Artificial Shading Can Adversely Affect Heat-tolerant Lettuce Growth and Taste, with Concomitant Changes in Gene Expression

in Journal of the American Society for Horticultural Science
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  • 1 Department of Horticultural Science, University of Minnesota, St. Paul, MN 55108
  • | 2 Southern Research and Outreach Center, University of Minnesota, Waseca, MN 56093
  • | 3 Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN 55108

Shading has been used to produce high-quality lettuce (Lactuca sativa) in locations where production conditions are not optimal for this cool-season crop. To learn what additional benefits shading provides if heat-tolerant cultivars are used and to understand the effects of shading on growth, sensory quality, chemical content, and transcriptome profile on heat-tolerant lettuce, we grew two romaine lettuce cultivars with and without shading using 50% black shadecloth in 2018 and 2019. Shading reduced plant leaf temperatures, lettuce head fresh weights, glucose and total sugars content, and sweetness, but not bitterness, whereas it increased lettuce chlorophyll b content compared with unshaded controls. Transcriptome analyses identified genes predominantly involved in chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism as upregulated in unshaded controls compared with shaded treatments. For the tested cultivars, which were bred to withstand high growing temperatures, it may be preferable to grow them under unshaded conditions to avoid increased infrastructure costs and obtain lettuce deemed sweeter than if shaded.

Abstract

Shading has been used to produce high-quality lettuce (Lactuca sativa) in locations where production conditions are not optimal for this cool-season crop. To learn what additional benefits shading provides if heat-tolerant cultivars are used and to understand the effects of shading on growth, sensory quality, chemical content, and transcriptome profile on heat-tolerant lettuce, we grew two romaine lettuce cultivars with and without shading using 50% black shadecloth in 2018 and 2019. Shading reduced plant leaf temperatures, lettuce head fresh weights, glucose and total sugars content, and sweetness, but not bitterness, whereas it increased lettuce chlorophyll b content compared with unshaded controls. Transcriptome analyses identified genes predominantly involved in chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism as upregulated in unshaded controls compared with shaded treatments. For the tested cultivars, which were bred to withstand high growing temperatures, it may be preferable to grow them under unshaded conditions to avoid increased infrastructure costs and obtain lettuce deemed sweeter than if shaded.

Lettuce (Lactuca sativa) is an important cool-season vegetable crop species, with more than 2 million tonnes produced in the United States in 2019 [U.S. Department of Agriculture (USDA), National Agricultural Statistics Service, 2020]. The ideal average daily temperature for field-grown lettuce is 18.5 °C, and high temperatures may negatively affect lettuce growth and development leading to tipburn, undesirable bitterness, and early bolting due to a reduced growing period (Waycott and Ryder, 1993). Bitterness, an important taste characteristic of lettuce usually associated with consumer rejection (Drewnowski and Gomez-Carneros, 2000), has been found to increase with higher growing season temperatures (Simonne et al., 2002).

Bunning et al. (2010) found that genotypic differences had a greater effect than environmental conditions on sensory and phenolic content of lettuces. Sensory analyses of lettuce have demonstrated genotypic differences (Simonne et al., 2002; Zhao and Carey, 2009), especially in bitterness, which may be attributed to differing quantities of bitter components, including sesquiterpene lactones, lactucin, and lactucopricrin (Price et al., 1990). Bitterness perception may be counteracted by sweetness, with the ratio of bitter-to-sweet compounds determining bitterness perception and liking (Chadwick et al., 2016). Breeding efforts may lead to new cultivars that are more heat tolerant and less bitter, but the development of new cultivars can be expensive and time-consuming (Driedonks et al., 2016).

In addition to the development of heat-tolerant cultivars, technologies that alter light intensity and temperature can be used to improve growth and productivity of crops under high temperature conditions. Several research groups have studied the effects of shading on lettuce growth, yield, bitterness, and chemical composition. The use of 39% white shadecloth over high tunnels reduced lettuce leaf surface temperature by 1.5 to 2.5 °C, as well as head weights of loose-leaf and butterhead, but not romaine, lettuces compared with lettuce grown in the open field (Zhao and Carey, 2009). In contrast, lettuce grown in the field under 50% black shade had greater marketable head weight and diameter, and chlorophyll and carotene content, than unshaded controls (Ilić and Fallik, 2017, Ilić et al., 2017). Mastilović et al. (2019) observed that shaded lettuce had lower soluble solids content, which is linked to sweetness, than those grown in the open field. Because consumers have a preference for sweeter lettuce (Chadwick et al., 2016), open field-grown lettuce may be preferable to shaded lettuce.

To produce lettuce under high temperatures, the use of both well-adapted cultivars and suitable production technologies are needed to provide effective solutions to these problems. Growers may first choose to raise heat-adapted cultivars before adopting shading technology, due to the increased costs and labor in using shade technology. The objective of this study was to evaluate whether shading technology would improve production and consumer appeal of heat-tolerant lettuce cultivars under high temperature growing conditions. In this study we evaluated lettuce growth, sensory properties, chemical content, and transcriptomic differences of heat-tolerant lettuces grown under 50% black shadecloth or unshaded field conditions.

Methods

Plant materials and growing conditions.

‘Salvius’ and ‘Sparx’ romaine lettuce were grown from seed (Johnny’s Selected Seeds, Winslow, ME). These cultivars have been reported to be heat-tolerant (Holmes et al., 2019) and were chosen at the recommendation of Minnesota growers. Seeds were sown in organic growing media. In 2018, the growing media that was used was Sunshine (Sun Gro Horticulture, Agawam, MA) or Seed Starter Mix (Purple Cow Organics, Middleton, WI). In 2019, the growing media consisted of Pro-Mix Mycorrhizae Organik (Premier Horticulture, Victor, NY) mixed with 7N-2.2P–8.3K organic fertilizer (BioWorks Verdanta EcoVita; BFG Supply Co., St. Paul, MN). All seedlings were grown in a greenhouse set at 21 °C day/night on a mist bench for 4 to 6 weeks, and then hardened off in an outdoor coldframe until transplanted in the field.

Field experimental design and treatments.

Experiments were replicated over 2 years at two locations (both USDA Plant Hardiness Zone 4b) on the St. Paul campus of the University of Minnesota (lat. 44.9740°N, long. 93.2277°W; Waukegan silt loam) and at the Southern Research and Outreach Center (SROC) in Waseca, MN (lat. 44.0707°N, long. 93.5264°W; Webster clay loam). Both field locations fall within Koppen climate zone Dfa (hot-summer humid continental).

