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Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water

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  • 'Wonderful' pomegranate (Punica granatum) is currently the industry-standard cultivar, accounting for more than 90% of all commercially planted trees. The purpose of this study was to determine the response of 'Wonderful' pomegranate trees to a range of salinity by irrigating them with a nutrient solution at an electrical conductivity (EC) of 1.2 dS·m−1 (control) or one of two saline solutions at EC of 5.0 (EC 5) or 10.0 dS·m−1 (EC 10) in two rounds of treatments. Pomegranate plants with saline solution treatments had no or minimal foliar salt damage. However, EC 10 reduced shoot dry weight (DW) by 15% relative to the control in the first round, and both EC 5 and EC 10 reduced shoot DW by 13% and 31%, respectively, in the second round compared to the control. The concentration of sodium (Na) was ≤ 1 mg·g−1 in the leaves and stems in all treatments but was much higher in the roots in EC 5 and EC 10. The concentration of chloride (Cl) in the leaves, stems, and roots increased by 36%−90%, 101%−156%, and 254%−299%, respectively, in EC 5 and EC 10 compared to the control. Salinity reduced the concentration of all macronutrients and some micronutrients, especially in the leaves, compared to the control. However, there was no or minimal effect on leaf gas exchange and SPAD readings. These results indicate that 'Wonderful' pomegranate is highly tolerant to salinity and has a strong ability to exclude Na accumulation in the leaves, thus avoiding salt damage.
  • The competition for consumer preference for fresh apples (Malus domestica) from exotic and tropical fruits is intense. Red-fleshed (RF) apple may not only provide a novel point of differentiation and enhanced visual quality, but also a source of increased concentration of potentially health-benefiting compounds within both the fresh fruit and snack/juice markets[1]. Two different types of RF apples have been characterised: Type 1 RF apple has red colouration not only in the fruit core and cortex, but also in vegetative tissues, including stems and leaves; Type 2 RF apples display red pigment only in the fruit cortex[1, 2]. To facilitate trade and lengthen the supply-window, harvested fruit are usually cold stored, which can induce a series of disorders, including physiological breakdown manifesting as a flesh browning disorder (FBD) in RF apples[3, 4]. FBD in RF apples can be caused by senescence, and there is also some evidence to suggest that a large proportion of RF apples are chilling-sensitive (Jason Johnston, Plant & Food Research Hawke's Bay, personal communication).

    Earlier studies suggested that Type 1 RF colour was determined by a promoter mutation of MdMYB10 that has a tandem replication of a myeloblastosis (MYB) binding cis-element (R6) within the promoter, resulting in autoregulation of MdMYB10[5]. However, van Nocker et al.[6] observed a large variation in the degree and pattern of red pigmentation within the cortex among the accessions carrying MdMYB10, and concluded that the presence of this gene alone was not sufficient to ensure the RF colour. A genome-wide association study (GWAS)[3], reported that, in addition to the MdMYB10 gene, other genetic factors (e.g. MdLAR1, a key enzyme in the flavonoid biosynthetic pathway) were associated with RF colour, too. Wang et al.[7] reported that many of the up-regulated genes in RF apples were associated with flavonoid biosynthesis (e.g., chalcone synthase (CHS), chalcone isomerase (CHI), dihydroflavonol 4-reductase (DFR), anthocyanin synthase (ANS), UDP-glucosyltransferase (UGT) and MYB transcription factors). Recently, MdNAC42 was shown to share similar expression patterns in RF fruit with MdMYB10 and MdTTG1, and it interacts with MdMYB10 to participate in the regulation of anthocyanin synthesis in the RF apple Redlove®[8].

    Several transcription factor genes (e.g., MYB, WRKY, bHLH, NAC, ERF, bZIP and HSF) were reported to be differentially expressed during cold-induced morphological and physiological changes in 'Golden Delicious' apples[9]. A study by Zhang et al.[10] showed that ERF1B was capable of interacting with the promotors of anthocyanin and proanthocyanidin (PA) MYB transcription factors, and suggested that ethylene regulation and anthocyanin regulation might be linked in either direction. It was reported that ethylene signal transduction pathway genes or response genes, such as ERS (ethylene response sensor), EIN3 (ethylene-insensitive3) and ERFs (ethylene response factors), may play an important role in the regulatory network of PA biosynthesis[11].

    Espley et al.[12] observed no incidence of FBD in cold-stored fruit of 'Royal Gala', but over-expression of MdMYB10 in 'Royal Gala' resulted in a high rate of FBD in RF fruit, which was hypothesised to be caused by elevated fruit ethylene concentrations before harvest and more anthocyanin, chlorogenic acid (CGA) and pro-cyanidins in RF fruit. In addition, the MYB10 transcription factor was shown to elevate the expression levels of MdACS, MdACO, and MdERF106 ethylene-regulating genes[12]. To elucidate the mechanism regulating the FBD of RF apples, Zuo et al.[13] analysed the transcriptome of tree-ripe apples at 0, 0.5 and 4 h after cutting, and reported that the differentially expressed genes at different sampling points were mainly related to plant–pathogen interactions.

    GWAS is a powerful technique for mining novel functional variants. One of the limitations of GWAS, using SNP arrays, is that they require genotyping of large numbers of individuals, which may be expensive for large populations. DNA pooling-based designs (i.e., bulk segregant analysis) test differential allele distributions in pools of individuals that exhibit extreme phenotypes (XP) in bi-parental populations, large germplasm collections or random mating populations[1416]. In addition to reducing the number of samples to be genotyped, the use of whole genome sequencing (WGS)-based XP-GWAS has the potential to identify small-effect loci and rare alleles via extreme phenotypic selection.

    In this WGS-based XP-GWAS, we investigated the genetic basis of RF and FBD by sequencing the pools of individuals that exhibited extreme phenotypes for these two traits, and analysed the differences in allele frequencies between phenotypic classes. This method combines the simplicity of genotyping pools with superior mapping resolution. We also examined the transcriptome from transgenic apple fruit harbouring the R6:MYB10 promoter as a model for red flesh in apple. Differences in gene expression of a highly pigmented line were compared with expression in control fruit and these genes were then used for comparison with the seedling population. Understanding the genetic basis of the link between RF and FBD will help in design of strategies for selection against FBD in high-quality Type 1 RF apple cultivars.

    A snapshot of visual variation in FBD and WCI is presented in Fig. 1. The average WCI and FBD across all ~900 seedlings ranged from 0 to 7, and from 0% to 58%, respectively. Based on the MLM analysis, the estimated narrow-sense heritability (h2) of WCI and FBD was 0.57 (standard error = 0.18) and 0.09 (standard error = 0.05), respectively. The estimated genetic correlation between WCI and FBD was 0.58, and several fruit quality traits displayed unfavourable correlations with WCI (Supplemental Fig. S1). Seedlings with higher WCI scores were generally characterised by poor firmness and crispness, plus higher astringency and sourness. Estimated phenotypic and genetic correlations between all pairs of traits are listed in Supplemental Table S1.

    Figure 1.  Transverse cross-sections of apple slices showing range in (a) flesh colouration, and (b) flesh browning disorder for Type 1 red-fleshed apple. The weighted cortical intensity (WCI) scores (0−9 scale) and the proportion of the cortex area showing symptoms of flesh browning disorder are also displayed.

    A few seedlings had no red pigment in the cortex, but the average WCI score across all seedlings was 2.25. About two-thirds of the seedlings did not display any FBD symptoms, but among the remaining seedlings, FBD ranged between 1% and 58% (Fig. 2). The average WCI score for the 'low' and 'high' WCI pool was 0.45 and 5.2, respectively, while the average FBD was 0% and 20.6% for the 'low' and 'high' FBD pool, respectively (Supplemental Table S2). The average WCI score of the seedlings in the FBD pools was similar (Low: 5.5; High: 4.6), while the average FBD of the high- and low-WCI pools was 4.5% and 0.1%, respectively.

    Figure 2.  The distribution of weighted cortex intensity (WCI) scores (a) and the internal flesh browning disorder IFBD%; (b) in the population of ~900 apple seedlings. The green and red circles highlight the individuals used to form the 'low' and 'high' pools of samples.

    After filtering, about 204,000 SNPs were used and the average sequencing depth of SNP loci was similar for the two pools (42 vs 44). There was a near-perfect correlation between the Z-test statistics and G-statistics, so only the latter are discussed hereafter. A plot of the G' values, smoothed over 2 Mb windows, is shown for all 17 chromosomes (Chrs) in Supplemental Fig. S2. XP-GWAS identified genomic regions significantly associated with FBD on 12 out of the 17 Chrs (Fig. 3), and putative candidate genes within ±1.0 Mb distanceof the significant G' peaks were identified (Table 1). Additional genomic regions, which did not meet the significance threshold but displayed distinguished G' peaks, were also identified across all chromosomes (Supplemental Table S3).

