TYPE Original Research PUBLISHED 12 August 2022 DOI 10.3389/fpls.2022.959155 Inheritance of esters and other OPEN ACCESS volatile compounds responsible for the fruity aroma in strawberry EDITED BY Jaime Prohens, Universitat Politècnica de València, Spain REVIEWED BY José L. Rambla, Pol Rey-Serra 1,2, Mourad Mnejja 1,2 and Amparo Monfort 1,2* University of Jaume I, Spain Fotini N. Lamari, 1 Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Barcelona, Spain, University of Patras, 2 Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Barcelona, Spain Greece *CORRESPONDENCE Cultivated strawberry, Fragaria × ananassa, has a complex aroma due to the Amparo Monfort amparo.monfort@irta.cat presence of more than 350 volatile organic compounds (VOCs). However, a SPECIALTY SECTION mixture of only 19 compounds, called Key Volatile Compounds (KVC), can This article was submitted to impart the main strawberry aroma. The octoploid nature of the cultivated Plant Breeding, strawberry species (2n=8x=56) adds complexity to the heritance of the a section of the journal Frontiers in Plant Science accumulation of the volatiles responsible for aroma. An F1 population cross RECEIVED 01 June 2022 between two breeding parental lines, FC50 and FD54, was phenotyped for ACCEPTED 27 July 2022 aroma by SPME GCMS during six harvests. A total of 58 compounds were PUBLISHED 12 August 2022 identified: 33 esters, nine terpenes, seven aldehydes, four lactones, two furans, CITATION one acid, one alkane and one alcohol, of which 16 were KVCs. A total of 179 Rey-Serra P, Mnejja M and Monfort A (2022) Inheritance of esters and other volatile QTLs were found, and 85 of these were detected in at least three harvests, of compounds responsible for the fruity which 50 QTLs were considered major (LOD>4.0) and detected in five or six aroma in strawberry. analyzed harvests. Several clusters of ester QTLs associated with fruity aroma Front. Plant Sci. 13:959155. doi: 10.3389/fpls.2022.959155 were discovered, such as QTLs for esters that share hexanoate group that were COPYRIGHT mapped in LG4A (Hexanoate_4A), those that share acetate and octyl groups © 2022 Rey-Serra, Mnejja and Monfort. in LG6A (Acetate_6A and Octyl_6A) or those with the same methyl group in This is an open-access article distributed LG7B (Methyl_7B). Different terpene QTLs associated with floral aroma appear under the terms of the Creative Commons Attribution License (CC BY). The use, grouped in a cluster in LG3C (Terpene_3C). Some of these clusters of QTLs distribution or reproduction in other were validated in a second F2 population, a cross of “Camarosa” and “Dover,” forums is permitted, provided the original that was also phenotyped for three years. Selected SNPs from floral and fruity author(s) and the copyright owner(s) are credited and that the original publication in aroma QTLs were tested in a third population, which will most likely be useful this journal is cited, in accordance with for marker-assisted breeding (MAB). accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. KEYWORDS Fragaria × ananassa, aroma, QTL, key volatile compounds, marker-assisted breeding Introduction Strawberries are one of the most consumed berries worldwide. During the last decades, strawberry breeding programs had been focused principally on fruit quality and disease resistance. However, consumers are demanding better tasting fruits (Folta and Klee, 2016) and for that reason, quality traits such as volatiles and polyphenolic content has gained importance in breeding programs. Volatile compounds, which increase during fruit ripening, are perceived as a healthy nutrient label (Goff and Klee, 2006). In strawberries a total of 979 volatile organic compounds (VOCs) were identified (Ulrich et al., 2018), but humans perceive only a few of them which contribute to the Frontiers in Plant Science 01 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 strawberry aroma (Schieberle and Hofmann, 1997; Ulrich et al., the other hand, some compounds, such as myrtenol, play an 1997; Nuzzi et al., 2008; Urrutia et al., 2017). The large variability important part in the aroma of woodland strawberry, and are not in strawberry volatile compounds can be explained by genetics, present in cultivated strawberry (Aharoni et al., 2004; Ulrich maturity stage and postharvest factors (Schwieterman et al., 2014). et al., 2007). Woodland strawberry has a particular flavor and a more intense Among all the VOCs identified, 19 volatile compounds have flavor than cultivated strawberry. Recent studies have shown that been described as those most relevant in strawberries as aroma only a few VOCs correlate with human preferences and these representatives and are considered as key volatile compounds enhance sweetness perception (Schwieterman et al., 2014; Fan (KVCs) in wild strawberry (Urrutia et al., 2017). Their importance et al., 2021). lies in overcoming the threshold of odour perception in humans, Fatty acids are the major precursor of aromatic compounds, and the combination of these 19 compounds produces the such as straight-chain acids, alcohols, aldehydes, esters, ketones characteristic strawberry aroma (Schieberle and Hofmann, 1997; and lactones. Esters, the most abundant chemical family in Ulrich et al., 1997, 2007). These KVCs are composed by 12 esters, strawberry fruits, are produced by the esterification of alcohols, two aldehydes, two furans, two terpenes and one lactone. Ester dehydrogenated by alcohol dehydrogenase (ADH), and acyl-CoA volatile compounds, such as butyl acetate, hexyl acetate, methyl catalysed by the alcohol acyl transferases (AAT). In this last step, butanoate, ethyl butanoate, butyl butanoate, methyl hexanoate or strawberry alcohol acyltransferase (SAAT) shows a wide range of ethyl hexanoate, deliver fruity and sweet notes. Other esters such substrate affinity (Aharoni et al., 2000; Beekwilder et al., 2004). as methyl cinnamate providing spicy notes, myrtenyl acetate Aldehydes and alcohols decrease during ripening at the same rate conferring herbaceous notes and methyl anthranilate that provides as the transcription level of AAT is increased in apricot (Gonzalez- strawberry-like flavor are only present in few cultivars. The Aguero et al., 2009) and during postharvest in peach (Zhang et al., γ-decalactone is a lactone that provides a peach aroma. Aldehydes, 2010). Acyl-coA can be synthesised by β-oxidation or via such as E-2-hexenal and Z-3-hexenal and their ester derivatives, lipoxygenases (LOX) being aldehydes and alcohol intermediates. E-2-hexenyl acetate and Z-3-hexenyl acetate, deliver herbaceous Branched and aromatic ester compounds are synthesised notes. Furans, mainly furaneol and mesifurane, contribute to the through amino acid metabolism. In strawberry, two eugenol strawberry aroma with caramel notes. Finally, terpene compounds, synthases (FaEGS1 and FaEGS2) are involved in the biosynthesis such as linalool and nerolidol, provide a floral aroma (Schieberle of eugenol and both are controlled by an R2R3 MYB transcription and Hofmann, 1997; Ulrich et al., 1997, 2007; Jetti et al., 2007; factor (FaEOBII; Aragüez et al., 2013; Medina-Puche et al., 2015). Nuzzi et al., 2008; Olbricht et al., 2008; Schwieterman et al., 2014). An anthranilate acid methyl transferase (FaAAMT) controls the Strawberry has an allo-octoploid genome composed by four last step of methyl anthranilate synthesis (Pillet et al., 2017). subgenomes, one related to Fragaria vesca, another to Fragaria However, this compound is only detected in few cultivars and in iinumae with the last two subgenomes being under discussion woodland strawberry (Ulrich et al., 1997; Schwieterman (Edger et al., 2020; Liston et al., 2020). The release of the et al., 2014). F. × ananassa genome (Edger et al., 2019), increased the genomic Furthermore, Z-3/E-2-hexenal isomerases are responsible for knowledge of the cultivated strawberry. To characterize trait converting Z-3-hexenal to E-2-hexenal as described in cucumber heritance, several dense genetic maps were constructed using the (Spyropoulou et al., 2017). Different studies reported that an IStraw90k (Bassil et al., 2015) and IStraw35k SNP arrays (Verma omega-6 fatty acid desaturase (FaFAD1) is necessary for the et al., 2017). Recently, new 850 and 50 k SNP arrays have been lactone γ-decalactone biosynthesis (Chambers et al., 2014; developed (Hardigan et al., 2020). Sanchez-Sevilla et al., 2014). QTLs for volatile compounds have