Fertilizer was applied at rates based on soil test results and previous crop histories. In St. Paul, urea was applied to plots in 2018, but 8N–0.9P–3.3K organic fertilizer (Sustane Natural Fertilizer, Cannon Falls, MN) was used in 2019. At the SROC, lettuce plots received 5N–1.3P–2.1K and 3N–3.1P–3.3K organic fertilizers [Chickity Doo (Pearl Valley Organix, Pearl City, IL) and Sustane Natural Fertilizer, respectively] in 2018. In 2019, the fertilizer used at the SROC was 5N–1.3P–2.1K organic fertilizer (Sustane Natural Fertilizer) with the addition of 0N–20.1P–0K triple superphosphate (Ag Partners, Morristown, MN), 0N–0N–49.8K potassium chloride, 44N–0P–0K slow-release urea (Jordan Seeds, Woodbury, MN), and 46N–0N–0N urea (Ag Partners). Raised beds (0.1 m high at the SROC or 0.3 m high in St. Paul × 1 to 1.5 m wide) were made and covered with white on black embossed plastic mulch (Ag Resource, Detroit Lakes, MN) and equipped with 0.38-mm low flow drip irrigation (Aqua-Traxx; DripWorks, Willits, CA), used as needed based on weather conditions. Transplants were planted 30 cm apart in single rows.

A split-plot design was used for field experiments in 2018 and 2019. Blocks (15.24 m long × 1.5 m wide) were split into shaded or unshaded (control) plots (6 m long × 1.5 m wide). Cultivars were randomized into one of two subplots within each shade treatment. There were four replicate blocks at each location per year, with two whole plots (shade or no shade) per block and two subplots (‘Salvius’ or ‘Sparx’) per whole plot. Treatments consisted of shading with netting that was designed to reduce photosynthetically active radiation by 50% (50% Pull and Cut Sunblocker; FarmTek, Dyersville, IA) attached to 1.5-m-tall hoops made from bent electrical conduit, and unshaded control treatments not covered by hoops or shading.

Lettuce plants were transplanted in four randomized blocks in each location on 1 June 2018 and 30 May 2019 at the SROC and 12 June 2018 and 26 May 2019 in St. Paul under each treatment. This was done to ensure that lettuce was grown under high temperatures. There were 19 plants staggered within each subplot. Lettuce was harvested at the SROC on 11 July 2018 and 15 July 2019 and in St. Paul on 16 July 2018 and 15 July 2019. All lettuce was harvested before bolting.

Field measurements.

Weather stations at both locations recorded daily temperature and precipitation data. In 2018, plant leaf temperatures were measured using handheld infrared (IR) thermometers pointed at leaf surfaces. In 2019, microtemperature sensors were clipped onto adaxial leaf surfaces (unshaded by other leaves) about halfway between the core and outer leaves on a leaf of one plant per treatment per row, and data were recorded with data loggers (WatchDog; Spectrum Technologies, Aurora, IL). There were no differences between temperature measurements made using the handheld IR thermometer (mean of five measurements = 24.9 ± 1.3 °C) and the micro sensor (23.2 ± 1.7 °C). Photon flux density measurements were made at solar noon on 19 July 2021 using a field spectroradiometer (SS-110; Apogee Instruments, Logan, UT) equipped with a 180° field of view head placed 0.5 m aboveground surface. Three measurements were made for each treatment—under the shadecloth and without the shadecloth.

Sensory evaluations.

The University of Minnesota Institutional Review Board approved all recruiting and experimental procedures of all sensory tests. All tasters were 6-n-propylthiouracil tasters or supertasters, so have the ability to taste bitterness (supertasters are particularly sensitive) and were compensated for the testing session.

Sensory evaluations of lettuce were performed by 12 trained panelists in 2019 at the Sensory Center at the University of Minnesota. Two different sensory studies were done. The objective of the first study was to measure the intensity differences in sweetness and bitterness across two lettuce cultivars (Salvius and Sparx), under two shading treatments (shaded and unshaded), at two locations (St. Paul and the SROC). Three lettuce heads of each cultivar and treatment were harvested from each block at the two test locations the day before testing and stored at 0 °C until transported the next day to the Sensory Center. Outer leaves of each head of lettuce were removed at the time of harvest. All lettuce was harvested before bolting.

All samples used for sensory evaluations were prepared from the middle leaves. On the day of testing, 12 to 13 leaves were removed from each head of lettuce and washed thoroughly with cold tap water. The leaves were laid out on absorbent mats (up&up™, Target Corp., Minneapolis, MN) labeled with each sample’s three-digit blinding code. Lettuces were examined for damage caused by insects or bacteria and patted dry with cloth dish towels. Leaves with no visible damage were portioned into 6-cm sections from the middle of the leaf and cut in half lengthwise to include both rib and leafy material. Lettuces with pest or abiotic damage were portioned to avoid the damaged section. If unable to get 12 portions from 12 separate leaves, two portions were taken from one larger undamaged leaf. Lettuce samples were presented to participants in sets of eight, with each set including one replicate of the two shadings by two cultivars by two locations in snack size zip-top bags (up&up™) coded with a random three-digit number. These bagged leaves were refrigerated until 30 min before serving, then allowed to equilibrate to room temperature.

Sixteen samples, replicates 1 and 2, were served on the first day (16 July 2019), and 16 samples, replicates 3 and 4, were served on the second day (17 July 2019). Panelists always tested replicate 1 before replicate 2, and replicate 3 before replicate 4. This was done to ensure that the same samples were not randomly served multiple times in a row and to expose panelists to the entire range of samples as early as possible. Samples within each replicate were served balanced for order and carryover effects using Williams Latin square designs. For the entirety of testing, panelists were instructed to use nose clips to ensure evaluation of basic tastes only. Panelists started each testing session with a set of two citric acid calibration solutions with immediate feedback. Panelists were instructed to use the citric acid scale intensity reference samples (Karalus et al., 2010) provided as needed. During the testing session, each panelist rated each sample for the intensity of sweetness and the intensity of bitterness. Panelists were instructed to “remove the entire piece of lettuce from your bag. Roll the lettuce and put the whole piece into your mouth at once. Chew the sample thoroughly and swallow before making your evaluations.” These attribute ratings were made on a 20-point line scale labeled none at the left end and intense at the right end. Panelists were provided with room-temperature filtered water to rinse their mouths between samples.

Weight and chemical measurements.

Six heads of lettuce were sampled from each subplot within a block, and fresh weights were measured in the field immediately after harvest in both 2018 and 2019. All lettuce was harvested before bolting.

Lettuce leaf soluble sugars, chlorophyll, and carotenoid levels were measured only in 2019. Middle leaves from one head of each cultivar and treatment from each of four blocks of both growing locations were assayed. For soluble sugar analyses, ≈0.5 mg of finely sliced fresh leaf material was placed into 2-mL screwcap tubes with 1 mL of 80% (v/v) ethanol and incubated in a water bath at 80 °C for 4 h. After this time, supernatants were transferred to new 2-mL screwcap tubes. The plant material was washed with 0.5 mL 80% (v/v) ethanol and the supernatants were combined, and then completely dried using a concentrator (Savant SpeedVac model SVC-100H; Thermo Fisher Scientific, Waltham, MA) connected to a refrigerated vapor trap (Savant model RVT400, Thermo Fisher Scientific). The dried materials were dissolved in 100 µL of deionized water. Sucrose, fructose and glucose contents were determined enzymatically using a sugar analysis kit (K-SUFRG; Megazyme, Wicklow, Ireland) according to manufacturer instructions.

