There are multiple detection methods for Salmonella. Traditional cultural methods for isolation include plating on selective agars and incubating for 24 h at 35 °C . Before plating, naturally-contaminated samples are often enriched with non-selective and/or selective broths such as lactose broth since a low concentration of Salmonella is expected. Standard methods and media used to isolate Salmonella can be found in the FDA Bacteriological Analysis Manual . In conjunction with traditional cultural methods, rapid biochemical or antigen-antibody-based methods can be used for quicker isolation and identification of Salmonella . Salmonella can also be identified through testing a combination of biochemical and serological reactions. Most Salmonella will provide a positive result for glucose , lysine decarboxylase , H2S , lysine carboxylase broth, phenol red dulcitol broth, polyvalent flagellar test, polyvalent somatic test, and methyl red test; and provide a negative result for urease, potassium cyanide broth, malonate broth, indole test, phenol red lactose broth, phenol red sucrose broth, and VogesProskauer test Salmonella can be further identified through phenotyping methods such as serotyping, phage typing, biotyping, and R typing . Finally, Salmonella can be identified through genotyping by PCR or pulse-field gel electrophoresis . PFGE was a highly used method to trace outbreaks, but whole genome sequencing is now the current method used by PulseNet . Whole genome sequencing is a laboratory procedure that determines the order of bases in the genome of an organism in one process . Because millions of bases make up the WGS for every organism, grow bag for tomato it is much more detailed method than Pulse Field Gel Electrophoresis which was the former gold standard method for differentiating among pathogen isolates .

The CDC started implementing the use of WGS as its main way tracking foodborne outbreaks in 2013 . They are able to compare genomes from outbreak strains to reference genomes from public data bases such as EnteroBase . Shiga toxin producing Escherichia coli. Shiga toxin producing E. coli is a gramnegative, non-spore-forming bacteria that can cause infection in humans. Like Salmonella, it belongs to the family Enterobacteriaceae. STEC can grow in temperatures ranging from 7 °C to 45 °C but has optimal growth from 35 °C to 42 °C . It can grow in a pH range of 4-10, and requires a water activity of 0.95 or higher . STEC can be carried by many types of animals and is commonly associated with ruminants such as cattle . STEC will be passive in many of these hosts, but can cause disease in humans. Symptoms of infection by STEC include bloody diarrhea, vomiting, and in certain cases hemolytic uremic syndrome . STEC infects humans by using attachment and effacement lesions encoded for on their LEE pathogenicity island . As the name suggests, the main toxins used by STEC are Shiga toxins, which is what leads to cell death in the host. Apart from being the most known disease-causing STEC serotype, E. coli O157:H7 informs most of what is known about STEC . The serotype E. coli O157:H7 was first identified in 1982 and was well studied during that decade . The pathogen rose to infamy in 1993 when a large outbreak occurred across multiple locations of the fast food chain Jack in the Box . The consumption of the chain’s undercooked hamburgers led to illness in more than 600 people . Because of this incident, the way food safety processes are handled, especially the inspection of meat and poultry, have drastically changed . This incident is also the reason why O157:H7 has been so well-studied compared to other STEC serotypes. Among other STEC serotypes E. coli O26 is less likely to cause HUS compared to O157, even though its toxins are similar . Hemolytic uremic syndrome, or HUS, is a severe condition that damages the blood vessels of the kidneys and leads to renal failure.

Once in the body, Shiga toxin can bind to globotriaosylceramide in vascular endothelial cells, and damages those cells by inhibiting protein synthesis. If those cells are part of the kidney, it can lead to HUS . In general, E. coli O157 is more likely to cause severe symptoms than other types of STEC . STEC and may also be of concern in low-moisture foods. While the main reservoir for E. coli O157:H7 is cattle, the pathogen can easily spread through fecal contamination of water and other foods . According to the World Health Organization , this contamination can occur at many stages of growing and processing produce, which has led to increases in outbreaks of the pathogen in fruits and vegetables . Because of the recent outbreaks associated with STEC in low-moisture foods, and the various stages at which contamination can occur, it is important to explore its ability to survive in dried fruit, which can have many processing steps . The detection of STEC can also be culturable or molecular based. Selective media often used for STEC plating include MacConkey agar, violet red bile agar, and Levine’s Eosine methylene blue agar . To differentiate E. coli O157 from other E. coli, sorbitol can be added to the agar since O157 will not usually ferment sorbitol . Because the number of E. coli cells present in food is low, enrichment is very important to make sure that any cells present are detected. Common enrichments forSTEC include brain heart infusion broth, tryptic soy broth, and modified buffered peptone water with pyruvate . For identification in pure cultures, agglutination assays are useful for serotyping . The enzyme-linked immunosorbent assay is becoming more common for identifying STEC. Use of this assay has led to a better understanding of the most common serotypes of STEC. While O157:H7 is the most common STEC serotype associated with disease, there is a decrease in proportion of that serotype when using ELISA compared to culture-based methods . When screening with biochemical tests, most pathogenic E. coli will have negative test results for H2S, urease, arabinose non-fermenting, and indole .

To further determine if pathogenic E. coli is STEC specifically, real-time PCR can be used. The genes that should be targeted during PCR are stx1, stx2, and uidA, with the latter being highly conserved in O157:H7 strains . As mentioned with Salmonella, the main outbreak identification tool used by PulseNet is WGS. It has more differentiation capability than past methods used like PFGE. Abdelhamid et al. looked at a recent outbreak of E. coli O157:H7 from cattle to human and found that WGS was able to distinguish which isolates from the cattle matched the isolates in the infected patients, while the use of PFGE was unable to differentiate between all the isolates tested. Listeria monocytogenes. L. monocytogenes is a gram-positive, non-spore forming bacteria belonging to the family Listeriaceae. It can grow in a temperature range of from -0.4 to 45 °C with optimal growth from 30 to 37 °C and can grow within a pH range of 4.4-9.6, but has optimal growth at 6-8 . L. monocytogenes can grow in foods with a water activity of 0.9 or higher .L. monocytogenes is found in a variety of places, including plants, animals, soil, water, and humans . It can cause listeriosis, which can be a very serious infection in high-risk groups but is unlikely to manifest severely in other groups of people . Foodborne Listeria needs only a few cells to infect and once in the digestive tract Listeria can invade cells and use cell-to-cell transmission to spread to the rest of the body. Symptoms of listeriosis in high-risk individuals can include miscarriage, sepsis, and meningitis, grow bag for blueberry plants while in the rest of the population people may experience only mild gastroenteritis. There is some debate of whether L. monocytogenes poses a significant risk in low moisture foods. There have been no documented outbreaks of L. monocytogenes associated with low-moisture foods and the current prevalence of the pathogen in low-moisture foods is likely low . However, L. monocytogenes can survive for long periods of time in low-moisture foods and there have been recalls associated with L. monocytogenes in these foods, including in dried fruits, nuts, biscuits, and oats . L. monocytogenes is notorious for its ability to grow in cold environments. This is why outbreaks of this pathogen are often found in refrigerated, ready-to-eat foods , as they do not require heating before consumption.

Dried fruits are an RTE and are often stored at refrigerated temperatures by processors, but due to the inability of pathogens to grow at low water activities, L. monocytogenes growth should not be a concern in dried fruits. Pathogen survival is still a concern though, as L. monocytogenes has been shown to have a desiccation tolerance of up to 1 year in certain low moisture foods. For instance, Kimber et al. found that 6 log CFU/gof L. monocytogenes inoculated onto raw almonds did not decline significantly when the almonds were stored at 4 °C for 12 months. Agar used for selective plating of Listeria include Oxford, Modified Oxford, PALCAM, Chromogenic Listeria agar, and lithium chloride-phenylethanol-moxalactam . Selective enrichment can be done with buffered Listeria enrichment broth . Proper subtyping is particularly important in identifying Listeria, as many different strains can have similar phenotypic qualities . The most common serotypes of L. monocytogenes isolated from patients are type 1 and type 4 . There can also be strains that have qualities that are unusual to Listeria that make identification more difficult. The FDA BAM mentions as examples isolates of Listeria innocua that are hemolytic and L. monocytogenes and Listeria welshimeri isolates that are rhamnose negative . When trying to differentiate L. monocytogenes specifically, the species should usually test negative for mannitol and xylose, and should test positive for rhamnose, virulence, and beta hemolysis . Again, sequencing plays an important role in identification of many pathogens such as Listeria. In fact, Listeria was the first bacteria the CDC began using WGS with and has since then spread its use to other organisms including Salmonella and E. coli . Intrinsic factors influencing pathogen survival. Pathogen survival can be influenced by many factors, including aw and pH. In general, the ability of microorganisms to survive common food processes increase when aw is lowered. However, while higher aw promotes growth, high aw also enhances lethality of thermal treatments . The mechanisms for thermal resistance are not completely agreed upon but are shown to be strongly influenced by aw . The lower the aw, the more difficult it is for the number of cells present to decline . For example, Keller et al. found that Salmonella inoculated onto pumpkin seeds became increasingly resistant to thermal inactivation when the aw decreased from its original value of 0.97 to below 0.20. The pumpkin seeds began with a Salmonella population of 7.48 ± 0.57 log CFU/g and dropped to 0.68 ± 0.81 log CFU/g after 6 h or drying at 60 °C . After 6 h, the aw dropped to below 0.20 and no more significant decrease in the Salmonella population was seen during 12 more h of drying at 60 °C . Just knowing the aw alone is not enough information to understand pathogen survival, as water activity is often working in conjunction with other factors such as temperature . pH is also known to have some effect on bacterial survival. While bacteria have a specific pH range in which they can grow, they can survive outside that pH range. Thermal resistance is decreased at lower pH, so pathogens are generally easier to inactivate in more acidic food matrices . Deng et al. inoculated dry infant cereals of pH 4.0 and 6.8 with 6 log CFU/g of E. coli O157:H7. After 24 weeks of storage at 5 °C the cereal with a pH of 4.0 had 3.19 log CFU/g of E. coli, while no E. coli was detected in the cereal at pH 6.8 . The phytochemicals found in dried fruits, including alkaloids, flavonoids, and phenolic compounds, can exhibit antibacterial activity . Jagathambal et al. screened various phytochemicals from dried figs to see if they had any inhibitory effects on various bacteria. The phytochemicals extracted from dried figswere able to inhibit Salmonella spp., Klebsiella spp., Haemophilus spp., and Serratia spp. with a minimum inhibitory concentration of 1.0 mg/mL . Mainasara et al. screened phytochemical from dates to see how inhibitory they could be against pathogens.

Limonene and a-terpinolene were the highest produced monoterpenes, which exhibited the strongest patterns consistently . To investigate the events leading to this accumulation of monoterpenes, we performed a Fisher’s exact test to identify enriched KEGG pathways in the modules. Terpenoid backbone biosynthesis was significantly enriched in the H-II-1 module . Figure 3.4 depicts the MEP terpene backbone biosynthesis producing GPP that leads to monoterpene biosynthesis. A flux of terpene biosynthesis occurred at the end of Stage II and the beginning of Stage III, which indicates precursors for monoterpene metabolism were being synthesized . From the WGCNA, we also identified the top 5% highest connected genes within the module network. In the H-II-1 module, the top highly connected genes included the gene encoding HDR and a limonene synthase in the terpene biosynthesis pathway.Color changes are a characteristic of fruit ripening. To further define ripening in the pistachio hull, we investigated the underlying biological cause of the change in fruit coloration from green-yellow to hues of red-pink observed in the hull during Stage IV . We found a significant correlation between red coloration increase in the hull with the H-IV-1 and H-IV-2 =0.73 modules during ripening. This was further supported by a Fisher’s exact test for enrichments of KEGG pathways in each module. The H-IV-2 module was significantly enriched for the carotenoid biosynthesis pathway. The B-carotene hydrolase was the highest expressed carotenoid gene in this module and is annotated to be involved in the production of lutelin and zeaxanthin. We examined the highest connectivity genes in this module and among them was a phytoene synthase gene with 887 connections, the rate-limiting step in the carotenoid pathway. Because pink coloration often comes from anthocyanins we also looked at anthocyanin biosynthesis in the hull. While expression was present in the phenylpropanoid and flavonoid pathways, grow bag expression was low in the steps exclusive toanthocyanin biosynthesis. We also found a high negative correlation between the hull redness and the H-III-1 module corresponding to a loss of green coloration.

