Insights into sucrose pathway of chicory stems by integrative transcriptomic and metabolic analyses

Phytochemistry 167 (2019) 112086

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Insights into sucrose pathway of chicory stems by integrative transcriptomic and metabolic analyses

T

Giulio Testonea,1, Anatoly Sobolevb,1, Maria Gonnellac, Massimiliano Rennac,d, Luisa Manninab,e, ⁎ Donatella Capitanib, Giuseppe Arnesif, Tiziano Biancarif, Donato Gianninoa, a

Institute of Agricultural Biology and Biotechnology - Unit of Rome, National Research Council (CNR), Via Salaria km 29.300, 00015, Monterotondo, Rome, Italy Institute for Biological Systems, “Annalaura Segre” Magnetic Resonance Laboratory, CNR, Via Salaria Km 29,300, 00015, Monterotondo, Rome, Italy c Institute of Sciences of Food Production, CNR, Via G. Amendola 122/O, 70126, Bari, Italy d Department of Agricultural and Environmental Science, University of Bari, Via Amendola 165/A, 70126, Bari, Italy e Department of Drug Chemistry and Technologies, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy f Enza Zaden Italia, Strada Statale Aurelia km. 96.400, 01016, Tarquinia, Viterbo, Italy b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cichorium intybus Compositae Stem sugar metabolism NMR profiling RNA-Seq Sucrose catabolism genes Gene network

The worldwide-cultivated chicory (Cichorium intybus L.) produces food and beneficial compounds, and young pre-flowering inflorescence stems are newly marketed vegetables. These sink-organs undergo growth by metabolizing sugars of leaf origin; the carbohydrate content and sweetness are crucial aspects for consumers’ nutrition and acceptance. NMR profiling of 31 hydrosoluble phytochemicals showed that stem contents varied as influenced by genotype, environment and interaction, and that higher sucrose levels were associated with the sweeter of two landraces. Integrative analyses of metabolic and transcriptomic profile variations allowed the dissection of sucrose pathway. Overall, 427 and 23 unigenes respectively fell into the categories of sucrose metabolism and sugar carriers. Among 10 differentially expressed genes, the 11474/sucrose synthase, 53458/ fructokinase, 9306 and 17035/hexokinases, and 20171/SWEET-type genes significantly associated to sugar content variation, and deduced proteins were characterised in silico. Correlation analyses encompassing sugar level variation, expressions of the former genes and of computationally assigned transcription factors (10938/NAC, 14712/bHLH, 40133/TALE and 17846/MIKC) revealed a gene network. The latter was minimally affected by the environment and accomplished with markers, representing a resource for biological studies and breeding.

1. Introduction Chicory (Cichorium intybus L., fam. Compositae) produces a wide range of vegetables and derivate foods worldwide (Street et al., 2013). The ‘Catalogna’ cultivated group (Raulier et al., 2016) embraces stemchicory ecotypes (Testone et al., 2016) that yield inflorescence stems (emerging from a leaf rosette) harvested at early growth and consumed as fresh, fresh-cut or fully processed vegetables (Elia and Santamaria, 2013; Renna et al., 2014). Stem-chicory cultivation has concentrated in south Italy (Apulia) as a domestication focus of local populations with ample trait diversity (Elia and Santamaria, 2013). Recently, some landraces were characterized for a few nutrients and antioxidant capacity (D'Acunzo et al., 2017; Montefusco et al., 2015; Renna et al., 2014). Biologically, these stalks undergo primary growth (longitudinally

oriented) by exploiting sugars produced by the rosette leaves. Sucrose is a major photoassimilate exported from photosynthetic (source) to nonphotosynthetic (sink) tissues and is employed into all organic growth supporting metabolic pathways (Peng et al., 2014). Sucrose is translocated by the uptake transporters/carriers (SUTs) and by the SWEETs (sugars will eventually be exported transporters) facilitators. Specifically, these latter promote sucrose efflux from mesophyll cells to cell wall, providing SUTs with the disaccharide that is loaded into sieve element/companion cell complexes (Chen et al., 2012). Referring to stems, SUTs and SWEETs can perform sucrose unload from sieves to sink tissues (Mizuno et al., 2016b; Sauer, 2007). Inside a sink tissue, sucrose is hydrolyzed by both invertases (INVs) and sucrose synthases (SUSs), which are the only two sucrose-cleaving enzyme families identified in several plants (Dennis and Blakeley, 2000) as well as in chicory (Druart et al., 2001; Wei et al., 2016). INVs encompass cell

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Corresponding author. E-mail address: [email protected] (D. Giannino). 1 Equal contribution to the work. https://doi.org/10.1016/j.phytochem.2019.112086 Received 11 January 2019; Received in revised form 21 June 2019; Accepted 6 August 2019 Available online 23 August 2019 0031-9422/ © 2019 Elsevier Ltd. All rights reserved.