Chlorophyll levels were determined according to Han et al. (2016). Approximately 0.5 g of fresh leaf tissue was finely sliced and transferred into 50-mL tubes to which 10 mL of 95% (v/v) ethanol was added. Tubes were covered with aluminum foil, and chlorophyll was extracted at room temperature for 24 h. Aliquots (210 µL) were measured at 649 nm (A649), 664 nm (A664), and 470 nm (A470) using a spectrophotometer (SpectraMax 190; Molecular Devices, San Jose, CA) equipped with data analysis software (SoftMaxPro 5.2, Molecular Devices). Chlorophyll a, chlorophyll b, and carotenoid concentrations were calculated using the following equations (Sumanta et al., 2014):
Chlorophylla=13.36A6645.19A649
Chlorophyllb=27.43A6498.12A664
Carotenoids=(1000A4702.13Chlorophylla97.63Chlorophyllb)/209

RNA extraction and sequencing.

One leaf was collected from each of four heads of ‘Sparx’ lettuce per treatment after sensory evaluation sampling in 2019 and pooled together for RNA analyses. Two biological replicates (biologically distinct samples) from each treatment (unshaded and shaded) were subjected to sequencing. RNA extraction was performed using an RNA extraction kit (RNeasy mini kit; Qiagen, Valencia, CA). RNA concentration was measured with a spectrophotometer (NanoDrop, Thermo Fisher Scientific) and tested for integrity using electrophoretic separation (RNA ScreenTape System, Agilent Technologies, Santa Clara, CA). RNAs with RNA Integrity Number ≥7.0 were selected for library preparation. cDNA libraries were constructed by the University of Minnesota Genomics Center using the Illumina TruSeq RNA sample preparation protocol stranded with ribosomal reduction. The libraries were sequenced on a sequencing system (Illumina HiSeq 2500 using the NextSeq Mid-Output platform; Illumina, San Diego, CA) to generate 150-bp paired-end sequence reads. Quality control of the sequence reads was performed using FastQC version 0.11.9 software (Andrews, 2010). Low-quality reads (Phred score < 30) and adapter contamination were removed using the Trimmomatic software tool (Bolger et al., 2014). The trimmed reads were mapped to the L. sativa cv. Salinas reference genome (Reyes-Chin-Wo et al., 2017) using HISAT2 software (Kim et al., 2019) with default settings. The number of reads aligned to each gene was counted using the featureCounts program from the Subread package (Liao et al., 2019).

Transcriptome analyses.

Differentially expressed genes (DEGs) between unshaded control and shaded lettuce were identified using the Bioconductor edgeR software package (Robinson et al., 2010). False discovery rate (FDR) <0.05 and |Log2(fold-change)| >2.0 were set as the thresholds for differential expression. Analyses to describe DEGs according to Gene Ontology project (GO) descriptors were performed using the Bioconductor goseq R software package (Young et al., 2010), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using the enrichKEGG function of the ClusterProfiler R package (Yu et al., 2012). Enriched GO terms and KEGG pathways were determined at FDR <0.05. Gene annotations were obtained from the National Center for Biotechnology Information database.

Quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) methodology was used to validate transcriptome analyses (i.e., confirm RNA sequencing results) because only two biological replicates had been used for RNA sequencing (RNAseq). qRT-PCR was done using a SYBR green reagent system (CFX96 Touch Real-Time PCR Detection System; Bio-Rad, Hercules, CA). First strand cDNA was synthesized with a reverse transcriptase kit (GoScript; Promega Corp., Madison, WI) in two steps. The first step consisted of adding 4.0 µL of RNA (1200 ng), 0.7 µL of oligo dt15 primer and 0.3 µL random primers. The mixture was heated to 70 °C for 5 min and then immediately chilled on ice for at least 5 min. Five microliters of the reaction mixture were added to 4.0 µL 5× reaction buffer, 2.0 µL MgCl2, 1.0 µL PCR nucleotide mix, 0.5 µL recombinant ribonuclease inhibitor, 1.0 µL reverse transcriptase, and 6.5 µL nuclease free water, in a final volume of 20 µL, incubated for 5 min at 25 °C, followed by 1 h at 42 °C, and finally inactivated for 15 min at 70 °C. cDNA was diluted 10× to be used for qRT-PCR reactions. qRT-PCR was performed for 26 DEG genes, including 15 downregulated, 11 upregulated, and four housekeeping genes (Supplemental Table 1). Primer pairs were designed using Primer-BLAST software (Ye et al., 2012). Each 10-µL qRT-PCR reaction contained a mixture of 2.0 µL cDNA, 5.0 µL cyanine dye (SYBR Green, Bio-Rad) and 0.2-µL pairs of primers (400 nM each). The thermal cycling conditions were initial denaturation for 5 min at 95 °C, 40 cycles at 95 °C for 20 s, 60 °C for 30 s, and 72 °C for 30 s. Melting curve analyses were performed at the end of qRT-PCR amplifications over the range of 65 to 95 °C, increasing temperature stepwise by 0.5 °C every 5 s. Gene expressions were normalized using tubulin beta-2 chain–like as a reference gene. Relative gene expression was calculated using the 2-ΔΔCT method (Livak and Schmittgen, 2001). Four biological replicates and two technical replicates (repeated measurements of each biological sample) were performed for each treatment.

Statistical analyses.

Linear mixed effects models were performed using the lme4::nlme package (Bates et al., 2015) for R software (version 1.2.1335 or 4.1.1; RStudio, Boston, MA) to model treatment effects on lettuce fresh weight (measured in 2018 and 2019) and carotenoid, chlorophylls, fructose, glucose, sucrose, and total sugar content (measured in 2019). Dependent variable transformations were carried out as needed to meet analysis of variance (ANOVA) assumptions. Fresh weights and total sugars were square root transformed, and glucose and sucrose contents were log-transformed. Treatment, cultivar, and their interaction were included as fixed effects, while blocking [blocks, location, and year (fresh weight data)] and whole plot effects (year by location by block by treatment) were included as random effects.

To determine if sweetness or bitterness intensities differed by treatment, cultivar, location, or their interactions, ANOVA [PROC GLM (SAS version 9.4; SAS Institute, Cary, NC)] were used for sensory evaluations. Sweetness and bitterness were the dependent variables; treatment, cultivar, location, and replicate were fixed predictors; participant was a random predictor. All main effects, two-way interactions, and three-way interactions of treatment, cultivar, and location were present in the model.

To identify DEGs in lettuce grown with and without shade, the Bioconductor edgeR package (Robinson et al., 2010) was used. To designate changes in gene expression, FDR <0.05 and |log2FC| >2.0 were set as the thresholds for differential expression. Correlations between results of qRT-PCR and RNAseq were analyzed using the Spearman coefficient.

Results

Field measurements.

In 2018 and 2019, the St. Paul and the SROC sites experienced temperatures for lettuce production that were above the optimal (18.5 °C) listed by Zhao and Carey (2009). For the period of lettuce production, average maximum temperatures in St. Paul were 29.1 and 24.4 °C in 2018 and 2019, respectively, whereas they were 27.3 and 25.8 °C at the SROC in 2018 and 2019, respectively.