A significant enrichment of photosynthesis genes in the same H-III-1 module , meaning gene expression of photosynthesis genes decreased after Stage III when fruit became less green.Pistachio kernels contain a high proportion of fatty acids and reach their maximum fat content as the kernel matures during ripening . To further understand the composition of the fat content, we measured unsaturated and saturated fatty acids across six time points during Stage III and IV of kernel development . Unsaturated fatty acids made up 87% of the total fatty acids present when the fruits were ready to be consumed . We confirmed that the unsaturated fatty acids were composed of a higher ratio of mono-unsaturated to poly-unsaturated . This ratio changed through time, such that by ripening MUFA were the predominant class of fatty acids present in the fruit. We determined alterations of metabolites within each class of fatty acid contributed to the changes in MUFA and PUFA ratios during maturation . To further understand what causes these alterations, we examined gene expression of kernel in gene modules associated with the increase in fat content. The module-trait relationships indicated that the increase in fat content was highly and significantly correlated with the K-III-1 module, along with K-IV-1, K-IV-2, and K-IV-3 . This same relationship was also evident for these same modules and the proportion of unsaturated fatty acids through time . We performed an enrichment of KEGG pathway annotations in kernel modules and found that fatty acid biosynthesis was significantly enriched in the K-III-1 module . The high expression of biosynthesis genes during Stage III indicates that fatty acids are produced early on at the start of kernel development, and taper off at the beginning of Stage IV . Within this module, 19 genes encoding fatty acid biosynthesis were found including key genes FAB2 and FAD2 which desaturates steric acid into oleic acid and oleic acid into linoleic acid, respectively . The FAB2 and FAD2 genes were the highest expressed genes in the pathway, and were among the top 5% of genes in the module. FAB2 peaked in expression with 12,500 normalized reads at 1508 GDD while FAD2 peaked with 14,700 normalized reads at 1749 GDD.

Consistent with the expression data, the metabolite data also showed that oleic and linoleic acid were the top two produced fatty acids, throughout development. Interestingly, the concentration of linoleic acid decreased over time while oleic acid increased, which was not evident in the expression data.Defining the biological events occurring during pistachio fruit development that lead to traits of interest can allow for breeding and management strategies to improve fruit quality. Further, a high-quality reference genome has been lacking, as previous genomes are incomplete and fragmented. Therefore, in order to facilitate molecular breeding and broaden the understanding of nut tree crop fruit developmental processes, we present for the first time an assembled 561 Mb reference-quality chromosome-scale genome of P. vera cv. Kerman. Based on k-mer distribution analysis with PacBio HiFi reads, the Kerman genome showed a moderate heterozygosity estimate in comparison with other outcrossing highly heterozygous crops, such as pear 1.6% and grape 1.6-1.7% . This is unexpected because the previously-reported heterozygosity levels of pistachio genome were higher, 1% and 1.72% , which was attributed to the nature of outcrossing by wind pollination and dioecy of pistachio trees . In addition, the genome size estimate of 521 Mb in our study was smaller than the first attempt for pistachio genome size estimate with 26.77 Gb whole genome sequencing data using 17-mers . However, later, the genome size estimated with the larger amount of data using 21-mers was rather similar in size to our assessment . In the final genome assembly, the Kerman genome size was larger than the estimated size but smaller than previously published genome assemblies of different pistachio cultivars, Batoury and Siirt and Bagyolu . Although the size variation of the estimated and assem-bled pistachio genome assemblies could be explained by possible genome size variation across different cultivars as documented in other plant genomes, it is likely that pseudoduplication in the assemblies, especially from the highly repetitive regions in chromosome arms in the case of pistachio, is the primary cause of assembly size variation . In 2015, Sola-Campoy and colleagues characterized massive enrichment of 180 bp repeat on one arm of 11 chromosomes in pistachio, which was also observed in the Kerman genome .

The largest region with dense distribution of 180 bp repeats reached about 9 Mb in chromosome 7, where no protein-coding gene was annotated . These extremely repetitive regions could have been the major issues of accurate pistachio genome assembly and chromosome construction. Although Omni-C reads are known to offer uni-form coverage across the genome without RE sites over represented, it was observed that the overall coverage of Omni-C reads was significantly lower in those regions in Omni-C analysis, likely due to the limitation of mapping capability . Therefore, more careful validation on those regions is needed to improve the pistachio genome. The annotation of intact and fragmented transposable elements in the Kerman assembly resulted in about 11% higher genome coverage than the estimated repetitiveness . As discussed in genome size and estimate, this is likely caused by the pseudo-duplication in the assembly or misestimation due to exceptionally repeat-dense regions in chromosome arms. Among 65% of repetitive regions, nearly 49% of the genome was composed of LTRs, which have been widely known as the dominant TE groups in plants and can play a major role in adaptation and evolution by introducing novel genetic material. The protein-coding gene annotation shows high completeness based on BUSCO assessment with almost 99% . However, minor improvements can still be made by filtering out false-positive gene models and recovering missing BUSCO genes. Macrosynteny patterns between Pistacia vera cv. Kerman, Mangifera indica , and Citrus sinensis provided evidence that P. vera has not experienced a lineage-specific whole genome duplication event , unlike the recent WGD which occurred in the mango genome as described in . The synteny between mango and pistachio genomes and the similarity between their fruit morphology and growth patterns provides an interesting evolutionary comparison within the Anacardiaceae family.During the growing season, pistachios undergo a unique asynchronous development of the kernel and maternal tissues. The hull and shell develop together in the first months, marking Stages I and II , grow bag gardening while embryo development takes place during Stages III and IV. In contrast to previous reports, we found that shell hardening continues to take place with kernel growth starting in late June at approximately 1000 GDD through late August at approximately 2000 GDD . The asynchronous developmental pattern between the fruit and embryo has not been well described in the literature for other tree crops. While peaches appear to exhibit a similar pattern in seed development, this trait does not seem to have been studied in a crop whose seed is consumed . Carbohydrate dynamics in the tree may offer some explanation of the asynchrony. Carbohydrates reserved from the prior year are utilized by the tree to produce buds and develop fruit in early spring, through Stage I . The lull in fruit growth identified as Stage II may serve as a transition between a net carbon loss and a net carbon gain in photosynthesis leading to the growth of the kernel. The RNAseq experiment assessed genetic changes through time and tissue type during fruit development. The shell and hull have the most similar gene expression patterns . This was obvious in the expression of hormone-related gene expression. The shell and hull tissues exhibited very similar expression patterns for each hormone biosynthesis pathway, while the kernel expression patterns were distinct . Interestingly, the hull and shell total gene expression became more similar over time . This contrasts with the morphology of the tissues, which early on in development are physically fused together and appear to become increasingly different through time as shells become woody and split, and the hull and shell tissues separate during ripening . The similarity in gene expression may be due to both tissues undergoing terminal developmental programs. This occurs earlier in the shell when the tissue reaches its peak firmness at the beginning of Stage IV, while in the hull this occurs at the end stages of Stage IV as ripening finishes. Shell lignification was previously reported to start as early as May-June, falling in Stage I-II . While the secondary cell walls become lignified, the shells are green and flexible at this point. However, as described above, the texture of the shell continues to change through Stage III leading to a woody tissue that then splits . The shell tissues appear to senesce and be fully lignified at around 2100 GDD, as RNA content became very low in shell tissues after this point. Our gene expres-sion analysis found a proportion of the genes involved in the phenylpropanoid pathway leading to monolignols to be expressed highest at Stage II followed by a sharp decline, marking the initial lignification . The genes exhibiting this pattern were among the highest expressed homologs; however, other copies of the genes displayed patterns with peak expression later on during Stage III or IV indicating lignin was still being produced, contributing to the increased firmness of the shell. This suggests that the lignification process does not complete until the shell reaches peak firmness, as has been described in walnuts . While continued lignification may be a factor leading to shell firmness changes, other factors such as cell wall modifications likely also contribute, but require further investigation. Overall, understanding the composition and alterations in the shell tissue will be important to ascertaining the underlying mechanisms leading to shell split for a higher quality nut.Although ripening has not previously been well explored in fruit tree crops, early reports suggest that pistachios are non-climacteric fruit . We confirmed ethylene is not produced in a climacteric pattern during ripening and remains at constant low levels, as shown through biosynthesis gene expression . In conjunction with this we found evidence that abscisic acid may be involved in regulating ripening in pistachio. NCED is the rate limiting enzyme in ABA biosynthesis . We found that a primary copy was expressed in the shell and hull tissues right before ripening changes began to occur, i.e., the transition between Stage III and Stage IV. This corresponded to an increase in ABA signaling genes such as, PYLs, PP2C, SNRK2, and ABFs, suggesting ABA is active at the onset of ripening .

Moving forward, we intend to generate a range of populations based on both phenotypic and genomic selection from this yield evaluation trial. These will then be evaluated alongside other elite material for DMY to assess if there has been any improvement. Due to the lengthy breeding process of perennial forages, it will take several years to determine whether these methods have been successful. To improve the predictive ability of the model moving forward a combination of evaluating a greater number of families and improving the quality of phenotypic data through better modeling will be imposed. Increasing the size of the training population could be facilitated without a significant increase in costs by using modern high-throughput phenotyping tools, such as dronebased remote sensing. With decreasing costs of genotyping, improved computational software and the availability of genomic resources , genomic selection is becoming increasingly available to more resource limited breeding programs like alfalfa. There is still much research required to assess whether actual yield gain can be achieved; however, these studies provide a baseline for future studies to investigate potential yield improvement.Yield is the most important trait for profitable forage production, yet the rate of genetic gain for dry matter yield in perennial forage crops is lower than the main cereal crops and has been essentially zero in alfalfa over the past 30 years . Limited resources, low heritability, square black flower bucket significant genotype by environment interaction and long selection cycles limit the rate of genetic gain in perennial forages in comparison to many annual food and feed crops .

Improvement of perennial forages is typically carried out through recurrent phenotypic selection with or without progeny testing to accumulate desired alleles at high frequency in a population . Ideally the number of families to be evaluated is very large, particularly with the advent of modern breeding methodology such as genomic selection. In reality, breeders must strike a balance between the available resources and the size and scope of breeding trials. Phenotypic evaluation of perennial forage traits requires significant investment of land, labor, and capital. Forage DMY and dormancy in alfalfa are two crucial traits that require significant resources to phenotype. The standard test for fall dormancy in alfalfa requires height measurements for each trial entry 25-30 days after the final harvest, often across multiple environments and years . To accurately assess forage yield, experimental units must be harvested, dried,and weighed to estimate dry matter content across multiple harvests and years, resulting in up to 40 total harvests over the lifetime of a trial . Further complexity is added to the breeding of perennial forages considering the diversity of evaluations trials often used, ranging from single plant evaluations to transplanted rows, seeded rows, or solid seeded swards. The choice depends on the traits of interest, the number of genotypes or families being evaluated, seed quantity, and the capital and labor resources available to the breeder, with most programs using a combination of sown and transplanted trials . Transplanted family rows are the most common as they are a cost-effective method of evaluating large numbers of trial entries for traits with high heritability. They are commonly used to screen populations for resistance or tolerance to various pests and diseases, investigating growth habit, dormancy, flowering time, and forage quality . In the past, forage yield has often been selected indirectly based on evaluation of vigor on spaced plants or short family rows . Although a useful method for evaluating other important, highly heritable traits, a poor correlation exists between these assessment methods and yield in a commercial setting . Large sown plots are commonly used for variety trials.