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stems were sweeter than ‘Molfettese’ ones (Table S2). Glucose, fructose and sucrose content ranges of chicory stalks are three fold lower than leaves (16.7 ± 5.8 vs 63.8 ± 0.4 g 100g−1) (Milala et al., 2009). The higher glucose and sucrose (but not fructose) levels of ‘Galatina’ vs ‘Molfettese’ confirmed the contents previously measured in Apulia (Renna et al., 2014), and G and E significant effects on glucose and sucrose were coherent with data reported for chicory leaves (Gent, 2012; Poli et al., 2002). The glucose content of stems prevailed in the contribution to TSI (51–56%) compared to fructose (27–36%), though the sweetness power of the latter is 1.7 fold higher than the former; sucrose contribution was of 8–20% while that of the others was below 1% (Table S2). Kestose is a key precursor of inulin, a typical storage carbohydrate of chicory, and stem amounts were over 10 fold lower than roots (Li et al., 2008), consistently with the sink status of growing stalks. Interestingly, raffinose ranges were comparable to those of legume seeds that can often exceed 4.5 mg g−1 of dry weight (McPhee et al., 2002), hence the chicory-stem consumption may need evaluation in anti-bloating diets considering that raffinose causes flatulence (Dahl et al., 2014). Glutamine and asparagine showed the highest contents among twelve hydro-soluble amino acids (Table 1; compound abbreviations are in Fig. 1 legend) and these two were higher in ‘Molfettese’ than ‘Galatina’ stems in both planting sites. The variation contents of both amino acids were under G influence while only Gln under that of GxE interaction. Chicory leaves are reported to have higher free amino acid contents than stems (84 vs 59 mg g−1) and the total ranges of ‘Catalogna’ stalks were consistent (Shad et al., 2013). However, the content profile of eleven amino acids enriched information on stem vegetables considering that we encountered data scarcity even for chicory leaves (Ćustić et al., 2002). Malic, tartaric and citric acid amounts exceeded those of fumaric, succinic and lactic acids that were below 0.1 mg 100g−1. G and E affected content variation of each organic acid in different manners; e.g. the MA level was specifically influenced by G, that of TA by both E and GxE. Focussing on the most abundant MA and CA, the chicory-stalk levels were much lower than lettuce leaves that have average values of 118 and 575 mg 100g−1 on fresh weight basis (Lee, 2018). As for polyols, myo-inositol and quinic acid contents were much higher than scyllo- and chiro-inositol. QA content of ‘Molfettese’ exceeded that of ‘Galatina’ in both growth areas, with significant effects by G and GxE, but not by E. QA of chicory stems (here converted into 29.2–67.3 mg 100g−1 of fresh weight) was much more abundant than fresh leaves quantified as ca. 4 mg 100g−1 (Zeb et al., 2018). Stalks also contained more MI but less SI and CI than chicory leaves; respectively converted into 42.5 ± 4.8, 2.5 ± 0.6, 3.4 ± 1.6 versus 18.2 ± 0.4, 5.3 ± 0.1, 19.9 ± 2.3 mg 100g−1 on fresh weight basis (HernandezHernandez et al., 2011). The polyol content was affected by G and E, but not by GxE. The chicoric acid amount was higher than monocaffeoyl tartaric acid in stems though much higher ranges (1.7–2.3 mg g−1) occur in Catalogna leaves (Ferioli, 2015).

wall, vacuolar and cytoplasmic isoforms (Wan et al., 2018) and irreversibly split sucrose into glucose and fructose (Granot et al., 2014). As for SUSs, they reversibly break sucrose in uridine diphosphate glucose and fructose (Druart et al., 2001). In order to enter any metabolic process, the two hexoses need phosphorylation performed by hexokinases and fructokinases (HXKs and FRKs) that play indispensable functions in sink organs (less necessary in photosynthetic tissues where phosphorylated hexoses derive from triose phosphates). FRKs specifically act on fructose, while HXKs can target glucose, fructose, mannose and glucosamine (Granot et al., 2014). HXKs and FRKs catalyze irreversible reactions (hexose-phosphate phosphatases have not been found in plants so far); hence, they play regulatory functions in sucrose metabolism. Genetically, the ‘Catalogna’ group shows high variation (Raulier et al., 2016) within C. intybus (2n = 2x = 18, allogamous) that has a genome of ca. 1,3 Gb and, so far, the use of its sequence has been under restrictions (Galla et al., 2016). However, sequences of chicory transcriptomes have been publicly available (Hodgins et al., 2014) and a ‘Catalogna’ transcriptome recently enriched (Testone et al., 2016) the chicory genetic and genomic scenario (Cadalen et al., 2010; De Simone et al., 1997) with useful tools for molecular marker assisted breeding. Sweetness is a crucial flavour in consumers’ acceptance of chicory vegetables (Appleton et al., 2018; Drewnowski and Gomez-Carneros, 2000). In order to widen the information on nutritive aspects of these novel foods, the content variation of sugar and several other hydrosoluble compounds was measured and found to be influenced by genotype, environment and interactions in stems of two landraces with divergent sweetness (higher in ‘Galatina’ than ‘Molfettese’). Subsequently, the study aimed at identifying those genetic pathways that could subtend sugar content differences by an integrative approach based on NMR-metabolic profiling, transcriptome mining and RNA-seq differential expression analyses. The sucrose pathway was dissected in order to identify a network participating to the differences in sucrose and glucose levels and consisting of biosynthetic genes and their putative regulators, which were minimally affected by the environment and accomplished with makers useful for breeding scopes. 2. Results and discussion 2.1. Contents of sugars and other nutrients Globally, 31 hydrosoluble compounds were assigned by NMR (Table S1), grouped by chemical similarity and quantified (Table 1). The data set was first explored by PCA (Fig. 1); the principal components 1 and 2 explained more than 57% and 36% of the total variance and could separate respectively the environment (E) from genotype (G) effects. Specifically, products from Lazio fell in the sector of PC1 positive values and oppositely to those from Apulia (negative value sector). As for PC2, ‘Molfettese’ genotypes fell in the positive value area diverging from ‘Galatina’ ones, sited in the range of negative values. Moreover, sugars were associated to ‘Galatina’. The metabolite contents and variations as affected by G, E and GxE interactions were further examined (Table 1). Synoptically, carbohydrates were the major fraction followed by amino acids, organic acids, polyols, phenylpropanoids (see total means). The most abundant carbohydrates were glucose, sucrose and fructose (91–137; 10–40; 25–31 mg 100g−1), followed by raffinose, kestose and galactose (0.9–6.9; 1.7–5.4; 0.1–0.4 mg 100g−1). The content of each sugar type was on average higher in ‘Galatina’ than ‘Molfettese’, commonly influenced by E, but differentially by G (e.g. fructose was not affected by G) and by GxE interactions, these latter were specifically acting on sucrose and raffinose amounts. The ‘Galatina’ genotype maintained higher levels of sucrose and glucose in both cultivation sites and, consistently, the total sweetness index (TSI) of the former was at least 1.2 fold higher than the latter in both sites, and G, E and GxE effects coincided with those on sugar content analyses (Table S2). A simple and mid-scale sensory test further confirmed that ‘Galatina’

2.2. Characterisation of genes subtending sucrose metabolism in chicory stems The annotation of a previously assembled ‘Galatina’ transcriptome (Testone et al., 2016) was improved by using sequences of the genomes of artichoke and lettuce (both Compositae species) that were released later on (Acquadro et al., 2017; Reyes-Chin-Wo et al., 2017). In order to investigate on genes likely to subtend sucrose content differences, RNAseq analysis was targeted to differentially expressed genes (DEGs) ascribed to sucrose metabolism (SM) and transport (ST). In support, a previous KEGG analysis pointed at DEG enrichment in sucrose and starch metabolism (Testone et al., 2016). Overall, 427 contigs belonged to the SM pathway and 23 contigs were annotated as sugar carriers (Table S3). To perform a solid gene expression analysis 159 SM and 18 ST contigs were selected based on transcript completeness (at least 80%) and respectively 9 SM and 1 ST contigs showed differential 2