been detected in previous Furanones can also produce volatile compounds such as studies. Zorrilla-Fontanesi et al. (2011) screened an F1 population mesifurane and furaneol. A quinone oxidoreductase (FaQR) is by GC–MS and mapped 70 QTLs for 48 different volatile required for furaneol biosynthesis (Raab et al., 2006) and an compounds, but only 35 of them were found to be stable over O-methyltransferases (FaOMT) methylate furaneol to produce time. A near-isogenic line (NIL) collection using F. vesca, as the mesifurane (Wein et al., 2002). recurrent parent, and F. bucharica, as the donor parent, revealed Finally, terpene compounds are also highly accumulated in 14 QTLs for different key volatile compounds (Urrutia et al., strawberry fruits. Terpenes are derived from isopentenyl 2017). Moreover, RNAseq analysis identified two genes involved pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) in the accumulation of two volatile compounds (Chambers et al., which can produce both monoterpenes and sesquiterpenes, 2014; Sanchez-Sevilla et al., 2014; Pillet et al., 2017). independently. In strawberry, nerolidol synthase (FaNES1) can The main goal of this study is to increase the knowledge of the synthesise the monoterpene linalool and the sesquiterpene genetic regions responsible for the complex aroma of cultivated nerolidol (Aharoni et al., 2004). FaNES1 is present only in strawberry fruits. We will do this with two populations (the F1 of cultivated but not in wild strawberries (Aharoni et al., 2004). “FC50” × “FD54,” and the F2 of “Camarosa” × “Dover”) for which Moreover, FaNES1 has been widely screened in cultivated, wild high-density linkage maps are already available (Rey-Serra et al., octoploid and diploid accessions, reporting that only octoploid 2021). The VOC diversity obtained by GC–MS was analyzed in strawberries have the functional allele (Chambers et al., 2012). On the F1 population from six harvests over three consecutive years, Frontiers in Plant Science 02 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 where a set of major and stable QTLs were identified. These were headspace vial for 30 min in the same conditions to extract the validated using a second F2 population and the SNPs associated volatile compounds. The extracted volatiles were desorbed in with aroma KVC QTLs were used to select these traits in a the GC injection port at 250°C for 5 min in splitless mode. breeding population. Volatile compounds were analyzed on a 7890A gas chromatograph coupled with a 5975C mass spectrometer (Agilent Technology, CA, United States). An HP-5MS UI GC Materials and methods column (30 m, 0.250 mm, 0.25 μm; J&W, CA, United States) and 1.2 ml/min constant helium flow was used for Plant material chromatographic separation. Oven conditions began at 40°C for 2 min, increasing by 5°C/min ramp until reaching a An F1 population (N = 70) derived from a cross between two temperature of 250°C and maintaining that temperature breeding lines, “FC50,” a seedling from the cross between “Donna” for 5 min. and “Cigaline” and “FD54,” an offspring from the cross between All profiles were analyzed using Enhanced ChemStation cultivars “Darselect Bright” and “Candonga” (hereafter software (Agilent Technologies, CA, United States) and compared “FC50 × FD54”). “FC50” has a wild strawberry aroma, whereas with mass spectra libraries, NIST08 and NIST11. In addition 19 “FD54” has a characteristic fruity aroma. At least six clonal runner key volatile compounds (KVCs) were identified by comparing the plants per progeny or parental lines were grown using standard retention time and ion fragmentation with each respective cultivation practices in the south-west of France (Le Barp, latitude: commercial standard (Sigma-Aldrich, MO, USA), and the 44°67′N, longitude 0°73′W) for three successive years (2016– remaining compounds by comparing the non-isothermal Kovats 2018). Ripe fruits were collected from six harvests over 3 years. retention index (KI). The KI (van den Dool and Kratz, 1963) is Harvests from the same year were collected in one to 2 weeks’ calculated using C7-C30 saturated standards (Sigma-Aldrich, intervals in May and June on 26/05/2016, 06/06/2016, 29/05/2017, MO, United States). Our results were compared with KI from 07/06/2017, 30/05/2018 and 07/06/2018. Each year, the plants NIST database.1 Only those peaks that could be clearly identified have been clonally replicated by runners. Grandparents were also were used for further analysis (Supplementary Table 1). Identified cultivated for further analysis. compounds areas were integrated by selecting a specific ion and A cross between two commercial cultivars, “Dover” and multiplying by a response factor to get the real compound area. “Camarosa” produced a hybrid “H-21” which was self-pollinated Then these areas were normalized by the 3-hexanone internal to obtain an F2 population with 117 progenies (hereafter “21AF”). standard peak from each sample. “Dover” was selected for its good agronomic performance and disease resistance, “Camarosa” for its fruit qualities such as its high polyphenol content. At least two clones per progeny or parental Data analysis line were grown using standard cultivation practices in the north- east of Spain (Caldes de Montbui, latitude: 41°36′N, longitude The statistical analysis of GC–MS data was performed using 2°10′E) for 3 years (2014, 2015, and 2018). Ripe fruits were picked R v3.6.2 (RCoreTeam, 2019) with the Rstudio v1.2.5033 interface up over several days in May and June 2014, 2015, and 2018 and in (RStudio, 2019). A violin plot was obtained using “ggplot2” R this F2 population the harvested fruits from the same year were package (version 3.3.0) that visualise relative VOCs content considered as an individual harvest. Each year, clonal runner distribution. and Pearson correlation between VOCs were plants were replicated. calculated using “cor” function and visualised as cluster network analyses (CNA) using “qgraph” function from “qgraph” R package (version 1.6.5) and Pearson correlation between harvests and Volatile compound analysis by GC–MS VOCs were visualised by “corrplot” R package (version 0.84). Heatmap representation of hierarchical cluster analysis was Each sample consisted of a pool of three to five strawberry performed using “heatmap.2” function from “gplots” R package fruits from each genotype and harvest, frozen in N2 (liq.), (version 3.0.3). Principal component analysis (PCA) was done powdered with a coffee grinder and stored immediately at with “fviz_pca_biplot” function from “factorextra” R package −80°C. To perform the GC–MS assay, 1 g of powdered (version 1.0.7). Omega squared values (ω2) were derived from strawberry fruit was weighted into a 10 ml screw cap headspace ANOVA residuals calculated with “aov” function and using the vial and 1 ml of saturated NaCl solution added together with formula: (SSi − dfi * MSerr) * (MSt + MSerr)−1. Boxplots were drawn 10 ppm of internal standard (3-hexanone; Sigma-Aldrich, MO, using “ggplot2” package (version 3.3.0) and “ggsignif ” function United States). A GC Sampler 80 (Agilent Technology, CA, (version 0.6.0) to calculate the t-test significant level. United States) was used for incubation, extraction and desorption. Vials were first heated at 50°C during 10 min with agitation at 500 rpm, then SPME fibre (50/30 μm DVB/CAR/ PDMS; Supelco, PA, United States) was exposed into the 1 https://webbook.nist.gov/chemistry/gc-ri/ Frontiers in Plant Science 03 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 Volatile QTL analysis hexanoate, ethyl hexanoate, E-2-hexenal, mesifurane, γ-decalactone and linalool which are KVCs (Table 1; To analyse the genetic regions responsible for the VOCs Supplementary Figure 1). Despite the fact that VOC content variance, a reduced “FC50 × FD54” genetic map was constructed differed among harvests, the “FD54” parental line displayed a with 1,464 markers grouped in 28 LGs and spanning significantly higher (p-value <0.05) concentration of most 2,273 cM. Likewise, we used a reduced genetic map from the F2 VOCs compared to the “FC50” parental line, such as some “Camarosa” × “Dover” population (“21AF”) with 1,457 markers esters (methyl 3-methylbutanoate, methyl decanoate and grouped in 29 LGs and spanning 1808.05 cM (Rey-Serra methyl benzeneacetate), aldehydes (hexanal, heptanal and et al., 2021). Z-2-heptanal), lactones (γ-octalactone and γ-decalactone) and VOC data was transformed with