These cultivars were selected based on leaf shape as described in Tatiana’s TOMATObase and The Heirloom Tomato . Tomato seeds were treated, germinated, and field planted as previously described . In both the 2014 and 2015 seasons, plants were laid out in a randomized block design and were planted and grown in soil, with furrow irrigation once weekly. Gas exchange and intercepted PAR measurements Gas exchange measurements were done in the field on attached leaves after the plants had recovered from transplanting. Measurements were made weekly from week 10 to week 15 , on week 17 , and weeks 18– 21 , on c. 60 plants each week, on three plants per cultivar wk–1 . Measurements were made on leaves fromthe upper and lower portions of the plants to eliminate positional bias within the plant, and measured for three leaves per plant. The A , gst , transpiration, and ɸPS2 of a 6 cm2 area of the leaflet were measured using the LI-6400 XT infrared gas exchange system , and a fluorescence head . The chamber was positioned on terminal leaflets such that the midvein was not within the measured area. Light within the chamber was provided by the fluorescence head at 1500 µmol m2 s 1 photosynthetically active radiation , raspberry plant container and the chamber air flow volume was 400 µmols s1 with the chamber atmosphere mixed by a fan.
CO2 concentration within the chamber was set at 400 µmols mol1 . Humidity, leaf and chamber temperature were allowed to adjust to ambient conditions; however, the chamber block temperature was not allowed to exceed 36°C. Measured leaflets were allowed to equilibrate for 2–3 min before measurements were taken, allowing sufficient time for photosynthetic rates to stabilize with only marginal variation. The amount of intercepted PAR was measured in four orientations per plant and an average PARi calculated. PARi was measured by placing a Line Quantum Sensor onto a base made from ¼” PVC piping, and a Quantum Sensor approximately 1 m above the plant on the PVC rig. Measurements from both sensors were taken simultaneously for each sample using a Light Sensor Logger . This allowed variation in overall light intensities such as cloud movement to be measured and accounted for in the total PARi.After gas exchange measurements, three plants per cultivar were destructively harvested each week. The final yield and fresh vegetative weight of each plant harvested was measured using a hanging scale in the field. Five leaves were collected at random from the bottom and top of the plant to capture all canopy levels, and approximately nine fruit were collected for BRIX measurements. FW was used owing to the large number of plants and measurements being done in situ in the field setting.
All measurements were made in kg. To measure the BRIX value of the tomatoes, the collected fruit was taken to the laboratory where the juice was collected and measured on a refractometer . The yield and BRIX for each plant were multiplied together to get the BRIX 9 yield index , which gives an overall fruit quality measure, accounting for variations and extreme values in either measurement. It should be noted that while BRIX is used as a standard quality measure, BY is a composite value that folds in yield to assess weight of soluble solids per plant and is being used to measure commercial quality and not consumer quality . BY measurements were done for both the 2014 and the more detailed 2015 fields. These data were compared to test for reproducibility of results . Subsequently, primary leaflets were used for imaging and analysis of shape and size as previously described , and the images then processed in IMAGEJ . The images were cropped to individual leaflets maintaining the exact pixel ratio of the original image, and then cropped again to only include the single leaflet using a custom Java script written for FIJI. Single leaflet images converted to a binary image as black on a white background, and smoothed to allow for the exclusion of any particulates in the image were then processed in R using MOMOCS , a shape analysis package. Leaflet images were imported and then aligned along their axes so that all images faced the same direction. They were then processed using elliptical Fourier analysis based on the calculated number of harmonics from the MOMOCS package. Principal component analysis was performed on the resulting eFourier analysis and the principal components were used for subsequent analysis. Traditional shape measures such as leaflet area, circularity, solidity, and roundness were done with the area measurement based on pixel density. These measures were compared with the PCs to determine the characteristics captured by each PC.