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Table 1 Content variation of hydro-soluble compounds from edible stems. Metabolite

Content (mg g−1 dry weight)1 Apulia

Carbohydrates Glucose Fructose Galactose Sucrose Raffinose Kestose Total Amino acids Alanine Asparagine Aspartic acid Glutamine Glutamic acid Isoleucine Phenylalanine Tyrosine Threonine Valine γ-aminobutyric acid Total Organic acids Citric acid Fumaric acid Malic acid Succinic acid Lactic acid Tartaric acid Total Polyols Myo-inositol Scyllo-inositol Chiro-inositol Quinic acid Total Phenylpropanoids Chicoric acid Monocaff. tartaric ac. Total Others Ethanolamine Choline Total

Variation source Lazio

‘Galatina'

‘Molfettese'

‘Galatina'

‘Molfettese'

G

E

GxE

137.57 ± 19.97 31.39 ± 2.22 0.27 ± 0.08 40.41 ± 6.42a 6.91 ± 2.69a 5.43 ± 2.36 221.99 ± 23.97

108.57 ± 13.66 28.41 ± 3.26 0.17 ± 0.03 25.15 ± 7.93 b 2.00 ± 0.58bc 3.70 ± 2.04 168 ± 20.67

112.35 ± 14.38 27.07 ± 5.27 0.43 ± 0.24 17.19 ± 2.95c 2.41 ± 0.78 b 3.28 ± 1.42 162.74 ± 18.92

91.37 ± 12.95 25.36 ± 3.96 0.27 ± 0.07 10.13 ± 1.78 d 0.93 ± 0.14c 1.71 ± 1.32 129.78 ± 16.26

*** n.s. ** *** *** ** ***

*** ** ** *** *** ** ***

n.s. n.s. n.s. * *** n.s. n.s.

1.58 ± 0.49 b 6.97 ± 2.83 0.96 ± 0.28 23.34 ± 7.77 b 1.13 ± 0.28 1.37 ± 0.48 0.85 ± 0.12 0.28 ± 0.07 0.50 ± 0.11 0.61 ± 0.22 0.86 ± 0.29 38.47 ± 11.95 b

3.10 ± 0.93a 12.62 ± 1.47 1.46 ± 0.16 46.3 ± 6.93a 1.55 ± 0.2 1.33 ± 0.45 0.47 ± 0.17 0.24 ± 0.10 0.87 ± 0.15 0.99 ± 0.28 1.18 ± 0.35 70.10 ± 7.60a

1.63 ± 0.67 b 8.95 ± 1.70 2.49 ± 0.53 26.42 ± 8.22 b 1.91 ± 0.51 2.42 ± 0.94 2.02 ± 0.86 0.86 ± 0.42 0.91 ± 0.30 1.68 ± 0.79 0.69 ± 0.67 49.97 ± 12.01 b

2.05 ± 0.56 b 12.55 ± 2.10 2.60 ± 0.30 38.48 ± 6.18a 2.00 ± 0.22 2.19 ± 0.60 1.17 ± 0.28 0.83 ± 0.27 1.07 ± 0.15 1.92 ± 0.44 0.69 ± 0.60 65.56 ± 9.87a

*** *** ** *** * n.s. *** n.s. *** n.s. n.s. ***

* n.s. *** n.s. *** *** *** *** *** *** * n.s.

* n.s. n.s. * n.s. n.s. n.s. n.s. n.s. n.s. n.s. *

2.09 ± 0.42 0.10 ± 0.02 26.54 ± 5.96 0.13 ± 0.03 0.08 ± 0.05 7.05 ± 1.11 ab 35.99 ± 5.46

2.18 ± 0.27 0.07 ± 0.02 19.72 ± 3.93 0.17 ± 0.08 0.05 ± 0.03 5.53 ± 1.36 b 27.71 ± 2.88

2.65 ± 0.7 0.16 ± 0.16 24.53 ± 5.73 0.13 ± 0.04 0.02 ± 0.02 7.61 ± 1.52a 35.1 ± 5.79

2.13 ± 0.39 0.08 ± 0.05 21.52 ± 4.33 0.14 ± 0.05 0.02 ± 0.02 8.08 ± 1.32a 31.98 ± 5.12

n.s. n.s. ** n.s. n.s. n.s. ***

n.s. n.s. n.s. n.s. *** *** n.s.

n.s. n.s. n.s. n.s. n.s. * n.s.

5.72 ± 0.72 0.27 ± 0.04 0.25 ± 0.08 4.68 ± 1.36c 10.92 ± 1.5c

7.04 ± 0.8 0.40 ± 0.15 0.18 ± 0.09 10.78 ± 3.25a 18.4 ± 3.56a

7.64 ± 1.51 0.49 ± 0.15 0.90 ± 0.34 5.63 ± 1.1c 14.65 ± 2.61 b

7.89 ± 0.94 0.52 ± 0.08 0.64 ± 0.16 8.37 ± 1.29 b 17.42 ± 2.03 ab

* * * *** ***

*** *** *** n.s. n.s.

n.s. n.s. n.s. ** **

0.71 ± 0.24 0.11 ± 0.04 b 0.83 ± 0.27

0.87 ± 0.32 0.24 ± 0.13a 1.12 ± 0.44

0.46 ± 0.30 0.11 ± 0.07 b 0.57 ± 0.33

0.50 ± 0.19 0.12 ± 0.06 b 0.61 ± 0.23

n.s. * n.s.

*** * ***

n.s. * n.s.

0.97 ± 0.18 1.12 ± 0.17 2.09 ± 0.27

0.96 ± 0.19 1.20 ± 0.18 2.16 ± 0.33

1.12 ± 0.27 1.50 ± 0.31 2.62 ± 0.53

1.08 ± 0.29 1.71 ± 0.35 2.79 ± 0.63

n.s. n.s. n.s.

n.s. *** ***

n.s. n.s. n.s.