    Figure 3.  G'-statistics across the linkage groups (LG) showing significant association with the flesh browning disorder (FBD) in apple The horizontal red lines indicate the significance threshold. The putative candidate genes (refer to Table 1) underpinning various G' peaks are also shown.
    Table 1.  A list of the genomic regions associated with internal flesh browning disorder (FBD) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    ChrGenomic region (Mb)Putative genes functions
    221.9–23.5Ethylene-responsive element binding factor 13 (MdERF13: MD02G1213600);
    33.1–4.9cinnamate 4-hydroxylase (C4H) enzyme (MD03G1051100, MD03G1050900 and MD03G1051000); MdWRKY2: MD03G1044400; MdWRKY33 (MD03G1057400)
    38.2–9.6ascorbate peroxidase 1 (MdAPX1: MD03G1108200, MD03G1108300)
    314.7–16.6senescence-related MdNAC90 (MD03G1148500)
    336.5–37.5Ethylene response sensor 1 (MdERS1: MD03G1292200); flavonoid biosynthesis protein MdMYB12 (MD03G1297100); Heat shock protein DnaJ (MD03G1296600, MD03G1297000); pectin methylesterase (MdPME) inhibitor protein (MD03G1290800, MD03G1290900, MD03G1291000).
    411.0–13.0phenylalanine and lignin biosynthesis protein MdMYB85 (MD04G1080600)
    422.1–24.4MYB domain protein 1 (MD04G1142200); HSP20-like protein (MD04G1140600); UDP-glucosyltransferase (UGT) proteins UGT85A7 (MD04G1140700, MD04G1140900); UGT85A3 (MD04G1140800); UGT (MD04G1141000, MD04G1141300); UGT85A2 (MD04G1141400); UGT85A4 (MD04G1141500); DNAJ heat shock protein (MD04G1153800, MD04G1153900, MD04G1154100)
    427.6–29.6HCT/HQT regulatory genes MD04G1188000 and MD04G1188400
    629.8–31.7Volz et al. (2013) QTL for IFBD; anthocyanin regulatory proteins MdMYB86 (MD06G1167200); triterpene biosynthesis transcription factor MdMYB66 (MD06G1174200); Cytochrome P450 (MD06G1162600; MD06G1162700, MD06G1162800, MD06G1163100, MD06G1163300, MD06G1163400, MD06G1163500, MD06G1163600, MD06G1163800, MD06G1164000, MD06G1164100, MD06G1164300, MD06G1164400, MD06G1164500, MD06G1164700)
    713.9–15.9heat shock protein 70B (MD07G1116300)
    717.4–19.0Drought-stress WRKY DNA-binding proteins (MdWRKY56: MD07G1131000, MD07G1131400)
    723.6–25.2MdPAL2 (MD07G1172700); drought-stress gene NGA1 (MD07G1162400); DNAJ heat shock family protein (MD07G1162300, MD07G1162200), stress-response protein (MdNAC69: MD07G1163700, MD07G1164000)
    97.9–11.8MD09G1110500 involved in ascorbate oxidase (AO); MdUGT proteins (MD09G1141200, MD09G1141300, MD09G1141500, MD09G1141600, MD09G1141700, MD09G1141800, MD09G1142000, MD09G1142500, MD09G1142600, MD09G1142800, MD09G1142900, MD09G1143000, MD09G1143200, MD09G1143400) involved in flavonoids biosynthesis; heat shock proteins 89.1 (MD09G1122200) and HSP70 (MD09G1137300); Ethylene-forming enzyme MD09G1114800; Anthocyanin regulatory protein MdNAC42 (MD09G1147500, MD09G1147600)
    914.9–16.5triterpene biosynthesis transcription factor protein MdMYB66 (MD09G1183800);
    111.7–3.0ethylene response factor proteins (MdEIN-like 3: MD11G1022400)
    1138.6–40.6Senescence-related gene 1 (MD11G1271400, MD11G1272300, MD11G1272000, MD11G1272100, MD11G1272300, MD11G1272400, and MD11G1272500); Chalcone-flavanone isomerase (CHI) protein (MD11G1273600) and MdbHLH3 (MD11G1286900, MDP0000225680); cytochrome P450 enzyme (MD11G1274000, MD11G1274100, MD11G1274200, MD11G1274300, MD11G1274500, and MD11G1274600); heat shock transcription factor A6B (MdHSFA6B: MD11G1278900) – involved in ABA-mediated heat response and flavonoid biosynthesis.
    122.2–3.5heat shock protein 70-1 ((MD12G1025600, MD12G1025700 and MD12G1026300) and heat shock protein 70 (MD12G1025800 and MD12G1025900 and MD12G1026000); ethylene (MD12G1032000) and auxin-responsive (MD12G1027600) proteins.
    127.2–8.4DNAJ heat shock domain-containing protein (MD12G1065200 and MD12G1067400)
    1327.5–30.5MD13G1257800 involved in polyphenol 4-coumarate:CoA ligase (4CL)
    1337.5–39.5pectin methyl esterase inhibitor superfamily protein MdPMEI (MD13G1278600)
    1425.4–27.5Drought-stress response gene MdWRKY45 (MD14G1154500); chalcone synthase (CHS) family proteins (MD14G1160800 and MD14G1160900); triterpene biosynthesis transcription factor MdMYB66 (MD14G1180700, MD14G1181000, MD14G1180900); anthocyanin biosynthesis protein (MdMYB86: MD14G1172900); cytochrome P450 proteins (MD14G1169000, MD14G1169200, MD14G1169600, MD14G1169700)
    150–1.5Ethylene synthesis proteins (MD15G1020100, MD15G1020300 and MD15G1020500); dihydroflavonol reductase (DFR) gene (MD15G1024100)
    154.6–6.8MdMYB73 (MD15G1076600, MD15G1088000) modulates malate transportation/accumulation via interaction with MdMYB1/10; MdNAC52: MD15G1079400) regulates anthocyanin/PA; heat shock transcription factor B4 (MD15G1080700); stress-response protein MdWRKY7 (MD15G1078200)
    1553.5–54.9MdC3H (MD15G1436500) involved in chlorogenic acid biosynthesis; MdEIN3 (MD15G1441000) involved in regulating ethylene synthesis and anthocyanin accumulation
    168.9–10.9SAUR-like auxin-responsive protein (MD16G1124300) and MdNAC83: (MD16G1125800) associated with fruit ripening; MD16G1140800 regulates proanthocyanidin; MdPAE genes (MD16G1132100, MD16G1140500) regulates ethylene production.
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    The ethylene-responsive factor 13 (MdERF13: MD02G1213600) resided within the significant region (21.9–23.5 Mb) on Chr2, while the ascorbate peroxidase 3 (MdAPX3: MD02G1127800, MD02G1132200) resided in the prominent region between 9.45 and 11.28 Mb) (Fig. 3, Table 1). There were several genomic regions showing association with FBD on Chr3. The first region (8.2–9.5 Mb) flanked MdAPX1 (MD03G1108200, MD03G1108300), while MdERF3 (MD03G1194300) and heat shock protein 70 (HSP70: MD03G1201800, D03G1201700) resided within the prominent peak region (25.5–27.5 Mb). Another significant region (36.5–37.5 Mb) at the bottom on Chr3 harboured ethylene response sensor 1 (MdERS1: MD03G1292200), a flavonoid-biosynthesis related protein MdMYB12 (MD03G1297100), HSP DnaJ and pectin methyl esterase (PME) inhibitor proteins (Fig. 3, Table 1).

    The significant G' region (27.6–29.6 Mb) on Chr4 flanked the genes MD04G1188000 and MD04G1188400 involved in the biosynthesis of hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyl transferase (HCT/HQT), while the adjacent region (22.1–24.3 Mb) harboured a cluster of UDP-glucosyltransferase (UGT) proteins and HSP (Table 1). Another region between 11.0 and 13.0 Mb encompassed phenylalanine and lignin biosynthesis gene MYB85 (MD04G1080600)[17]. The only significant region associated with FBD on Chr6 spanned between 29.8 and 31.7 Mb, which included a SNP earlier reported associated with FBD[4]. This region also harboured several MYB proteins (MdMYB86: MD06G1167200; MdMYB98: MD06G1172900; MdMYB66: MD06G1174200) and a large cluster of cytochrome P450 proteins (Table 1).

    A significant FBD-associated region (13.9–15.9 Mb) on Chr7 encompassed the HSP 70B (MD07G1116300), while another significant region between 23.6 Mb and 25.2 Mb flanked the gene coding for phenylalanine ammonia lyase 2 (MdPAL2: MD07G1172700), a drought stress gene NGA1 (MD07G1162400), DNAJ HSP, and stress-response protein (MdNAC69: MD07G1163700, MD07G1164000) (Fig. 3, Table 1). A large significant region spanning between 7.9 and 11.8 Mb on Chr9 encompassed the gene MD09G1110500 putatively involved in ascorbate oxidase (AO), HSP (HSP70: MD09G1137300; HSP89.1: MD09G1122200), an ethylene-forming enzyme (MD09G1114800), and MdNAC42 (MD09G1147500, MD09G1147600). Another significant genomic region on Chr9 was between 14.9 and 16.5 Mb, which harbours the MYB domain protein MdMYB66 (MD09G1183800) (Table 1).

    A sharp G' peak region (1.7–3.0 Mb) on Chr11 associated with FBD encompassed ethylene insensitive 3 (MdEIN3: MD11G1022400) along with a cluster of UGT proteins, WD-40, and bHLHL proteins (Table 1). A significant region between 38.6 and 40.6 Mb at the bottom of Chr11 was dominated by clusters of senescence-related genes and cytochrome P450 enzymes. This genomic region also flanked a chalcone-flavanone isomerase (CHI) family protein (MD11G1273600) and a bHLH protein (MdbHLH3: MD11G1286900, MDP0000225680), along with the heat shock transcription factor A6B (HSFA6B: MD11G1278900) (Table 1, Supplemental Table S3).

    The region (2.2–3.3 Mb) associated with FBD on Chr12 flanked genes for the HSP 70 and 70-1, NAC proteins, ethylene and auxin-responsive proteins (Table 1). An adjacent significant region (7.2–8.4 Mb) flanked DNAJ HSP, along with bZIP, bHLH and WD-40 repeat-like proteins (Table 1; Supplemental Table S3). There was a large genomic region on Chr13 showing a significant association with FBD. In this region, the first G' peak (27.5–30.5 Mb) harboured MD13G1257800, which regulates polyphenol 4-coumarate: CoA ligase (4CL) synthesis. The second G' peak region (32.6–34.7 Mb) corresponded to an earlier mapped QTL for flesh browning in white-fleshed apples[18].

    The significant region (25.4–27.5 Mb) on Chr14 harboured various genes for proteins with different putative functions, such as AP2 proteins, WD-40 repeat family proteins, bHLH proteins, MdNAC83 (MD14G1150900), MdWRKY45 (MD14G1154500), chalcone synthase (CHS) family proteins (MD14G1160800 and MD14G1160900), cytochrome P450 proteins, and several MYB domain proteins (MdMYB86: MD14G1172900; MdMYB98: MD14G1179000; MdMYB66: MD14G1180700, MD14G1181000, MD14G1180900) (Fig. 3, Table 1, Supplemental Table S3). A sharp G' peak (15.2–17.2 Mb) on Chr14 did not meet the significance threshold corresponding to the FBD QTL in white-fleshed apples[18].

    The upper 1.5 Mb region on Chr15 associated with FBD encompassed several transcription factor families, including WD-40 repeats, bHLH, bZIP, ethylene synthesis proteins, and dihydroflavonol reductase (DFR) protein (MD15G1024100) (Fig. 3, Table 1; Supplemental Table S3). Another significant region on Chr15 (4.6−6.8 Mb) harboured HSF B4, MdMYB73 (which interacts with MdMYB1/10 to modulate malate transportation) and MdNAC52 (MD15G1079400), which regulates anthocyanin and PA synthesis by directly regulating MdLAR[19]. A significantly associated region at the bottom of Chr15 (53.5–54.9 Mb) harboured MdNAC35 (MD15G1444700), MdC3H (MD15G1436500) involved in the production of p-coumarate 3-hydroxylase (C3H) enzyme, which plays a role in chlorogenic acid biosynthesis, and MdEIN3 (MD15G1441000).

    The significant region between 8.9 and 10.9 Mb on Chr16 harboured a gene for SAUR-like auxin-responsive protein (MD16G1124300) and MdNAC83 (MD16G1125800), both of which have been reportedly associated with apple fruit ripening[20]. This region also encompassed MD16G1140800, which regulates PA[11], and a gene for the pectin acetyl esterase protein MdPAE10 (MD16G1132100) involved in ethylene production and shelf-life[21]. A sharp G' peak region (20.4–22.4 Mb) in the middle of Chr16 flanked MdERF1B (MD16G1216900) and MdMYB15 (MD16G1218000 and MD16G1218900), involved in altering anthocyanin and PA concentrations[10] (Fig. 3, Table 1, Supplemental Table S3).

    The average sequencing depth of the SNP loci (~160,000) retained for marker-trait association was similar for the two WCI pools (41 vs 44). The significant regions were located on Chrs 2, 4, 6, 7, 10, 15 and 16 (Fig. 4). The genomic intervals within ±1.0 Mb of the significant G' peaks, and the putative candidate genes within those intervals, are listed in Table 2. Additional genomic regions, which did not meet the significance threshold but displayed distinct G' peaks, were also identified across most chromosomes ( Supplemental Fig. S2, Supplemental Table S3). A significant region on Chr4 encompassed chalcone synthase (CHS) genes, along with an ERF (MD04G1009000) involved in regulating PA biosynthesis[11].