log2 and analyzed separately terpenes (β-pinene, D-limonene, α-terpineol, linalool and ® for each harvest in the MapQTL 6 software (van Ooijen, 2009) geraniol), whereas the concentrations of methyl anthranilate using the Interval Mapping method (IM) and nonparametric and methyl nicotinate were significantly higher in the “FC50” Kruskal–Wallis test (KW). Same criteria as Rey-Serra et al. (2021) parental line than in “FC54.” In addition, “FC50 × FD54” was followed to classify QTLs. A QTL was determined with a progeny showed greater variation for each VOC than the minimum LOD score of 2.5 by IM and when detected in at least parental lines (Supplementary Figure 1). two harvests. The threshold to identify a major QTL was calculated VOC distribution in each harvest was checked by a Shapiro– by a permutation test and established at a LOD score of four. KW Wilk test which revealed that few compounds (methyl butanoate, has been used to determine the most accurately linked marker. R2 methyl hexanoate, hexanal, E-2-hexenal, Z-2-heptanal and determines the percentage of phenotypic variation expressed by γ-decalactone) were normally distributed. However, when the the QTL. The MapChart2.2 software (Voorrips, 2002) was used to VOC content was transformed to log2, the majority (42/58) visualize each QTL in our genetic map with fill and color presented a normal distribution in at least three harvests parameters that distinguish the stability and chemical family of (Supplementary Table 3). each VOC, respectively. The QTL size was determined according to the overlapping 1-LOD confidence interval to define either side of the peak. Volatile correlation analysis To identify the physical position of each marker, SNP probes were blasted to the F. × ananassa cv. “Camarosa” genome (Edger A Cluster network analysis (CNA) was drawn to visualise the et al., 2019) as explained in Rey-Serra et al. (2021). Once the relationships between different VOCs (Figure 1; genome distances of the QTLs had been delimited, genes inside Supplementary Table 4). This analysis clearly clustered the each QTL were analyzed by gene homology using NCBI, different terpene compounds that show a strong correlation and SwissProt, Tair10 and TrEMBL database obtained from https:// separates them from the rest of the VOCs. Esters, which are the www.rosaceae.org/species/fragaria_x_ananassa/genome_v1.0.a1 most abundant compounds, were shown to be highly correlated (Jung et al., 2019). with each other, while branched esters display less correlation with other compounds. Lactones showed a high correlation with each other and with few esters. Similarly, furan compounds revealed a Results high correlation with each other and with hexyl butanoate. Only the aldehyde family presented a low correlation between its Volatile compound composition components. Indeed, few weak negative correlations were detected. To further understand the relationship between compounds To identify the compound variability in the strawberry aroma, in different harvests, Pearson correlation between VOCs, full ripe fruits from 68 plants of the “FC50 × FD54” population independently of the harvest, were calculated collected in six different harvests over three consecutive years (Supplementary Table 5). The results showed that ester compounds (2016–2018) were analyzed by GC–MS. From hundreds of peaks sharing the same acyl-CoA group were highly correlated (0.43– present in strawberry aroma profile, 58 volatile organic 0.90 correlation range between VOCs and harvests), such as compounds (VOCs) could be clearly identified (Table 1; propyl butanoate with ethyl butanoate, isopropyl butanoate with Supplementary Table 2) including, 33 (57%) esters, nine (16%) butyl butanoate, and methyl hexanoate with propyl hexanoate and terpenes, seven (12%) aldehydes, three (5%) lactones, two (3%) with isoamyl hexanoate. Similarly, esters having the same alcohol furans and four (7%) from compounds different to other families. group showed high correlations (between 0.30 and 0.97), such as Out of the 19 compounds considered as key volatile compounds methyl hexanoate with methyl octanoate and with methyl (KVCs), 16 were identified (Table 1). Myrtenyl acetate and methyl decanoate, ethyl hexanoate with ethyl 2-hexenoate, with ethyl cinnamate peaks were not present in this population and the octanoate and with ethyl butanoate, methyl octanoate with methyl Z-3-hexenal peak was almost undetectable. decanoate, hexyl butanoate with hexyl acetate, and octyl acetate The compounds that showed the highest peaks were with octyl 3-methylbutanoate and with octyl hexanoate methyl butanoate, ethyl butanoate, butyl butanoate, methyl (Supplementary Figure 2). Frontiers in Plant Science 04 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 TABLE 1 VOCs summary in “FC50×FD54” population. Volatile compound Abb. Type FC50 FD54 FC50 × FD54 Mean ± SD Mean ± SD Mean ± SD Range Butyl acetate* BA Ester 0.092 ± 0.043 0.118 ± 0.058 0.104 ± 0.075 0.007–0.528 Isopentyl acetate IPA Ester 0.042 ± 0.011 0.074 ± 0.061 0.065 ± 0.060 0.005–0.537 Hexyl acetate* HA Ester 0.152 ± 0.062 0.461 ± 0.385 0.332 ± 0.534 0.013–6.350 Octyl acetate OA Ester 0.026 ± 0.042 0.129 ± 0.103 0.045 ± 0.099 0.002–0.953 Methyl butanoate* MB Ester 2.938 ± 1.132 2.104 ± 0.605 3.507 ± 1.580 0.140–9.569 Ethyl butanoate* EB Ester 2.724 ± 1.837 1.630 ± 0.886 3.598 ± 3.182 0.064–21.773 Isopropyl butanoate IPB Ester 0.411 ± 0.122 0.417 ± 0.253 0.443 ± 0.292 0.012–1.445 Methyl 3-methylbutanoate M3MB Ester 0.041 ± 0.013 0.126 ± 0.064 0.083 ± 0.060 0.011–0.438 Propyl butanoate PB Ester 0.056 ± 0.029 0.056 ± 0.033 0.065 ± 0.048 0.003–0.262 Butyl butanoate* BB Ester 0.419 ± 0.205 0.511 ± 0.478 0.630 ± 0.705 0.002–4.076 Hexyl butanoate HB Ester 0.073 ± 0.040 0.418 ± 0.509 0.239 ± 0.399 0.003–4.972 Octyl butanoate OB Ester 0.031 ± 0.042 0.680 ± 0.718 0.380 ± 0.843 0.002–7.411 1-Methyloctyl butanoate MOB Ester 0.350 ± 0.690 0.040 ± 0.046 0.060 ± 0.226 0.001–3.183 Octyl 3-methylbutanoate O3MB Ester 0.004 ± 0.002 0.274 ± 0.275 0.046 ± 0.100 0.002–1.113 Methyl 4-methylpentanoate M4MP Ester 0.017 ± 0.012 0.017 ± 0.017 0.024 ± 0.024 0.002–0.167 Methyl hexanoate* MH Ester 2.876 ± 1.078 2.961 ± 1.802 3.840 ± 2.258 0.028–12.808 Ethyl hexanoate* EH Ester 1.592 ± 1.552 1.777 ± 1.478 3.231 ± 3.626 0.009–20.876 Isopropyl hexanoate IPH Ester 0.084 ± 0.030 0.116 ± 0.075 0.143 ± 0.145 0.002–1.186 Isoamyl hexanoate IAH Ester 0.014 ± 0.007 0.021 ± 0.016 0.045 ± 0.063 0.001–0.419 Hexyl hexanoate HH Ester 0.007 ± 0.003 0.101 ± 0.140 0.032 ± 0.048 0.003–0.372 Octyl hexanoate OH Ester 0.005 ± 0.002 0.202 ± 0.228 0.091 ± 0.248 0.002–2.833 Methyl E-2-hexenoate M2H Ester 0.100 ± 0.039 0.054 ± 0.035 0.069 ± 0.063 0.003–0.388 Ethyl 2-hexenoate E2H Ester 0.006 ± 0.004 0.010 ± 0.008 0.020 ± 0.031 0.002–0.381 Methyl octanoate MO Ester 0.062 ± 0.026 0.363 ± 0.310 0.206 ± 0.234 0.001–1.768 Ethyl octanoate EO Ester 0.007 ± 0.005 0.036 ± 0.024 0.067 ± 0.142 0.001–1.273 Methyl decanoate MD Ester 0.001 ± 0.000 0.023 ± 0.011 0.011 ± 0.013 0.001–0.097 Methyl benzene acetate MBA Ester 0.037 ± 0.011 0.081 ± 0.033 0.054 ± 0.031 0.007–0.266 Methyl nicotinate MN Ester 0.038 ± 0.027 0.002 ± 0.002 0.016 ± 0.028 0.001–0.236 Methyl salicylate MS Ester 0.090 ± 0.054 0.040 ± 0.032 0.090 ± 0.085 0.002–0.506 Methyl anthranilate* MA Ester 0.252 ± 0.252 0.009 ± 0.008 0.065 ± 0.180 0.001–1.752 Z-3-hexenyl acetate* Z3HA Ester 0.028 ± 0.024 0.039 ± 0.036 0.030 ± 0.019 0.004–0.134 E-2-hexenyl acetate* E2HA Ester 0.069 ± 0.065 0.156 ± 0.192 0.166 ± 0.199 0.009–2.509 E-2-hexenyl butanoate E2HB Ester 0.032 ± 0.021 0.086 ± 0.159 0.105 ± 0.182 0.002–2.070 Hexanal Hex Aldehyde 0.144 ± 0.050 0.526 ± 0.266 0.353 ± 0.220 0.064–1.272 Heptanal Hep Aldehyde 0.026 ± 0.001 0.042 ± 0.008 0.043 ± 0.018 0.017–0.139 Nonanal Non Aldehyde 0.230 ± 0.051 0.166 ± 0.103 0.209 ± 0.153 0.007–1.070 Decanal Dec Aldehyde 0.011 ± 0.007 0.043 ± 0.062 0.021 ± 0.023 0.003–0.315 E-2-Hexenal* E2Hal Aldehyde 1.990 ± 0.521 2.290 ± 1.430 1.865 ± 1.434 0.064–9.109 Z-2-Heptenal Z2H Aldehyde 0.024 ± 0.002 0.048 ± 0.019 0.048 ± 0.018 0.014–0.121 Benzaldehyde B Aldehyde 0.154 ± 0.122 0.187 ± 0.150 0.200 ± 0.160 0.007–1.260 Dodecane Dode Alkane 0.039 ± 0.042 0.011 ± 0.010 0.029 ± 0.056 0.001–1.017 Octanoic acid Oac Acid 0.074 ± 0.072 0.203 ± 0.209 0.176 ± 0.312 0.003–2.936 Octanol Ool Alcohol 0.082 ± 0.063 0.071 ± 0.049 0.068 ± 0.053 0.009–0.386 2-Heptanone Hep2 Ketone 0.156 ± 0.083 0.291 ± 0.268 0.169 ± 0.146 0.010–1.293 