1, mean ± standard deviation; mean ratios of dry vs fresh weight for ‘Galatina’ and ‘Molfettese’ were respectively 6.6 and 6.7% in Apulia, and 5.7 and 5.9% in Lazio. Conversion factors into mg g−1 fresh weight are 15.1–14.9 and 17.5–16.9, accordingly. G, genotype; E, environment. Significance letters refer to GxE interactions; n.s., non-significant; *, **, *** = significant at P ≤ 0.05, 0.01 and 0.001, respectively.

using sequences from the major vegetables of the Cichorieae tribe plus those from A. thaliana as herbaceous model species that provides exhaustive information on protein function. Overall, the chicory and endive sequences (C. intybus L. and C. endivia L.) joined the same phyletic groups that diverged from those of lettuce (Lactuca sativa L.). As for SUS, four contigs could be ascribed to SUS types I, II and III (Fig. 3A), and the type I 11474/SUS protein was predicted to be cytosolic (Fig. S1C). The 53458/FRK protein fell in the phyletic group of reference A. thaliana FRK5 (group A, Fig. 3B) and computed as cytosolic (Fig. S1C), while other three chicory FRKs fell in the group B and C. Among the five chicory-stem hexokinases (Fig. 3C), the 17035 and 9306/HXKs were closest to the AtHXK1 and 2 and computed to be respectively mitochondrial and cytosolic (Fig. S1C). Referring to the ten chicory SWEET-like genes, the contig20171 encoded a product falling into clade IV (Fig. 3D) of the vacuolar AtSWEET16 and 17 and likely to localize in the tonoplast (Fig. S1C). The retrieval of 4 SUS, 4 FRK, 5 HXK and 10 SWEET full or nearly full coding sequences from chicory transcriptome confirmed the occurrence of family genes like in other plant

transcription in pairwise comparison between the genotypes in each growth site. We further selected those genes that showed conserved transcription patterns in both cultivation sites; they were assumed as minimally affected by the environment and named core-DEGs. The core-DEGs are good candidates to account for sucrose content differences that occur independently of the growth site and are a valuable source for breeding. The core-DEGs consisted of 10 sequences, subdivided into 6 sucrose metabolism, 1 SWEET and 3 cellulose/cell wall genes (Table 2). The 11474/sucrose synthase (SUS), 53458/fructokinase (FRK), 9306 and 17035/hexokinase (HXK), and 20171/SWEET-type genes were further studied (Table 2, grey shade), while trehalose and cellulose and cell wall pathway genes were not further investigated since the enzyme-targeted metabolites were not quantified. The digital expression trends of SM and SWEET genes were confirmed by qPCR assays (Fig. 2 A-F and K). As for deduced proteins, SUS, FRK, HXK, and SWEET were characterised by phylogenetic analysis (Fig. 3), amino acid identity and cell localisation prediction (Fig. S1). The phylogenetic trees were built up

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Fig. 1. Principal component analysis. PCA biplot showing the spatial distribution of 31 hydrosoluble compounds in ‘Molfettese’ (Mol) and ‘Galatina’ (Gal) types cultivated in Apulia (A) and Lazio (L). GLC, Glucose; FRU, Fructose; GAL, Galactose; SUCR, Sucrose; RAF, Raffinose; KES, Kestose; Ala, Alanine; Asn, Asparagine; Asp, Aspartic acid; Gln, Glutamine; Glu, Glutamic acid; Ile, Isoleucine; Phe, Phenylalanine; Tyr, Tyrosine; Thr, Threonine; Val, Valine; GABA, γ-Aminobutyric acid; CA, Citric acid; FA, Fumaric acid; MA, Malic acid; SA, Succinic acid; LA, Lactic acid; TA, Tartaric acid; MI, Myo-inositol; SI, Scyllo-inositol; CI, Chiro-inositol; QA, Quinic acid; CHA, Chicoric acid; MCTA, Monocaffeoyl tartaric acid; ETA, Ethanolamine; CHN, Choline.

Table 2 Differentially expressed genes of ‘Molfettese’ vs ‘Galatina’ stems with conserved patterns in two growth sites and belonging to sucrose metabolism. Contig

11474 9306 17035 53458 44405 9341 20171 Contig 59447 624 16154

ERb

Protein features a

Sucrose metabolism and transport

Reference

EC

Sucrose synthase Hexokinase 1 Hexokinase 1 Fructokinase 2 Trehalose-P phosphatase D Trehalose-P phosphatase F SWEET sugar transporter Cellulose and cell wall pathway Cellulose synthase A cat. sub. 3 Callose synthase 5 β-1,3-glucanase

Ci; ABD61653.1; 99 Ls; XP_023751733.1; Ls; XP_023761880.1; Ls; XP_023746181.1; Ls; XP_023750943.1; Ls; XP_023762434.1; Cc; KVI01593.1; 86

DEG analysisc Apulia

Lazio

90 89 94 92 91

2.4.1.13 2.7.1.1 2.7.1.1 2.7.1.4 3.1.3.12 3.1.3.12 –

H M H H M M M

2.48*** 1.63*** 1.54*** 3.22*** 1.41* 1.5*** −2.42***

1.10*** 1.07*** 1.18*** 1.94*** 1.11* 1.01*** −1.86***

Ls; XP_023756500.1; 91 Ls; XP_023763287.1; 94 Ci; CAA09765.1; 67

2.4.1.12 2.4.1.34 3.2.1.39

H M H

1.21*** −1.86*** 1.44***

1.03*** −1.12*** 2.63***

a , the column sequentially reports: the species abbreviation (Ls, Lactuca sativa; Ci, Cichorium intybus; Cc; Cynara cardunculus), the NCBI accession number and the identity percentage. b , ER, expression range. H, high (RPKM > 8); M, moderate (RPKM 1 ÷ 8); L, low (RPKM 0.1 ÷ 1). c , Positive and negative values respectively indicate up- and down-regulation in ‘Molfettese’ vs ‘Galatina’ stem comparisons.

coding genes was differentially expressed and a deeper characterisation was beyond the scope of this work. However, INV genes also formed a family in chicory in agreement with plant species (Wan et al., 2018).

species (Aguilera-Alvarado and Sanchez-Nieto, 2017; Chen et al., 2012; Granot et al., 2014). The ‘Catalogna’ sequences enriched the gene number within these families, considered that there is literature paucity on FRKs, HXKs and SWEETs of chicory and that only one chicory SUS has been characterized (Wei et al., 2016). The deduced protein variability supports the origin from distinct alleles or alternative splicing events, consistently with the predicted isoforms of lettuce orthologues (phytozome.jgi.doe.gov). The phylogenetic clustering of C. intybus and C. endive proteins was in agreement with the species relatedness (Raulier et al., 2016). Chicory proteins distant from those with ascertained functions may have enzymatic specificity; hence, experimental evidence will be necessary to either validate substrate affinity and computationally inferred localisation. Finally, considering the role of INVERTASES in sucrose to fructose and glucose release, none of the