    Figure 4.  G’-statistics across the linkage groups (LG) showing significant association with the weighted cortex intensity (WCI) in apple. The horizontal red lines indicate the significance threshold. The putative candidate genes (refer to Table 2) underpinning various G' peaks are also shown.
    Table 2.  A list of the genomic regions significantly associated with the weighted cortex intensity (WCI) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    ChrGenomic region (Mb)Putative genes functions
    211.2–13.2UDP-glucosyltransferase (UGT) proteins (MD02G1153000, MD02G1153100, MD02G1153200, MD02G1153300; MD02G1153400; MD02G1153500; MD02G1153700; MD02G1153800; MD02G1153900);
    40–1.2Chalcone synthase (CHS) genes (MD04G1003000; MD04G1003300 and MD04G1003400); DNAJ heat shock protein (MD04G1003500); MdERF (MD04G1009000) involved in regulating PA biosynthesis.
    69.5–11.0Ubiquitin protein (MD06G1061100); stress-response WRKY protein MdWRKY21 (MD06G1062800);
    612.1–13.6pectin methylesterase inhibitor superfamily protein (MdPME: MD06G1064700); phenylalanine and lignin biosynthesis gene (MdMYB85)
    616.0–17.6MD06G1071600 (MDP0000360447) involved in leucoanthocyanidin dioxygenase (LDOX) synthesis
    624.0–26.0UDP-glycosyltransferase proteins (MD06G1103300, MD06G1103400, MD06G1103500 and MD06G1103600); auxin response factor 9 (MdARF9: MD06G1111100) and heat shock protein 70 (Hsp 70; MD06G1113000).
    74.1–5.6Ethylene insensitive 3 protein (MdEIN3: MD07G1053500 and MD07G1053800) involved in proanthocyanidins (PA) biosynthesis; ubiquitin-specific protease (MD07G1051000, MD07G1051100, MD07G1051200, MD07G1051500, MD07G1051300, MD07G1051700 and MD07G1051800).
    1015.9–18.3bHLH proteins (MD10G1098900, MD10G1104300, and MD10G1104600); UGT protein (MD10G1101200); UGT 74D1 (MD10G1110800), UGT 74F1 (MD10G1111100), UGT 74F2 (MD10G1111000).
    1035.6–38.6Stress-response WRKY proteins (MdWRKY28: MD10G1266400; MdWRKY65: MD10G1275800). ethylene responsive factors (MdERF2: MD10G1286300; MdERF4: MD10G1290400, and MdERF12: MD10G1290900) and a NAC domain protein (MdNAC73: MD10G1288300); polyphenol oxidase (PPO) genes ((MD10G1298200; MD10G1298300; MD10G1298400; MD10G1298500; MD10G1298700; MD10G1299100; MD10G1299300; MD10G1299400).
    1524.8–26.8heat shock factor 4 (MD15G1283700), drought-stress response WRKY protein 7 (MdWRKY7: MD15G1287300), MdMYB73 (MD15G1288600) involved in ubiquitination and malate synthesis
    1531.7–34.2MYB domain protein 93 (MdMYB93: MD15G1323500) regulates flavonoids and suberin accumulation (Legay et al. 2016); ubiquitin -specific protease 3 (MD15G1318500),
    161.5–3.4MYB domain proteins (MD16G1029400) regulates anthocyanin; senescence-associated gene 12 (MD16G1031600); ethylene response factor proteins (MdERF118: MD16G1043500, and MD16G1047700 (MdRAV1); MdMYB62 ( MD16G1040800) flavonol regulation; malate transporter MdMa2 (MD16G1045000: MDP0000244249), Ubiquitin-like superfamily protein (MD16G1036000),
    165.4–7.4MdMYB88 (MD16G1076100) regulates phenylpropanoid synthesis and ABA-mediated anthocyanin biosynthesis; MdMYB66 (MD16G1093200) regulates triterpene biosynthesis, MdWRKY72 (MD16G1077700) mediates ultraviolet B-induced anthocyanin synthesis.
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    There were distinct G' peaks within a large genomic region (spanning between 9.0 and 18.0 Mb) significantly associated with WCI on Chr6 (Fig. 4). The G' peak region 12.1–13.6 Mb encompassed MdMYB85 (MD06G1064300), and a gene for a pectin methyl esterase

    (PME) inhibitor protein (MD06G1064700); while the region between 16.0 and 17.6 Mb flanked the genes involved in leucoanthocyanidin dioxygenase (LDOX) synthesis (Table 2). The auxin response factor 9 (MdARF9: MD06G1111100) and the HSP70 (MD06G1113000) resided in the significant region between 24.0 and 26.0 Mb on Chr6 (Table 2).

    The genomic region (4.1–5.6 Mb) with significant association with WCI on Chr7 flanked MdEIN3 (MD07G1053500, MD07G1053800), which plays an important role in the regulatory network of PA biosynthesis[11]. A significant region (35.6–38.7 Mb) on Chr10 encompassed gene clusters for bHLH and WRKY proteins along with an ethylene repressor factor (MdERF2: MD10G1286300) (Fig. 4, Table 2; Supplemental Table S3). This region also harboured MdERF4 (MD10G1290400), MdERF12 (MD10G1290900) and NAC domain genes (MdNAC73: MD10G1288300), which have been reported to be associated with fruit ripening[20]. In addition, there was a cluster of polyphenol oxidase (PPO) genes residing in this region (Table 2).

    On Chr15, the significant WCI-associated region spanning between 31.7 Mb and 34.2 Mb encompassed several TF families, including MdMYB93 (MD15G1323500) (Fig. 4, Table 2, Supplemental Table S3). A distinguished G' peak region (24.9–26.9 Mb) on Chr15 harboured genes for WD-40 repeat-like proteins, bHLH proteins, redox responsive transcription factor 1 (MD15G1283200), cytochrome P450 proteins, HSF4 (MD15G1283700), and MdWRKY7 (MD15G1287300). The MdMYB73 (MD15G1288600) residing in this region has been shown to interact with MdMYB1/10 and regulates several functions, including cold-stress response, ubiquitination and malate synthesis[22, 23].

    The significant 1.5–3.4 Mb region on Chr16 flanked an anthocyanin repressor MYB protein (MD16G1029400), senescence-associated gene 12 (MD16G1031600), malate transporter MdMa2 (MD16G1045000: MDP0000244249), and two ethylene response factor genes (MdERF118: MD16G1043500; MdRAV1: MD16G1047700), which interact in retaining flesh firmness[24]. The MdMYB62 (MD16G1040800) gene residing in this region is phylogenetically linked to MdMYB8 (MD06G1217200), which plays a major role in flavonoid biosynthesis[25]. Another significant region (5.4–7.4 Mb) on Chr16 harboured several bHLH genes, including MdMYB88 (MD16G1076100) involved in phenylpropanoid synthesis resulting in drought resistance[26] and ABA-mediated anthocyanin production[27]. The MdMYB66 (MD16G1093200) gene, which is involved in triterpene biosynthesis, and the MdWRKY72 (MD16G1077700) gene involved in anthocyanin synthesis, were also present in this region (Table 2, Fig. 4).

    The genomic regions tagged by XP-GWAS were enriched for regulatory functions, and several of these have a paralog (Table 3). For example, paralogues for a trio of genes (MdMYB86, MdMYB98 and Cytochrome 450) resided in the FBD-associated genomic regions on Chr6 and Chr14. Several pairs of genes resided together in the FBD-associated paralog regions; for example, MdNAC90 and germin-like protein 10 on Chr3 and Chr11; WRKY55 and WRKY70 on Chr1 and Chr7; and ERF1 and ERF5 on Chr4 and Chr6. The ethylene response factor MdERF1B had a paralog in the FBD-associated region on Chr13 and Chr16. Interestingly, three copies of MdNAC83 (Chrs 14, 16 17) and four copies of MdMYB66 (Chrs 4, 6, 9 and 14) resided in the FBD-associated regions. A pair of genes (RAV1 and MYB62) resided together in the WCI-associated paralog regions on Chrs13 and 16 (Table 3).

    Table 3.  A list of homologues genes/genomic regions associated with the internal flesh browning disorder (IFBD) and red flesh (WCI) in apples. Putative candidate genes residing within these regions are also listed using GDDH13v1.1 reference genome assembly.
    TraitChr (genomic region: Mb)Gene namePredicted gene IDPutative function
    IFBDChr6 (29.8–31.7 Mb)MdMYB66MD06G1174200Suberin/triterpene deposition
    IFBDChr14 (25.4–27.5 Mb)MdMYB66MD14G1180700, MD14G1181000, MD14G1180900Suberin/triterpene deposition
    IFBDChr4 (7.0–8.1 Mb)MdMYB66MD04G1060200Suberin/triterpene deposition
    IFBDChr9 (14.9–16.5 Mb)MdMYB66MD09G1183800Suberin/triterpene deposition
    IFBDChr6 (29.8–31.7 Mb)MdMYB86MD06G1167200Anthocyanin regulation
    IFBDChr14 (25.4–27.5 Mb)MdMYB86MD14G1172900Anthocyanin regulation
    IFBDChr6 (29.8–31.7 Mb)MdMYB98MD06G1172900Drought stress response
    IFBDChr14 (25.4–27.5 Mb)MdMYB98MD14G1179000Drought stress response
    IFBDChr6 (29.8–31.7 Mb)Cytochrome P450MD06G1162600, MD06G1162700, MD06G1162800, MD06G1163100, MD06G1163300, MD06G1163400, MD06G1163500, MD06G1163600, MD06G1163800, MD06G1164000, MD06G1164100, MD06G1164300, MD06G1164400, MD06G1164500, MD06G1164700Flavonoid and triterpenic metabolism
    IFBDChr14 (25.4–27.5 Mb)Cytochrome P450MD14G1169000, MD14G1169200, MD14G1169600, MD14G1169700Flavonoid and triterpenic metabolism
    IFBDChr3 (14.7–16.6 Mb)MdNAC90MD03G1148500Senescence-related
    IFBDChr11 (16.5–18.5 Mb)MdNAC90MD11G11679000Senescence-related
    IFBDChr14 (25.4–27.5 Mb)MdNAC83MD14G1150900Senescence/ripening-related
    IFBDChr16 (8.9–10.9 Mb)MdNAC83MD16G1125800Senescence/ripening-related
    IFBDChr17 (0–0. 7 Mb)MdNAC83MD17G1010300Senescence/ripening-related
    IFBDChr3 (14.7–16.6 Mb)Germin-like protein 10MD03G1148000Polyphenol oxidase
    IFBDChr11 (16.5–18.5 Mb)Germin-like protein 10MD11G1167000, MD11G1167100, MD11G1167400, MD11G1169200Polyphenol oxidase
    IFBDChr1 (26.9–28.5 Mb)MdWRKY55MD01G1168500Drought stress response
    IFBDChr7 (30.3–31.9 Mb)MdWRKY55MD07G1234600Drought stress response
    IFBDChr1 (26.9–28.5 Mb)MdWRKY70MD01G1168600Drought stress response
    IFBDChr7 (30.3–31.9 Mb)MdWRKY70MD07G1234700Drought stress response
    IFBDChr4 (7.0–8.1 Mb)MdERF1MD04G1058000Ethylene responsive factor
    IFBDChr6 (5.5–7.3 Mb)MdERF1MD06G1051800Ethylene responsive factor
    IFBDChr4 (7.0–8.1 Mb)MdERF5MD04G1058200Ethylene responsive factor
    IFBDChr6 (5.5–7.3 Mb)MdERF5MD06G1051900Ethylene responsive factor
    IFBDChr13 (18.0–20.0 Mb)MdERF1BMD13G1213100Ethylene response factor 1
    IFBDChr16 (20.4–22.4 Mb)MdERF1BMD16G1216900Ethylene response factor 1
    WCIChr13 (2.6–4.4 Mb)MdRAV1MD13G1046100Ethylene responsive factor
    WCIChr16 (1.5–3.4 Mb)MdRAV1MD16G1047700Ethylene responsive factor
    WCIChr13 (2.6–4.4 Mb)MdMYB62MD13G1039900Flavonol biosynthesis
    WCIChr16 (1.5–3.4 Mb)MdMYB62MD16G1040800Flavonol biosynthesis
    IFBDChr9 (7.9–11.8 Mb)MdNAC42MD09G1147500, MD09G1147600Anthocyanin accumulation
    WCIChr17 (11.4–12.4 Mb)MdNAC42MD17G1134400Anthocyanin accumulation
    IFBDChr9 (7.9–11.8 Mb)HSP 70MD09G1137300Heat stress response
    WCIChr17 (11.4–12.4 Mb)HSP 70MD17G1127600Heat stress response
    IFBDChr4 (11.0–13.0 Mb)MdMYB85MD04G1080600Phenylalanine and lignin biosynthesis
    WCIChr6 (12.1–13.6 Mb)MdMYB85MD06G1064300Phenylalanine and lignin biosynthesis
    IFBDChr15 (13.2–14.2 Mb)MdEBF1MD15G1171800Ethylene inhibition
    WCIChr8 (15.2–17.2 Mb)MdEBF1MD08G1150200Ethylene inhibition
    IFBDChr15 (53.5–54.9 Mb)MdEIN3MD15G1441000Ethylene insensitive 3 protein
    WCIChr8 (30.3–31.6 Mb)MdEIN3MD08G1245800Ethylene insensitive 3 protein
    IFBDChr11 (1.7–3.0 Mb)MdEIN3MD11G1022400Ethylene insensitive 3 protein
    WCIChr7 (4.1–5.6 Mb)MdEIN3MD07G1053500, MD07G1053800Ethylene insensitive 3 protein
    IFBDChr15 (4.6–6.8 Mb)MdMYB73MD15G1076600, MD15G1088000Cold-stress response & malate accumulation
    WCIChr15 (24.8–26.8 Mb)MdMYB73MD15G1288600Cold-stress response & malate accumulation
    IFBDChr15 (4.6–6.8M b)MdWRKY7MD15G1078200Anthocyanin accumulation
    WCIChr15 (24.8–26.8 Mb)MdWRKY7MD15G1287300Anthocyanin accumulation
    IFBDChr15 (43.5–45.5 Mb)MdMYB93MD15G1369700Flavonoid & suberin accumulation
    WCIChr15 (31.7–34.2 Mb)MdMYB93MD15G1323500Flavonoid & suberin accumulation
     | Show Table
    DownLoad: CSV