γ-octalactone GO Lactone 0.004 ± 0.004 0.043 ± 0.030 0.023 ± 0.021 0.001–0.180 γ-decalactone* GD Lactone 0.046 ± 0.063 4.046 ± 2.068 2.720 ± 1.901 0.001–14.162 γ-dodecalactone GDo Lactone 0.124 ± 0.189 0.292 ± 0.398 0.195 ± 0.288 0.001–1.591 Mesifurane* M Furan 3.375 ± 1.271 1.828 ± 1.410 2.419 ± 2.242 0.023–13.937 (Continued) Frontiers in Plant Science 05 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 TABLE 1 Continued Volatile compound Abb. Type FC50 FD54 FC50 × FD54 Mean ± SD Mean ± SD Mean ± SD Range Furaneol* F Furan 0.280 ± 0.087 0.111 ± 0.079 0.118 ± 0.144 0.006–1.018 β-Pinene BP Terpene 0.096 ± 0.079 0.278 ± 0.127 0.199 ± 0.162 0.001–1.002 D-Limonene DL Terpene 0.035 ± 0.030 0.115 ± 0.054 0.080 ± 0.063 0.002–0.415 α-Terpineol AT Terpene 0.076 ± 0.057 0.375 ± 0.186 0.171 ± 0.162 0.002–1.242 Linalool* L Terpene 0.854 ± 0.395 2.421 ± 0.691 1.488 ± 0.919 0.010–5.554 Geraniol G Terpene 0.002 ± 0.001 0.008 ± 0.006 0.004 ± 0.005 0.000–0.034 β-Farnesene BF Terpene 0.161 ± 0.182 0.414 ± 0.338 0.296 ± 0.278 0.003–1.460 α-Curcumene AC Terpene 0.032 ± 0.044 0.063 ± 0.073 0.052 ± 0.064 0.001–0.403 α-Farnesene AF Terpene 0.096 ± 0.097 0.292 ± 0.217 0.203 ± 0.191 0.003–1.268 Nerolidol* N Terpene 0.382 ± 0.300 0.962 ± 0.670 0.603 ± 0.529 0.003–2.706 Volatile compound, its abbreviation and compound family. Relative average and standard deviation (SD) of 58 VOCs content detected in parental lines “FC50” and “FD54” and relative average, standard deviation and range for their F1 progenies. Volatile compound in bold and * mark KVCs, Values in bold indicate the highest significant (p < 0.05) value in parental lines. Lactones also presented high correlations, the highest being (Figure 2). Most of the lines (64%), were grouped together in a big between γ-dodecalactone and γ-decalactone (0.52–0.83). cluster A, which is separated into two subclusters, A1 and A2. Likewise, furans showed a greater correlation between each other Cluster A1 included 18% of the individuals such as “FC50” and than with other compounds (Supplementary Figure 2). “Donna” and was characterized by its relatively low VOC content. The most stable correlations across the different harvests were Cluster A2, the largest group, comprised 46% of the individuals detected between terpene compounds. Indeed, the minimal such as “Candonga,” and was distinguished by its high content of correlation between monoterpenes was 0.74, while for several VOCs, but also by its low terpene content. Cluster B, sesquiterpenes it was 0.77. However, the minimal correlation represented by a unique sample, “Cigaline,” was characterized by between monoterpene and sesquiterpene compounds was quite its high content of octanol, methyl nicotinate, butyl acetate and low (0.32). Notably, linalool showed the highest correlations ethyl E-2-hexanoate. Cluster C corresponded to 28% of the among all terpene compounds ranging from 0.60 to 0.99 individuals that includes “FC54.” This cluster was characterized by (Supplementary Figure 2). showing a high terpene and several ester contents. The last group, Moreover, high correlations between compounds belonging to cluster D with only 5 individuals (7%), includes “Darselect Bright,” different families were also observed. For instance, some butanoate and differs by its high content of almost all VOCs except terpenes related esters and furans presented a high correlation between each (Figure 2). other. Likewise, some lactones with ester compounds and some terpenes with compounds from other families with a maximum correlation of 0.76 (Supplementary Figure 2). Analysis of the environmental effect on To better understand the relationship between VOCs and VOCs progenies, parental lines (“FC50” and “FD54”) and grandparental lines (“Donna,” “Cigaline,” “Darselect Bright” and “Candonga”), a In response to varying environmental conditions, plants could hierarchical cluster analysis (HCA) using the average of six alter the biosynthesis of some VOCs in different ways. We assumed harvests was performed. A heatmap representation of the HCA that each harvest, cultivated in the open field, to be under different shows three main compounds and four individual clusters environmental conditions and therefore, its VOC accumulation (Figure 2). Compound cluster 1 agglomerated 43% of the VOCs, would be affected. To figure out how different environmental which is divided into two subclusters, 1A and 1B. Compound conditions might affect VOC content, a Principal component cluster 1A, contained 21% of the VOCs and grouped esters derived analysis (PCA) was performed. The first three dimensions from octyl-CoA and lactones. Compound cluster 1B, comprised explained 26.5% (Dim1), 11.9% (Dim2) and 8.2% (Dim3) of the 22% of the VOCs and clustered aldehydes and hexyl-CoA esters. VOC variance. Each harvest partially overlapped the others in Compound cluster 2, the smallest one, grouped 9% of the VOCs, both PCA plots. The closest harvests were from the same year, but being all butanoate derived esters. Finally, compound cluster 3 slight differences arose between years, the most divergent being agglutinated 48% of the VOCs that join terpenes, hexanoate 2018 (Supplementary Figure 3). derived esters and branched ester, where the latter is the furthest Regarding the contribution of VOCs to each PCA plot, the from the others. plot of Dim1 and Dim2 showed that terpene, lactone and The hierarchical cluster analysis defines four groups of aldehyde compounds were clustered together and esters such as genotypes which could be interesting for breeding purposes the octyl group and hexanoate related esters were forming a Frontiers in Plant Science 06 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 FIGURE 1 Cluster network analysis (CNA) for VOCs in “FC50×FD54.” Compounds are represented as nodes and colored according to the chemical families. Positive (blue) and negative (red) correlation are represented as link between compounds, the widest links being the strongest correlation. Showing correlations with p value <0.05. For the correspondences between the full name and their abbreviation names consult Table 1. small and dense group. Other ester compounds were scattered significantly on the environmental effect and only 18 VOCs (Supplementary Figure 3A). However, no clear pattern was exhibiting a significant effect of GxE interaction. found when plotting together the Dim 1 and Dim 3 In general, the genotypic factor was the main contributor to (Supplementary Figure 3B). the observed variance over environment and GxE. At least 25% of To dissect the variance caused by the genotype (G), the the observed variance was explained by the G factor in 26 VOCs. environment (E) or their interaction (GxE) regarding the VOC This genetic variance was particularly high in linalool, nerolidol accumulation, the significance of each factor and trait was and octyl 3-methyl butanoate, explaining about 40% of their calculated and quantified (Supplementary Table 6). The analysis variance. In contrast, a high influence of the E factor was detected of variance (ANOVA), taking into account the genotype, year and in some VOCs, explaining more than 30% of their total observed interaction between both (G + E + GxE), revealed 50 VOCs with variance, such as methyl salicylate, nonanal, octanol and significant genetic differences (p-value<0.05), 48 VOCs depending γ-nonalactone. The GxE effect never exceeded 20% of the total Frontiers in Plant Science 07 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 FIGURE 2 Heatmap representation of hierarchical clustering analyses (HCA) in VOCs in the “FC50×FD54” population. Clusters are named by capital letters for individuals and numbers for VOCs. Stars indicate KVCs. observed variance in any VOCs. The part of the VOC variability correlation between each other ranging between 0.60 and 0.71 that cannot be attributed to any of the above factors, was named according to the year (Supplementary Figure 4B). The performed “error” (Supplementary Table 6). hierarchical cluster analysis classified the genotypes by groups in relation to their KVC accumulation. (Supplementary Figure 4C). The same overlapping behavior, as for the F1 population, was Key volatile compounds in the “21AF” observed in the PCA plots when comparing all KVCs from population different years together (Supplementary Figure 4D). Fully ripe fruits from a second population (“21AF”) were similarly analyzed by GC–MS for three harvests (2014, 2015, and