2.3. Correlation analyses between gene expression and sugar content SUS/FRK/HXK transcriptions were characterized by negative correlation (Fig. 4A) vs SUCR/FRU/GLC contents (significant r ranges: 0.59 to −0.91), while that of SWEET was positive just vs glucose (r = 0.76). Moreover, SUS/FRK/HXK transcriptions showed one-another positive correlations in all pairwise comparisons (r: 0.65–0.94), whereas each of these three genes was negatively correlated with 20171/SWEET (r ≤ −0.72). These results supported that SM genes shared co-expression in sucrose catabolism that was antithetic to that of 20171/ 4

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Fig. 2. Gene expression analyses. A) Heat map of sucrose metabolism (SM), sugar transporter (ST) and transcription factors (TF) gene expressions (log2 fold change) in ‘Molfettese’ vs ‘Galatina’ from Apulia and Lazio growth sites. B-J) Gene expression analyses of core-DEGs in ‘Molfettese’ vs ‘Galatina’ by RNA-seq (black bars) and quantitative PCR (qPCR, grey bars) analyses. Nine DEGs (4 SM genes, 1 SWEET transporter and 4 putative transcription factors) were chosen and their expression was monitored in both landraces in Apulia (A) and Lazio (L) growth sites. Different letter indicate significant differences (P < 0.05) according to ANOVA and Tukey's HSD analyses. K) Correlation of gene expression fold changes (log2 fold change) inferred by bioinformatics predictions (log2FC by RNA-seq; x axis) and experimentally measured (log2FC by qPCR; y axis). Regression line and coefficient of determination (R2) are shown.

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Fig. 3. Phylogenetic trees of chicory sucrose metabolism proteins. Unrooted phylogenetic trees of sucrose synthases (A), fructokinases (B), hexokinases (C), and SWEET transporters (D). The bootstrap values are based on 1000 replicates and length of the lines indicates the relative distances between nodes. The analysis involved amino acid sequences of the Cichorieae tribe and those from Cichorium intybus are in bold. Sequences from core-DEGs are highlighted. Ci, C. intybus; Ce, C. endivia; Lsat, Lactuca sativa. As for Arabidopsis sequences, the TAIR accession number are: AtSUS1, AT5G20830.1; AtSUS2, AT5G49190.1; AtSUS3, AT4G02280.1; AtSUS4, AT3G43190.1; AtSUS5, AT5G37180.1; AtSUS6, AT1G73370.1; AtFRK1, At2g31390; AtFRK2, At1g06030; AtFRK3, At1g06020; AtFRK4, At3g59480; AtFRK5, At4g10260; AtFRK6, At1g66430; AtFRK7, At5g51830; AtHXK1, AT4G29130; AtHXK2, AT2G19860; AtHXK3, AT1G47840; AtHXL2, AT3G20040; AtHXL1, AT1G50460; AtHXL3, AT4G37840; AtSWEET1, AT1G21460; AtSWEET2, AT3G14770; AtSWEET3, AT5G53190; AtSWEET4, AT3G28007; AtSWEET5, AT5G62850; AtSWEET6, AT1G66770; AtSWEET7, AT4G10850; AtSWEET8, AT5G40260; AtSWEET9, AT2G39060; AtSWEET10, AT5G50790; AtSWEET11, AT3G48740; AtSWEET12, AT5G23660; AtSWEET13, AT5G50800; AtSWEET14, AT4G25010; AtSWEET15, AT5G13170; AtSWEET16/17, AT3G16690; AtSWEET17, AT4G15920.

except for the non-significant cases of 10938/NAC and 14712/bHLH vs fructose. The 17846/MIKC gene expression had positive correlation just with glucose contents (r = 0.73). Taken together these results support that 10938/NAC, 14712/bHLH and 40133/TALE and SM are co-expressed to negatively regulate sucrose and glucose levels, whereas 17846/MIKC and 20171/SWEET concurrent down regulation may act to raise glucose content. The regulation of sucrose metabolism occurs at multiple levels and, so far, the literature on protein-promoter regulation has regarded RIN-MAD-box type TF that targets SUS, FRK and HXK promoters in tomato (Qin et al., 2016) and the LEC2 putative control on SUS gene in Arabidopsis (Angeles-Nunez and Tiessen, 2012), while the SWEET regulation has been assessed for some effectors of pathogen origin (Yuan and Wang, 2013). Correlation analyses from chicory stems pointed at 10938/NAC, 40133/TALE and 14712/bHLH as candidates that co-work with the 11474/SUS (r > 0.87) in the sucrose breakdown and hexose fate. The first two TF respectively shared identities with Arabidopsis NST1 and KNAT7 proteins that hierarchically regulate xylan biosynthesis in inflorescence stem (He et al., 2018). Xylans are cell wall hemicelluloses made from units of xylose that derive from UDP-glucose produced by SUS. The occurrence of binding sites recognised by NACs and TALEs in lettuce SUS ortholog supports the speculation that they can control sucrose metabolism also in chicory stalks. Chicory 14712/bHLH is the putative orthologue of Arabidopsis bHLH25 that, so far, has an unexplored function in sugar metabolism. However, bHLH factors are proposed to control SUS expression (Payyavula et al., 2013), which is supported by the BS enrichment (over 30) in the lettuce ortholog of 14712/bHLH. The high positive correlation of both 17846/MIKC and 20171/SWEET genes vs glucose levels and the high number of MIKC-specific BS in lettuce SWEET promoter suggest the associated participation in the glucose content cascade. The Arabidopsis orthologues of the latter genes are MAF4 (MADS AFFECTING FLOWERING4) and SWEET16 both involved in sugar-mediated cold perception/sensing (Kim and Sung, 2014; Klemens et al., 2013). Hence, the tight correlation of the chicory MIKC/SWEET16 and the landracespecific different expression might mirror the different cold adaptation described for ‘Molfettese’ and ‘Galatina’ (Elia and Santamaria, 2013). Finally, the TF and target gene relationships in chicory stems were predicted by in silico analyses and need the support of experimental assays.