Lettuce leaf temperatures were unaffected by shading treatments in 2018 (data not shown), but temperatures were only measured at noon by handheld thermometers. In 2019, when thermistors connected to recorders were used to record leaf temperatures, the 50% shadecloth lowered plant maximum, but not minimum, temperatures (Fig. 1A and B). The maximum leaf temperature reduction caused by shadecloth at St. Paul was observed when the leaf temperature was reduced from 38.8 to 27.2 °C (Fig. 1A). At the SROC, the maximum leaf temperature reduction due to shadecloth was from 40.2 to 28.7 °C (Fig. 1B). Average leaf temperature decreases for shaded lettuce compared with unshaded controls were 4.4 and 6.0 °C in St. Paul and the SROC, respectively.

Fig. 1.
Fig. 1.

Daily (month/day) surface leaf temperatures measured in 2019 using microtemperature sensors clipped onto adaxial leaf surfaces (unshaded by other leaves) about halfway between the core and outer leaves on a leaf of one plant per treatment per row of (A) lettuce under 50% shadecloth (shaded) and control (unshaded) at St. Paul, MN, and (B) lettuce under 50% shadecloth (shaded) and control (unshaded) at the Southern Research and Outreach Center in Waseca, MN. (C) Light spectra recorded with a field spectroradiometer on 19 July 2021 at solar noon without or with the shadecloth.

Citation: Journal of the American Society for Horticultural Science 147, 1; 10.21273/JASHS05124-21

The photosynthetic photon flux density under the shadecloth ranged from 572 to 595 μmol·m−2·s−1·nm−1, ≈35% of that of full sunlight, which ranged from 1591 to 1880 μmol·m−2·s−1·nm−1. The spectral pattern was not changed by the shadecloth (Fig. 1C).

Lettuce quality.

Cultivar and location effects were not significant for lettuce fresh weight, sweetness, or sugar content. Fresh weight was the same in both 2018 and 2019. Shading reduced lettuce fresh weights of ‘Salvius’ and ‘Sparx’ (P < 0.001) heads in both 2018 and 2019 (results shown in Fig. 2A aggregated across years and locations). The most abundant sugar found in lettuce was fructose (Fig. 2B). Total sugar and glucose content (P = 0.04 and 0.02, respectively), but not fructose (P = 0.06) or sucrose (P = 0.07) contents, differed between unshaded and shaded lettuce. Mean total sugar and glucose contents were 128.7 and 15.3 μg·mL−1, respectively, for unshaded control lettuce, and 79.4 and 8.1 μg·mL−1 respectively, for shaded lettuce. Treatment (P < 0.001), cultivar (P = 0.02), and location (P < 0.001) had significant effects, but none of the interactions of these factors significantly affected sweetness ratings of lettuce in 2019. Sensory evaluation participants rated unshaded lettuce as slightly sweeter (mean sweetness intensity of 2.9) than shaded (mean sweetness intensity of 2.3) lettuce (Fig. 2D) and did not discern differences in bitterness based on shading treatment (data not shown). ‘Sparx’ was rated sweeter than ‘Salvius’, and lettuce grown at the SROC was rated sweeter than those grown in St. Paul (data not shown).

Fig. 2.
Fig. 2.

Quality of ‘Salvius’ and ‘Sparx’ (pooled for location) lettuce produced under unshaded (control) or 50% black shadecloth conditions. (A) Head fresh weights pooled for 2018 and 2019, showing median and interquartile range, and distribution of raw data for each cultivar (n = 8 per cultivar). (B and C) Marginal mean ± 95% confidence interval (CI) of fructose, glucose, sucrose, and total sugars in 2019. (D) Bootstrapped (n = 100,000) mean sweetness ratings ± 95% CI of lettuce harvested in 2019. Sweetness ratings were made on a 20-point line scale, with 0 = no sweetness and 20 = intense sweetness, but all ratings were <5, so the graphed y-axis maximum = 4. (E–G) Chlorophyll a and b and chlorophyll a-to-b ratio of 2019 lettuce (marginal means ± 95% confidence interval). CI bars that are not visible are obscured by the mean (N = 16 for means shown in B–G).

Citation: Journal of the American Society for Horticultural Science 147, 1; 10.21273/JASHS05124-21

In 2019, shaded lettuce in our study had higher chlorophyll b [P = 0.02 (Fig. 2F)], but not total chlorophyll (P = 0.11), chlorophyll a [P = 0.20 (Fig. 2E)], or carotenoid (P = 0.34) content. The ratio of chlorophyll a to chlorophyll b was greater in unshaded control lettuce (2.31 ± 0.08 μg·mL−1) than in shaded lettuce (2.18 ± 0.08 μg·mL−1), with P = 0.01 (Fig. 2G). Interactions of location or cultivar with treatment were not significant for chlorophyll b content or chlorophyll a-to-b ratio.

Transcriptome analyses.

RNA sequencing generated 111 million paired-end 150-bp reads, where 16.4% of the raw reads were removed after adaptor trimming and quality filtering, resulting in an average of 25 million reads per sample. The clean reads ratio was higher than 83.6%. Genome mapping by HISAT2 successfully aligned 20.5 to 28.7 million reads to the lettuce genome. Using log2-fold change ≥2 and FDR <0.05 as cutoff values, 313 DEGs between unshaded and shaded lettuces were identified. Among the DEGs, 254 and 59 were upregulated in unshaded and shaded lettuce, respectively, and clustered by treatment (Fig. 3). There was a 0.86 Spearman coefficient (P < 0.001) between RNAseq and qRT-PCR data, indicating that the qRT-PCR data validated the RNAseq results, despite using only two biological replicates per treatment.

Fig. 3.
Fig. 3.

Heatmap showing how many of the 313 genes identified from the RNA sequencing experiment as differentially expressed between unshaded control and shaded lettuce were upregulated (red) or downregulated (green) relative to the other treatment; 1 and 2 refer to the two biological replicates that were sequenced for the unshaded control or shaded treatments.

Citation: Journal of the American Society for Horticultural Science 147, 1; 10.21273/JASHS05124-21

Analyses of identified DEGs using GO terms sorted their putative gene products into 10 biological process, three cellular component, and three molecular function categories (Supplemental Table 2). The greatest numbers of DEGs were involved in processes such as plasma membrane function, extracellular region metabolism (gene products not attached to the cell surface), transmembrane transport, calcium ion binding, response to salt stress, and response to water deprivation (Fig. 4A). These DEGs under the enriched GO terms were mostly upregulated in unshaded controls compared with shaded lettuce. An insignificant number of genes were upregulated in shaded compared with unshaded lettuce.

Fig. 4.
Fig. 4.

Functions of differentially expressed genes (DEGs) between unshaded control and shaded lettuce identified from RNA sequencing and based on (A) Gene Ontology project (GO) and (B) Kyoto Encyclopedia of Genes and Genomes analyses. DEGs in the GO analyses were categorized as biological processes, cellular components, and molecular functions.

Citation: Journal of the American Society for Horticultural Science 147, 1; 10.21273/JASHS05124-21

KEGG enrichment analysis distributed DEGs into five categories, in which all DEGs were upregulated in unshaded compared with shaded lettuce (Fig. 4B). The categories included phenylpropanoid biosynthesis, plant-pathogen interaction, α-linoleic acid metabolism, aromatic amino acid metabolism, and flavonoid biosynthesis.