These trials require large quantities of seed, cover a large area, and provide phenotypic data for relatively few trial entries. In a breeding program, these trials are typically used to compare advanced breeding populations to released cultivars for key traits such as stand establishment, DMY, forage quality, flowering time, and dormancy. Although useful for obtaining phenotype data that well represents a commercial forage operation, it is usually not feasible to evaluate hundreds of families in this way. Transplanted mini-sward plots provide a compromise between family rows and large sown plots. They seek to provide a better estimation of forage DMY than family rows without the need for large quantities of seed or significant land area that large sown plots require. In these trials asmall number of plants are planted close to one another to mimic the competition observed in commercial forage stands. In recent decades, remote sensing has been widely adopted in agricultural research , offering a plethora of non-destructive vegetative data with massively reduced labor requirements. Remote sensing has the potential to address the lack of yield improvement in alfalfa and increase the rate of genetic gain for yield in other perennial forages by enabling breeders to greatly increase the size of trials without the associated increase in labor costs. This is particularly important for breeding programs looking to use genomic selection, where the size of the training population is a key component of predictive ability . Remote sensing techniques have been shown to enable accurate estimation of biomass yield in alfalfa at the field level , and at the large plot level in breeding trials . However, its accuracy has not been widely reported across the range of plot types used in forage breeding or for estimating fall dormancy in alfalfa. The overall objective of this research project was to assess the accuracy of drone-based remote sensing versus traditional phenotyping for forage biomass yield and alfalfa fall dormancy across a variety of plot types used in perennial forage breeding. The goal is to give breeders the ability to evaluate a wider range of material without the associated increase in labor and costs. In addition, we aim to provide recommendations for researchers looking to incorporate similar technology into their breeding programs.

This experiment was carried out across several trials previously established as part of the UC Davis forage breeding program located on the UC Davis Plant Sciences Farm in Davis, CA on a Yolo silt clay loam . It is a Mediterranean environment with hot, dry summers, cool winters and moderate annual rainfall which falls predominantly in the cooler months from November-March . Soil tests were conducted prior to planting to adjust P, K, and pH according to soil test recommendations. The trials consist of three alfalfa breeding trials and a forage grass variety trial.The trial consisted of 72 released cultivars, experimental cultivars, germplasm populations, and eleven standard test check cultivars . Plants were germinated in 128-cell flats in the greenhouse in February before transplanting to the field in April 2018. This experiment consisted of four replications laid out in a randomized complete block design. Plots consisted of a single row of 25 plants spaced 30 cm apart with a 90 cm gap between plots and 60 cm spacing between rows. Fertilizer was applied to maintain P and K at appropriate levels for a high yielding alfalfa stand, with weeds and insect pests monitored and control measures applied when necessary. Plants were initially watered using sprinkler irrigation until fully established, following which they were flood irrigated to satisfy full evapotranspiration requirements.This trial contained 80 half-sib families of an experimental population UC2588 that had been selected for tolerance to lygus feeding. We had had sufficient seed of each family to plant solid seeded plots. This experiment was established following the NAAIC standard procedures for variety yield trials . It consisted of two replications laid out in a randomized complete block design with ten rows and twenty ranges. Plots were 1 m x 3 m and were drilled using a small plot planter at a seeding rate of 15 kg ha-1 with 1.5 m gaps between ranges. UC Impalo was sown as a border between ranges and around the exterior of the trial. As with the 2018 dormancy trial, crop nutrient demand, weeds and pests were monitored and adjusted when necessary. Sprinklers were used immediately after sowing to get the trial established, square black flower bucket wholesale followed by flood irrigation to meet water demand.This trial included a total of 198 entries of which 193 were half-sib families from two closely related elite UC Davis populations derived from various UC Davis germplasm that underwent selection for root rot and other stresses in El Centro and Davis, California. In addition, three cultivars: Highline, UC Impalo and CUF 101 were included as repeated checks and the remaining two entries were balanced bulks from each of the two populations . The trial was sown in the greenhouse in March 2020 and transplanted two months later in early May at two locations on the UC Davis research farm in Davis, California. Each site has the same layout consisting of two replicates with 7 rows and 29 ranges for a total of 203 plots per rep, 812 plots overall. Plots consisted of 24 plants laid out in a regular 4 × 6 grid with 20 cm spaces between plants. There was a 30 cm space between rows and a 110 cm space between ranges to allow room for mechanical harvesting.

This trial was managed as a high-yielding alfalfa stand, soil tests were conducted each year, with amendments made accordingly. The trial was established using sprinkler irrigation, which was switched to flood irrigation after plants were well established. Irrigation water was added to roughly match crop ET. Weeds were managed by a combination of manual removal and herbicides, and insect pests were monitored and controlled with insecticide application as necessary, primarily for alfalfa weevil control in spring.A grass variety trial containing 88 cultivars was sown in October 2020. Plots are 1.5 m x 4.5 m and were drilled using a small plot planter. Table 1 outlines the seeding rates used for each species. The trial was separated by species with two blocks of tall fescue, two blocks of orchard grass, one block of timothy and reed canary grass, and the remaining species in the final block. Each block contained four rows of plots with 14 ranges. The blocks are separated by borders of either tall fescue, timothy, or orchard grass to allow irrigation pipes to be laid across the field without lying on top of the plots. This trial was irrigated by sprinklers on a weekly to biweekly basis as needed to approximate ET demand. N, P and K levels were monitored, and fertilizer applied when necessary. N was applied at 100 kg ha-1 in spring and again after first harvest.Plant height measurements for the alfalfa fall dormancy standard test were measured following the protocol outlined by Teuber et al. . Twenty-five days after the final fall harvest, the natural plant height was measured on each of the 25 plants per plot. Natural plant height was deemed to be the distance from the soil surface to the top of the tallest stem as the plant stands in the field . The measurements were then averaged over the whole plot to generate a single data point for each plot. Biomass yield data were collected using a small self-propelled plot harvester. Harvests occurred in alfalfa when the field had reached 10% bloom with the first harvest usually occurring in late March/April and the final harvest in October. For alfalfa trials, subsamples were taken during each harvest, weighed wet, dried for at least 4d at 60C, and weighed dry to adjust moisture percentage. Several subsamples were taken from each replication as composite samples from all entries, rather than for every entry, and the average dry matter was used to adjust the wet weights. In the grass trial, harvests occurred when the most plots of tall fescue and orchard grass had reached the late boot stage, with the first harvest in April and subsequent harvests every 6-8 weeks for a total of four harvests per year. All species were harvested at the same time for logistical reasons, even though this was likely not ideal for individual species . All forage was clipped uniformly at 7.5 cm, weighted, and removed from the trial area. Subsamples were taken from every plot in the grass trials, weighed wet, dried for at least 4d at 60C, and weighed dry to adjust moisture percentage.Prior to remote sensing data collection, the borders surrounding the trial and between plots were mown. Drone flights and preliminary image processing were conducted following methods modified from Parker et al. .

Women reported working, on average, less than 10 days a month during five months compared to 15 to 21 days per month during the peak season. Although a significant proportion of the individuals surveyed lived in or close to towns, roughly 85% of the jobs reported by this sample of workers were in agriculture. Females had somewhat greater packing shed employment experience than males. Surprisingly, women had higher average daily earnings than did men. Women worked more frequently on a piece rate basis , which paid more than comparable wage employment, and women were employed primarily during the peak season, when earnings were highest.Most workers lived in households with several workers. Twenty-five percent of the females surveyed and half of the males provided more than 50% of their household’s annual income. Only a third of the females who were widows or separated were their household’s major earner . Still, interviews indicated that many women had been able to separate from their husbands and/or live apart from their parents because of income obtained as a temporary fruit laborer. Although female-headed households tended to have lower incomes than male-headed households, many female heads of households spoke with satisfaction that their work allowed them to support themselves. Worker’s household characteristics influenced the number of days employed each year. Figure 3 shows that married men worked the most, especially if they had young children, approximately 275 days per year. Single males worked much less, about 170 days. Men who were separated or widowed worked an amount intermediate between these levels. The significant affect of marriage on the number of days worked suggests that marriage affected the motivation to work and that search effort was an important determinant of employment.

Women averaged significantly fewer days worked per year than men did. Some women worked more than 220 days per year, flower buckets wholesale but no female category had such a high average. Women also showed less variation in the number of days worked with respect to their household situation, at least as here categorized, and the variation shown was directly reversed from that of men. For example, married women with young children worked the least of individuals in the sample, while single women who were not living with their parents worked the most of all female categories. There is thus evidence that married women with young children had a higher reservation wage than other workers. However, women lacking income from a husband or parents worked substantially even when they had young children.Female labor force participation varied greatly by season, declining sharply from February to May, remaining low through September, and then rising steadily to February. Labor force participation was less variable for males. Daily earnings varied seasonally more in agricultural than in non-agricultural jobs, especially for jobs held by women. Women tended to earn more than men in agricultural jobs during the peak season, but less during the slack season, while the situation was reversed for non-agricultural jobs. As agricultural wages declined, a rising proportion of workers was employed in non-agricultural jobs . While female temporary workers face greater wage variation than men and vary their labor participation more, they also suffered substantially more unemployment . The female unemployment rate exceeded 50% during five months. Male unemployment was also high, but averaged only about half as much. 4.1. Labor Market Participation Equation and Expected Earnings Jarvis and Vera Toscano explored adjustment in this market to identify whether seasonal differences in labor force participation was attributable to the existence of specific ‘barriers’ to employment, differences in preferences or differences in observed worker characteristics.

Specifically, they modeled labor force participation for male and female workers by estimating a random effects probit that allowed for unobserved heterogeneity in preferences. Table 5 reports the results. For women, the estimated coefficients on the explanatory variables were generally highly statistically significant and in line with prior expectations. Few of the estimated coefficients were statistically significant for men, a result consistent with the relatively constant male labor force participation rate.13 Women participated in the labor force less than men did. Female labor force participation increased with age. Since rising education was associated with higher daily earnings, education may have altered the preference for work versus leisure. Marriage reduced labor force participation for females, perhaps due to increased household responsibilities and/or a social-cultural bias against work, but did not affect male participation. Female labor participation declined as the number of the worker’s children aged 0-5 years increased, but this effect was reduced if another adult female lived in the household, suggesting that childcare was gender specific and indicating the importance of childcare for female labor force participation. Men and women were more likely to participate during the peak season and less during the slack season as compared to the transition months of April and October through December, a result probably linked to expected earnings. Jarvis and Vera Toscano examined the sensitivity of labor force participation decisions to changes in expected earnings using a probit equation that included the same regressors plus estimated earnings . The coefficient on expected earnings was positive and significant and the other coefficients were closely similar to those obtained cols. 1 and 2. Though labor force participation for men and women responded strongly and positively to the expected wage, the female participation rate varied substantially more because females tended to have a higher reservation wage. Still, female unemployment was generally much higher than male unemployment .