    Paralogs of several regulatory functions were also found in the regions associated with either FBD or WCI; for example, a significant region (7.9–11.8 Mb) harbouring a gene trio (MdNAC42, HSP70 and HSP89.1) on Chr9 was associated with FBD, but the paralogs of this trio also resided in a distinct G' region associated with WCI on Chr17 (Table 3, Supplemental Table S3). Some other examples included MdMYB85 (Chr4 for FBD, and Chr6 for WCI), MdEBF1 (Chr8 for WCI, and Chr15 for FBD), MdEIN3 (Chr8 for WCI, and Chr15 for FBD), and EIN3 (Chr7 for WCI, and Chr11 for FBD). A pair of genes (MdMYB73 and MdWRKY7) resided together in the separate regions associated with FBD (4.6–6.8 Mb) and WCI (24.8–26.8 Mb) within Chr15 (Table 3).

    The red pigmentation in fruit flesh differed amongst transgenic lines, with fruit from line A10 presenting the most deeply pigmented tissues (Supplemental Fig. S3), while those from lines A2 and A4 were similar in having a lower intensity of pigmentation. No pigmentation was observed in the flesh of control 'Royal Gala' (RG) fruit. RNAseq analysis of a representative transgenic line (A2) compared with RG revealed that a total of 1,379 genes were differentially expressed (log 2-fold), with 658 genes upregulated and 721 genes downregulated. This list was then assessed for commonality with the genomic regions and candidate genes from the XP-GWAS.

    Genes that contained mis-sense SNPs (which would result in a change in predicted protein) and that were also differentially expressed between A2 red-fleshed transgenic line and white-fleshed 'Royal Gala' (control) apples included anthocyanin-related flavanone 3-hydroxylase, chalcone synthase and dihydroflavonol 4-reductase (Table 2 & 4). Many more upstream DNA variants (e.g. in potential promoter-controlling elements) were seen in this group of differentially expressed genes (DEGs) that were also in regions underlying WCI or FBD. Genes encoding enzymes that may be involved in FBD, such as a Rho GTPase activating protein, peroxidase, lipoxygenase 1, and ethylene-forming enzyme (ACO4), were DEGs and showed mis-sense SNPs, including a potential stop codon in the Rho GTPase-activating protein (Table 4). This intersection between DNA change and differential expression warrants further research to evaluate the functions of these genes.

    Table 4.  List of candidate genes associated with WCI or FBD in the XP-GWA and R6:MdMYB10 apple datasets.
    Mutations in GWAS apple populationPredicted gene functionExpression in R6 and ‘Royal Gala’ apples
    GeneMissense
    SNPs
    SNP
    Stop
    Upstream
    variants
    DatasetLocusAnnotation and TAIR IDAverage
    RPKM
    R6 flesh
    Average RPKM
    WT flesh
    log2Fold
    Change
    MD02G11322001FBD Supplemental Table S3Chr02:9450615-11289014flavanone 3-hydroxylase
    (F3H, TT6, F3'H) AT3G51240
    387752.47
    MD02G11336003FBD Supplemental Table S3Chr02:9450615-11289014fatty acid desaturase 5
    (FAD5) AT3G15850
    18109.98
    MD02G11537001WCI Table 2Chr02:11278254-13234556UDP-Glycosyltransferase, lignin related AT2G1856010385341.03
    MD03G10592002FBD Supplemental Table S3Chr03:3177994-4910866Peroxidase AT5G053402514.21
    MD03G1143300422FBD Table 1Chr03:14797661-16611554bZIP transcription factor (DPBF2, AtbZIP67) AT3G44460114852-2.80
    MD03G1147700121FBD Table 1Chr03:14797661-16611554Rho GTPase activating protein AT5G22400205891.29
    MD04G1003400331WCI Table 2Chr04:1-1284894Chalcone synthase (CHS, TT4) AT5G1393014433122.34
    MD04G120410063FBD Table 1Chr04:27629536-29612282lipoxygenase 1 (LOX1, ATLOX1) AT1G550205462681.09
    MD06G1160700129FBD Table 1Chr06:29862341-31737341peptide met sulfoxide reductase AT4G251302121254562.04
    MD06G116140014FBD Table 1Chr06:29862341-31737341Pectin lyase-like protein AT5G63180614129530-2.22
    MD07G1240700126FBD Supplemental Table S3Chr07:30357332-31979025Fe superoxide dismutase 2 AT5G511001051826-3.99
    MD07G13069006FBD Supplemental Table S3Chr07:34607570-36531467UDP-glucosyl transferase 78D2 AT5G170506771112.78
    MD08G12491002WCI Supplemental Table S3Chr08:30486569-31607516HSP20-like chaperone (ATHSP22.0) AT4G102503186456.12
    MD09G111400032FBD Table 1Chr09:7999212-11883202fatty acid desaturase 5 (FAD5) AT3G158507608.68
    MD09G11468001FBD Table 1Chr09:7999212-11883202PHYTOENE SYNTHASE (PSY) AT5G1723038456160901.32
    MD10G132810045FBD Supplemental Table S3Chr10:40235258-41736791ethylene-forming enzyme (ACO4) AT1G050108769033910331.21
    MD15G10236001FBD Table 1Chr15:1-1487288jasmonic acid carboxyl methyltransferase (JMT) AT1G19640472111552.07
    MD15G10241008FBD Table 1Chr15:1-1487288dihydroflavonol 4-reductase (DFR, TT3, M318) AT5G428008723091.63
    MD17G11334004WCI Supplemental Table S3Chr17:11422073-12433463PHYTOENE SYNTHASE (PSY) AT5G172306001721.87
    MD17G12606002FBD Supplemental Table S3Chr17:31098955-32776972dehydroascorbate reductase 1 (DHAR3) AT5G167101867−1.78
     | Show Table
    DownLoad: CSV

    Several gene families (e.g. chalcone synthase, UGT, anthocyanin synthase, HSP, PAL, ERF, WRKY proteins, ABA, and bZIP transcription factors) residing in WCI-associated genomic regions have been reported to be associated with RF in apple[7, 12, 28]. Several of these genes are implicated in stress responses, suggesting that flavonoids and anthocyanin biosynthesis could also be associated with stress (e.g., drought, water loss) tolerance of RF apples[7].

    MdMYB73, which may play a role in the cold-stress response[23, 29], resided in the WCI-associated regions. Ethylene is among the various modulators of environmental stresses induced by factors such as drought and cold temperatures[9, 28]. Several ERF proteins (e.g. MdERF1B, MdERF3) interact with the promoters of MYB domain proteins to regulate anthocyanin and proanthocyanidin (PA) accumulation in apple[10, 11, 30]. The WCI-associated genomic regions in our study flanked several ERFs previously reported to have roles in anthocyanin and PA regulation. MdERF4 (MD10G1290400), one of the three ERFs residing in the WCI-associated region on Chr10, has high phylogenetic similarity with MdERF38, which interacts with MdMYB1 to regulate drought-related anthocyanin biosynthesis[31]. Some ERFs (e.g. MdERF2: MD10G1286300; MdERF4: MD10G1290400, and MdERF12: MD10G1290900) and MdNAC73 residing in the WCI-associated regions have been reported to be associated with fruit maturation[20] – suggesting an interaction between ethylene production and RF colour[12].

    The WCI-associated region on Chr15 included a gene MdMYB73 (MD15G1288600) involved in malate acid synthesis. Previous studies have reported that MdMYB1 regulates both anthocyanin and malate accumulation, and perhaps MdBT2 regulates MdMYB73-mediated anthocyanin accumulation[22, 23]. The malate transporter gene MdMa2 and the MdMYB7 gene, involved in the regulation of anthocyanin and flavanols in RF apple[32], resided together in the WCI-associated upper region of LG16. Taken together, these results support the hypothesis that there is interplay between anthocyanin and malate accumulation in the RF apple. There was a cluster of ubiquitin-specific proteases underpinning the WCI-associated regions on Chrs 7 and 15, which is supported by earlier reports suggesting that ubiquitin-specific proteases respond to auxin and might suppress anthocyanin biosynthesis proteins[33]. The auxin response factor 9 (MdARF9: MD06G1111100), which has also been shown to suppress anthocyanin biosynthesis in RF callus samples[33], also resided in the WCI-associated genomic region on LG7.

    Seedlings in both the low- and high-FBD pools carried the MdMYB10 gene, which suggests that MdMYB10 itself is not the causal factor of FBD in RF apples. Long-term cold storage generally results in senescence-related flesh breakdown, and several transcription factor genes (e.g. MYB, WRKY, NAC, ERF, cytochrome P450, and HSP) have been shown to express differentially during long-term cold storage[9]. The FBD-associated genomic regions in our study harboured several ERFs, suggesting that ethylene synthesis proteins may be contributing to cell wall disassembly, allowing PPO enzymes to come into contact with phenolic compounds and potentially leading to FBD symptoms[18].