Examination of genes related to photosynthesis and carbohydrate metabolism showed that a few genes were upregulated in unshaded compared with shaded lettuce (Table 1), including those coding for early light-induced proteins and the RuBisCO small and large subunits. Only 19% of differentially expressed genes were upregulated in shaded compared with unshaded control lettuce (Supplemental Table 3). Genes related to phenylpropanoid and flavonoid biosynthesis were upregulated in unshaded compared with shaded lettuce. No genes related to bitterness, such as squalene synthase (Testone et al., 2019) or sesquiterpene synthase (Bennett et al., 2002), were differentially expressed between unshaded and shaded lettuce.

Table 1.

Differentially expressed genes with Gene Ontology project (GO) functions associated with chlorophyll biosynthesis, photosynthesis, and carbohydrate metabolism. GO analyses were performed on data from RNA sequencing results using the goseq R Bioconductor software package (Young et al., 2010). A positive Log2 value indicates that the gene was upregulated in unshaded lettuce.

Table 1.

Discussion

Shading decreased average temperatures of lettuce compared with unshaded controls (Fig. 1A and B) and was most effective in reducing temperature when leaf temperatures were above 35 °C. The heat-tolerant lettuce cultivars, Sparx and Salvius, grew well in unshaded conditions in both locations and years. However, shade reduced lettuce fresh weights of both tested cultivars (Fig. 2A). These results agree with those described by Zhao and Carey (2009) for romaine lettuce grown under high tunnels shaded with 39% white shadecloth, which had lesser head fresh weights compared to those grown in the open field; the white shadecloth decreased leaf surface temperature. However, our results contrast with those observed for butterhead lettuce shaded with 50% black netting (Ilić et al., 2017), and green- and red-leaf lettuce shaded with 50% black netting (Li et al., 2017), in which head fresh weights were greater than those grown in the open field. Li et al. (2017) reported that lettuce head dry weights did not differ among treatments, suggesting that differences among treatments in their study may have been due to differences in water content.

Unshaded ‘Sparx’ and ‘Salvius’ lettuces were rated by sensory evaluation participants as sweeter than shaded counterparts (Fig. 2D). These evaluations were positively correlated with the higher total sugars contents of unshaded compared with shaded lettuce (Fig. 2C) and the upregulation of genes related to glucose metabolism (Table 1). Our data were similar to results reported for soluble sugars by Mastilović et al. (2019). Unshaded lettuce has previously been shown to be more bitter than shaded lettuce (Zhao and Carey, 2009), but no difference in bitterness was discerned between the shaded and unshaded lettuce cultivars used in this study.

The higher content of sugars in the unshaded lettuce may be attributed to higher photosynthetic rates compared with shaded lettuce, although we did not measure lettuce photosynthetic rates. These results suggest that greater photosynthesis in the unshaded lettuce, and consequently higher accumulation of sugars, may have resulted in sweeter unshaded than shaded lettuce. Shading treatments affected lettuce chlorophyll b content (Fig. 2F), but not total chlorophyll or chlorophyll a (Fig. 2E) content, in our study. Shaded lettuce had higher mean chlorophyll b content than unshaded lettuce, but the difference between treatments was small (means of 1.04 μg·mL−1 for the unshaded vs. 1.18 μg·mL−1 for the shaded lettuce). These data are different from those reported by Ilić and Fallik (2017) and Ilić et al. (2017) for butterhead lettuce, in which shading with black netting increased chlorophyll a, chlorophyll b, and carotenoid contents on a fresh weight basis compared with unshaded lettuce. Shade-tolerant species have been shown to have higher proportions of chlorophyll b relative to chlorophyll a (Boardman, 1977). It may be that the lettuce cultivars used in this study are more shade tolerant than the butterhead lettuce studied by Ilić et al. (2017). Also, genes associated with rubisco subunits were upregulated in unshaded lettuce (Table 1), which may have led to greater plant growth and soluble sugar accumulation under high light and high temperature in unshaded conditions. The higher fresh weights and sweetness of the cultivars grown in unshaded compared with shaded conditions in our study may be attributed to many different mechanisms, including carbohydrate metabolic processes, two of which were also upregulated in unshaded compared with shaded lettuce (Table 1). The extent to which RNAs were translated into active proteins could be studied in future work.

The differing results among the literature and our results highlight variability among genotypes in interactions with the environment. While greater fresh weight under shading conditions were observed for the cultivars Tizian (Ilić et al., 2017), Two Star (green-leaf) and New Red Fire [red leaf (Li et al., 2017)], opposite results were observed for the cultivars used in this study. Lafta et al. (2021) found significant genotype × environment differences in head weight, core length, and head diameter, among other traits in crisphead lettuce. The ability of the cultivars used in this study to tolerate heat may be related to modifications in many pathways that are involved in photosynthesis and secondary metabolism (Fig. 3, Table 1). As temperatures increase, lettuce growers will need to adapt by using as many tools as possible, including growing heat-tolerant cultivars. It could be possible that heat-tolerance limits of lettuce may be extended by growing them under shade.

Literature Cited

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    • Search Google Scholar
    • Export Citation
  • Bates, D., Maechler, M., Bolker, B. & Walker, S. 2015 Fitting linear mixed-effects models using lme4 J. Stat. Softw. 67 1 48 https://doi.org/10.18637/jss.v067.i01

    • Search Google Scholar
    • Export Citation
  • Bennett, M.H., Mansfield, J.W., Lewis, M.J. & Beale, M.H. 2002 Cloning and expression of sesquiterpene synthase genes from lettuce (Lactuca sativa L.) Phytochemistry 60 255 261 https://doi.org/10.1016/s0031-9422(02)00103-6

    • Search Google Scholar
    • Export Citation
  • Boardman, N.K 1977 Comparative photosynthesis of sun and shade plants Annu. Rev. Plant Physiol. 28 355 377 https://doi.org/10.1146/annurev.pp.28.060177.002035

    • Search Google Scholar
    • Export Citation
  • Bolger, A.M., Lohse, M. & Usadel, B. 2014 Trimmomatic: A flexible trimmer for Illumina sequence data Bioinformatics 30 2114 2120 https://doi.org/10.1093/bioinformatics/btu170

    • Search Google Scholar
    • Export Citation
  • Bunning, M.L., Kendall, P.A., Stone, M.B., Stonaker, F.H. & Stushnoff, C. 2010 Effects of seasonal variation on sensory properties and total phenolic content of 5 lettuce cultivars J. Food Sci. 75 156 161 https://doi.org/10.1111/j.1750-3841.2010.01533.x

    • Search Google Scholar
    • Export Citation
  • Chadwick, M., Gawthrop, F., Michelmore, R.W., Wagstaff, C. & Methven, L. 2016 Perception of bitterness, sweetness and liking of different genotypes of lettuce Food Chem. 197 66 74 https://doi.org/10.1016/j.foodchem.2015.10.105

    • Search Google Scholar
    • Export Citation
  • Drewnowski, A. & Gomez-Carneros, C. 2000 Bitter taste, phytonutrients, and the consumer: A review Am. J. Clin. Nutr. 72 1424 1435 https://doi.org/10.1093/ajcn/72.6.1424