Although wages varied greatly by season, Jarvis and Vera Toscano found they did not vary sufficiently to fully equate the supply and demand of labor and achieve zero unemployment. Four factors were advanced to explain this high unemployment. First, frictional unemployment was high as a result of individuals entering and/or leaving the labor force, changing jobs, and searching for employment in a spatially dispersed market where jobs were relatively short lived and search costs relatively high. Second, many or all firms may have paid an efficiency wage or piece rate to motivate workers, thereby causing the unemployment rate to remain above zero even during periods when labor demand is high. Third, the average reported wage in agriculture lay above the average reported wage in the non-agricultural sector throughout the year. Thus, waiting for an agricultural job could easily have been the better strategy for most workers even when few agricultural jobs were available. Fourth, some workers, especially females, may incorrectly report having been in the labor force and actively seeking work. Alternatively, they may have considered themselves in the labor force, but searched only within a small, local area, where there were few jobs.The average wage rose by about 50% from the slack season to the peak season, a surprisingly large variation. To understand the determinants of changes in daily earnings over the one-year period, Jarvis and Vera Toscano estimated an earnings equation where the dependent variable was the log of average daily earnings and the regressors included both supply and demand side factors. Human capital variables such as education and experience were hypothesized to influence worker productivity and earnings, while monthly dummies reflected the net influence of seasonal fluctuations in agricultural labor supply and demand. Wages were hypothesized to vary in response to the worker’s decision to seek either piece rate or wage employment, and either non-agricultural or agricultural employment. Such choices were assumed dependent on a worker’s willingness to supply effort and preference for factors such as work environment and a shorter commute time to work. Since dummy variables were used to measure the effect of working at a piece rate as opposed to a wage, flower harvest buckets the other coefficients measured the effect of the respective independent variables on the daily wage. Consistent estimates of the earnings function were obtained using the two-step estimator proposed by Vella and Verbeek. The results for both men and women are reported in Table 6. The earnings of both men and women increased with schooling, suggesting that education significantly increased labor productivity in agricultural work, although the higher return was probably partly due to the innate ability that allowed individuals to successfully complete additional schooling. Experience had a significant positive impact on female daily earnings in jobs throughout the year; the analogous coefficient was not significant for males. The square of experience had a significant negative coefficient, indicating that rising experience had a non-linear effect.

A dummy variable was also used to measure the earnings effect of working on a piece rate basis. A piece rate system was frequently used to motivate and remunerate temporary agricultural workers in the fruit sector and a substantial theoretical literature indicates that the piece rate system should increase worker’s productivity and workers’ incomes . There have been few empirical studies. The estimated coefficient on the piece rate dummy indicates that piece rate jobs in this case earned a daily premium of about 12 percent relative to wage jobs. A dummy variable was also used to measure the effect of working in the agricultural as opposed to the non-agricultural sector. Agricultural work paid substantially more, particularly for women . Men’s wages in this sample were about 18 percent higher when working in agriculture, while women’s wages were about 37 percent higher. Agricultural jobs were probably even more attractive than shown for women since there were few piece rate jobs available in non-agricultural work. As earlier noted, women’s average daily earnings were higher than men’s average daily earnings . Women working as temporary agricultural laborers were thought to earn relatively high wages in the Chilean fruit sector , and the results in Jarvis and Vera-Toscano supported that view. Nonetheless, women earned substantially less than men did in wage employment once earnings were adjusted for observed and unobserved characteristics. The estimated gender wage differential was about 25 percent. Although females had higher average daily earnings than men, women earned less than men when working for a wage, but not when working for a piece rate. Jarvis and Vera-Toscano suggested that these results indicated discrimination in the wage market. There may be less possibility of discrimination when workers are employed at piece rate since pay is directly linked to productivity. The large magnitude of the gender wage differential suggests an area for further analysis.Newman and Jarvis found that women were highly informed about many aspects of the packing shed jobs that they accepted, e.g., shed-related characteristics that affected workers’ productivity, fringe benefits, and the expected duration of the job. Women’s willingness to accept work at a specific piece rate was strongly influenced by these characteristics. Piece rates for the same tasks were found to vary by as much as 100%among different packing sheds and these differentials were well explained econometrically by the observed heterogeneity among workers and firms. For example, most processing sheds provided workers with some combination of fringe benefits that included meals, snacks, transportation to and from work, childcare, interest-free loans, and higher quality bathrooms. Supervisors and managers in different sheds treated created different quality work environments. According to the theory of equalizing wage differentials, sheds that provide more and better fringe benefits and/or a better work environment should have paid lower piece rates. This hypothesis was supported by the data. Similarly, Newman and Jarvis hypothesized that firms’ investments in technology, improved plant organization, or the ability to process grapes that were in better condition would raise worker productivity. Further, so long as workers were aware of firm-influenced productivity differences, such higher productivity should lead to lower, not higher piece rates. To the extent that firms possessed improved technology that allowed their workers to achieve higher productivity or were better organized and could provide a constant flow of good quality grapes to workers, allowing workers to process more boxes per time period, the firm should pay a lower piece rate. This followed from the assumption that each worker should earn an income consonant with her opportunity cost in equilibrium. If a firm’s characteristics allowed its workers to produce more output, ceteris paribus, worker competition for the jobs at the firm should have caused the piece rate to decline until its workers’ incomes were equal to what they would earn elsewhere. This hypothesis was also supported by the econometric results. Workers could easily ascertain the piece rates paid by different firms, but the effect of firm characteristics on a worker’s productivity should have been harder topredict.

Plants employ a large array of strategies, both direct and indirect, to minimize the effect of seed predators. These include alternative strategies of regeneration, seed banks, abundant seed crops, altering phenology, spatial and temporal variation in seed productions and increasing plant defenses . It has also been demonstrated that pre-dispersal predation can facilitate the stratification of the seeds facilitating germination . A hybrid-derived lineage that has already successfully invaded a new habitat represents a useful model because, when combined with its progenitors, it allows for a replicated study of the invasion process. Here, I focus on the hybrid-derived California wild radish, which is an invasive lineage in western North America . Its progenitors, the cultivated radish Raphanus sativus and the wild radish R. raphanistrum, were introduced in western United States around mid 1800 . These two lineages naturally hybridize, and there is evidence for the hybridderived lineage to have originated from interspecific bidirectional hybridization between them . Genetically based differences between both progenitor lineages and a partial and temporary reproductive isolation during the first generations of hybrids results from a single reciprocal translocation . The polymorphism in fruit, flower color and shape, root morphology, chemical and structural defenses found in Raphanus lineages has been the focus of numerous studies in ecology, evolution, genetics and agricultural and food chemistry . Anecdotal observations have reported that bird pre-dispersal predation of seeds can be extensive in all three Raphanus lineages and birds are arguably the primary consumers of pre-dispersed seeds over invertebrate granivory . When granivory is excluded, the hybrid-derived California wild radish exhibits superior fitness compared to its parental lineages in common garden experiments across its Californian distribution . However, black plastic plant pots wholesale the hybrid’s relative fitness and that of one of the progenitors in the presence and absence of granivores, is unknown.

Our aim was to answer the following questions: what species of bird granivore is the main consumer of radish seeds? how much does the bird granivore affect relative fitness and relative potential fitness? are the variables likely to affect the birds’ selection of individual plants such as days to germination, plant final weight, total fruit production and potential reproduction, correlated with fruit damage? and are there viable seeds in the debris due to granivory resulting from the bird foraging behavior under damaged plants? In addition, the comparison among lineages allowed us to better understand novel biotic interactions in a successful invasive hybrid-derived lineage and to propose a mechanism that led to the replacement of both progenitors .Seed sources – The seeds used to breed the mother plants in the present study came from plants reared in a common garden during Spring 2005 and Winter 2006. The seed sources for the first generation of maternal plants are described in table 2.1. The second generation seeds are the result of natural open pollination in common gardens at the Agricultural Operations fields at the University of California-Riverside . More details on how the first generation plants were grown can be found in Ridley and Ellstrand . Common garden and experiment design – The common garden experiment took place during Spring and Summer seasons of 2010 at AgOps-UCR. Three replicate sites, each one consisting of two plots of 7 m by 7 m, were planted with 36 plants placed in a 6 x 6 grid with 1 m spacing in rows and columns. One of the plots at each of the three sites was covered with 3/4″ x 3/4″ orchard mesh to exclude above ground vertebrate damage while the other plot remained unprotected. These two conditions created two different treatments for the plants to grow in: protected from vertebrate seed predators and unprotected to vertebrate seed predators. In both cases the plants were exposed to open pollination, invertebrates and potentially underground vertebrates. All plots were oriented in the same North-South direction. We selected at random 8 seeds from 4 different mothers within each of the 9 above-mentioned populations for a total of 288 seeds. These seeds were divided in groups of 36, such that all mothers were represented in those 8 groups by 2 seeds. That is, each population had 4 seeds, for a total of 12 seeds per lineage.

These 8 groups of seeds were germinated in Petri dishes at the beginning of March and transplanted into seed starting trays filled with sterilized UC Soil Mix III at a climate controlled greenhouse. Once the seedlings had attained a three-leaf stage, 6 of the 36- grouped seeds were transplanted to the pre-water and plowed field plots. The two additional groups of seeds were used to replace any seed that did not germinate or any seedlings that did not survive the transplanting process. The plants were watered once daily for 10 min with a sprinkler system until most of the plants had started to flower. To maintain favorable abiotic conditions for the plants that flowered later, the watering persisted only every-other-day for 5 min. Granivores – We visited the sites at AgOps at least every two days to ensure that the experimental conditions were kept consistent during the entire length of the study. During those visits I also spent time observing the foraging behavior of the birds that began when fruits had attained a fully formed size. Once I became familiar with the birds foraging patterns, I spend an afternoon filming their behavior. Videos were captured with a digital video camera on a tripod. Videos are available as supplemental information.Fruit damage, fecundity, fitness related values and debris due to granivory – Variables related to morphology, damage and fitness were recorded before planting, during the experiment, and after the surviving plants were collected. All seeds were weighed to within 0.01 mg with an analytical balance . The germination and growth of seeds in the dish was recorded daily. At the end of the experiment when the plants were dry and had senesced, I recorded the final plant weight to within 0.001 g. To calculate fecundity and fruit damage, I counted total number of: damaged fruits that included all fruits with clear signs of missing or damaged sections, whole dropped fruits that were found detached from the dry plant, and whole attached fruits. With these variables I calculated fruit damage and fecundity. We counted total numbers of: flower buds, flowers, whole empty pedicels , and broken or pedicel scars on the stems. We also collected the fruit material or debris accumulated under heavily damaged plants, herein referred to as “debris due to granivory”, to discern what was discarded during the birds foraging behavior.