    Pectin methyl esterase (PME) genes resided in the FBD-associated significant regions on Chrs 3, 13 and 17. Volatile generation and senescence degradation have been suggested to be bio-markers of FBD, and the expression levels of methyl esters were found to be associated with FBD and senescence in 'Fuji' apples after cold storage[34]. The MdPME2 gene has also been reported to be associated with apple flesh firmness and mealiness[21, 35]. The co-location of genes encoding cell wall-degrading enzymes (MdPME) and QTLs for FBD has been reported in apple[18, 36].

    The clusters of cytochrome P450 enzymes and senescence-related genes resided in some of the FBD-associated regions in this study. There are no earlier reports of the involvement of P450 enzymes in apple FBD expression, but some genes related to cytochrome P450 were found to be upregulated during litchi fruit senescence[37]. Pericarp browning in litchi is mainly attributable to the degradation of anthocyanin, and the ABA-initiated oxidation of phenolic compounds by PPO[37]. Flavonoids are among the major polyphenols in RF apples[5], and cytochrome P450 is part of the regulatory mechanism for flavonoid metabolism[38]. Association of FBD with the genomic region harbouring P450 would suggest its role in enhanced polyphenol synthesis causing FBD. The co-occurrence of a senescence-related gene MdNAC90 (MD03G1148500; MD11G11679000) and the PPO regulator germin-like proteins (in the FBD-associated paralog regions on Chrs 3 and 11) lends support to an interplay between senescence and oxidation of phenolics and anthocyanins.

    A significant region on Chr9 encompassed a cluster of UGT proteins, which play a role in the regulation of flavonoids and phenolic compounds, as well as converting phloretin to phloridzin[39]. Cytochrome P450, which resided within several FBD-associated regions, is reportedly involved in flavonoid metabolism, such as chlorogenic acid (CGA) acid and phloridzin, which have also been positively associated with suberin production and cell wall disassembly[40, 41].

    Reactive oxygen species (ROS) play an important role in regulating physiological processes in plants, such as senescence[37]. Legay et al.[42] suggested that MdMYB93 (MD15G1369700), found residing in the FBD-associated regions in this study, plays a critical role in remobilisation of flavonoid/phenolic compounds, which can be utilised for detoxification of ROS in the case of oxidative stress. However, flavonoid biosynthesis has also been linked with suberin production causing cuticle cracks in apples[42, 43], and the development of cuticular cracks could accelerate flesh browning as a result of an enhanced oxidative process[44].

    Several HSP (e.g. HSP70, HSP70-1, HSP60, HSP89.1, and HSP DNAJ) and HSF (e.g. HSF4, HSFB4, HSFA6B) resided in the FBD-associated genomic regions. Ferguson et al.[45] showed that, during summer, apple flesh temperature could reach as high as 43 °C, and that an increase in the expression of HSP in apples was associated with high daily flesh temperatures, suggesting a role of HSP to counter heat stress. HSPs have been reported to interact with AP2/ERFs and to play a role in flavonoid biosynthesis and drought tolerance in apple[46]. Heat stress affects lignin accumulation and its substrate, O-phenols, and has been reported to play role in enzymatic browning[47]. Additionally, HSF that regulate HSP expression have also been reported to be regulated by cold stress to generate heat-induced cold tolerance in banana[48]. HSF1 was shown to transcriptionally regulate the promoters of HSP to enhance chilling tolerance in loquat fruit[49]. Activity of the enzymes (PAL, C4H, 4CL) of the phenylpropanoid pathway was positively correlated with loquat fruit lignification, whilst suppression of their expression by heat shock treatment and low-temperature conditioning significantly reduced fruit lignification[50].

    Wang et al.[7] showed that ascorbate peroxidase (APX) was among the genes that were upregulated in RF apple compared with in white-fleshed apples. Several genes (e.g. MdAPX1, MdAPX3, MdAPX4, and MdDHAR1) involved in ascorbate synthesis resided in the genomic regions associated with FBD. Co-localisation of MdDHAR and ascorbic acid (AsA) synthesis genes in the FBD-linked genomic regions have been reported[51], suggesting that the low AsA content increases fruit susceptibility to FBD[52]. It has been shown that the expression of MdMYB1 and MdDHAR genes was strongly correlated in RF apples, and that AO and APX were upregulated by anthocyanin regulatory genes[31].

    There were several genes associated with CGA biosynthesis residing in the FBD-associated regions on various linkage groups, including the genes MYB19 (MD07G1268000) and MdC3H (MD15G1436500). Higher concentrations of CGA were reported in transgenic apple lines carrying MdMYB10[12], suggesting a role of CGA metabolism in the expression of FBD[53]. Interestingly, some of the FBD-associated regions (e.g. Chrs 9, 11, 13, 14 and 17) reported here in RF apples coincide with those reported earlier for FBD in white-fleshed apples[18, 36, 53], suggesting some common underlying genetic mechanisms.

    As discussed above, ERFs have been reported to be involved in the accumulation of anthocyanin and PA biosynthesis, while ethylene synthesis proteins also contribute to cell membrane breakdown, allowing the PPO enzyme to come into contact with phenolic compounds, potentially leading to FBD symptoms[12, 18]. We observed clusters of anthocyanin biosynthesis proteins (bHLH), ERFs (MdERF2, MdERF4, MdERF12) and PPO genes together in the genomic region associated with WCI on Chr10. The co-occurrence of these gene families perhaps facilitates potential interactions that contribute to the genetic correlation between WCI and FBD. We noted that genes involved in flavonoid regulation and ethylene synthesis occurred together in the FBD-associated regions on several chromosomes (e.g. MD16G1140800 and MdPAE10: MD16G1132100; MdERF1B: MD16G1216900 and MdMYB15: MD16G1218000, MD16G1218900) – suggesting these genes could be in linkage disequilibrium and this would contribute to the expression of WCI and FBD.

    MYB7 (MD16G1029400) resided in the WCI-associated upper region of Chr16, and the expression level of MYB7 was shown to be correlated with that of LAR1 in peach fruit[54]. The MdLAR1 protein (MDP0000376284), which is located about 1.2 Mb upstream of MYB7 (MD16G1029400), was reported to be associated with WCI and FBD in apple[3]. Mellidou et al.[36] reported that 4CL (MD13G1257800 in the FBD-linked region on Chr13), which catalyses the last step of the phenylpropanoid pathway, leading either to lignin or to flavonoids, was upregulated in browning-affected flesh tissues. The gene MdMYB85, involved in the regulation of flavonoid and lignin biosynthesis, resided in the WCI-associated region on Chr6 and FBD-associated paralogous region on Chr4. Metabolic interactions between anthocyanin and lignin biosynthesis have been reported for apple[55] and strawberry[56], while flesh lignification and internal browning during low-temperature storage in a red-fleshed loquat cultivar was shown to be modulated by the interplay between ERF39 and MYB8[57].

    The co-localisation of MdMYB66 and cytochrome P450 proteins, along with the anthocyanin regulatory protein MdMYB86, in paralogous FBD-associated genomic regions on Chr6 and Chr14 suggests that these genes interact as a 'hub' contributing to the WCI-FBD genetic link. The paralogs of some other genes were found to be residing in the regions associated with either WCI or FBD. For example, MdNAC42 and HSP70 co-localised in the FBD-associated region on Chr9, but this same pair of genes also resided in the most prominent region associated with WCI on Chr17. Similarly, paralogs of MdEIN3 resided in the WCI-associated region on Chr7 (MD07G1053500, MD07G1053800) and FBD-associated region on Chr11 (MD11G1022400). Interestingly, the paralogs of MdMYB73 and MdWRKY7 co-localised in the WCI- (24.6–26.8 Mb) and FBD-associated (4.6–6.8 Mb) regions on Chr15. The WCI-associated region at the bottom of Chr11 hosted a cluster of senescence-related genes, along with the anthocyanin biosynthesis gene MdbHLH3 (MD11G1286900), suggesting they might interact in the genetic nexus between FBD and WCI.

    FBD in RF apples can be caused by senescence, injury via extreme temperature exposure (chilling or heat), or enzymatic (cut fruit) reaction. Genes reported to be connected to all three factors were located in various FBD-associated genomic regions in this study. Postharvest strategies that both delay senescence and limit exposure to low temperatures may be needed to manage FBD. We also hypothesise that high ascorbic acid content could help to minimise expression of FBD in Type-1 RF cultivars. The adverse genetic correlation between WCI and FBD appears to arise from dual and/or interactive roles of several transcription factors, which would pose challenges for designing a conventional marker-assisted selection strategy. The use of bivariate genomic BLUP to estimate breeding values to simultaneously improve adversely correlated polygenic traits (e.g. WCI and FBD), could be an alternative approach[58, 59].

    A population of 900 apple seedlings composed of 24 full-sib families was generated in 2011 by selected crossings between six red-leaved pollen parents and six white-fleshed female parents. All six pollen parents inherited their red-leaf phenotype from the same great-grandparent 'Redfield'[1]. Each pollen parent was involved in four crosses, and the female parents were involved in three to six crosses each. Foliage colour of young seedlings is a phenotypic marker for Type 1 RF apple. The main purpose of this trial was to understand the flesh colour variation and FBD in the Type 1 RF seedlings, so only the seedlings with red foliage (i.e. carrying MdMYB10) were kept for this trial. The number of seedlings per family varied from 10 to 95. The seedlings were grafted onto 'M9' rootstock and were planted in duplicate at the Plant & Food Research orchard in Hawke's Bay, New Zealand (39°39′ S, 176°53′ E) in 2015.

    Phenotyping for RF and FBD was conducted over two consecutive fruiting seasons (2017 and 2018). Fruit were harvested once, when judged mature, based on a change in skin background colour from green to yellow, and when the starch pattern index (SPI) was between 1.0 and 2.0 (on a scale of 0 to 7). In each season, six fruit were harvested from each plant and stored for 70 d at 0.5 °C, followed by 7 d at 20 °C before fruit evaluation. Fruit were cut in half across the equator and the proportion of the cortex area (PRA) that was red in colour, and the intensity of the red colour (RI) (= 1 (low) to 9 (high)) was scored. A weighted cortical intensity (WCI) was then calculated (PRA × RI) as an estimation of the amount of red pigment in the fruit. The proportion of the cortex area showing symptoms of FBD was also recorded. WCI and FBD were averaged over all fruit for a particular seedling. Fruit were also assessed for the following eating quality traits on a 1 (lowest) to 9 (highest) scale: firmness, crispness, juiciness, sweetness, sourness and astringency, to understand the genetic correlations of eating-quality traits with WCI and FBD.

    The binary vector pSA277-R6:MYB10 was transferred into Agrobacterium tumefaciens strain LAB4404 by electroporation. Transgenic 'Royal Gala' plants were generated by Agrobacterium-mediated transformation of leaf pieces, using a method previously reported[5]. Wild-type 'Royal Gala' and three independent transgenic lines (A2, A4 and A10) of R6:MdMYB10 were grown under glasshouse conditions in full potting mix with natural light. The resulting fruit were assessed for flesh colour phenotypes at harvest (around 135 d after full bloom). Fruit peel and cortex from three biological replicates were collected and frozen in liquid nitrogen, with each replicate compiled from five pooled mature fruit for each transgenic line or wild-type control.