    • Search Google Scholar
    • Export Citation
  • Driedonks, N., Rieu, I. & Vriezen, W.H. 2016 Breeding for plant heat tolerance at vegetative and reproductive stages Plant Reprod. 29 67 79 https://doi.org/10.1007/s00497-016-0275-9

    • Search Google Scholar
    • Export Citation
  • Han, Y., Chen, Z., Shanshan, L., Ning, K., Ji, X., Liu, X., Wang, Q., Liu, R., Fan, S. & Zhang, X. 2016 MADS-Box genes and gibberellins regulate bolting in lettuce (Lactuca sativa L.) Front. Plant Sci. 7 1889 https://doi.org/10.3389/fpls.2016.01889

    • Search Google Scholar
    • Export Citation
  • Holmes, S.C., Wells, D.E., Pickens, J.E. & Kemble, J.M. 2019 Selection of heat-tolerant lettuce (Lactuca sativa L.) cultivars grown in deep water culture and their marketability Horticulturae 5 50 https://doi.org/10.3390/horticulturae5030050

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    • Search Google Scholar
    • Export Citation
  • Ilić, S.Z., Milenković, L., Dimitrijević, A., Stanojević, L., Cvetković, D., Kevrešan, Ž., Fallik, E. & Mastilović, J. 2017 Light modification by color nets improve quality of lettuce from summer production Scientia Hort. 226 389 397 https://doi.org/10.1016/j.scienta.2017.09.009

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    • Search Google Scholar
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  • Kim, D., Paggi, J.M., Park, C., Bennett, C. & Salzberg, S.L. 2019 Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype Nat. Biotechnol. 37 907 915 https://doi.org/10.1038/s41587-019-0201-4

    • Search Google Scholar
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  • Lafta, A., Sandoya, G. & Mou, B. 2021 Genetic variation and genotype by environment interaction for heat tolerance in crisphead lettuce HortScience 56 126 135 https://doi.org/10.21273/HORTSCI15209-20

    • Search Google Scholar
    • Export Citation
  • Li, T., Bi, G., LeCompte, J., Barickman, T.C. & Evans, B.B. 2017 Effect of colored shadecloth on the quality and yield of lettuce and snapdragon HortTechnology 27 860 867 https://doi.org/10.21273/HORTTECH03809-17

    • Search Google Scholar
    • Export Citation
  • Liao, Y., Smyth, G.K. & Shi, W. 2019 The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads Nucleic Acids Res. 47 e47 https://doi.org/10.1093/nar/gkz114

    • Search Google Scholar
    • Export Citation
  • Livak, K.J. & Schmittgen, T.D. 2001 Analysis of relative gene expression data using real-time quantitative PCR and the 2ΔΔC(T) method Methods 25 402 408 https://doi.org/10.1006/meth.2001.1262

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Price, K.R., DuPont, M.S., Shepherd, R., Chan, H.W.-S. & Fenwick, G.S. 1990 Relationship between the chemical and sensory properties of exotic salad crops-coloured lettuce (Lactuca sativa) and chicory (Chicorum intybus) J. Sci. Food Agr. 53 185 192 https://doi.org/10.1002/jsfa.2740530206

    • Search Google Scholar
    • Export Citation
  • Reyes-Chin-Wo, S., Wang, Z., Yang, X., Kozik, A., Arikit, S., Song, C., Xia, L., Froenicke, L., Lavelle, D.O., Truco, M.J., Xia, R., Zhu, S., Xu, C., Xu, H., Xu, X., Cox, K., Korf, I., Meyers, B.C. & Michelmore, R.W. 2017 Genome assembly with in vitro proximity ligation data and whole-genome triplication in lettuce Nat. Commun. 8 14953 https://doi.org/10.1038/ncomms14953

    • Search Google Scholar
    • Export Citation
  • Robinson, M.D., McCarthy, D.J. & Smyth, G.K. 2010 edgeR: A Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 139 140 https://doi.org/10.1093/bioinformatics/btp616

    • Search Google Scholar
    • Export Citation
  • Simonne, A., Simonne, E., Eitenmiller, R. & Coker, C.H. 2002 Bitterness and composition of lettuce varieties grown in the southeastern United States HortTechnology 12 721 726 https://doi.org/10.21273/HORTTECH.12.4.721

    • Search Google Scholar
    • Export Citation
  • Sumanta, N., Haque, C.I., Nishika, J. & Suprakash, R. 2014 Spectrophotometric analysis of chlorophylls and carotenoids from commonly grown fern species by using various extracting solvents Res. J. Chem. Sci. 4 63 69 https://doi.org/10.1055/s-0033-1340072

    • Search Google Scholar
    • Export Citation
  • Testone, G., Mele, G., di Giacomo, E., Tenore, G.C., Gonnella, M., Nicolodi, C., Frugis, G., Iannelli, M.A., Arnesi, G., Schiappa, A., Biancari, T. & Giannino, D. 2019 Transcriptome driven characterization of curly- and smooth-leafed endives reveals molecular differences in the sesquiterpenoid pathway Hort. Res. 6 1 19 https://doi.org/10.1038/s41438-018-0066-6

    • Search Google Scholar
    • Export Citation
  • U.S. Department of Agriculture, National Agricultural Statistics Service 2020 Vegetables 2019 summary 1 Oct. 2021. <https://www.nass.usda.gov/Publications/Todays_Reports/reports/vegean20.pdf>

    • Search Google Scholar
    • Export Citation
  • Waycott, W. & Ryder, E.J. 1993 Adaptation of lettuce to high-temperature environments 285 295 Kuo, C.G. Adaptation of food crops to temperature and water stress: Proceedings of an international symposium. AVRDC Publ. No. 410/93 Asian Vegetable Res. Dev. Cent. Taipei, Taiwan

    • Search Google Scholar
    • Export Citation
  • Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S. & Madden, T.L. 2012 Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction BMC Bioinformatics 13 134 https://doi.org/10.1186/1471-2105-13-134pmid:22708584

    • Search Google Scholar
    • Export Citation
  • Young, M.D., Wakefield, M.J., Smyth, G.K. & Oshlack, A. 2010 Gene ontology analysis for RNA-seq: Accounting for selection bias Genome Biol. 11 R14 https://doi.org/10.1186/gb-2010-11-2-r14

    • Search Google Scholar
    • Export Citation
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    • Search Google Scholar
    • Export Citation
  • Zhao, X. & Carey, E.E. 2009 Summer production of lettuce, and microclimate in high tunnel and open field plots in Kansas HortTechnology 19 113 119 https://doi.org/10.21273/HORTSCI.19.1.113

    • Search Google Scholar
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Supplemental Table 1.

List of gene names for forward (F, Plus) and reverse (R, Minus) primers, primer sequences, start and stop sites of target genes, primer melting temperature (Tm), expected product size, and product predicted description for quantitative reverse transcription polymerase chain reactions of genes used to validate RNA sequencing results.

Supplemental Table 1.
Supplemental Table 1.
Supplemental Table 2.