Volatile QTL detection 2018) to measure their KVCs (Supplementary Table 7). In this population, methyl anthranilate and furaneol were not detected. A total of 58 VOCs were characterized by GC–MS in the The distribution analysis shows that the KVCs in each year were “FC50 × FD54” population, of which 16 were considered KVCs. normal distributed and overlapped on violin plot representation By the genetic analyses, a total of 179 QTLs were discovered, (Supplementary Figure 4A; Supplementary Table 8). As described exhibiting a LOD > 2.5 in at least two different harvests, for 55 in F1 population, high correlations were detected between ester different VOCs (Supplementary Table 9). From these, 94 QTLs compounds that share the same acyl-CoA group (0.49–0.79 in were detected in more than three harvests and 22 in five to six range), such as between butyl acetate and hexyl acetate, or the harvests (Figure 3). Up to 50 QTLs were considered major same alcohol group, such as between methyl butanoate and methyl (LOD > 4.0) and described with more detail in Table 2. A total of hexanoate or between ethyl butanoate and ethyl hexanoate. Also, 44 QTLs were mapped for 14 KVCs, of which 11 were major. Only high correlations were found between other ester compounds, two KVCs, ethyl butanoate and E-2-hexenyl acetate, did not show such as between hexyl acetate and butyl butanoate or between any significant QTL. hexyl acetate and ethyl hexanoate. In addition to esters, two Focusing on KVC_QTLs of the ester family, two butyl terpene compounds, linalool and nerolidol, also exhibited high acetate QTLs were mapped: BA_5A, with R2 ≤ 27.0, and Frontiers in Plant Science 08 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 FIGURE 3 VOC_QTL location in the “FC50×FD54” genetic map. Box size correspond to the 1-LOD confidence interval. Stability is represented by box filling degree: consistent in five or six harvests (filled) and detected in three or four harvests (semi-filled). Chemical family compounds are differentiated by colors: acids (light blue), alcohols (red), aldehydes (yellow), alkanes (dark red), esters (green), furans (purple), lactones (turquoise) and terpenes (dark blue). BA_6A, a major QTL detected in five of the six analyzed detected for methyl hexanoate that are MH_4A present in four harvests with R2 ≤ 35.2. Significant QTLs for hexyl acetate of the six harvests with R2 ≤ 44.5 and MH_7B observed in half were detected and the most important ones are HA_1A and of the harvests with R2 ≤ 36.3. Six different QTLs were detected HA_5A with R2 ≤ 24.6. For methyl butanoate, different QTLs for methyl anthranilate, of which two were constant in three were discovered such as MB_2B, a major QTL with R2 ≤ 28.5 of the six harvests that are MA_1A, which explained R2 ≤ 34.6 and MB_3A with R2 ≤ 20. For butyl butanoate, a single QTL and MA_5D with R2 ≤ 29.0. A third interesting major QTL for was mapped in LG6A with R2 ≤ 27.4. Two major QTLs were MA was consistent in all harvests, which in LG7A explained a Frontiers in Plant Science 09 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 TABLE 2 VOC_QTLs detected in “FC50×FD54” and “21AF” population. QTL ID KVC Har. LOD R2 Genetic map F. × ananassa genome LG QTL boundaries QTL QTL Nb. genes (cM) interval interval (pb) (cM) FC50 × FD54 77.92  BA_6A X 5 5.19 35.2 6A 68.03 9.89 5,541,008 753  OA_6A 6 14.41 69.4 6A 73.39 92.94 19.55 11,087,539 1,652  MB_2B X 4 4.07 28.5 2B 58.73 76.17 17.44 6,021,672 1,021  IPB_4B 3 5.53 37.6 4B 1.86 12.14 10.29 152,931 13  M3MB_3B 4 6.52 41.5 3B 64.92 69.99 5.07 5,783,583 653  PB_5A 4 4.13 29.2 5A 53.10 84.11 31.01 16,168,702 2,154  PB_5D 3 4.91 33.2 5D 5.91 15.94 10.03 6,955,239 1,108  HB_1A 4 5.06 35.1 1A 28.15 56.67 28.52 6,127,553 1,037  HB_6A 4 4.64 32.7 6A 3.65 31.50 27.85 6,369,285 1,096  OB_1A 3 5.72 38.6 1A 43.14 50.98 7.84 2,217,834 339  OB_6A 6 11.26 61.7 6A 77.92 90.13 12.21 6,023,570 830  O3MB_2D 4 4.51 31.9 2D 3.71 13.37 9.67 6,336,099 653  O3MB_6A 6 14.4 70.7 6A 73.39 85.13 11.74 6,023,570 830  MH_4A X 4 6.91 44.5 4A 57.48 62.90 5.42 7,402,050 1,233  MH_7B X 3 5.49 36.3 7B 27.13 49.80 22.67 10,345,159 1,754  IPH_4A 4 4.51 31.9 4A 57.48 61.90 4.42 7,402,050 1,233  IPH_7B 6 9.08 53.9 7B 27.13 44.57 17.44 4,953,047 765  IAH_4A 4 5.25 36.1 4A 54.49 60.08 5.59 7,402,050 1,233  HH_4A 3 4.48 31.8 4A 54.49 62.90 8.41 7,402,050 1,233  OH_6A 6 12.59 65.2 6A 73.39 90.13 16.74 6,023,570 830  M2H_4A 3 4.32 30.8 4A 56.49 61.90 5.41 7,402,050 1,233  MO_4A 4 7.22 46 4A 58.48 61.90 3.42 7,402,050 1,233  MO_7D 5 4.03 28.6 7D 6.36 20.70 14.33 7,059,468 849  MD_4A 6 9.98 57.3 4A 57.48 62.90 5.42 7,402,050 1,233  MBA_1A 3 4.37 31.1 1A 1.82 8.85 7.03 1,881,958 382  MBA_3C 6 4.45 31.1 3C 53.25 86.54 33.29 14,891,344 1,682  MN_3C 6 14.93 72 3C 18.55 35.35 16.80 2,519,086 358  MS_3C 6 13.63 68 3C 18.55 21.35 2.80 2,519,086 358  MA_1A X 3 5.07 34.6 1A 31.87 38.92 7.06 2,839,656 526  MA_5D X 3 4.16 29 5D 4.85 10.09 5.24 5,079,981 809  MA_7A X 6 7.3 46.4 7A 24.26 46.52 22.26 16,373,010 2,767  Dode_6B 3 4.15 28.9 6B 92.16 126.82 34.65 5,991,681 1,078  Dode_7B 4 6.85 44.2 7B 19.56 40.22 20.66 2,871,386 395  Oac_1C 3 4.85 33.9 1C 0.00 21.93 21.93 3,554,988 667  Oac_7D 5 6.25 41.3 7D 0.00 14.05 14.05 11,737,287 1,243  Ool_6D 3 4.54 31.6 6D 50.90 61.34 10.43 4,473,829 502  GDo_6D 4 4.06 28.8 6D 53.81 66.63 12.82 9,105,053 1,010  F_1C X 3 4.74 33.2 1C 0.00 5.52 5.52 1,505,596 283  BP_3C 6 7.15 45.6 3C 1.82 7.43 5.62 954,222 171  DL_3C 5 7.32 45.2 3C 1.82 7.43 5.62 954,222 171  AT_3C 5 6.2 39.9 3C 1.82 5.52 3.71 954,222 171  L_3C X 5 7.06 44 3C 1.82 7.43 5.62 954,222 171  G_3C 6 7.01 43.8 3C 1.82 7.43 5.62 954,222 171  BF_3C 4 7.23 44.8 3C 1.82 35.35 33.53 6,795,619 1,077  BF_4A 3 5 34.7 4A 57.48 73.96 16.48 11,331,026 1,980  AC_3C 5 7.25 44.9 3C 1.82 34.35 32.53 6,795,619 1,077 (Continued) Frontiers in Plant Science 10 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 TABLE 2 Continued QTL ID KVC Har. LOD R2 Genetic map F. × ananassa genome LG QTL boundaries QTL QTL Nb. genes (cM) interval interval (pb) (cM)  AC_4A 4 4.94 34.4 4A 57.48 73.96 16.48 11,331,026 1,980  AF_3C 5 7.31 45.2 3C 1.82 35.35 33.53 6,795,619 1,077  N_3C X 5 7.17 44.6 3C 1.82 35.35 33.53 6,795,619 1,077  N_4A X 3 4.76 33.4 4A 57.48 73.96 16.48 11,331,026 1,980 21AF  BA_6A X 3 5.08 21.5 6A 59.39 66.61 7.22 3,623,024 481  HA_6A X 3 5.70 22.1 6A 60.99 70.18 9.19 1,739,375 237  MB_1A X 3 4.58 17.3 1A 24.32 31.79 7.46 3,002,207 481  MB_7B X 3 9.13 32.1 7B 21.18 26.40 5.22 594,943 93  MH_1A X 3 4.37 17.4 1A 21.25 37.88 16.63 3,838,906 635  MH_7B X 3 10.57 37.1 7B 21.18 26.40 5.22 594,943 93  GD_3B X 3 7.61 30.0 3B 72.76 79.29 6.53 2,441,208 368  L_3C X 3 7.61 27.3 3C 0.00 14.02 14.02 31,373 9  N_3C X 3 9.61 34.4 3C 0.00 0.86 0.86 31,373 9 List of major (LOD > 4) and stable (more than 2 harvests) VOC_QTLs ordered by compounds. Maximum values of LOD score, R2 explain % of phenotypic variance, QTL interval determined by 1-LOD confidence interval, QTL position in F. × ananassa genome and number of genes in each region. range of R2 between 22.7% and 46.4% of the phenotypic major QTL GDo_6D was co-located in the lower part of the variance (Supplementary Table 9). LG6D with GD_6D. For the terpene family, two major QTLs were detected in five The QTL analysis in the 21AF population showed a total of the six harvests and mapped together with the two most of 51 QTLs for 14 KVCs, but only nine of these were major important terpenes, linalool and nerolidol, in LG3C (L_3C and and consistent in all harvests. Six of these QTLs were for N_3C) with R2 ≤ 44.6 of both variances. For nerolidol, another ester compounds, one for lactones and two for terpenes major QTL was identified N_4A, with R2 ≤ 33.4 (Table 2). (Table 2; Supplementary Table 9). Concerning the ester With respect to lactone family, one QTL was discovered for QTLs, methyl butanoate and methyl hexanoate QTLs were γ-decalactone in LG6D R2 ≤ 25.5. For furans, two QTLs were mapped in the same region in LG1A (MB_1A and MH_1A) constant in three of the six harvests, F_1C a major QTL for and in LG7B (MB_7B and MH_7B), while two other ester furaneol, with R2 ≤ 33.2, and M_6A a QTL for mesifurane with compounds, butyl acetate and hexyl acetate, were located in R2 ≤ 25.1. LG6A (BA_6A and HA_6A). For terpenes, linalool and Looking at map distribution, QTLs for ester compounds were nerolidol QTLs were located at the beginning of LG3C, and present in almost all linkage groups. Major QTLs of octyl ester for