The PC values were used for all subsequent leaflet shape and size analyses. Total leaf area for each plant was measured by imaging the whole plant and a 4 cm2 red square and then processed in the EASY LEAF AREA software .Five plants per line were used to analyze leaflet sugar content. The plants were grown under the same conditions as field plants with the following exceptions. Plants remained in the glasshouse after transfer to 1 gallon pots. All plants were watered with nutrient solution and grown until mature leaves could be sampled. Using a hole punch, a disk with an area of 0.28 cm2 was taken from the leaflets and extracted from the disks using a modified extraction method from the Ainsworth laboratory . Leaf disks were placed in 2 mM HEPES in 80% EtOH and heated to 80°C for 20 min and the liquid collected and stored at 20°C. The entire process was repeated twice. They were then placedin 2 mM HEPES in 50% EtOH and heated, collecting the liquid and storing at 20°C followed by another 2 mM HEPES in 80% treatment. The collected liquid was then used to measure the amount of sugar present per area of disk. To measure leaf sugar content a working solution of 100 mM HEPES , 6.3 mM MgCl2 , and 3 mM ATP and NADP at pH 7 was prepared. From the working solution, an assay buffer was made adding 50 U of glucose-6- phosphate dehydrogenase , and 295 or 280 µl of the working solution was added to a 96-well plate for sucrose standards or samples, respectively. Standards were added at a 60-fold dilution and samples were added at a 15-fold dilution. Then 0.5 U of hexokinase , 0.21 U of phosphoglucoisomerase , and 20 U of invertase were added to each well and the plates allowed to sit overnight to reach equilibrium. The plates were measured on a UV spectrometer at 340 nm, followed by analysis in JMP .All statistical analyses were performed using JMP software. To determine statistical significance, measurements were modeled using general linear regression model and tested by a one-way ANOVA followed by Tukey’s honestly significant difference, if necessary. These modeled data for all measured values were compiled into a table and used to create a model using partial least-squares path modeling in SMARTPLS 3.0 . Modeled data were used for the statistical analyses as many measurement types varied in number of data points, and therefore a set of generated predicted values of equal size was used to make an equal data matrix . Partial least squares-PM was used to explore the cause-and-effect relationships between the measured variables through latent values. PLS-PM is effective in both exploring unknown relationships and combining large-scale data, such as field, physiological, container raspberries and morphological data, that otherwise are not well described together . In addition to running the PLSPM, 1000 bootstraps were performed to obtain statistical significance and confidence intervals of the path coefficients and the R 2 values of each latent variable. The path coefficients are the standardized partial regression coefficients , and represent the direction and strength of causal relationships of direct effects. Indirect effects are the multiplied coefficients between the predictor variable and the response variable of all possible paths other than the direct effect . To determine the best path model, the latent variables were combined using our best understanding of biological relationships, and a general model using all data was generated. The paths between LVs were altered until a best-fit model was found. PLSPredict was then used on the dataset to ensure that the model did not over or under fit the data, and for predictive performance of each manifest variable . This structural model, and not the fit values, was retained for use in predictive modeling of a separate dataset. PLSPREDICT, with the structural model developed as described earlier, was used on a separate dataset to determine the efficacy of the model.
Two commercial cultivars, M82 and ‘Lukullus’, were used and only the leaf shape values were entered as exogenous variables. The predicted values for each output variable were compared with the actual measured values to determine how well the model predicted these variables.To perform phylogenetic analysis, all single nucleotide polymorphisms detected by CLC Genomics Workbench 11.0 from whole genome sequencing were exported as a vcf file. The SNPRELATE package for R was used to determine the variant positions that overlapped between cultivars and then all sequences combined into a single gds file . This file was run through SNPhylo with the following parameters: the linkage disequilibrium was set to 1.0, as we wanted to exclude as few variants as possible based on this factor, the minor allele frequency was set to 0.05, and the missing rate was set to 0.1. In all, 1000 bootstraps were performed for confidence intervals and significance. Solanum pimpinellifolium was used as the outgroup. The bootstrapped output tree was displayed in MEGA7 . Analysis of c gene flow was performed using PHYLONETWORKS . All common SNPs from chromosome 6were run through the TICR pipeline and then analyzed using PHYLONETWORKS with default settings, except for the number of runs which was set to 20. After the hybrid network for chromosome 6 was obtained, bootstrap analysis was done in PHYLONETWORKS using default settings with the following exceptions: ftolRel was set to 0.01, ftolAbs was set to 0.001, liktolAbs was set to 0.0001, and Nfail was set to 5. These adjustments were made to decrease processing time. The bootstrapped tree was output in DENDROSCOPE .Tomato is one of the highest-value and most extensively used vegetable crops worldwide. However, to meet increasing demand, modern tomato cultivars have been selected for qualities such as size and firmness instead of taste . Consequently, most of modern commercial varieties have lost their flavor and are often tasteless . Flavor of fruit is the sum of interactions between taste and aroma, whereas sugars and acids are the two of primarily components to activate taste receptors and aroma components such asvolatile compounds activate olfactory receptors . Though the relative contribution of taste and aroma to fruit flavor has not been clearly defined , plenty of studies have shown the importance of sugars and acids in determining fresh fruit flavor . For tomato, the levels of sugars and acids not only contribute to tomato taste , but also are major factors affecting tomato overall flavor intensity , and increasing sugar content of the fruit will enhance tomato flavor . Recent studies have shown that fruit sugar accumulation in modern tomato is two to three-fold less than that in wild species , which can account for the decline in flavor quality of tomato fruit. Fruits are the primary photosynthetic sinks and over 80% of sugars in the fruit are produced in the leaf through photosynthesis and subsequently translocated through the phloem . Therefore, factors involved in regulating leaf photosynthesis, as well as sugar biosynthesis and sugar transport would influence sugar levels in fruit. Leaves are the principle site of plant photosynthesis, and leaf traits directly impact the efficiency of light capture and photosynthetic carbon fixation Thus, changes in leaf traits could have an effect on fruit yield and quality. Studies evaluating the influence of leaf area on tomato yield have shown high leaf area index can lead to an increase in tomato yield as a result of better light interception . Recently, leaf shape was shown to be strongly correlated with fruit sugar levels in tomato, with rounder and more circular leaves having higher sugar content in their fruit .