List of Gene Ontology project (GO) unique identifiers, names (ID), and term names of all genes determined by RNA sequencing analyses to be differentially expressed between unshaded control and shaded lettuce. Differentially expressed genes were identified using the Bioconductor edgeR software package (Robinson et al., 2010). Enriched GO terms were determined using a false discovery rate <0.05 and generated using the Bioconductor goseq R software package (Young et al., 2010).

Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 2.
Supplemental Table 3.

List of genes determined to be differentially expressed from RNA sequencing analyses between lettuce grown unshaded (control) or shaded and their putative functions as annotated in the National Center for Biotechnology Information database. Log2FC = log-fold change >2 of unshaded control lettuce relative to the shaded lettuce; genes with positive Log2FC were expressed to a greater degree in control than shaded lettuce. All gene functions were identified for Lactuca sativa.

Supplemental Table 3.
Supplemental Table 3.
Supplemental Table 3.
Supplemental Table 3.
Supplemental Table 3.
Supplemental Table 3.

Contributor Notes

We thank Aaron Blythe for input on research design; Doug Brinkmann, Courtney Tchida, Adam Sauve, Jessyca Martínez-Vélez, and Taylor Bushelle for technical assistance; Juan E.A. Llorens for assistance with transcriptome analysis; Gary Oehlert for statistical consultation; and Dominic Petrella and Yinjie Qiu for reviewing drafts of this manuscript. Funding was provided by the U.S. Department of Agriculture (USDA) Marketing Service (grant 64436 via the Minnesota Department of Agriculture Specialty Crops Block Grant Program) and the Minnesota Experiment Station (projects MN21-043 and 18-138). The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of USDA.

C.B.S.T. is the corresponding author. E-mail: c-tong@umn.edu.

  • View in gallery

    Daily (month/day) surface leaf temperatures measured in 2019 using microtemperature sensors clipped onto adaxial leaf surfaces (unshaded by other leaves) about halfway between the core and outer leaves on a leaf of one plant per treatment per row of (A) lettuce under 50% shadecloth (shaded) and control (unshaded) at St. Paul, MN, and (B) lettuce under 50% shadecloth (shaded) and control (unshaded) at the Southern Research and Outreach Center in Waseca, MN. (C) Light spectra recorded with a field spectroradiometer on 19 July 2021 at solar noon without or with the shadecloth.

  • View in gallery

    Quality of ‘Salvius’ and ‘Sparx’ (pooled for location) lettuce produced under unshaded (control) or 50% black shadecloth conditions. (A) Head fresh weights pooled for 2018 and 2019, showing median and interquartile range, and distribution of raw data for each cultivar (n = 8 per cultivar). (B and C) Marginal mean ± 95% confidence interval (CI) of fructose, glucose, sucrose, and total sugars in 2019. (D) Bootstrapped (n = 100,000) mean sweetness ratings ± 95% CI of lettuce harvested in 2019. Sweetness ratings were made on a 20-point line scale, with 0 = no sweetness and 20 = intense sweetness, but all ratings were <5, so the graphed y-axis maximum = 4. (E–G) Chlorophyll a and b and chlorophyll a-to-b ratio of 2019 lettuce (marginal means ± 95% confidence interval). CI bars that are not visible are obscured by the mean (N = 16 for means shown in B–G).

  • View in gallery

    Heatmap showing how many of the 313 genes identified from the RNA sequencing experiment as differentially expressed between unshaded control and shaded lettuce were upregulated (red) or downregulated (green) relative to the other treatment; 1 and 2 refer to the two biological replicates that were sequenced for the unshaded control or shaded treatments.

  • View in gallery

    Functions of differentially expressed genes (DEGs) between unshaded control and shaded lettuce identified from RNA sequencing and based on (A) Gene Ontology project (GO) and (B) Kyoto Encyclopedia of Genes and Genomes analyses. DEGs in the GO analyses were categorized as biological processes, cellular components, and molecular functions.

  • Andrews, S 2010 FastQC: A quality control tool for high throughput sequence data 15 Apr. 2020. <http://www.bioinformatics.babraham.ac.uk/projects/fastqc>

    • Search Google Scholar
    • Export Citation
  • Bates, D., Maechler, M., Bolker, B. & Walker, S. 2015 Fitting linear mixed-effects models using lme4 J. Stat. Softw. 67 1 48 https://doi.org/10.18637/jss.v067.i01

    • Search Google Scholar
    • Export Citation
  • Bennett, M.H., Mansfield, J.W., Lewis, M.J. & Beale, M.H. 2002 Cloning and expression of sesquiterpene synthase genes from lettuce (Lactuca sativa L.) Phytochemistry 60 255 261 https://doi.org/10.1016/s0031-9422(02)00103-6

    • Search Google Scholar
    • Export Citation
  • Boardman, N.K 1977 Comparative photosynthesis of sun and shade plants Annu. Rev. Plant Physiol. 28 355 377 https://doi.org/10.1146/annurev.pp.28.060177.002035

    • Search Google Scholar
    • Export Citation
  • Bolger, A.M., Lohse, M. & Usadel, B. 2014 Trimmomatic: A flexible trimmer for Illumina sequence data Bioinformatics 30 2114 2120 https://doi.org/10.1093/bioinformatics/btu170

    • Search Google Scholar
    • Export Citation
  • Bunning, M.L., Kendall, P.A., Stone, M.B., Stonaker, F.H. & Stushnoff, C. 2010 Effects of seasonal variation on sensory properties and total phenolic content of 5 lettuce cultivars J. Food Sci. 75 156 161 https://doi.org/10.1111/j.1750-3841.2010.01533.x

    • Search Google Scholar
    • Export Citation
  • Chadwick, M., Gawthrop, F., Michelmore, R.W., Wagstaff, C. & Methven, L. 2016 Perception of bitterness, sweetness and liking of different genotypes of lettuce Food Chem. 197 66 74 https://doi.org/10.1016/j.foodchem.2015.10.105

    • Search Google Scholar
    • Export Citation
  • Drewnowski, A. & Gomez-Carneros, C. 2000 Bitter taste, phytonutrients, and the consumer: A review Am. J. Clin. Nutr. 72 1424 1435 https://doi.org/10.1093/ajcn/72.6.1424

    • Search Google Scholar
    • Export Citation
  • Driedonks, N., Rieu, I. & Vriezen, W.H. 2016 Breeding for plant heat tolerance at vegetative and reproductive stages Plant Reprod. 29 67 79 https://doi.org/10.1007/s00497-016-0275-9

    • Search Google Scholar
    • Export Citation
  • Han, Y., Chen, Z., Shanshan, L., Ning, K., Ji, X., Liu, X., Wang, Q., Liu, R., Fan, S. & Zhang, X. 2016 MADS-Box genes and gibberellins regulate bolting in lettuce (Lactuca sativa L.) Front. Plant Sci. 7 1889 https://doi.org/10.3389/fpls.2016.01889

    • Search Google Scholar
    • Export Citation
  • Holmes, S.C., Wells, D.E., Pickens, J.E. & Kemble, J.M. 2019 Selection of heat-tolerant lettuce (Lactuca sativa L.) cultivars grown in deep water culture and their marketability Horticulturae 5 50 https://doi.org/10.3390/horticulturae5030050