    Total RNA of 36 samples (3 R6:MYB10 lines and 1 wild type control, 3 time points, 3 biological replicates) was extracted, using Spectrum Plant Total RNA Kit (SIGMA). Removal of genomic DNA contamination and first-strand cDNA synthesis were carried out using the mixture of oligo (dT) and random primers according to the manufacturer's instructions (QuantiTect Reverse Transcription Kit, Qiagen). Real-time qPCR DNA amplification and analysis was carried out using the LightCycler 480 Real-Time PCR System (Roche), with LightCycler 480 software version 1.5. The LightCycler 480 SYBR Green I Master Mix (Roche) was used following the manufacturer's method. The qPCR conditions were 5 min at 95 °C, followed by 45 cycles of 5 s at 95 °C, 5 s at 60°C, and 10 s at 72 °C, followed by 65 °C to 95 °C melting curve detection. The qPCR efficiency of each gene was obtained by analyzing the standard curve of a cDNA serial dilution of that gene. The expression was normalized to Malus × domestica elongation factor 1-alpha MdEF1α (XM_008367439) due to its consistent transcript levels throughout samples, with crossing threshold values changing by less than 2.

    Individual fruit measurements were first averaged for each seedling. As the phenotyping was repeated over two years, we used a mixed linear model (MLM) accounting for this 'permanent environmental effect', as previously described[58]. Pedigree-based additive genetic relationships among seedlings were taken into account for estimation of genetic parameters using ASReml software[60]. Product-moment correlations between best linear unbiased predictions (BLUP) of breeding values of all seedlings for different traits were used as estimates of genetic correlation among traits.

    A selective DNA pooling procedure was adopted to construct DNA pools. A high-pool and a low-pool were constructed separately for the two traits (WCI and FBD). Genomic DNA was extracted from the leaves of selected seedlings, and quantified by fluorimetry using the picogreen reagent (Cat#P11496, Thermo). The low and high pools consisted of 35 seedlings each, and normalised amounts (~300 ng) of DNA from individuals were pooled. The pools were dried down with DNA Stable reagent (Cat#93021001, Biomatrica) in a centrifugal evaporator and shipped for sequencing. Each DNA pool was sequenced using paired-end 125 bp reads on the Illumina HiSeq 2500 platform. The quality of raw sequence reads was checked with FastQC/0.11.2 and MultiQC/1.2. Based on the quality control reports, the reads were aligned to the published apple reference genome GDDH13 v1.1[61] using the program bowtie2/2.3.4.3[62] with trimming from both ends before alignments and aligning in full read length ("-5 6 -3 5 –end-to-end"). The mapping results were marked for duplicate alignments, sorted, compressed and indexed with samtools/1.12[63]. Based on the alignment of binary alignment map (BAM) files of the high and low pools, single nucleotide polymorphism (SNP) identification was performed using samtools/1.12 ('samtools mpileup') and bcftools/1.12 ('bcftools call –mv')[63, 64]. Variant sites with missing genotype in any of the pools, or having the same genotype between the pools, were discarded. To minimise the influence of sequencing quality on association analysis, the identified SNPs were further filtered according to the following criteria: 1) a Phred-scaled quality score > 20; and 2) the read depth in each pool was neither < 35, nor > 500.

    The allele frequencies between each pair of bulk DNAs (low versus high WCI; low versus high FBD) were compared at each SNP locus. Differences in the allele frequencies between the low and high pool were expected to be negligible for unlinked SNP markers, but allele frequency differences would be larger for SNPs closely linked to the underlying quantitative trait loci (QTLs) contributing to the extreme phenotypes. A nonparametric test (G-statistic = 2 × Σni ln(ni/nexp), where ni (i = 1 to 4) represented counts of reference and alternate alleles at a particular SNP generated from sequencing of the low and high pool, and nexp was the expected allele count assuming no allele frequency divergence between the two DNA pools[65].

    We then calculated a modified statistic (G'), which took into account read count variation caused by sampling of segregants as well as variability inherent in short-read sequencing of pooled samples[65]. Using R package QTLseqr[66], firstly a G-statistic was calculated for each SNP marker, and then a weighted average using Nadaraya-Watson kernel was obtained to yield a G' statistic for a sliding genomic window of 2 Mb size. The Nadaraya-Watson method weights neighbouring markers' G-values by their distance from the focal SNP so that closer SNPs receive higher weights. The 95th percentile value of G' was used as a threshold to identify significant hotspots and to identify the putative candidate genes residing within the ±1 Mb region around the G' peak. For comparison purposes, a standard two-sided Z-test[14] was also performed to determine the significance of allele frequency differences at SNP loci between the pools for each trait.

    The GDDH gene models intersecting with the XP-GWA hotspots were pulled out with bedtools/2.30.0 ("bedtools intersect -wo -nonamecheck"). The selected genes were further blasted to TAIR10 ("-evalue 1e-5") and the annotated functions from Arabidopsis genes with the best blast score, the highest % identity, and the longest aligned length, were used. Then the expressions of genes located in the GWA hotspots were extracted from the RNAseq analysis of the R6:MdMYB10 representative transgenic line, and the log 2-fold change between the R6:MdMYB10 and 'Royal Gala' apples were calculated.

    This research was funded in 2017/18 by the Strategic Science Investment Fund of the New Zealand Ministry of Business, Innovation and Employment (MBIE) and from 2019 by the Plant & Food Research Technology Development – Pipfruit programme. We thank our colleague Jason Johnston for providing some pictures of the flesh browning disorder in red-fleshed apples. Richard Volz and Jason Johnston provided constructive comments and suggestions on the manuscript.

  • The authors declare that they have no conflict of interest.

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  • Cite this article

    Sun Y, Niu G, Masabni JG. 2024. Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water. Technology in Horticulture 4: e002 doi: 10.48130/tihort-0023-0030
    Sun Y, Niu G, Masabni JG. 2024. Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water. Technology in Horticulture 4: e002 doi: 10.48130/tihort-0023-0030

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Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water

Technology in Horticulture  4 Article number: e002  (2024)  |  Cite this article

Abstract: 'Wonderful' pomegranate (Punica granatum) is currently the industry-standard cultivar, accounting for more than 90% of all commercially planted trees. The purpose of this study was to determine the response of 'Wonderful' pomegranate trees to a range of salinity by irrigating them with a nutrient solution at an electrical conductivity (EC) of 1.2 dS·m−1 (control) or one of two saline solutions at EC of 5.0 (EC 5) or 10.0 dS·m−1 (EC 10) in two rounds of treatments. Pomegranate plants with saline solution treatments had no or minimal foliar salt damage. However, EC 10 reduced shoot dry weight (DW) by 15% relative to the control in the first round, and both EC 5 and EC 10 reduced shoot DW by 13% and 31%, respectively, in the second round compared to the control. The concentration of sodium (Na) was ≤ 1 mg·g−1 in the leaves and stems in all treatments but was much higher in the roots in EC 5 and EC 10. The concentration of chloride (Cl) in the leaves, stems, and roots increased by 36%−90%, 101%−156%, and 254%−299%, respectively, in EC 5 and EC 10 compared to the control. Salinity reduced the concentration of all macronutrients and some micronutrients, especially in the leaves, compared to the control. However, there was no or minimal effect on leaf gas exchange and SPAD readings. These results indicate that 'Wonderful' pomegranate is highly tolerant to salinity and has a strong ability to exclude Na accumulation in the leaves, thus avoiding salt damage.

    • Pomegranate (Punica granatum L.) has long been cultivated throughout central and southeast Asia, the Indian subcontinent, Iran, the Caucasus region, the Middle East, the Mediterranean Basin, and north and tropical Africa[1]. It is also suited to grow in the United States Department of Agriculture (USDA) cold hardiness zones 8−11, but performs best in regions with long, hot, dry summers[1,2] and with well-drained alkaline, loamy soils[3]. In the United States, pomegranate has been successfully grown in arid and semiarid regions such as Arizona, California, Nevada, New Mexico, Texas, and Utah[4]. It has recently gained increased popularity due to its nutritious fruits with unique flavor, taste, and medicinal properties[2,5]. Between 2002 and 2017, the production acreage of pomegranates in the United States doubled or tripled[4,6]. California is the leading producer, accounting for more than 98% of pomegranate production in the country[4,6]. In the crop year 2020−2021, California harvested a total of 18,885 acres of pomegranates, with an average yield of 6.72 tons per acre and a total production of 46,938 tons valued at $130.69 million[7]. This upward trend is expected to continue due to growing public awareness of the benefits of pomegranate fruits, advancements in industrial processing methods for separating arils from fruits, and improvements in cultivation techniques[2,5,8,9].

      Pomegranate cultivation is often hindered by various abiotic stresses, such as drought and salinity[2,5]. Salinity is a major environmental factor that limits crop growth and productivity in many parts of the world. The presence of high salt concentrations in the soil can result in water stress, ion toxicity, and nutrient imbalances, ultimately leading to reduced plant growth and yield[1012]. Therefore, it is crucial to understand the salinity tolerance of pomegranate and identify suitable cultivars for salt-affected soils. Salinity tolerance varies among different cultivars of pomegranate. For example, 'Malas-Saveh' is more sensitive to salinity than 'Shishe-Kab'[13]. In a pot experiment, 10 commercial Iranian cultivars exhibited varying degrees of salinity tolerance when irrigated at different salinity levels (4, 7, or 10 dS·m−1)[14]. Similarly, seven-year-old 'Manfalouty', 'Wonderful', and 'Nab-Elgamal' pomegranate trees showed different responses when irrigated with saline groundwater at a salinity level of 6.0 dS·m−1[15]. Identifying salt-tolerant cultivars is critically important for sustainable pomegranate production, especially in regions where low-quality water is used for irrigation and salt-prone conditions are prevalent.

      Pomegranate has been found to exhibit a relatively high tolerance to salinity stress[1417]. For example, 'Malas Shirin' pomegranate, grown in a 1:1 sand-perlite medium, showed tolerance up to 40 mM NaCl (approximately 3.65 dS·m−1) when irrigated with a complete Hoagland's solution[18]. However, in another study, irrigation with saline groundwater with an EC of 6.0 dS·m−1 resulted in reduced growth, flowering, and yield of seven-year-old 'Manfalouty', 'Wonderful', and 'Nab-Elgamal' pomegranate trees[15]. It also increased the incidence of fruit cracking, although the total sugar and acidity percentages of the fruit remained unchanged. Furthermore, pomegranate trees showed minimal foliar salt damage and only slight growth reduction when irrigated with a saline solution up to an EC of 15.0 dS·m–1[17]. Similarly, Liu et al.[19] observed minimal leaf burn, necrosis, or discoloration in all pomegranate cultivars irrigated with a saline solution at an EC of 20.8 dS·m–1 for 35 d. These findings indicate that pomegranate exhibits a remarkable ability to tolerate high levels of salt stress.

      Although the USDA National Clonal Germplasm Repository for Tree Fruit and Nut Crops houses nearly 200 pomegranate accessions[20], the 'Wonderful' cultivar is widely recognized as the standard in commercial pomegranate production, with more than 90% of all commercial trees being this variety in the US[5]. However, there is still a need for further research to investigate the salt tolerance of this prevalent pomegranate cultivar. The purpose of this study was to determine the growth and physiological responses of 'Wonderful' pomegranate plants to saline water in a greenhouse.