Potential seed viability was determined by visually inspecting the seed coat and by putting pressure on each seed between the thumb and the index fingers; when unviable, seeds had black and/or wrinkled seed coats and crumbled easily. All the previously described values and those in table 2 allow us to calculate relative fecundity and relative potential fecundity of plants in unprotected and protected plots. Because I did not count number of seed per fruits, I calculated the number of seeds based on the average number of seeds per fruit per populations. These average values,listed in table 2, were obtained from a previous study where I counted total number of seeds from 884 fruits that belonged to the same populations represented here . We consider these values appropriate to extrapolate the number of seed in our study because: the plants that produced them developed from pure lineage seeds from the same populations represented in our study listed in table 2.1, the plants were grown under similar conditions to the present study, and the plants were exposed to open pollination . Total number of seeds were extrapolated for a given plant by: multiplying total number of whole fruits per plant by the average value of seeds in table 2.2 according to the population of origin, black plastic plant pots bulk followed by multiplying total number of damaged fruits by the 2/3 of the average number of seeds according to the population of origin in table 2.2, and finally by adding the numbers obtained for whole and damaged fruits. Fecundity and female fitness values were calculated as follows. The average number of extrapolated seeds per population was calculated by dividing the total number of extrapolated seeds divided by the total number of fruits per population. Relative fecundity is the average number of extrapolated seeds divided by the highest average number of extrapolated seeds. Potential reproduction was calculated by adding flower buds, flowers, whole empty pedicels broken to whole, damaged and dropped fruits for a given plant. The average potential reproduction was calculated by adding the potential reproduction for a given population or lineage and dividing by the total number of plants and multiplied by 100. Finally, the percentage of the relative potential fecundity was calculated by dividing a given average potential reproduction to the highest average one among for populations and lineages separately and then multiplying by 100. Our fitness values did not explicitly include male fitness. Nevertheless I know based on prior studies in plants of the Raphanus lineage that male fertility is highly influenced by environmental factors and weakly correlated with female fertility values . Data analysis – Data were normalized as needed either with log-normal or Box Cox transformations using functions in R . Significant P values were adjusted a posteriori with sequential Bonferroni test to adjust for type I error . We used one-way analysis of variance to tests the effects of treatments and lineages on total fruit production. Variables related with fruit damage and with fitness were compared in pairs among lineages and between treatments with Wilcoxon tests. The effect of the treatments on relative fecundity and relative potential fecundity as well as average number of fruits and seeds were tested for significance with Fisher exact tests. These tests were performed to individually compare CAwr values to its progenitors. We also compared fecundity values to the highest ones with chi-square tests. Total number of fruit damaged was correlated using Spearman correlation coefficients and covariance to variables possibly related to final fruit production and general performance. Those variables included: days to germination, final plant weight as well as total number of fruits and total potential reproduction. In this case each lineage was tested independently.Flower buds, flowers and pedicels – No differences were found between lineages and treatments in average number of flower buds and open flowers . With respect to the average number of pedicels, values for CAwr from protected and unprotected plots are significantly different and higher than both progenitors under protected treatment as well as for the cultivar under unprotected treatment, respectively . Fruits with and with no damage – We only found damaged fruits in plants that were collected in unprotected plots . Consistent with our previous results, average numbers of fruits with damage are significantly different among lineages and treatments as revealed by Wilcoxon tests . Whole undamaged fruits were categorized as either attached to the dry plant or detached and on the ground. The cultivar Rs differs significantly from CAwr and Rr on lower average number of whole dropped fruits, whereas both wild lineages, CAwr and Rr, are comparable . No differences are found in the average number of whole attached fruits among lineages with the exception of CAwr and Rs from protected plots . The average proportions of damage, calculated as total number of damaged fruits over the total fruits produced for each lineage and population, are listed in Table 2.3. When the damage is estimated based on seeds removed, calculations of damage per population are reduced by at least 33 % and at most by 60% relative to the damage calculated based on fruit damaged. Damage, based on seeds removed, was calculated as the total number of seed removed divided by total number of seeds produced. As mentioned earlier, I did not count the total number of seeds per fruits during this experiment. Fruits from Cst-CAwr suffer higher damage than interior populations . Fruit production – Total fruit production does not differ under protected or unprotected treatments but does differ among lineages . The cultivated Rs lineage produced fewer fruits in protected treatments relative to both wild lineages, significantly differing from both CAwr and Rr . However, under unprotected conditions, Rs only substantially differs from unprotected and protected CAwr fruit production . Fecundity and fitness related values – CAwr has significantly higher fecundity in protected plots than in unprotected ones, as shown in table 2.4.

Glucose-1-phosphate adenylyltransferase was highly down-regulated in symptomatic fruits while invertase was clearly up-regulated in the peel of apparently healthy and symptomatic fruits from the infected orchard. Sucrose symporter was up-regulated in infected, symptomatic fruits. The terpenoid pathway was affected by HLB disease, as shown by the down-regulation of terpene synthase cyclase . Induction of lipid transfer protein was observed in the absence of symptoms. The expression of other genes such as acidic cellulase and methyltransferase2 was diminished in infected fruits.Huanglongbing, a highly destructive disease of citrus, threatens citrus-producing areas worldwide. Previous studies monitored transcriptional changes in leaves on a large scale using microarrays to investigate host responses and reveal the mechanisms underlying disease development. The present work focuses on transcriptional regulation in fruit peel to analyze the response to CaLas infection at different disease stages. Next-generation sequencing technology , as used here, can find differential expression of an increased number of transcripts, many of which may not be present in EST databases or represented in microarrays. NGS data can be used for specific transcriptome assemblies that become resource datasets for annotation of genomes and annotation of differentially regulated genes and proteins analyzed using any ‘‘omic’’ technique. However, 30 planter pot the absence of a completed genome sequence limited the advantages of RNA-Seq technologies. The aim of this work was to provide data using NGS technology for a comprehensive analysis of metabolic changes in fruit induced by HLB disease.

These findings will uncover the fruit disorder mechanisms and facilitate development of short-term therapeutic strategies for already-infected trees. Toward this end, the experimental design included four types of fruit: healthy control fruit from an HLB-free orchard, apparently healthy fruit from non-symptomatic trees in an orchard affected by HLB, and asymptomatic and symptomatic fruit from infected symptomatic trees in the affected orchard. This design made it possible to identify differentially expressed genes at different disease stages. A comparison between apparently healthy and asymptomatic fruit revealed genes induced early in disease development. Comparing asymptomatic and symptomatic fruit identified genes involved in the host response during disease progression. Comparing symptomatic and apparently healthy fruit revealed host response genes related to the presence of HLB symptoms. Healthy fruit from the HLB-free location were also compared to the three sample types from the infected location. Differences in gene expression in these comparisons are likely to result from environmental and agronomic variability due to the difference in location, in addition to the effects of HLB. A range of 1154 to 1762 differentially regulated genes were found using RNA-Seq comparing the three categories of fruits from the infected orchard with those taken from a location free of HLB . In qRTPCR analysis, 31 of 33 differentially regulated genes confirmed the pattern of expression found by RNA-Seq. Comparisons among the three fruit categories in the infected orchard resulted in fewer differentially regulated genes . This was expected, since samples within the same orchard grew under similar environmental and agronomic conditions. A similar contrast between samples from the infected and uninfected orchards is seen in principal component analysis. The transcripts that most strongly characterize the asymptomatic and apparently healthy samples are common between the two samples.

The symptomatic and uninfected orchard samples are both distinct groups and widely separated in the biplot . A visual summary outlines the most important transcriptional changes in the networks among genes, pathways, and cell functions in the fruit peel . Gene set enrichment analysis identified several pathways significantly affected by HLB as symptoms appear, considering both within-tree and between-tree comparisons, such as those for phenylpropanoids, starch and sucrose metabolism, carbon fixation, ascorbate, and alpha-linolenic acid . Other pathways were also affected by HLB in the two comparisons within the same orchard. Transcripts encoding different subunits of the photosystem II reaction center and cytochrome b6-f complex subunit were more abundant in symptomatic HLB-infected fruit than in healthy fruit. Photosynthesis is central to all aspects of plant biology, since it provides energy for growth and reproduction, but its regulation by biotic and abiotic stresses is still unclear. The induction of photosynthetic light reactions in the fruit is consistent with the observation that symptomatic HLB-infected fruit often remains green. The retention of green color and increase of photosynthesis reactions is probably linked to the lower amount of ethylene detected insymptomatic fruits. Photosynthesis is usually downregulated by pathogen attacks. The upregulation of photosynthetic reactions in the fruit does not contradict this common consideration because CaLas infections typically occur in young leaves. The transcriptomic changes observed in the fruit are probably linked to the source-sink disruption caused by leaf infections. Protein degradation and modification pathways were significantly changed by CaLas infection, as shown by the upregulation of genes such as C3HC4-type ring finger proteins involved in ubiquitin-mediated degradation. Interestingly, heat shock proteins HSP82 and HSP70, highly interactive proteins in the PPI network inferred in citrus, were down-regulated at different stages of the disease. Heat shock proteins are highly conserved proteins induced in cells subjected to elevated temperatures or other environmental stresses.

These proteins act as molecular chaperones to stabilize, reduce misfolding, or facilitate refolding of proteins that have been denatured during stress events. In plant cells, HSP70 and HSP90 are involved in signal transduction leading to plant defense responses. Both proteins interact with a salicylic acid-induced protein kinase and their silencing affected the hypersensitive response in Nicotiana benthamiana while inducing non-host resistance to Phytophthora infestans. HSP90 also modulates the innate immune responses involving gene-for-gene specific interactions, acting as a scaffold protein in a complex that mediates signal transduction. Based on these findings, we speculate that down-regulation of heat shock proteins observed at different stages of HLB disease might increase protein misfolding in the fruit peel. Genes encoding ATP synthase gamma and delta chains were also induced in symptomatic fruit, supporting the idea that CaLas may act as an energy parasite by scavenging ATP from its host with a pathogen-specific ATP/ADP translocase. Indeed, the recently sequenced genome of CaLas revealed the presence of an ATP/ADP translocase in addition to ATP synthase. ATP scavenging may be a possible mechanism of pathogenicity, affecting the fruit peduncle, columella, and seed coat. Photosynthesis regulation in infected fruit peel may also reduce transport of sugars and ions such as nitrate, sulfate, and potassium. In this study, several differentially expressed genes were associated with transport of ions, including ammonium, sulfate, and phosphate . Inorganic ions can modify sugar metabolism and photosynthesis. Polarized vesicle trafficking, transport, and secretion of plant materials are associated primarily with non-specific resistance during host-pathogen interactions. ABC transporters play important roles in this process, since they are also involved in virulence, host range, and symptom elicitation. Citrus proteins related to sugar and PDR/ABC transporters implicated in secretion of antimicrobial terpenoids were specifically induced by HLB at the symptomatic stage . Interestingly, among the genes identified in the CaLas genome were those for phosphate and zinc uptake into the cell. This implies that mineral uptake by the pathogen may be enhanced due to induction of endogenous genes as well as host genes. However, plastic growers pots phloem necrosis induced by CaLas contributes to the impaired nutritional transport functions and source-sink communication observed in this study. Starch accumulation in leaf chloroplasts of sweet orange trees infected with CaLas has previously been demonstrated and is characteristic for HLB. Genes encoding the large subunit of ADP-glucose pyrophosphorylase, the key enzyme catalyzing the first and limiting step in starch biosynthesis, were up-regulated in infected leaves. Interestingly, a gene encoding the large subunit of glucose-1-phosphate adenylyltransferase was downregulated in infected and symptomatic fruit in the present study. Also of interest are contrasting patterns of expression for different isoforms of starch synthase and starch cleavage genes, leaving the dynamics of starch accumulation in the fruit unclear . This is in contrast to experimental observation of starch accumulation in infected leaf tissues. In leaves, this accumulation was linked to up-regulation of genes involved in starch biosynthesis and down-regulation of its conversion to maltose. It has been hypothesized that starch accumulation is an effect of phloem plugging/necrosis during HLB infection, although it usually occurs before these symptoms are visible. Differences in starch accumulation are probably linked to the different types of organs: mature leaves are ‘‘source’’ and fruit are ‘‘sink’’. However, further analysis of fruit starch accumulation must be performed to validate this hypothesis.