lactones, the γ-decalactone QTL was mapped at the end compounds were mapped in LG6A, LG6B and LG7B, being the of LG3B (Table 2). QTL in LG6A the most significant and stable. In LG1A, few ester QTLs were mapped closely to the OB_1A QTL, such as HA_1A, HB_1A, PB_1A and MA_1A. Several QTLs mainly related to QTL validation in the two populations hexanoate but also to octanoate and decanoate (MH_4A, IAH_4A, IPH_4A, HH_4A, M2H_4A, MO_4A and MD_4A) were mapped In order to compare and validate the QTLs detected in the two at the end of LG4A. Moreover, QTLs of MH_7B, IPH_7B and populations studied, an analysis of synteny was carried out and the MO_7B were detected in LG7B and were mapped close to some consistency of the QTLs checked over different harvests and octyl ester QTLs (Figure 3; Supplementary Table 9). populations. Two ester QTLs BA_6A and MH_7B, and the terpene All identified terpenes showed QTLs that were major and QTL in LG3C, had been mapped in the same regions of the same consistent in at least five of the six harvests (L_3C, BP_3C, LGs in both “FC50 × FD54” and “21AF” populations (Figure 3). DL_3C, AT_3C, G_3C, N_3C, AF_3C, BF_3C and AC_3C). All In addition, other major QTLs have been detected that may be of of them shared the same region at the beginning of LG3C. In interest for breeding. However, they were significant in only one addition to these major QTLs, stable QTLs for monoterpene of the studied populations, such as GD_3B and MH_1A detected compounds were detected in LG3A and for sesquiterpenes in in the “21AF,” and MH_4A and MA_7A discovered in LG1B, LG4A and LG4C (Figure 3; Supplementary Table 9). The “FC50 × FD54” (Supplementary Figure 5). To improve synteny QTLs for lactone compounds were found in few LGs, and the analysis, high-dense genetic maps were used. Frontiers in Plant Science 11 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 Ester QTLs were spread over different LGs in both and one to decanoate group (methyl decanoate; Figure 3; populations. Focusing on the interesting QTL cluster in LG6A, Supplementary Table 9). The AB genotype of the Affx-88856759 BA_6A and HA_6A detected in the “21AF” genetic map in the marker in the “FC50 × FD54” population showed a significant same region as BA_6A, O3MB_6A, OA_6A, OB_6A and OH_6A accumulation of methyl hexanoate (p-value <0.001) compared to identified in the “FC50 × FD54” genetic map (Figure 3). All of the BB genotype (Supplementary Figure 5B). But this QTL was these QTLs are related to acetate and octyl groups (Acetate_6A not detected in the “21AF” population, probably masked by the and Octyl_6A). When comparing the relative butyl acetate content effects of major QTLs in LG1A and LG7B. The Hexanoate_4A in both populations, the detected content was 10-fold higher in QTL was located at the end of LG4A which corresponds to the “21AF” than in “FC50 × FD54” (Figure 4A). In the case of the beginning of the described as F. vesca-like chromosome. The QTL Affx-8884155 marker, different segregation behaviors were interval size is 7.4Mbp containing a total of 1,233 annotated genes observed in both populations. The homozygous allele (A) of the (Table 2). Methyl anthranilate is an aromatic ester compound “FC50 × FD54” genetic map corresponded to the highest butyl providing wild strawberry aroma that was only detected in acetate accumulation (p-value<0.001), whereas in the “21AF” “FC50 × FD54.” Looking at the segregation of the Affx-8897207 genetic map, it was found to be homozygous B. The Acetate_6A marker located in LG7A, the AB genotype showed a significantly was localized in the chromosome 6A. The smallest QTL interval higher methyl anthranilate content than the AA genotype (p-value was detected for the hexyl acetate in the “21AF” genetic map, <0.001; Supplementary Figure 5C). This MA_7A QTL was located which corresponds to a physical distance of 1.7Mbp and comprises in the middle of the F. vesca-like chromosome. This QTL interval 237 annotated genes (Table 2). was relatively large (16.4Mbp) and therefore 2,767 genes were Different ester QTLs with a common methyl group were annotated inside (Table 2). mapped in LG7B (Methyl_7B) such as MH_7B, IPH_7B and The terpene family, which provides floral aroma, was MO_7B in the “FC50 × FD54” genetic map and MH_7B and identified with five monoterpenes (linalool, β-pinene, D-limonene, MB_7B in the “21AF” genetic map. Relative methyl hexanoate α-terpineol and geraniol) and four sesquiterpenes (nerolidol, content was twice to three times higher in the “FC50 × FD54” α-farnesene, β-farnesene and α-curcumene) in the “FC50 × FD54” population than in “21AF.” When observing the Affx-88897295 population (Figure 3), and with linalool and nerolidol in the marker, individuals carrying the BB genotype showed a higher “21AF” population, since the VOC analysis was restricted to the methyl hexanoate content than those with the AB genotypes in key compounds. Different major QTLs for all these compounds both populations. In fact, individuals with the AA genotype, were colocalized at the beginning of LG3C Terpenes_3C in both present only in the “21AF” population, revealed an almost populations (Table 2). Focussing on the L_3C QTL, similar undetectable methyl hexanoate content (Figure 4B). Therefore, the linalool relative contents were observed in both populations B allele of this marker appears to be responsible for the (Figure 4C). The genetic region of this QTL in the “21AF” genetic accumulation of methyl hexanoate. The LG7B corresponded to the map showed a gap which limited the synteny to a few markers. F. iinumae-like subgenome. The smallest QTL size for the Despite this, good synteny was observed throughout both genetic Methyl_7B was of only 0.6 Mbp and included 93 genes (Table 2). maps. Looking at the common markers inside this QTL, such as Two of the ester QTLs were detected only in the “21AF” the Affx-88832495 marker, individuals with the AA genotype population, these were MH_1A and MB_1A, both of which share showed significant differences in the accumulation of linalool a methyl group named as Methyl_1A (Supplementary Figure 5A). compared to the individuals having the AB genotype in However, different QTLs related to the acetate group were detected “FC50 × FD54” or the BB genotype in “21AF.” This finding in the same region of LG1A as the “FC50 × FD54” population. suggested that the presence of the A allele at this locus increased Focusing on the Affx-88812680 marker located in the Methyl_1A linalool content in both populations, while the B allele, particularly QTL interval, individuals with the homozygous A allele showed a in homozygosity, appeared to correlate with a very low significantly higher methyl hexanoate content than those carrying linalool accumulation. the B allele. Since the AA genotype is not present in the Since markers from IStraw35k and 90 k arrays are not “FC50 × FD54” population, no significant differences were subgenome-specific, markers from the linalool QTL were located observed between “FC50 × FD54” genotypes. The Methyl_1A QTL by blast analysis in all homeologous chromosomes and especially covered a physical distance of 3Mbp and contained 481 annotated abundant in the chromosomes 3A (F. vesca-like) and 3C. To genes (Table 2). identify the chromosome, selected markers from the subgenome- Regarding the major ester QTLs involving eight different specific 50 k SNP array, were tested in “FC50 × FD54” and those compounds related to the hexanoate group, these were all located from chromosome 3C showed the same segregation as those at the end of the LG4A (Figure 3) and named as mapped in LG3C (data not shown). Hexanoate_4A. However, different stability degrees were observed In the “FC50 × FD54” genetic map, the shortest terpene QTL in these compounds. Four of these compounds were directly interval size was observed in monoterpene compounds with an related to hexanoate group (methyl, ethyl, isoamyl and hexyl interval of 0.9Mbp and includes 171 annotated genes (Table 2). hexanoates), two were related to 2-hexenoate group (methyl and However, most of the markers from the “21AF” genetic map were ethyl 2-hexenoate), one to octanoate group (methyl octanoate) mapped in the other homeologous rather than in chromosome 3C. Frontiers in Plant Science 12 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 A B C FIGURE 4 Selected major QTLs detected in the “FC50xFD54” and “21AF” maps, and boxplot of related VOCs for a common marker located in the