SWEET. Data literature report that the correlated increase of SUS activity and gene expression goes in parallel with sucrose content decline in taproots (Liu et al., 2018) and elongating stems (Sturm and Tang, 1999; Wang et al., 2013). Consistently, the 11474/SUS transcript and sucrose abundance negative correlation suggests an analogous role for this gene during early growth of chicory stem. FRK and HXK expressions recur in plant stems, some members of the former control vascular development in herbaceous species (Stein et al., 2016, 2018), while the HXK loss of function affects hypocotyl length in Arabidopsis (Moore et al., 2003). Relatedly, the FRK and HXK abundant transcription during chicory stalk growth might be necessary in processes of bundle differentiation and elongation. Moreover, HXK overexpression caused increase of amino acids contents in tomato leaves and fruits (Menu et al., 2004; Roessner-Tunali et al., 2003) likely due to the enhanced carbon partitioning toward the amino acid pool. Contextually, it is tempting to speculate that the higher transcription of 53458/FRK, 9306 and 17035/HXK genes in ‘Molfettese’ vs ‘Galatina’ may reflect a stronger glucose recruitment into amino acid synthesis as supported by the higher contents measured in ‘Molfettese’ (Table 1), though it cannot be excluded that changes may be due to different import levels from the leaves. The positive correlation of 20171/SWEET expression and glucose contents of chicory stems finds supports by the concurrent upregulation of SWEET16/17-like genes and sugar accumulation observed in rice and sorghum stems (Hashida et al., 2018; Mizuno et al., 2016a). Moreover, differential expression between ‘Molfettese’ vs ‘Galatina’ may subtend the occurrence of different intracellular hexose homeostasis, considering that Arabidopsis SWEET 16 and 17 are tonoplast hexose bidirectional carriers (Chardon et al., 2013; Klemens et al., 2013). 2.4. Identification of candidate transcription factors involved in stem sucrose metabolism Out of thirty-two TF genes (transcript completeness 80%) that conserved differential pattern between genotypes independently of the growth site, those related to sucrose metabolism were addressed using significant correlation versus SM/SWEET gene expression and versus sugar content variation. As a result, we could select the contigs 10938/ NAC, 14712/bHLH, 40133/TALE and 17846/MIKC. The first three shared the up-regulation with the SM genes (Fig. 2A, also compare Fig. 2B–E and 2G, H, J) in ‘Molfettese’ vs ‘Galatina’, while 20171/ SWEET and 17846/MIKC together showed the opposite behaviour (Fig. 2A, F and 2I). To explore if these TF may target the SM/SWEET genes, the lettuce genome sequence was used (alternatively to the lack of chicory one) to assess the abundance of binding sites (BS) within SM/ SWEET genes highly similar to those of chicory (Table 3 and Table S4). Each gene harboured sequences reckoned by all TFs, except for the 17035/HXK-TALE coupling, and the high BS number supported direct interaction in all the other combinations. Moreover (Fig. 4A), the 10938/NAC, 14712/bHLH and 40133/TALE expressions were confirmed to have positive correlation with those of the SUS/FRK/HXK group (r: 0.66–0.98) and negative vs 20171/SWEET (r < −0.72). Diversely, 17846/MIKC transcription had strong negative correlation vs that of SM (r ≤ −0.71) and positive vs that of 20171/SWEET (r = 0.88). Looking at the TFs and sugar relationships (Fig. 4A), the 10938/NAC, 14712/bHLH and 40133/TALE transcription levels showed negative correlation vs the SUCR/FRU/GLC amounts (r: 059 to −0.86),

2.5. Gene network subtending sugar content differences A metabolic network was hypothesised (Fig. 4B) to provide explanations of sugar content differences between ‘Molfettese’ vs ‘Galatina’ stems independently of the growth site by fixing a set of thresholds and conditions. The correlations between SM/SWEET genes and respective targeted sugar were pictured for r > |0.7|; consequently, the fitting couples were 11474/SUS-SUCR (r = −0.74), 53458/FRK-FRU (r = −0.70), 9306/HXK-GLC (r = −0.77), 17035/HXK-GLC (r = −0.79) and 20171/SWEET-GLC (r = −0.76), while the non-significantly correlated HXKs-FRU were discarded. Moreover, protein cell locations were computationally predicted. Gene-metabolite modules (TF-SM/SWEET-sugar) were structured based on the simultaneous occurrence of r > |0.7| in the TF vs SM/SWEET correlation and of maximum r (in absolute terms) in the TF vs sugar correlation. For example, NAC, bHLH and MIKC showed r > |0.7| vs 11474/SUS; however, the 7

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Fig. 4. Correlation plot and network acting in the sucrose metabolic pathway of chicory-stems. A) Correlogram of the Pearson's coefficient (r) between glucose, fructose and sucrose contents versus the expression levels of sucrose metabolism/transport and transcription factor genes. The r coefficients and significances (asterisks) are disposed in a symmetric matrix and a heat map is used to indicate the strength of correlation among the variables. Red and blue squares indicate negative and positive correlations, respectively. Non-significant values are typed in grey. *, **, *** = significant at P ≤ 0.05, 0.01 and 0.001, respectively. B) Arrowed lines depict the metabolic reactions performed by the deduced proteins (circles). Curved lines represent correlations (negative-orange, positive-blue) of gene expression variation (up- and down-regulation respectively green and red) versus sugar (hexagons) content variation (grey scale) of ‘Molfettese’ stems as compared to ‘Galatina’ ones. The criteria for selecting the module “transcription factor-gene target-metabolite” and the protein cellular location are in the text. INV, invertase; SUS, sucrose synthases; UDP-G, uridine diphosphate glucose; HXK, hexokinase, FRK, fructokinase; F6P, fructose 6-phosphate; G6P, glucose 6-phosphate. . (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

differences in a specific environment and does not consider events regulating sucrose metabolism by post-translational modifications, such as those performed by Snf1-related protein kinases (Halford and Hey, 2009) since these latter were missing in the transcriptome. On the other hand, the network offers a gene pool - minimally affected by the environment and accomplished with markers - useful for genetic breeding purposes and product traceability.