An increased abundance of transcripts for genes involved in the first steps of glycolysis and sucrose metabolism was observed in fruits from infected trees. In the cytosol, an invertase gene was upregulated in asymptomatic and symptomatic fruit, affecting the sugar balance and communication between sink and source tissues. We speculate that this leads to increased glucose and fructose and decreased sucrose in fruit cells, which should be further validated with carbohydrate analysis. It is important to note that elevated glucose and fructose has been demonstrated in citrus leaves infected with CaLas. Interestingly, different types of invertases were up-regulated in leaves and fruits . Over-expression of yeast invertase in the cell wall of transgenic tobacco disrupted sucrose export, allowing soluble sugars and starch to accumulate, which consequently inhibited photosynthesis and resulted in stunted growth and bleached or necrotic leaf areas. As previously suggested for leaves, it is possible that the differential expression of key genes involved in sucrose and starch metabolism, as observed in CaLas-infected citrus fruit, might affect the osmotic potential and induce plasmolysis, thus altering the ripening process and producing typical HLB symptoms. However, further analysis of sugar concentrations will be necessary to clarify the causes and effects of disease symptoms in the fruit. Other studies on CaLas-infected leaves have shown increased sucrose and glucose, but not fructose. In fruit, it is possible that altered fructose and glucose concentrations might be responsible for physiological disorders and affect source-sink relationships with leaves. The gene set enrichment analysis confirmed that sucrose and starch metabolism were highly affected by the disease. Integrated analysis of leaf and fruit data indicates that sugar and starch metabolism play a key role in the metabolic dysfunction induced by HLB disease. However, few studies have been conducted to address the effects of altering sugar metabolism on resistance to pathogen infections. Invertase plays a key role in the activation of stress responses and may function as an extracellular indicator for pathogen infection. Indeed, transgenic plants overexpressing sugar metabolism enzymes such as a heterologous invertase from yeast have helped clarify source-sink relationships. The expression of different viral movement proteins in transgenic plants and the resulting effects on photosynthesis, carbohydrate accumulation, and partitioning emphasize the importance of sugars in activating defense responses against biotic attacks. Sink metabolism may be essential to satisfy the energy requirements of activating the cascade of defense responses. Interestingly, sucrose synthase was also more abundant in symptomatic fruit while sucrose transporter genes were downregulated by HLB. The Genevestigator database, indicated that these proteins may be down-regulated by hormones such as ethylene, methyl-jasmonates, and indol-3-acetic acid. The concept of sucrose metabolism regulators as a potential target for HLB therapeutics is intriguing. Increasing evidence indicates an extensive cross-talk between sugar, hormone, and light signal transduction networks in plants. Hormone pathways were significantly altered in fruit peel inresponse to CaLas infection. Two genes involved in auxin synthesis, GH3.1 and GH3.4, were induced in affected fruit. Gibberellin regulation has been observed in other fruit disorders such as albedo breakdown disorder and applications of GA3 before fruit color break can reduce the occurrence of some fruit disorders. It is possible that sugar metabolism changes observed in the fruit might be linked with the down-regulation of gibberellins that regulate energy and carbohydrate metabolism. Previous studies have demonstrated that regulation of gibberellic acid-induced gene expression is affected by sugar and hormone signaling. That cytokinins play a role in sugar regulation has been demonstrated. Therapeutic approaches using small-molecule hormones such as cytokinins and gibberellins may allow modification of fruit metabolism to mitigate the negative impact of HLB on fruit quality and productivity.Ethylene regulates a variety of developmental processes and stress responses in plants, including seed germination, cell elongation, senescence, fruit ripening, and defense. Nonetheless, ethylene can promote either disease resistance or susceptibility, depending on the host–pathogen interaction. In our study, considerable changes were observed in the transcriptional profiles of genes related to ethylene biosynthesis and signal transduction. ACC synthase and ACC oxidase play pivotal roles in ethylene biosynthesis and their expression is often affected by pathogen attack. It was unclear how ethylene concentration changes in fruit in response to HLB.

One of the reasons for this could be the nature of phenotypic complexity which is the result of reticulated interactions among many different physiological and cellular processes and environmental conditions . We used genetic and phenotypic analyses in the tomato IL’s, coupled with meta-analyses of existing data, to identify several co-regulatory relationships between carbon metabolism and leaves . This study proposed that leaf shape may affect the sugar content of tomato fruit through developmental and photosynthetic mechanisms. We used these relationships in directed experiments to explore additional avenues for fruit quality improvement.Previous studies have shown that there is a direct regulation of yield and BRIX in tomato through leaf shape by modeling these relationships using PLS-Path Modeling . This correlation was performed in heirloom cultivars which retain a large amount of genetic diversity compared to commercial varieties. This can make identifying the causative gene/s difficult. Another study utilizing monogenic mutants and their isogenic backgrounds showed a correlation between decreased vascular density and BRIX in tomato fruit. Here we used 17 tomato introgression lines , backcrossed IL , and sub IL’s to identify a specific gene which may influence Brix and yield through regulation of leaf shape and vascular density, bHLH032. bHLH032 is a SPATULA like transcription factor which when knocked out via CRISPR resulted in plants with decreased vascular density and increased BRIX x Yield .Leaf shape for these lines was measured and compared to their BRIX and yield traits from the same plants. Approximately 7637 leaflets were measured over two growing seasons, and their shape characterized using PCA analysis .

Nine lines showed significantly rounder leaflets compared to M82, including BIL 260, sub ILsub IL 4-3-4, BIL 063, blueberry in container and sub ILsub IL 5-4-1 . Additionally, two lines, sub ILsub IL 8-1-3 and IL 9-1-2, were significantly narrower than M82, with leaflets of the remaining lines similar in shape to M82 . BIL 260 and sub IL 4-3-4 had improved yield, and BIL 260 had improved BRIX at terminal harvest . Of the remaining lines, BIL 338 and BIL 378 were trending higher in yield than M82 at terminal harvest but were not significantly different . The inverse relationship between BRIX and yield has long been established , however BIL 260 breaks this relationship with an increase in both traits with yield greater than M82 by approximately 3-fold and increased BRIX . sub IL 4-3-4 also had an increased yield at just over 2 times that of M82 but had no significant difference in BRIX from M82 . To further quantify the total output of BIL 260 and sub ILs 4-3-4, they were grown with M82 over three independent field seasons, and the BRIX and yield of their terminal harvest multiplied to obtain the BY value index, a composite value which accounts for variations and extreme values in either measurement . The BY of BIL 260 is significantly higher than that of M82 at approximately 20, while M82 has a BY of 14 . The BY of sub ILs 4-3-4 was elevated but not significantly different from M82, most likely due to only an increase in yield and not BRIX .Because leaves are the primary site of photosynthesis, and responsible for most of the sugar production in plants, we checked to see if the photosynthetic rate , and stomatal conductance were altered in these lines. Several lines showed decreased photosynthesis compared to M82 , but only BIL 260 had increased photosynthesis . This increase is small but significant despite the large range of photosynthesis found in M82 . Several lines had reduced gst compared to M82, corresponding to lines which had decreased photosynthesis .

These same lines had reduced, but not significantly different, yields and BRIX values compared to M82 with the exception of BIL 338 and BIL 378 which each had slightly higher yield values . Both BIL 260 and sub IL 4-3-4 had similar gst as M82, despite the increase in photosynthesis in BIL 260 . Finally intrinsic water use efficiency was calculated for M82, BIL 260, and sub IL 4-3-4, with both introgression lines having significantly higher values suggesting both lines utilize water more efficiently than M82 . How this may impact the other measured physiological traits was not specifically studied in this research.To further understand the sugar usage in BIL 260 and sub IL 4-3-4 we analyzed sugar and starch mobilization and transport in leaves. This measurement was done at two-hour intervals across 24 hours for each of the three lines that were the focus of a detailed analysis, to identify time points where differences in their sugar and starch usage may occur . Figure 2d shows the sugar content of the leaves for all three lines measured as μmols per gram fresh weight. Much of the increase in sugar took place between 7am and 7pm hours, as would be expected as these were daylight hours , and for all three lines the concentration and change in sugar content was similar . However, between 1 am and 5 am there was an initial decrease in sugar content in M82 but then a subsequent large increase. In contrast, leaf sugar concentration in BIL 260 and sub IL 4-3-4 continued to decrease over this time period, suggesting that the sugar was either being utilized in the leaf or exported out of the leaves . Figure 2e shows the starch content from the same leaves and at the same time points. During daylight hours the change in starch content in the leaf is similar, though much higher in M82 at solar noon and interestingly all three genotypes have a decrease in starch content during the 1am to 5am time period .

This decrease in starch content and the concomitant increase in sugar content over this time in M82 suggests starch mobilization to sugar for usage in the leaves as no photosynthesis is taking place at this time . However, BIL 260 and sub IL 4-3-4 had a continued decline in sugar content of their leaves despite similar starch mobilization to that found in M82, which suggests that either the sugar is being utilized in the leaves at a more rapid rate or is being exported from the leaves at an increased rate . Despite the decrease in sugar content in BIL 260 and sub IL 4-3-4 being similar, BIL 260 has less sugar at 5am than either M82 or sub IL 4-3-4, likely due to even higher export rates .BIL 260 and sub IL 4-3-4 are both introgression lines which contain a small portion of Chromosome 4 from the S. pennelli genome in a majority M82 background. Because they both have yield difference but only BIL 260 has increased fruit BRIX and lower vascular density, we performed Whole Genome Sequencing to determine the exact location of the introgressions. In both lines the introgression is limited to chromosome 4, and there are no other introgressions present in the genome . The introgression spans the centromeric region and contains approximately 800 genes in each. However, there are a small number of genes introgressed from S. pennellii that are uniquely present in BIL 260 but not sub IL 4-3-4 on both the 5’ and 3’ ends of the introgression . On the 5’ end of the introgression there are 75 additional genes introgressed in BIL 260, while three additional genes are present on the 3’ end. Among the 78 additional genes from S. pennellii three are transcription factors, specifically bHLH032 , R2R3MYB20 , and a GRAS transcription factor . We hypothesized that these genes could be responsible for the differences seen between BIL 260 and sub IL 4-3-4 in factors such as yield, BRIX, vascular density, plastic planters bulk and sugar transport. As such we performed an RNA-Seq analysis for these lines and M82 covering three time points , and three tissue types in a field setting . The RNA-Seq data was split by tissue type and analyzed across all three time points. UMAP dimensionality reduction and Mean Shift clustering were performed on the DEGs for each tissue type . To determine the tissue type of most interest we looked for the three unique transcription factors from BIL 260 in the DEG lists and then for their position in the clustering. For SAM tissue the bHLH032 transcription factor was a DEG and present in clustering but the other transcription factors were not . In mature leaves bHLH032 was again a DEG present in the clustering but the other two were not . Young leaves, defined as those leaves fully developed but not expanded, had all three of these transcription factors of interest present in the DEG list .

The GRAS transcription factor was within Cluster 6 which had GO enrichment for protein folding and intracellular protein transport. The R2R3Myb was found within Cluster 31 which had no significant GO enrichment . bHLH032 was found within Cluster 19 which had GO enrichment terms relating to sugar metabolism and extracelluar regions . Additionally, the ortholog of bHLH032 in Arabidopsis is Abnormal Shoot 5 , which when over expressed causes upward curled leaves and increased vascular density . It is also shown thatABS5 interacts with Lonesome Highway to initiate vascular development in combination with TMO5 . Given the leaf and vascular changes observed in BIL 260 along with the increased BRIX and yield, we chose to pursue cluster 19 and the bHLH032 TF. Gene co-expression analysis was done on cluster 19 to identify any differences between BIL 260 and M82 . The M82 network for cluster 19 contains bHLH032 as a peripheral gene connecting to the network through Solyc08g067030 . A BLAST search of the protein sequence for this gene showed three potential Arabidopsis orthologs, At4g32460 , At5g11420, and At5g25460 . All three genes have expression in either mature or developing leaves, and BDX is involved in vascular development . Additionally, a phospolipase-A2 like gene was also correlated with the BDX/DGR2 gene . An aldose-1-epimerase , is also present and could indicate a connection with sugar metabolism or processing. The BIL 260 network did not contain the bHLH032 gene but did still have BDX/DGR2 connected to the central network . The aldose-1-epimerase was no longer within the central part of the network, but a peripheral gene, and not connected BDX/DGR2 . The same was found for the phospholiapse-A2 like gene, indicating a large rearrangement of the network including the loss of bHLH032 . The absence of bHLH032 from the BIL 260 network suggested either a loss of function or reduction in expression for the S. pennelli version of this gene, and so we analyzed the expression values found in the RNA-Seq data . The overall expression of bHLH032 in BIL 260 was lower than that in M82, with a p-value of 0.06 across all weeks. At week 10, when plants were flowering and beginning early fruiting, the expression of bHLH032 in young leaves was much lower than that found in M82 . This was confirmed by the eFP browser for tomato which also showed that in S. pennellii bHLH032 has a much lower expression than M82 .There are approximately 800 genes found within the BIL 260 introgression from S. pennellii, and as such the differences in BY, leaf shape, and vasculature could be a result of other genes within the introgression and not the bHLH032 despite the gene co-expression network results. To determine if this was the case we back-crossed BIL 260 with M82 to break up the introgression region. The F1 populations were grown in the greenhouse for seed, and then 400 F2 plants were taken to the field for characterization. The BY for all lines, including the parent lines M82 and BIL 260, were measured . There was a wide range of BY’s with the majority of plants falling within the parent plants range, but transgressive phenotypes were seen as both lower and higher than either M82 or BIL 260 . The vegetative biomass was also measured and compared to fruit mass to see if there was an equivalent change in both, but vegetative biomass remained consistent across all F2 plants while yield was highly variable . From these data we selected lines that had both high and low BY as well as round and narrow leaflets to grow the F3 and F4 populations . Additionally, all 400 lines were sequenced using GT-Seq .