QTL. (A) BA_6A QTL for acetate and octyl groups with Affx-8884155. (B) MH_7B QTL for methyl group with Affx-88897295. (C) L_3C QTL for terpene content with the Affx-88832495 marker. Red boxes on the left cover 1-LOD confidence intervals, and red line shows the SNP position in both maps. Markers colored in dark grey and chromosome in light grey. Right: Boxplot with each sample represented as a dot (progenies in grey, father line in red, mother line in green and hybrid for the “21AF” data in yellow), and the blue square indicates the average of each group. Significant levels p<0.001 (***), p<0.01 (**) and p<0.05 (*). γ-decalactone is known to provide peach-like aroma in Affx-88845940 with the γ-decalactone content revealed that the strawberries. In “21AF,” we found a major QTL, consistent in all BB genotype correlates with a very low content of γ-decalactone harvests, in LG3B. Comparing the genotyping data of the marker and that both genotypes, AA and AB, which presented small Frontiers in Plant Science 13 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 A B FIGURE 5 (A) Marker linked summary for the Terpene_3C and Hexanoate_4A QTLs in the “C19” population. Bold numbers indicate the genotype linked to the compound accumulation. (B) Linalool (left) and methyl hexanoate (right) boxplot for the “C19” population. Dot colors: progenies (black), “FC041” (red) and “FC081” (green). Blue squares represent means. Significant level<0.1 (·). differences between them, are related to high content of The analysis of the volatilome for the “FC50 × FD54” γ-decalactone. This means that A allele determines the population along six harvests identified 58 VOCs, with 16 of these γ-decalactone accumulation (Supplementary Figure 5D). This being KVCs. The total number of VOCs in the “FC50 × FD54” QTL is located at the end F. iinumae-like chromosome spanning a population appears to be lower than in previous studies such as physical size of 2.4Mbp that encloses 368 annotated genes the 87 VOCs found in an F1 population (Zorrilla-Fontanesi et al., (Table 2). 2012), the 81 identified in a parental collection (Schwieterman et al., 2014) or the 100 discovered in a F. vesca NIL collection (Urrutia et al., 2017). These differences might be caused by the SNPs for terpene and hexanoate differences that exist between wild and cultivated strawberry, the marker-assisted breeding genetic differences for VOC segregation in the used parental lines and the metabolite identification method used. In our study, A different F1 population, called “C19,” a cross between Kovats retention index, used for melon (Mayobre et al., 2021), was “FC041” and “FC081” breeding lines, selected for their special adopted to identify volatiles, while commercial standards were aroma, composed of 46 progenies and was chosen for the study of used for KVCs. Ulrich et al. (2018) reviewed that between 50 and its VOCs. The GC–MS analysis revealed that 14 out of the 19 75 compounds were identified using few strawberry accession KVCs were present in the C19 progenies. lines as also seen in our results. More than half of the VOCs SNP markers were selected for the Terpene_3C and identified in our analysis were esters (57%), followed by terpenes Hexanoate_4A QTLs to construct a Fluidigm array. ® (16%), aldehydes (12%) and lactones (5%). These proportions are Hybridization array analyses showed association between four comparable to those found in previous studies (Zorrilla-Fontanesi SNPs with linalool and three SNPs with methyl hexanoate et al., 2012; Schwieterman et al., 2014; Urrutia et al., 2017). accumulations. The alleles that increased linalool (Affx-88843366, Generally, the high correlation between compounds of the R2 ≤ 26) are from the parental line “FC081” and those that same chemical family suggests that these compounds are regulated increased methyl hexanoate (Affx-88856956, R2 ≤ 21) are from in the same way. But also, some high correlations were observed “FC041” (Figure 5). between compounds of different chemical families. Although these correlated compounds did not necessarily share the same biosynthetic pathway, they could have some common Discussion transcription factors regulating their activity or some linked genes involved in different volatile pathways. Hence, when selecting a Volatile organic compounds are key factors for fruit consumer compound, you may be simultaneously selecting others related acceptance. For this reason, breeding programs are demanding (Fan et al., 2021). Although the genetic factor was the main new tools that allow marker-assisted breeding (MAB) in order to contributor to the phenotypic variance, several compounds were satisfy the market demand. In this study, we present the aromatic rather dependent on the environment. profile of two strawberry populations and reveal the genome In our analysis, a high number of QTLs (179) were discovered regions responsible for VOC accumulation. Additionally, some for 55 identified VOCs in the “FC50 × FD54” population. Among SNP markers are suggested for MAB. them, 94 QTLs were detected in at least three of the six harvests Frontiers in Plant Science 14 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 for 43 VOCs. This relatively low QTL stability is in concordance assembling and therefore an homeologous FaNES1 gene could with previous studies in strawberry (Zorrilla-Fontanesi et al., also be located in 3C. Nevertheless, the involvement of a new gene 2012; Urrutia et al., 2017). For KVCs, 11 of the 44 discovered in the accumulation of these compounds should not be discarded. QTLs in the “FC50 × FD54” genetic map were detected in at least In cultivated strawberry, ester compounds are the most three of the six harvests for 17 different compounds. However, in abundant family providing a wide range of fruity aroma. Several the “21AF” genetic map, only nine of the 51 detected QTLs were ester QTLs were found in different LGs such as LG1A, LG4A, observed in two different harvests for seven KVCs. Despite the LG6A and LG7B. According to our study, QTLs for different ester large variation observed between seasons and populations, a good compounds were mapped in the same region regardless of number of the detected QTLs would be valuable for breeding. mapping populations. In LG1A, methyl butanoate and methyl Besides, some QTLs controlling the same trait localize in hexanoate QTLs mapped in the “21AF” genetic map while hexyl different homeologous groups (HG) as observed in most of the acetate, hexyl butanoate and octyl butanoate QTLs mapped in QTL analyses in cultivated strawberry (Zorrilla-Fontanesi et al., “FC50 × FD54.” These “21AF” QTLs might be related to 2011, 2012; Lerceteau-Köhler et al., 2012; Cockerton et al., 2018, Methyl_1A, whereas the “FC50 × FD54” QTLs may be related to 2019). In our “FC50 × FD54” population, six VOC_QTLs were Hexyl_1A. In the literature, different ester QTLs, including one for mapped in HGs, with always one QTL in one HG being more methyl hexanoate, were also mapped in LGI-1 (Zorrilla-Fontanesi stable and significant than the others. We therefore suggest that et al., 2012). Other examples are the QTLs in LG6A mapped for the same genes located in different HGs can contribute to the same different compounds in different populations, such as octyl group trait, but one would have a predominant role and the others would compounds, Octyl_6A, discovered in “FC50 × FD54” and acetate modulate the accumulation of the VOCs (Lerceteau-Köhler related compounds, Acetate_6A, in “21AF.” In previous studies, et al., 2012). QTLs for different compounds were also found clustered together Some QTLs have been validated in different genetic without any obvious relationship between these compounds, such backgrounds. An interesting example is the Terpenes_3C QTL, as in the F1 population used by Zorrilla-Fontanesi et al. (2012) responsible for the pleasant floral aroma, identified at the and in a multiple family studied with GWAS by Fan et al. (2021). beginning of LG3C. In the “21AF” population this QTL showed Where the BA_6A QTL was mapped in both populations, the higher LOD score than in the “FC50 × FD54” population. The most significant marker shared, showed different genotypes for LOD score difference is mainly due to the exclusive presence in butyl acetate accumulation, this means that we are still quite far the “21AF” population of the allele associated with a very low from the causal gene (Figure 4A). Our Hexanoate_4A QTL, terpene