Table 3 Binding sites in sucrose genes putatively reckoned by TFs. Contig

11474/SUS 53458FRK 9306/HXK 17035/HXK 20171/SWEET

Lettuce orthologuea

Lsat_1_v5_gn_5_138880 Lsat_1_v5_gn_4_132241 Lsat_1_v5_gn_1_36821 Lsat_1_v5_gn_5_134201 Lsat_1_v5_gn_3_12760

Number of BSb NAC

TALE

bHLH

MIKC

9 32 13 7 8

1 7 3 0 8

31 26 55 29 35

22 26 1 6 49

3. Conclusions This study widened the information on sugar and other hydrosoluble nutrient contents of chicory stems and assessed the influence of G, E and GxE on amount variations, exerting impact on product valorisation and on crop performance in ex-situ cultivation. The integrative analyses (NMR profiles-transcriptome mining and RNA-seq analyses) pointed at sucrose catabolism as effector of sugar content differences that affect the sweetness grade of two landraces. A gene set of sucrose metabolism and transport and related regulatory transcription factors was identified to act in the sugar content differences. The characterization of these genes paves the way for further studies on the complex relationships in sucrose metabolism of inflorescence stems and for application in stem-chicory genetic breeding.

a

The lettuce orthologues and relative promoters were mined from lettuce genome v.8 available at phytozome.jgi.doe.gov. Regions targeted for binding site analysis spanned 3,000bp upstream the transcription start sites of genes. b BS, transcription factor binding sites. Table 4 Gene differences between ‘Molfettese’ vs ‘Galatina’ in the sugar-sink network. Contig

Sucrose pool 11474/SUS 53458/FRK 9306/HXK 17035/HXK 20171/SWEET TFs 10938/NAC 14712/bHLH 17846/MIKC 40133/TALE

Mola

SNP number and typesb

Variation source G

E

GxE

ORF

NCD

up up up up dw

** * *** *** *

n.s. n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s. n.s.

– 6, syn; 2, miss – 1 syn –

– 1, 3′UTR – – –

up up dw up

** *** *** ***

n.s. n.s. n.s. n.s.

n.s. n.s. n.s. n.s.

1, syn; 2, miss 1, syn; 1 miss – 1 miss

– 2, 3′UTR – 2, 5′UTR

4. Experimental 4.1. Plant materials, growth conditions and sampling The local varieties (syn. landraces) ‘Galatina’ and ‘Molfettese’ (Fig. S2) belong to C. intybus subsp. intybus, Fam. “Compositae” (syn. “Asteraceae”), subfam. “Cichorioideae”, tribe “Cichorieae” (Barcaccia et al., 2016), and to the sub-group ‘Catalogna’ of the cultigroup ‘leaf chicory’ (Raulier et al., 2016). These stem-chicory landraces were grown in Apulia (41°08′54''N 16°36′26''E Molfetta) and Lazio (42°15′N 11°44′E, Tarquinia, central Italy); details on coordinates and soil-climatic conditions were previously reported (D'Acunzo et al., 2017; Testone et al., 2016). Comparable crop density (8.3 plants m−2) and agro-techniques were used in these areas; nursery sowing was in August followed by open-field transplant after one month. In Tarquinia, ‘Molfettese’ was harvested three weeks before ‘Galatina’ (9/1/2013 vs 29/1/ 2013, mean temperature values one week before harvest were respectively 9.0 ± 0.9 °C and 6.9 ± 1.4 °C; month mean was 8.2 ± 2.2 °C; www.idrografico.roma.it/annali). In Molfetta, the harvest of ‘Molfettese’ occurred two weeks before ‘Galatina’ (14/1/2013 vs 25/1/2013; mean temperature values were respectively 9.4 ± 0.1 °C and 9.5 ± 0.3 °C; month average was 9.7 ± 0.2 °C; https://www. wunderground.com). Fifteen plants of comparable weights were selected, which had mean weights of 905 vs 850 g for ‘Galatina’ and ‘Molfettese’ from Tarquinia, and 820 vs 940 g for ‘Galatina’ and ‘Molfettese’ from Molfetta and characterized by non-significant differences by ANOVA. We generated three replicate batches, each of 10 stems with marketing standards (length 11.5 ± 1.5 cm, median section diameter: 2.7 ± 0.3 cm). After explanting from the plant rosette, stems were rapidly sliced in a cold room (7 °C), frozen in liquid nitrogen, stored either at −80 °C for RNA isolation or lyophilized at −50 °C for 72 h (laboratory freeze dryer with stoppering tray dryer, FreeZone®, Labconco Corp., Kansas City, MO, USA) and stored at −20 °C for NMR

a , Transcript expression regulation of ‘Molfettese’ genes as compared to those in ‘Galatina’. up, up-regulation; dw, down-regulation. b , Single nucleotide polymorphisms of ‘Molfettese’ vs ‘Galatina’. ORF, open reading frame; NCD, non-coding sequence; Syn, synonymous; miss, missense substitutions. G, genotype; E, environment. Significance letters refer to GxE interactions.

bHLH-SUS-SUCR module was picked due to the highest correlation between bHLH and SUCR (r = −0.67). The model proposes that the expression variation of these TF regulates the transcription of sucrose target genes (e.g.: higher catabolism), which, in turn, may account for lower SUCR and GLC contents of ‘Molfettese’ vs ‘Galatina’ stems, recurring in two different cultivation areas. In this network, the genotype was the major variation source that affected the transcription of all genes, whereas the environmental and GxE effects were not significant (Table 4). Structurally, gene transcripts of 53458/FRK, 17035/HXK, 10938/NAC, 14712/bHLH and 40133/TALE harboured landrace-specific single nucleotide polymorphism (SNP), including synonymous and missense substitutions (Tables 4 and S5). Even though SUS genes did not contain genotype specific SNP in the coding sequences, the transcriptional differences may reside in promoter variability or intron regulatory specificity, considering that the SUS first intron can regulate transcription in other plant species (Li et al., 2017), which was not addressed here for chicory. On one hand, the model has limitations because it excludes those DEGs that may contribute to sugar content 9

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sequence validation of thirteen sucrose genes by PCR full-transcript cloning, five of them were those included in the gene-metabolite network (Table S6). Incomplete sequences (less than 90% of full-length) were excluded together with putative redundant isoforms as assigned by mapping analysis of chicory transcripts on the lettuce genome (NCBI accession: GCF_002870075.1_Lsat_Salinas_v7) by the GMAP (Wu and Watanabe, 2005). Consequently, Ci_contig1353 (HXK), Ci_contig4631, -40680, -44967 (SWEET) were not included (Table S3). Subsequently, the Clustalw algorithm carried out multiple sequence alignments and the neighbor-joining method was used to perform phylogenetic analyses using MEGA (Tamura et al., 2013). The lettuce proteins marked with “Lsat” were retrieved from lettuce genome v.8 available at phytozome.jgi.doe.gov.