Six seedlings of each genotype were planted per pot for each replicate. The 76 IL’s were divided into four cohorts of 20 randomly assigned genotypes. These cohorts were placed across four temporal replicates in a Latinsquare design as described in . The seedlings were harvested 5 d after transplanting . Cotyledons and mature leaves .1 cm in total length were excluded, and remaining tissues above the midpoint of the hypocotyl were pooled, for all individuals in a pot, into 2-mL microcentrifuge tubes and immediately frozen in liquid nitrogen. Two IL’s, IL7.4 and IL12.4.1, were not included in the final analysis due to seed contaminations.Seeds 76 IL’s along with the parents were sterilized using 70% ethanol, followed by 50% bleach, and finally rinsed with sterile water. This experiment was replicated three times each in 2011 and 2012. Ten to 12 seeds of each IL were sown into Phytatray II containers with 0.53 Murashige and Skoog minimal salt agar. Trays of each IL were randomly assigned to either a sun or shade treatment consisting of 110 mmol PAR with a red to far-red ratio of either 1.5 or 0.5 at 22°C with 16-h-light/8-hdark cycles for 10 d. Three genotypes were excluded from the analyses due to poor germination or their necrotic dwarf phenotypes . After 10 d, seedlings were removed from the agar and placed onto transparency sheets containing a moistened kimwipe to prevent dehydration and scanned using an Epson V700 at 8-bit grayscale at 600 dpi. Image analysis was carried out using the software ImageJ . For hypocotyl length analysis of backcross inbred lines between S. pennellii and S. lycopersicum cv M82, growing berries in containers seeds were sterilized in 50% bleach and then rinsed with sterile water.

The seeds were then placed in Phytatrays in total dark at room temperature for 72 h and then moved to 16 h light/8 h dark for 4 d. Seedlings were transferred to soil using a randomized design and assigned to eithera sun or shade treatment for 7 d. Images were taken with an HTC One M8 Dual 4MP camera and hypocotyl lengths measured in ImageJ using the Simple Neurite Tracer plugin.RNA-seq libraries were prepared and the reads were preprocessed as de- scribed in Chitwood et al. and are outlined here. mRNA isolation and RNA-seq library preparation were performed from 80 samples at a time using a high-throughput RNA-seq protocol . The prepared libraries were sequenced in pools of 12 for replicates 1 and 2 and in pools of 80 for replicates 3 and 4 at the UC Davis Genome Centre Ex- pression Analysis Core using the HiSeq 2000 platform . Preprocessing of reads involved removal of lowquality reads , trimming of low-quality bases from the 39 ends of the reads, and removal of adapter con- tamination using custom Perl scripts. The quality-filtered reads were sorted into individual libraries based on barcodes, and then barcodes were trimmed using custom Perl script.Mapping and normalization were done on the iPLANT Atmosphere cloud server . S. lycopersicum reads were mapped to 34,727 tomato cDNA sequences predicted from the gene models from the ITAG2.4 genome build . A pseudo reference list was constructed for S. pennellii using the homologous regions between S. pennellii scaffolds v.1.9 and S. lycopersicum cDNA references above. Using the defined boundaries of IL’s, custom R scripts were used to prepare IL-specific references that had the S. pennellii sequences in theintrogressed region and S. lycopersicum sequences outside the introgressed region. The reads were mapped using BWA using default parameters except for the following that were changed: bwa aln: -k 1 -l 25 -e 15 -i 10 and bwa samse: -n 0.

The bam alignment files were used as inputs for express software to account for reads mapped to multiple locations . The estimated read counts obtained for each gene for each sample from express were treated as raw counts for DE analysis. The counts were then filtered in R using the Bioconductor package EdgeR version 2.6.10 such that only genes that had more than two reads per million in at least three of the samples were kept. Normalization of read counts was performed using the trimmed mean of M-values method , and normalized read counts were used to identify genes that are differentially expressed at the transcript level in each IL compared to cv M82 parent as well as between two parents, S. pennellii and M82. The DE genes for each IL were compared to those between the two parents to identify genes that were differentially expressed for the IL but not for S. pennellii compared to cv M82. Those genes were considered to show transgressive expression pattern at the transcript level for the specific IL, whereas other DE genes were considered to show the transcript expression similar to S. pennellii.RNA-seq libraries were prepared and the reads were preprocessed as de- scribed in Chitwood et al. and are outlined here. mRNA isolation and RNA-seq library preparation were performed from 80 samples at a time using a high-throughput RNA-seq protocol . The prepared libraries were sequenced in pools of 12 for replicates 1 and 2 and in pools of 80 for replicates 3 and 4 at the UC Davis Genome Centre Ex- pression Analysis Core using the HiSeq 2000 platform . Preprocessing of reads involved removal of low-quality reads , trimming of low-quality bases from the 39 ends of the reads, and removal of adapter contamination using custom Perl scripts. The quality-filtered reads were sorted into individual libraries based on barcodes, and then barcodes were trimmed using custom Perl script.Mapping and normalization were done on the iPLANT Atmosphere cloud server . S. lycopersicum reads were mapped to 34,727 tomato cDNA sequences predicted from the gene models from the ITAG2.4 genome build .

A pseudo reference list was constructed for S. pennellii using the homologous regions between S. pennellii scaffolds v.1.9 and S. lycopersicum cDNA references above. Using the defined boundaries of IL’s, custom R scripts were used to prepare IL-specific references that had the S. pennellii sequences in the introgressed region and S. lycopersicum sequences outside the introgressed region. The reads were mapped using BWA using default parameters except for the following that were changed: bwa aln: -k 1 -l 25 -e 15 -i 10 and bwa samse: -n 0. The bam alignment files were used as inputs for express software to account for reads mapped to multiple locations . The estimated read counts obtained for each gene for each sample from express were treated as raw counts for DE analysis. The counts were then filtered in R using the Bioconductor package EdgeR version 2.6.10 such that only genes that had more than two reads per million in at least three of the samples were kept. Normalization of read counts was performed using the trimmed mean of M-values method , and normalized read counts were used to identify genes that are differentially expressed at the transcript level in each IL compared to cv M82 parent as well as between two parents, S. pennellii and M82. The DE genes for each IL were compared to those between the two parents to identify genes that were differentially expressed for the IL but not for S. pennellii compared to cv M82. Those genes were considered to show transgressive expression pattern at the transcript level for the specific IL, whereas other DE genes were considered to show the transcript expression similar to S. pennellii.Transcript level patterns were correlated with three phenotypes collected from the IL’s along with the parents. Normalized estimated read counts with 3 to 4 independent replicates per IL were log2 transformed prior to the analyses. Leaf number and complexity were collected from the IL’s as outlined in Chitwood et al. under both sun and shade treatments. Hypocotyl lengths were measured as detailed above. To test whether the transcript level for a given gene was correlated with a particular phenotype, blueberry containers boostrapping analyses were performed. Transcript levels and phenotype data were randomly per- muted using the sample function against IL and then merged. For each analysis, 1,000 replications were performed and the P values were calculated from the Spearman’s rho value distributions. P values were adjusted for multiple comparisons using the BH correction . Significant correlations were identified as those with an adjusted P value, 0.05, and the mean rho value was used to designate the correlation as either positive or negative . All analyses were implemented using the statistical software R and custom scripts .eQTL mapping analyses were performed to determine whether the transcript level of a gene is correlated with the presence of a specific introgression from S. pennellii into S. lycopersicum cv M82. This correlation was examined at the level of “bin,” with a bin defined as a unique overlapping region between introgressions. Examining eQTL at the bin level enables those eQTL to be mapped to considerably smaller intervals than the IL’s themselves . eQTL mapping analyses were performed on the normalized estimated read counts with 3 to 4 independent replicates per IL, which were log2 transformed prior to the analyses. To test whether the transcript level for a given gene is correlated with the presence of a particular bin, a Spearman’s rank correlation test was used with ties resolved using the midrank method. P values were adjusted for multiple comparisons using the BH correction . Significant eQTL were identified as those with an adjusted P value, 0.05, and Spearman’s rho was used to designate the eQTL as up or down .

Significant eQTL were also designated as cis if the gene was located on the bin with which it is correlated; trans if the gene was correlated with a bin that is neither the bin it is on nor a bin that shares an overlapping IL with the correlated bin; or chromo0 if the gene lies in the unassembled part of the genome. When a gene has a designation cis- eQTL, and a secondary correlation was found with a bin that shares an overlapping introgression, this secondary correlation was not designated as an eQTL. When a gene does not have a designated cis-eQTL and a correlation was found with a bin that shares an overlapping introgression, this correlation was designated as a trans-eQTL. All analyses were implemented using the statistical software R and custom scripts .t-SNE or t-distributed stochastic neighbor embedding is a nonlinear dimensionality reduction method, which faithfully maps objects in high dimensional space into low dimensional space . Crowding is avoided through the longtailed t-distribution, which forces nonneighbor clusters farther away from each other in V-space than those clusters actually are in H-space . The ex- aggerated separation of non-neighboring clusters improves 2D resolution, allowing identification of novel groupings not readily apparent in other clustering methods. However, this method is resource intensive, and with higher dimensionality, the number of genes that can be analyzed is limited. We have used Barnes-Hut-SNE, a newer implementation of t-SNE that greatly increases the speed and number of genes that can be analyzed, for the present analysis . BarnesHut-SNE accomplishes this efficiency through the use of a Vantage Point tree and a variant of the Barnes-Hut algorithm . For clustering, 2D maps were generated using a perplexity of 30 and without the initial PCA step from the Barnes-Hut-SNE R implementation . Theta was set to 0.3 based on van der Maaten to maintain an accurate dimensionality reduction without sacrificing processing speed.The DBs can algorithm was used to select modules from the Barnes-Hut-SNE results . This algorithm had the advantage of both selecting modules and removing any genes that fell between modules. The scanning range and minimum seed points were selected manually and used to deter- mine if any one point is a member of a cluster based on physical positioning within the mapping relative to neighboring points. A minpts of 25 was used to capture smaller modules on the periphery, and an epsilon of 2.25 was used to avoid the overlapping of internal and closely spaced modules.Box plots were generated from normalized transcript abundance values for each module. The ribbon plot was generated from correlated abundance values from leaf development and photosynthesis related modules. These plots were generated using ggplot form the ggplot2 R Package . The median transcript levels of the genes mapped to a module were calculated for each IL and replicated for all modules.

Specifically, a principal coordinate analysis of weighted UniFrac distances indicated significant clustering of individual microbial communities by phenological stage . In a pairwise comparison of community compositional differences between each phenological stage, the leaf bacteriome had the greatest number of significant adjusted P values, with 21 of the 21 pairwise comparisons being significantly different, followed by the leaf mycobiome . Rainfall, fertilizer applications, temperature, and irrigation hours fluctuated across our sampling period . Rainfall was sparse in this sample location , ranging from 0.00 to 2.55 inches each month , and total rainfall was a minor determinant of community structure across all four communities, explaining only 0.8 to 2.0% of the variation . Similarly, fertilizer application describes a small percentage of the variation in the data for all four communities examined. We evaluated temperature based on the average temperature and interactive effects it might have with water availability in order to capture the full range of conditions that could affect microbial community composition. Temperature had a minor impact on communities, as this factor describes only 0.6 to 2.8% of the variation in the data that include temperature as an interaction factor. In addition to phenology, interactions between phenology and sample year were a driving factor of leaf bacterial community composition, explaining 8.2% of the changes across the data . Taken together, these beta diversity analyses indicate that plant phenological stage was the major driving factor in community composition for bacterial and fungal communities associated with leaves and roots.