accumulation in homozygosis (Figure 3C). Even if mapped only in “FC50 × FD54,” which seems to validate the Terpenes_3C is localized in the 3C chromosome, some of its cluster of ester QTLs mapped in LGIV-1 of the “1,392 × 232” markers map to the 3B and 3D chromosome, indicating a high population (Zorrilla-Fontanesi et al., 2012). synteny between the homeologous chromosomes. A terpene QTLs for methyl hexanoate, isopropyl hexanoate and methyl synthase cluster or a non-specific terpene substrate protein placed octanoate were co-localized in the LG7B. These three compounds in this QTL region might explain the accumulation of the terpene shared a methyl group, and we called the resulting QTL components. Another hypothesis is that some post-transcriptional Methyl_7B. The MH_7B presented higher significance in the modifications may alter the volatile terpene accumulation. “21AF” compared to the “FC50 × FD54” genetic maps. This Similar to our finding, F. × ananassa nerolidol synthase, difference is due to the homozygous genotype of the allele linked FaNES1, has been characterized as important for linalool and to the low accumulation of methyl hexanoate that is only present nerolidol synthesis (Aharoni et al., 2004), mapped in LGIII-4 in the “21AF” population (Figure 4B). The last step for ester (Zorrilla-Fontanesi et al., 2012) and annotated in 3D. Additionally, biosynthesis is catalysed by alcohol acyl transferase (AAT). This when having the functional enzyme, cultivated and wild octoploid enzyme can use a wide variety of substrates (Aharoni et al., 2000). strawberries produce higher linalool content (Chambers et al., Seven AAT genes were annotated in the F. × ananassa genome, 2012). Other nerolidol synthases, FaNES1-like and FaNES2, are located in LG1D (2), 7A (3), 7B (1) and 7C (1). These genes are annotated in 3B. These three genes show the same syntenic mostly conserved in Fragaria genus, such as FcAAT1 in F. chiloensis position as our QTL located in 3C. A very recently published (Gonzalez-Aguero et al., 2009) and four AAT genes in diploid genetic analysis of flavor compounds in cultivated strawberry, F. vesca genome annotated in LG6 (1) and LG7 (3) being one found also a cluster of terpene QTLs on chromosome 3C (Barbey FvAAT enzyme already characterized (Beekwilder et al., 2004). et al., 2021). Although a non-functional allele was described in The annotated gene encoding an AAT named as FxaC_26g18860 F. vesca (Aharoni et al., 2004; Chambers et al., 2012), terpene (Liu et al., 2021) is a candidate for our Methyl_7B QTL. QTLs were also mapped in Fvb3 (Urrutia et al., 2017) and a cluster Methyl anthranilate, derived from the degradation of amino of seven FvNES1 genes exists in the updated F. vesca genome acids, is defined as the wild strawberry aroma which is appreciated annotation (Li et al., 2019). Since these results are based on the by consumers and consequently by aroma breeding programs. In version 1.0.a1 of the cultivated strawberry genome (Edger et al., the “FC50 × FD54” population, several methyl anthranilate QTLs 2019) which presents high synteny between the homeologous were mapped in different LGs, such as in LG1A, LG4C, three LGs chromosomes, mismatches may be likely to occur during of the HG5 and LG7A. Methyl anthranilate was not detected in Frontiers in Plant Science 15 frontiersin.org Rey-Serra et al. 10.3389/fpls.2022.959155 the “21AF” population as this compound is known to be present RTA2013-00010-00-00 and PID2020-119052RR-I00 from in only a few cultivars (Olbricht et al., 2008; Schwieterman et al., FEDER/Ministerio de Ciencia, Innovación y Universidades – 2014). The anthranilate acid methyl transferase gene, FaAAMT, Agencia Estatal de Investigación and by the CERCA mapped in the F. vesca chromosome 4 (Pillet et al., 2017), may Programme/Generalitat de Catalunya. We acknowledge be responsible of the MA_4C QTL that we detected. The HG5 and financial support from the Spanish Ministry of Science and LG7A QTLs are probably related to the methyl anthranilate QTLs Innovation-State Research Agency (AEI), through the “Severo found in LG5 and LG7 in F. vesca (Urrutia et al., 2017). Markers Ochoa Programme for Centres of Excellence in R&D” in the region of a highly stable and major QTL, such is MA_7A, SEV-2015-0533 and CEX2019-000902-S. would be appropriate for MAB to select towards methyl anthranilate accumulation. The peach aroma is attributed to γ-decalactone presence and Conflict of interest amount. A total of six QTLs for lactone compounds were detected in the two studied populations in LG2A, LG3B, LG4A, LG6B and The authors declare that the research was conducted in the LG6D. The major GD_3B QTL was only detected in the “21AF” absence of any commercial or financial relationships that could population. Although the allele linked to the high γ-decalactone be construed as a potential conflict of interest. accumulation was present in both populations, only the “21AF” population exhibited the low γ-decalactone accumulation genotype. This QTL is likely related to the omega fatty acid Publisher’s note desaturase gene, FaFAD1, involved in the γ-decalactone synthesis and mapped in LGIII-2 (Zorrilla-Fontanesi et al., 2012; Chambers All claims expressed in this article are solely those of the et al., 2014; Sanchez-Sevilla et al., 2014). Unfortunately, this gene authors and do not necessarily represent those of their affiliated is not annotated in the F. × ananassa genome due to an 8 kb organizations, or those of the publisher, the editors and the deletion in non-producing γ-decalactone cultivars (Oh reviewers. Any product that may be evaluated in this article, or et al., 2021). claim that may be made by its manufacturer, is not guaranteed or The “FC50 × FD54” volatilome revealed that this population endorsed by the publisher. is suitable for the discovery of volatile QTLs. Some of these QTLs are related to liking compounds (Schwieterman et al., 2014; Ulrich and Olbricht, 2016; Fan et al., 2021) and validate QTLs from Supplementary material previous studies. Still, much more effort is needed to narrow down these genomic regions in order to come across candidate genes The Supplementary material for this article can be found online that allow a better understanding of how these compounds at: https://www.frontiersin.org/articles/10.3389/fpls.2022.959155/ accumulate and to therefore find diagnostic markers for selection full#supplementary-material in strawberry breeding. SUPPLEMENTARY FIGURE 1 Violin plot representing VOC distributions of parents and progeny of the “FC50×FD54” population. Harvest colors: 2016 (reddish), 2017 (orangish) Data availability statement and 2018 (bluish). Dots: “FC50” (green), “FD54” (red). Stars indicate KVCs. SUPPLEMENTARY FIGURE 2 Heatmap visualization of Pearson correlation between VOCs in six The original contributions presented in the study are included harvests of the “FC50xFD54” population (p-value < 0.05). Stars in the article/Supplementary material, further inquiries can indicate KVCs. be directed to the corresponding author. SUPPLEMENTARY FIGURE 3 Principal component analysis (PCA) plot in VOCs: (A) Dim1 vs Dim2 and (B) Dim1 vs Dim3. Harvest: 2016 (reddish), 2017 (orangish) and 2018 (bluish). Author contributions SUPPLEMENTARY FIGURE 4 KVC contents in the “21AF” population. A. Violin plot representing KVC AM and MM contributed to the conception and design of the distributions. Dots: “Dover” (green), “Camarosa” (red) and H-21 (yellow). B. Heatmap visualization of Pearson correlation in three years (p-value study. PR-S performed the experimental and statistical analysis < 0.05). C. Heatmap representation of hierarchical clustering analyses and wrote the first draft of the manuscript. All authors contributed (HCA) and D. Principal component analysis (PCA) plot in three years. to the article and approved the submitted version. SUPPLEMENTARY FIGURE 5 QTLs of interest found in one of the two populations studied. A. MH_1A (Methyl hexanoate) QTL synteny and boxplot of the Affx- 88812697 marker for “FC50 × FD54” and “21AF” population. B. MH_4A Funding (Methyl hexanoate) QTL synteny for “FC50 × FD54” and “21AF” population and boxplot of the Affx-88856759 marker. C. MA_7A (Methyl anthranilate) QTL synteny for “FC50xFD54” and “21AF” PR-S was supported by a PhD grant CPD2015-0185 from population and boxplot of the Affx-88897207 marker. D. GD_3B MINECO (FPI-INIA). 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