analyses. Three biological replicates were used in all experiments. 4.2. Metabolite extraction, NMR assignment and profiling Water-soluble metabolites were extracted with acetonitrile/water (1:1 v/v). Freeze-dried chicory stems were powdered using ceramic pestle and mortar. Powdered tissue (25 mg) was mixed with 0.90 mL of acetonitrile/water (1:1 v/v) mixture and stirred for 30 s. After 5 min of centrifugation (14500 g), 0.74 mL of supernatant was filtered through cotton wool in a glass vial. Solvent was evaporated by N2 flux at room temperature. The dried residue was dissolved in 0.75 mL of 400 mM phosphate buffer (pH = 7) in D2O containing 1 mM 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid sodium salt (TSP) as internal standard. The NMR spectra of aqueous extracts were recorded at 27 °C on a Bruker AVANCE 600 NMR spectrometer operating at the proton frequency of 600.13 MHz. TSP signal of methyl group (δ = 0.00 ppm) was used as internal standard for 1H spectra. Each 1H spectrum was acquired by coadding 256 transients with a recycle delay of 3 s. The residual HDO signal was suppressed using a pre-saturation. The experiment was carried out by using a 45° pulse of 7.0 μs, 32 K data points. All the spectra were processed by means of the Bruker TOPSPIN software (version 1.3). After Fourier transformation, manual phase correction and baseline correction selected resonances in 1H NMR spectra (Table S1) were integrated to calculate metabolite concentrations. The integral value of TSP methyl groups (9H) was used as a reference for quantification. The content of selected metabolites was expressed as in mg g−1 on dry weight basis. 2D NMR experiments, namely 1H–1H total correlation spectroscopy (TOCSY), 1H–13C heteronuclear single quantum coherence (HSQC), and 1H–13C heteronuclear multiple bond correlation (HMBC), were performed using the same experimental conditions previously reported (Capitani et al., 2014). The mixing time for the 1H–1H TOCSY was 80 ms. The 1H–13C HSQC experiment was performed using a coupling constant 1JC–H of 150 Hz, whereas the 80 ms delay for the evolution of long-range couplings was used in 1H–13C HMBC experiments.

4.5. Transcription factor identification and network assembly Chicory TF gene pool was first annotated by blasting against the PlantTFDB v.4.0 database. A first round of selection was for TFs that had sequence completeness (> 80%) and maintained differentially expressed profiles in the two growth sites. To screen for those involved in sucrose metabolism two simultaneous conditions were applied: 1) significant correlation threshold of r > |0.7| in the TF/SM gene and TF/ sugar content comparisons; 2) the occurrence of TF-targeted binding sites located in sucrose genes of the lettuce genome, taken as phylogenetically related model (Table 3 and Table S4). As for transcription binding site analysis in lettuce promoters, the 3000 base pair long genomic sequence upstream the ATG of the lettuce orthologues were analyzed by PLANTPAN 2.0 (http://plantpan2.itps.ncku.edu.tw/index. html). The skeleton of sucrose network was constructed using Cytoscape (Shannon et al., 2003) to visualize interactions and giving as general input a threshold of significant correlation r > |0.7| for all two-way comparisons (e.g. SM vs SWEET transcription; SM expression vs sugar abundance; SWEET expression vs sugar content). Moreover, the co-existence of r > |0.7| in the TF vs SM/SWEET correlation and of maximal r in the TF transcription vs sugar level correlation was an additional condition to depict the three component modules (TF-SM/SWEETsugar). The rendering was further integrated with protein subcellular localization predicted (Fig. S1) by DeepLoc-1.0 (www.cbs.dtu.dk/ services/DeepLoc-1.0/).

4.3. Annotation refinement, data availability and gene expression analyses The annotation of ‘Galatina’ transcriptome (Testone et al., 2016) was improved by BLASTX (cut-off E-value≤10−5) analysis against protein sequences from Cynara cardunculus and Lactuca sativa of the Cichorieae tribe hosted in RefSeq (NCBI Reference Sequence Database, release 87). Blast2GO 4.1 was used to retrieve KEGG annotations from the best hits and results were integrated. The reference assembly and RNA-seq data sets have been stored in the National Center for Biotechnology Information database (NCBI, www.ncbi.nlm.nih.gov) under the BioProjects accession PRJNA328202 for C. intybus and PRJNA417356 for C. endivia. The real time quantitative PCR (qPCR) and normalization procedures were previously detailed (Testone et al., 2016). Briefly, total RNA (1 μg) derived from a pool (n = 5) of comparable stems at harvesting time and was reverse-transcribed at 55 °C by SuperscriptIII (Life Technologies). cDNA (1 μL of a 1:10 dilution) was amplified by Eco Real-Time PCR System (Illumina) using 1x Quantimix easy master mix (Biotools) and 0.3 μM of each primer (Table S6) in a 10 μL final volume. Triplicate reaction conditions included: 95 °C for 10 min for polymerase activation, 45 cycles at 95 °C for 10 s, 60 °C for 15s.

4.6. Statistical analyses All data (three biological and analytical replicates) were analysed according to a completely randomized design in a two-way ANOVA (genotypes x environments) by a General Linear Model (GLM, SAS Software, Cary, NC, USA). The separation of means was obtained by Least Significant Difference (LSD) test. For a visual analysis of the data, Principal Component Analysis (PCA) was performed on mean centred and standardized data (unit variance scaled). The data matrix submitted to PCA was made of 4 observations (2 growing sites x 2 genotypes) and 31 variables. The results were shown as biplots of scores (treatments) and loadings (variables) using XLStat Pro (Addinsoft, Paris, France). Pearson correlations were calculated using the “rcorr” function in the Hmisc package within the R environment (v 3.4.3). Declaration 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.

4.4. Phylogenetic tree construction Previously, sequencing error impacts were minimized by several tools applied in the transcriptome assembly pipeline. Moreover, the six RNA-resequencing datasets (per each genotype; 3 replicates x 2 growth sites) was used to further check the sugar-gene sequences and several stringent filtering criteria for SNPs were applied (Testone et al., 2016). To increase the accuracy of phylogenetic analyses, we carried out

Funding This work was supported by the Italian Ministry of Economy and Finance to the Italian CNR for the project “CISIA-made in Italy” - Law n. 10

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191/2009.

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