    • Search Google Scholar
    • Export Citation
  • Ilić, S.Z. & Fallik, E. 2017 Light quality manipulation improves vegetable quality at harvest and postharvest: A review Environ. Exp. Bot. 139 79 90 https://doi.org/10.1016/j.envexpbot.2017.04.006

    • Search Google Scholar
    • Export Citation
  • Ilić, S.Z., Milenković, L., Dimitrijević, A., Stanojević, L., Cvetković, D., Kevrešan, Ž., Fallik, E. & Mastilović, J. 2017 Light modification by color nets improve quality of lettuce from summer production Scientia Hort. 226 389 397 https://doi.org/10.1016/j.scienta.2017.09.009

    • Search Google Scholar
    • Export Citation
  • Karalus, M., Pontet, C. & Vickers, Z. 2010 Experimentally created intensity scales for the five basic tastes: Sweet, sour, salty, bitter and umami 1 Oct. 2021. <https://sensorycenter.cfans.umn.edu/calibrated-scales-used-umn-sensory-center>

    • Search Google Scholar
    • Export Citation
  • Kim, D., Paggi, J.M., Park, C., Bennett, C. & Salzberg, S.L. 2019 Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype Nat. Biotechnol. 37 907 915 https://doi.org/10.1038/s41587-019-0201-4

    • Search Google Scholar
    • Export Citation
  • Lafta, A., Sandoya, G. & Mou, B. 2021 Genetic variation and genotype by environment interaction for heat tolerance in crisphead lettuce HortScience 56 126 135 https://doi.org/10.21273/HORTSCI15209-20

    • Search Google Scholar
    • Export Citation
  • Li, T., Bi, G., LeCompte, J., Barickman, T.C. & Evans, B.B. 2017 Effect of colored shadecloth on the quality and yield of lettuce and snapdragon HortTechnology 27 860 867 https://doi.org/10.21273/HORTTECH03809-17

    • Search Google Scholar
    • Export Citation
  • Liao, Y., Smyth, G.K. & Shi, W. 2019 The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads Nucleic Acids Res. 47 e47 https://doi.org/10.1093/nar/gkz114

    • Search Google Scholar
    • Export Citation
  • Livak, K.J. & Schmittgen, T.D. 2001 Analysis of relative gene expression data using real-time quantitative PCR and the 2ΔΔC(T) method Methods 25 402 408 https://doi.org/10.1006/meth.2001.1262

    • Search Google Scholar
    • Export Citation
  • Mastilović, J., Kevrešan, Ž., Jakšić, A., Milovanović, I., Trajković, R., Stanković, M., Milenković, L. & Ilić, Z.S. 2019 Influence of light modification on postharvest butter lettuce quality: Differences between external and internal leaves Zemdirbyste-Agr. 106 65 72 https://doi.org/10.13080/z-a.2019.106.009

    • Search Google Scholar
    • Export Citation
  • Price, K.R., DuPont, M.S., Shepherd, R., Chan, H.W.-S. & Fenwick, G.S. 1990 Relationship between the chemical and sensory properties of exotic salad crops-coloured lettuce (Lactuca sativa) and chicory (Chicorum intybus) J. Sci. Food Agr. 53 185 192 https://doi.org/10.1002/jsfa.2740530206

    • Search Google Scholar
    • Export Citation
  • Reyes-Chin-Wo, S., Wang, Z., Yang, X., Kozik, A., Arikit, S., Song, C., Xia, L., Froenicke, L., Lavelle, D.O., Truco, M.J., Xia, R., Zhu, S., Xu, C., Xu, H., Xu, X., Cox, K., Korf, I., Meyers, B.C. & Michelmore, R.W. 2017 Genome assembly with in vitro proximity ligation data and whole-genome triplication in lettuce Nat. Commun. 8 14953 https://doi.org/10.1038/ncomms14953

    • Search Google Scholar
    • Export Citation
  • Robinson, M.D., McCarthy, D.J. & Smyth, G.K. 2010 edgeR: A Bioconductor package for differential expression analysis of digital gene expression data Bioinformatics 26 139 140 https://doi.org/10.1093/bioinformatics/btp616

    • Search Google Scholar
    • Export Citation
  • Simonne, A., Simonne, E., Eitenmiller, R. & Coker, C.H. 2002 Bitterness and composition of lettuce varieties grown in the southeastern United States HortTechnology 12 721 726 https://doi.org/10.21273/HORTTECH.12.4.721

    • Search Google Scholar
    • Export Citation
  • Sumanta, N., Haque, C.I., Nishika, J. & Suprakash, R. 2014 Spectrophotometric analysis of chlorophylls and carotenoids from commonly grown fern species by using various extracting solvents Res. J. Chem. Sci. 4 63 69 https://doi.org/10.1055/s-0033-1340072

    • Search Google Scholar
    • Export Citation
  • Testone, G., Mele, G., di Giacomo, E., Tenore, G.C., Gonnella, M., Nicolodi, C., Frugis, G., Iannelli, M.A., Arnesi, G., Schiappa, A., Biancari, T. & Giannino, D. 2019 Transcriptome driven characterization of curly- and smooth-leafed endives reveals molecular differences in the sesquiterpenoid pathway Hort. Res. 6 1 19 https://doi.org/10.1038/s41438-018-0066-6

    • Search Google Scholar
    • Export Citation
  • U.S. Department of Agriculture, National Agricultural Statistics Service 2020 Vegetables 2019 summary 1 Oct. 2021. <https://www.nass.usda.gov/Publications/Todays_Reports/reports/vegean20.pdf>

    • Search Google Scholar
    • Export Citation
  • Waycott, W. & Ryder, E.J. 1993 Adaptation of lettuce to high-temperature environments 285 295 Kuo, C.G. Adaptation of food crops to temperature and water stress: Proceedings of an international symposium. AVRDC Publ. No. 410/93 Asian Vegetable Res. Dev. Cent. Taipei, Taiwan

    • Search Google Scholar
    • Export Citation
  • Ye, J., Coulouris, G., Zaretskaya, I., Cutcutache, I., Rozen, S. & Madden, T.L. 2012 Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction BMC Bioinformatics 13 134 https://doi.org/10.1186/1471-2105-13-134pmid:22708584

    • Search Google Scholar
    • Export Citation
  • Young, M.D., Wakefield, M.J., Smyth, G.K. & Oshlack, A. 2010 Gene ontology analysis for RNA-seq: Accounting for selection bias Genome Biol. 11 R14 https://doi.org/10.1186/gb-2010-11-2-r14

    • Search Google Scholar
    • Export Citation
  • Yu, G., Wang, L., Han, Y. & He, Q. 2012 clusterProfiler: An R package for comparing biological themes among gene clusters OMICS 16 284 287 https://doi.org/10.1089/omi.2011.0118

    • Search Google Scholar
    • Export Citation
  • Zhao, X. & Carey, E.E. 2009 Summer production of lettuce, and microclimate in high tunnel and open field plots in Kansas HortTechnology 19 113 119 https://doi.org/10.21273/HORTSCI.19.1.113

    • Search Google Scholar
    • Export Citation
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