    • On 4 Jan 2016, 'Wonderful' pomegranate plants were obtained from Marcelino's Nursery (Tornillo, TX, USA) in 3.8-L containers. On 18 Feb 2016, all plants were pruned to a height of 30 cm and then transplanted into 5.8-L black Poly-tainer pots (22.5 cm × 19.5 cm) containing a soilless growing substrate. The substrate consisted of 45% to 55% Canadian sphagnum peat moss, vermiculite, composed bark, dolomite limestone (used as a pH adjuster), and 0.0001% silicon dioxide (SiO2) from calcium silicate to promote root growth (Metro-Mix 360 RSI; SunGro® Horticulture, Agawam, MA, USA). The plants were grown in a greenhouse located in El Paso, TX, USA (lat. 31°41'45" N, long. 106°16'54" W, elev. 1,139 m) and irrigated using an injector (Dosatron International, Clearwater, FL, USA) with a water-soluble fertilizer solution (15N-2.2P-12.5K, Peters 15-5-15 Cal-Mag Special; Scotts, Marysville, OH, USA) at a nitrogen (N) concentration of 105 mg·L−1 and an electrical conductivity (EC) of 1.2 ± 0.1 dS·m−1 (mean ± SD). To control aphids, abamectin (AVID® 0.15 EC, 2% Abamectin, Syngenta Crop Protection, Greensboro, NC, USA) was sprayed at a rate of 0.1 mL/gal a.i. on all plants as needed.

    • On 12 Apr 2016, all plants were pruned again to a height of 30 cm due to their rapid growth. Two weeks later (i.e., 25 Apr), uniform plants were selected and divided into three groups to initiate the treatments. The first group of plants was irrigated with a nutrient solution at an EC of 1.2 dS·m−1, serving as the control. The other two groups of plants were irrigated with one of the saline solutions at ECs of 5.0 dS·m−1 (EC 5) or 10.0 dS·m−1 (EC 10). All plants across groups were irrigated weekly with 1.5 L of treatment solution, resulting in a leaching fraction of approximately 21% ± 4.1%. The nutrient solution was prepared by adding 15N-2.2P-12.5K (Peters 15-5-15 Ca-Mg Special; Scotts, Marysville, OH, USA) to reverse osmosis water, resulting in a nitrogen concentration of 150 mg·L−1 and an EC of 1.2 ± 0.1 dS·m−1. The saline solutions at EC of 5.0 ± 0.2 dS·m−1 (EC 5) were prepared by adding 20.6 mM sodium chloride (NaCl) and 10.4 mM calcium chloride (CaCl2) to the nutrient solution. Similarly, the saline solutions at EC of 9.8 ± 0.3 dS·m−1 (EC 10) were prepared by adding 47.8 mM NaCl and 24.0 mM CaCl2 to the nutrient solution. This combination was used because NaCl represents the common salt found in reclaimed water[21], and CaCl2 mitigates potential calcium (Ca) deficiencies caused by high levels of Na[22]. The nutrient and saline solutions were prepared in 100-L tanks, and the EC was verified using an EC meter (Model B173; Horiba, Kyoto, Japan) prior to irrigation. Between treatment solution irrigations, plants were irrigated with the control nutrient solution whenever the substrate surface became dry. The frequency of irrigation varied based on environmental conditions and treatment solutions. Plants subjected to higher salinity levels required less irrigation compared to those in the control group due to reduced water use resulting from decreased transpiration and leaf area.

    • To determine the EC of the leachate solution, the pour-through technique described by Cavins et al.[23] and Wright[24] was employed. After the treatment solution was applied and the container had drained for a minimum of 30 min, 100 mL distilled water was poured onto the substrate. A saucer was placed under the container to collect the leachate solution. The EC of the leachate was then measured using the EC meter. For each treatment, six plants were selected to measure the EC after each application of the treatment solutions.

    • Throughout the experimental period, the greenhouse was maintained at an average air temperature of 27.1 ± 2.7 °C during the day and 22.3 ± 4.2 °C at night. Daily light integral (DLI) averaged 10.4 ± 1.7 mol·m−2·d−1 and relative humidity (RH) averaged 45.6% ± 13.0%.

    • Irrigation treatments were applied eight times weekly from 25 Apr to 28 Jun 2016. On 7 Jul, after 73 d of growth, the new pomegranate shoots, which were identifiable visually, were harvested (first harvest). At harvest, plant height (cm) was measured from the rim of the pot to the top growing point. The leaves were separated from the stems, and both were dried in an oven at 70 °C for 6 d. The dry weights of the leaves and stems were recorded. Since pomegranate trees grew rapidly, we harvested the new shoots 73 d after initiation of the first-round treatments to make the plant size manageable. To examine the response to salinity for a longer period, we imposed the second round of saline solution treatments on the same trees. Between Jul 7 and Jul 22, which is the break between the two rounds of treatments, the trees were irrigated with the control nutrient solution. On 22 Jul, the second-round treatments were initiated by irrigating the trees with the nutrient solution or one of the saline solutions (EC 5 or EC 10) weekly for eight weeks. On 16 Sep, the new shoots, developed during the second-round treatment and visually identifiable, were harvested (second harvest). Plant height was recorded as previously described. The length of new shoots (> 5 cm) was measured, and the total length of new shoots was recorded as shoot length. Roots were also harvested, cleaned, dried, and weighed. The dry weights of the leaf, stem, and root were recorded. The dried leaf, stem, and root samples were ground into powder and used for mineral nutrient analysis (see below).

    • Before each harvest date, the extent of foliar salt damage was assessed visually using a reference scale ranging from 0 to 5. The rating scale was as follows: 0 indicated that the plant was dead; 1 represented over 90% foliar damage, characterized by salt-induced leaf burn, necrosis, or discoloration; 2 indicated moderate foliar damage ranging from 50% to 90%; 3 indicated slight foliar damage, encompassing less than 50%; 4 indicated good quality with minimal foliar damage; and 5 represented excellent condition without any foliar damage[17]. It is important to note that the foliar salt damage rating was independent of plant size and solely focused on assessing the extent of damage caused by salt stress.

    • Prior to each harvest date, the leaf SPAD readings were recorded using a handheld meter (Minolta Camera Co., Osaka, Japan), which quantified the optical density [Soil-Plant Analysis Development (SPAD) reading]. Healthy and fully expanded leaves located in the middle of the shoots were selected for measurement. Twenty measurements per treatment were taken to ensure the accuracy and reliability of the readings.

    • The maximal photochemical efficiency (Fv/Fm) of photosystem II (PS II) was measured before both harvest dates using a Hansatech Pocket PEA chlorophyll fluorimeter (Hansatech Instruments, Norfolk, UK), following the methodology described by Strasser et al.[25, 26]. For the measurements, healthy and fully expanded leaves were selected, and a total of 20 measurements per treatment were taken. The measurements were conducted on a sunny day between 10:00 and 14:00 HR, and plants were well watered to avoid any potential drought stress. Before taking the Fv/Fm measurements, the selected leaves were acclimated in darkness for at least 30 min. Minimal fluorescence values in the dark-adapted state (F0) were obtained by applying a low-intensity red LED (light emitting diode) light source (627 nm) for 50 µs. Subsequently, maximal fluorescence values (Fm) were measured after applying a saturating light pulse of 3,500 μmol·m−2·s−1. The Fv/Fm, which represents the maximum photochemical quantum use efficiency of PS II in the dark-adapted state, was calculated using the formula: Fv/Fm = (Fm − F0)/Fm.

    • Leaf net photosynthesis (Pn), stomatal conductance (gs), and transpiration (E) were recorded before both harvest dates using a CIRAS-2 portable photosynthesis system (PP Systems, Amesbury, MA, USA) with an automatic universal PLC6 broadleaf cuvette. For each treatment, measurements were taken for 10 plants. A fully expanded leaf at the top of each plant was chosen for the measurement. The environmental conditions within the cuvette were maintained at a leaf temperature of 25 °C, a photosynthetic photon flux density (PPFD) of 1,000 μmol m−2·s−1, and a CO2 concentration of 375 μmol·mol−1. Data recording took place once the environmental conditions and gas exchange parameters within the cuvette reached a stable state. These measurements were carried out on a sunny day between 10:00 and 14:00 HR to ensure consistent light conditions. The plants were adequately watered to prevent water stress and maintain optimal physiological conditions during the measurements.

    • To analyze the concentration of shoot mineral elements Na, Cl, Ca, potassium (K), iron (Fe), sulfur (S), zinc (Zn), copper (Cu), manganese (Mn), and boron (B), eight plants per treatment were randomly selected at the second harvest. All dried leaves, stems, and roots of each plant were ground using a stainless Wiley mill (Thomas Scientific, Swedesboro, NJ, USA) to pass a 40-mesh screen. To determine Cl concentration, the ground samples were extracted using 2% acetic acid (Fisher Scientific, Fair Lawn, NJ, USA), following the method described in Gavlak et al.[27] and determined using an M926 Chloride Analyzer (Cole Parmer Instrument Company, Vernon Hills, IL, USA). To determine the concentrations of other elements, the ground samples were sent to the Soil, Water and Forage Testing Laboratory at Texas A&M University (College Station, TX, USA). The samples were digested in nitric acid according to the protocol described by Havlin & Soltanpour[28] and analyzed for mineral elements using Inductively Coupled Plasma-Optical Emission Spectrometry (SPECTRO Analytical Instruments Inc., Mahwah, NJ, USA). The concentration of mineral elements was reported on a dry weight basis, as described by Isaac & Johnson[29].

    • Treatments were a completely randomized design with 20 plants/treatment. Data were analyzed by analysis of variance (ANOVA) using JMP (Version 12, SAS Institute Inc., Cary, NC, USA) and means were separately using Tukey's honest significant difference (HSD).

    • From 25 Apr to 28 Jun, EC of the leachate solution increased from 3.1 to 5.8 dS·m−1 in the control, 5.5 to 12.4 dS·m–1 in EC 5, and 9.7 to 21.5 dS·m–1 in EC 10, and during the second- round treatment ranged from 4.9 to 5.8 dS·m−1, 12.5 to 15.7 dS·m–1, and 18.1 to 20.1 dS·m–1 in control, EC 5, and EC 10, respectively (Fig. 1). Similar results were observed in our previous reports[17, 30]. Monitoring EC of leachate solution is essential for growing high-quality container plants and in woody plants and provides clues about responses to salinity before deficiency or toxicity symptoms appear in plants[23].

      Figure 1. 

      The electrical conductivity (EC) of the leachate collected from potted plants of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1, CNT) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)). Vertical bars represent standard deviations of six samples per treatment.

    • Leaf burn, necrosis, discoloration, and reduced plant growth are common symptoms that plants experience under salinity stress[10]. On the first harvest date, the plants had no foliar salt damage (Table 1). However, salt treatment impacted plant height (p = 0.006), leaf DW (p = 0.02), and shoot DW (p = 0.002). Compared to the control, EC 10 decreased plant height, leaf DW, and shoot DW by 9%, 18%, and 15%, respectively. By the second harvest date, salt treatment affected the visual score (p = 0.04), leaf DW (p = 0.01), stem DW (p < 0.0001), and shoot DW (p < 0.0001), but root DW was not affected by salt treatment (Table 1). Plants in EC 5 and EC 10 had minimal foliar salt damage with average visual scores of 4.7 and 4.9, respectively. Leaf DW was 24% less in EC 10 than in the control. Compared to the control, EC 5 and EC 10 reduced the stem DW by 20% and 38%, respectively, and shoot DW by 13% and 31%, respectively. In addition, salt treatment affected the shoot length (P = 0.001; data not shown). The shoot length was 25% less in EC 10 than in the control. These results are similar to previous work carried out by Naeini et al.[18], Okhovatian-Ardakani et al.[14], El-Khawaga et al.[15], and Sun et al.[17]. These researchers observed that increasing salinity levels inhibit pomegranate growth in terms of shoot length, leaf area, or shoot biomass and therefore, would likely also reduce fruit yield.

      Table 1.  Foliar damage, plant height, and leaf, stem, and shoot dry weight (DW) of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)).

      TreatmentFoliar damage*Height (cm)Leaf DW (g)Stem DW (g)Shoot DW (g)Root DW (g)
      First harvestControl5.0 a**84.7 a40.3 a32.3 a72.6 a−***
      EC 54.8 a84.3 a38.5 ab31.6 a70.1 a
      EC 105.0 a77.1 b33.1 b28.7 a61.8 b
      Second harvestControl5.0 a68.3 a25.6 a26.5 a52.1 a25.3 a
      EC 54.7 b69.6 a24.1 ab21.3 b45.4 b24.6 a
      EC 104.9 ab65.2 a19.4 b16.5 c35.9 c20.3 a
      * Visual damage was rated using a reference scale from 0 to 5, where 0 = dead; 1 = over 90% foliar damage (salt damage: leaf burn, necrosis, or discoloration); 2 = moderate (50% to 90%) foliar damage; 3 = slight (less than 50%) foliar damage; 4 = good quality with minimal foliar damage; and 5 = excellent without foliar damage[17]. ** Means with the same letters within the column and harvest date are not significantly different among treatments by Tukey's honest significant difference (HSD) multiple comparisons at α = 0.05. *** Data for the first harvest was not collected in the first harvest because the plants continued to grow for the second round of treatments.
    • It is well known that salinity stress usually impacts plant chlorophyll content, photosynthesis, and stomatal conductance[12]. Salt treatment did not affect Fv/Fm, E, and gs of 'Wonderful' pomegranate on either harvest date (Table 2). Pn and SPAD readings were similar among treatments at the first harvest date; however, by the second harvest date, Pn and SPAD readings were reduced by 13% and 10%, respectively, in EC 10. These results indicated that elevated salinity slightly impacted the photosynthetic apparatus of the pomegranate plants. Khayyat et al.[31] reported that salinity reduced the chlorophyll content and photosynthetic efficiency of 'Malas-e-Saveh' and 'Shishe-Kab' pomegranate. Hasanpour et al.[32] also observed that salinity reduced the chlorophyll index and chlorophyll fluorescence. In a previous study, we found that salt treatment did not affect the SPAD readings but decreased the Pn, E, and gs by an average of 18%, 24%, and 33%, respectively, across 22 cultivars[17].

      Table 2.  SPAD meter readings, maximum photochemical efficiency (Fv/Fm) of photosystem II, net photosynthesis (Pn), transpiration (E), and stomatal conductance (gs) of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)).

      TreatmentSPADFv/FmPn (µmol·m−2·s−1)E (mmol·m−2·s−1)gs (mmol·m−2·s−1)
      First harvestControl54.3 a*0.79 a10.6 a4.1 a250 a
      EC 552.9 a0.80 a12.8 a4.3 a277 a
      EC 1054.4 a0.80 a12.1 a4.1 a273 a
      Second harvestControl44.3 a0.79 a15.6 ab4.8 a421 a
      EC 542.2 ab0.79 a16.3 a4.8 a403 a
      EC 1039.9 b0.80 a13.6 b4.2 a321 a
      * Means with the same lowercase letters within the column and harvest date are not significantly different among treatments by Tukey's honest significant difference (HSD) multiple comparison at α = 0.05.
    • Plants adapt to salinity stress through osmotic adjustment, Na or Cl exclusion, or tolerance to high Na or Cl concentrations in the shoots[11]. The amount of Na in plant tissue usually increases with increasing NaCl concentration in irrigation water[13, 14, 17, 18, 31, 3335]. In our study, the Na concentration in the leaf and stem tissue of 'Wonderful' pomegranate was similar among treatments (Fig. 2); however, more Na accumulated in the root tissue when the plants were irrigated with EC 5 and EC 10. The concentration of Na in leaves and stems was less than that in roots (p < 0.0001). Surprisingly, the leaf and stem Na concentration was less than 1 mg·g−1 on a dry weight basis. We also observed similar results in another experiment on 22 pomegranate cultivars[17]. Moreover, Na concentrations in the roots in this study averaged 0.8, 3.7, and 4.5 mg·g−1 in the control, EC 5 and EC 10 treatments, respectively. These results indicate that pomegranate avoids foliar salt damage by limiting the transport of Na to the shoots[34, 35].

      Figure 2. 

      Concentration of Na, Cl, Ca, and K in the leaves, stems, and roots of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1, CNT) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)). The same lowercase letters above the error bars indicate the treatments are not significantly different based on Tukey's honest significant difference (HSD) test at α = 0.05.

      Increased Cl concentration was observed in all three plant parts in EC 5 and EC 10 (Fig. 2). The Cl concentration in leaves was less than that in stems and roots (p < 0.0001), but the difference in Cl concentration between leaves and roots was smaller than that of Na. Compared to the control, Cl concentration in leaves, stems, and roots increased by 36%−90%, 101%−156%, and 254%−299%, respectively. Higher concentrations of Cl in plant tissues with increasing salinity are well documented[13, 14, 18, 31, 3335]. Sun et al.[17] reported that the average leaf Cl concentration averaged 10.03 mg·g−1 DW in 22 pomegranate cultivars and 17% higher than in the control. Thus, pomegranate plants are capable of restricting either the uptake or transport of Cl[34, 35]. It seems that Na and Cl in pomegranate leaves are relatively low under saline conditions and, therefore, only have a slight effect on photosynthesis and other related parameters in 'Wonderful' pomegranate. Otherwise, high concentrations of Na or Cl in the leaves would have damaged the chloroplast, thus inhibiting photosynthesis[12].

      Salinity dominated by Na salts reduces Ca availability, transport, and mobility to growing regions of the plant, which subsequently affects the quality of both vegetative and reproductive organs[36]. In our study, leaf Ca concentration declined as EC of the saline solution increased, but this was not the case for Ca in stems and roots (Fig. 2). This result agrees with a previous report indicating that leaf Ca concentration declined with increasing salinity in pomegranate[31]. In another experiment, 64% of pomegranate cultivars receiving salt treatment had a significant or slight decrease in Ca concentration[17].

      Salinity dominated by Na salts also reduces K acquisition[36, 37]. In the present study, salinity reduced K concentration in leaves and roots, but not in the stems (Fig. 2). In another experiment, salt treatment reduced leaf K in 13 out of 22 pomegranate cultivars[17]. This is probably a strategy of the plants to reduce salt stress as K plays an important role in adjusting the osmotic potential of plant cells, as well as activating enzymes related to respiration and photosynthesis[12].

      Excessive Na and Cl uptake competes with the uptake of other nutrients, such as N, P, Mg, S, and B, resulting in nutritional disorders and reducing plant quality[36]. Elevated salinity reduced P (p < 0.02), Mg (p < 0.03), and B (p < 0.0005) concentrations in all plant parts in this study (Figs 3 & 4). Salinity also reduced the concentration of Zn (p = 0.005) and S (p < 0.0001) but increased Fe (p = 0.02), Cu (p = 0.008), and Mn (p < 0.0001) in the leaves. Salinity reduced the concentration of Fe (p < 0.0001) and S (p = 0.0003) but increased Zn (p = 0.01), Cu (p = 0.05), and Mn (p = 0.02) in the stems. However, salinity had no effect on the concentration of Zn, Fe, Cu, Mn, and S in the roots.

      Figure 3. 

      Concentration of P, Mg, Fe, and S in the leaves, stems, and roots of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1, CNT) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)). The same lowercase letters above the error bars indicate the treatments are not significantly different based on Tukey's honest significant difference (HSD) test at α = 0.05.

      Figure 4. 

      Concentration of Zn, Cu, Mn, and B in the leaves, stems, and roots of 'Wonderful' pomegranate grown in a greenhouse and irrigated with a control nutrient solution (electrical conductivity (EC) = 1.2 dS·m−1, CNT) or one of two saline solutions (EC = 5.0 (EC 5) or 10.0 dS·m−1 (EC 10)). The same lowercase letters above the error bars indicate the treatments are not significantly different based on Tukey's honest significant difference (HSD) test at α = 0.05.

      Leaf Ca, K, P, and B were still within the optimum range for pomegranate in each treatment, but Mg, Zn, Fe, Cu, and Mn were below the recommended levels for each nutrient[38]. Hasanpour et al.[32] observed that salinity inhibits the transport of micronutrients to the shoots in pomegranate. Khayyat et al.[31] also observed that leaf Mg and Fe concentrations declined in 'Malas-e-Saveh' pomegranate as the salinity of irrigation water increased. In contrast, salinity increased Zn and Cu in the roots and shoots of 'Rabab' and 'Shishegap' pomegranates[32].

    • 'Wonderful' pomegranate was observed to be very tolerant to saline irrigation water in the present study, as evidenced by minimal salt damage to the leaves and only a slight growth reduction even at salinity levels as high as 10.0 dS·m−1. The results suggested that the cultivar was capable of reducing salt damage by restricting the uptake and/or transport of Na and Cl to the stems and leaves. Thus, 'Wonderful' pomegranate could serve as an alternative crop in arid and semiarid regions with limited potable water. Further investigations are still needed to quantify the effects of elevated salinity on the fruit yield of 'Wonderful' pomegranates when alternative water resources are used for irrigation to maintain profitability and sustainability in agriculture.

    • The authors confirm contribution to the paper as follows: study conception and design: Niu G, Sun Y, Masabni J; project administration and supervision: Niu G, Sun Y; data collection: Sun Y; analysis and interpretation of results: Sun Y, Niu G; draft manuscript preparation: Sun Y. All authors reviewed the results and approved the final version of the manuscript.

    • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

      • This research is supported in part by the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Hatch project TEX090450 and UTA01666, USDA Agricultural Marketing Service Specialty Crop Block Grant, and Texas A&M AgriLife Research. It is approved as Utah Agricultural Experiment Station (UAES) journal paper number 9736. The authors are solely responsible for the content of this publication, and it does not necessarily represent the official views of the funding agencies. Mention of a trademark, proprietary product, or vendor does not constitute a guarantee or warranty of the product by the USDA and does not imply its approval to the exclusion of other products or vendors that may also be suitable. The authors would like to acknowledge the in-kind support of plant materials from Marcelino's Nursery, Tornillo, TX, USA.

      • The authors declare that they have no conflict of interest. Genhua Niu and Youping Sun are the Editorial Board members of Technology in Horticulture who were blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of these Editorial Board members and the research groups.

      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (4)  Table (2) References (38)
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    Sun Y, Niu G, Masabni JG. 2024. Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water. Technology in Horticulture 4: e002 doi: 10.48130/tihort-0023-0030
    Sun Y, Niu G, Masabni JG. 2024. Growth, gas exchange, and mineral nutrition of 'Wonderful' pomegranate irrigated with saline water. Technology in Horticulture 4: e002 doi: 10.48130/tihort-0023-0030

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