Significant compositional shifts were also visible at the phylum level, container growing raspberries particularly in the leaf bacterial community . Other covariates tested were minor or insignificant contributors to citrus-associated leaf and root microbiome composition.We identified core microbial taxa for each of our seven phenological stages. Our core bacterial and fungal leaf and fungal root microbiomes include genera that were greater than 0.01%, and core root bacteriome included genera greater than 0.1%, relative abundance in at least 75% of the samples within a phenological stage. All of our downstream analyses used genera that met our core taxa cutoffs in at least one phenophase. We assessed our core taxa and separated them into three categories: high stability, defined as core member of six or more phenophases, medium stability, core member of three, four, or five phenophases, or low stability, core member of two or fewer phenophases. We determined that of the identified core there were 3 leaf bacterial, 8 leaf fungal, 62 root bacterial, and 22 root fungal core genera that had high stability across phenophases . This suggests that both bacterial and fungal root communities have a substantially greater number of consistent or stable microbial features across the developmental cycle. However, our experimental design did not differentiate between endophytes versus epiphytes and, thus, may have missed some fine resolution microbial community shifts occurring between the endosphere and episphere. There were two bacterial and one fungal genera that were highly stable in both roots and leaves . A phylogenetic analysis of the core genera indicates that both bacterial and fungal root communities were rich in highly stable and phylogenetically diverse core taxa . Root core genera from the bacterial clade Alphaproteobacteria and the fungal family Pleosporomycetidae were all or nearly all binned as highly stable, indicating that genera in these clades were consistently high in relative abundance across all phenological stages.

Medium- and low-stability core genera appear randomly dispersed across the root community phylogeny, with no obvious patterns.However, leaf bacterial and fungal core community phylogenetic trees contained high, medium, and low stability patterns at the class and phylum levels . All core genera in the fungal class Tremellomycetes had medium to high stability. In contrast, all core genera in the fungal class Sordariomycetes had low stability across phenophases and met the defined core cutoffs only during fruit set or mature fruit stages. The leaf taxa within the bacterial class Gammaproteobacteria consisted of genera with high, medium, and low stability across the phenophases. Interestingly, all theGammaproteobacteria were core members of the full flowering or floral bud development microbiomes regardless of their stability in other phenophases. Another distinct phylogenetic pattern observed in the leaf community was genera in the bacterial phylum Actinobacteria that had low or medium stability across all phenophases. However, 95.0% of core genera in the Actinobacteria clade were core during fruit set and/or fruit development. The only exception to this within the Actinobacteria clade was Bifidobacterium, which was associated only with full flowering and was not a core member of fruit set or fruit development microbiomes . Lastly, the leaf bacterial class Betaproteobacteria contains low- to medium-stability core genera with the most dispersed stage associations. Overall, these data indicate that root bacterial and fungal communities have greater stability across phenophases than those of leaves . Additionally, core taxa had phylogenetically related trends within the high-, medium-, and low-stability classifications, indicating that conserved, vertically descended microbial traits may play a role in determining bacterial and fungal associations across phenophases, particularly in above ground leaf tissue.We completed a genus-level differential relative abundance analysis on our list of core taxa that were $0.01% relative abundance and $75% prevalence in one or more phenophases.

Our differential relative abundance analysis can determine finer-scale phenophase associations beyond just classification as a core microbiome member by looking for increases in relative abundance, proportionate to other members of the microbial community , during specific phenophases. Ecologically dominant taxa are predicted to have a proportionately larger contribution to community function. Among all the phenophases, those associated with flowering had striking microbial enrichments, particularly among the leaf bacteria. Acinetobacter was a core member of five phenophases but was significantly enriched during full flowering compared to other phenophases . Acinetobacter had a gradual enrichment from flush and floral bud development to full flowering. This gradual enrichment signature indicates that Acinetobacter was present throughout the year but has a high temporal turnover rate that is in sync with the transitions from flush to floral bud development and then to full flowering. We also observed bacteria that were sharply enriched during full flowering rather than undergoing gradual enrichments over the phenophases that lead up to full flowering . These include Snodgrassella, Frischella, Gilliamella, and Bifidobacterium . The sharp enrichment patterns during full flowering suggest that these taxa were introduced into the community via a dispersal event.We also identified bacterial leaf genera that had significant depletions during floral bud development and/or full flowering . Four Actinobacter genera, Corynebacterium, Dietzia, Georgenia, and Ornithinimicrobium, were significantly depleted during floral bud development and full flowering . Bacillus, Methylobacterium, Romboutsia, and Sphingomonas also significantly decreased in relative abundance during floral bud development and/or full flowering . For all differentially abundant genera, including bacteria and fungi, across all phenophases, see Fig. S5 and Table S4.We performed a network analysis on the foliar bacterial communities from all samples. We focused on significantly enriched and/or depleted populations and populations with direct connections or putative first-degree interactions . The goal of this approach was to give a broad overview of bacterial interactions across phenophases and identify taxa that potentially interact with specific phenophase-enriched taxa and potentially play a role in observed seasonal community compositional shifts. Rhizobium, Sphingomonas, an unknown bacterium, an unknown Bacillaceae , Acinetobacter,and Romboutsia have the highest normalized betweenness centrality scores ranging from 0.110 to 0.187. Betweenness centrality is a proxy for influence within a network because it measures how often a particular node is the shortest connection or bridge between two other nodes. These high betweenness centrality scores and placement within the network indicate that these genera are potentially keystone taxa that may perform a stabilizing role in the microbial communities across phenological transitions and events . Groups of taxa connected by putative positive interactions cluster together to form distinct modules. These modules are separated by putative negative interactions. Our analysis organized bacterial taxa that were enriched in fruit set and fruit development into a single highly connective community module .

This suggests that fruit set- and fruit development-associated microbiomes are compositionally similar and few microbe-microbe interactions change during the transition from fruit set to fruit development. Leaf bacteria associated with flowering also formed a module within the network . Specific bacteria within the fruit set/development and flowering modules also interact with taxa that were enriched in the other four phenophases, blueberries in pots which cluster together into a third module . Overall, these predicted positive interactions represent inter- or codependent microbe-microbe relationships, and the putative negative interactions indicate potential direct or indirect competition. These predicted microbe-microbe interactions within the microbiome likely affect community compositionin addition to the exogenous influences of abiotic environmental conditions and biotic host physiological factors .The majority of studies examining how plant developmental stage affects the plant’s microbiome have focused on bacteria associated with the rhizosphere of annuals or herbaceous perennials such as maize , rice , sorghum , wheat , Arabidopsis , and Boechera . These important studies indicate that rhizosphere-associated microbiomes can shift in association with plant developmental stages in both domesticated and wild plants that have short-lived aboveground tissues. Studies of the endophytic xylem sap microbiome in grapevine, a deciduous perennial, also showed that microbial shifts were linked to changes in phenological stage . However, much less is known about how overall plant phenology affects above- and belowground microbiomes of evergreen woody perennials that have lifespans that can be decades long and can retain their leaves for multiple years, compared to annuals or deciduous perennials that produce and shed all their leaves each season. Here, we investigated microbiome compositional dynamics in above and below ground tissues of mature 20-year-old Citrus sinensis trees to determine if temporal microbiome fluctuations were associated with host phenological events. The unique contribution of our research was the separation of leaf development from tree phenology. We did this by analyzing the changes in the foliar microbiome on fully mature leaves, which developed in the leaf cohort from the previous year, in relation to the phenological stages of the current year. Thus, the leaves were exposed to the same starting inoculum, minimizing the bias of any potential priority effects . Our results indicate that the phyllosphere microbiome has an active and dynamic relationship with host phenology. More specifically, microbial shifts occurred as trees transitioned from the spring leaf flushing stage and entered flowering. The transition from spring flush to floral bud development and full flowering aligns with important transitions in source-to-sink transport of photosynthate in the tree . During foliar flushing periods, young leaves are a primary carbohydrate sink as they rapidly expand and mature. This source-to-sink transport of photosynthate shifts during floral bud break and development, when mature leaves transition to serve as source tissues to developing floral tissues that are also primary sink tissues. In addition to changes in source-to-sink transport, there are also significant changes in water dynamics within the canopy of the tree associated with full flowering. Flowers have the highest transpiration rate of the tree even compared to the leaves, which drastically increases the amount of water being transported into the overall canopy of the tree . Interestingly, the significant shift in overall foliar community composition from flushing to full flowering was not coupled with a change in species richness, indicating that the same taxa were present, just in different relative abundances in relation to one another. This demonstrates that foliar microbiome assemblage is changing in sync with tree physiology and development. Empirical data, including presence/absence and relative abundance, can also be used to infer patterns or microbial enrichments and/or depletions relative to othertaxa in the community, as well as ecological mechanisms that contribute to plant microbiome assembly, such as microbial species turnover and dispersal . Interestingly, microbial enrichment and depletion patterns of specific taxa suggest that microbial species turnover and dispersal events within the citrus microbiome occur in sync with phenological stage transitions. These enrichment/depletion patterns for specific taxa were more apparent in leaves than in the root compartment. Specifically, the bacterial genus Acinetobacter was enriched in leaves as trees transitioned from spring flush to floral bud development and peaked in relative abundance during full flowering, which is when mature leaves shift toward becoming source tissues for developing flowers and fruit. This may create a microenvironment that selects for an increase in relative abundance of these taxa when carbohydrate is translocating out of the leaves.

Several studies have compared hydrolyzable tannin profiles in developing pomegranate fruits. However, the fruit developmental stages were defined by different standards, such as days after fruit set/full bloom, physico-chemical properties, or physiological attributes of the fruit . Developing fruits of two cultivars, “Wonderful” and “Rosh Hapered,” grown in Israel were collected during a span of 8 or 10 weeks . Three major hydrolyzable tannins, gallagic acid, punicalin isomers , and punicalagins, were quantified in water extracts of the developing fruits. Fruits of “Ruby” grown in South Africa were harvested at five stages according to days after full bloom . Total hydrolyzable tannins in aril juice declined during the progression of fruit maturation, and were accompanied by decreases in ellagic acid and gallic acid . Relative amounts of hydrolyzable tannins in fruit peel, aril juice, and seed of developing pomegranate fruits were also investigated. Fruits of the Chinese cultivar “Taishanhong” were harvested at 10-day intervals for nine collections. Unicalagins, ellagic acid, and gallic acid were higher in fruit peel than aril juice and in seed; all three metabolites showed decreased accumulation in the three tissues during fruit development . When quantified by absorption of the methanolic extracts at 550 nm, total hydrolyzable tannins gradually decreased in fruit peels at low, low-medium, medium, plant pots with drainage and medium-high stages of the Spanish cultivar “Mollar de Elche.” In contrast, they were not detectable in aril juice at all stages.

In seeds total hydrolizable tannins increased at medium and then decreased at medium-high stages . Overall, despite the differences in the genetic background, growth conditions, harvesting scheme, and extraction and quantification methods, there is a consistent trend of decreasing hydrolyzable tannin accumulation in fruit peels, aril juice, and seed through pomegranate fruit development.To understand the impact of growth environment on hydrolyzable tannin profiles, fruit peel and aril juice hydrolyzable tannins were compared for 11 accessions grown in the Mediterranean or desert climate in Israel . Mediterranean climate promoted high levels of hydrolyzable tannins in aril juice in most of the accessions evaluated; in contrast, desert climate had a positive impact on hydrolyzable tannins in fruit peels . It was reported that the sweet/sour phenotype and environment interactions had the most influence on total tannin variations in aril juice of 10 commercial cultivars grown in four different regions in China, followed by the growth environment . There were negative correlations of overall average temperature with total polyphenol, total tannin, and punicalagin concentrations. The sweet/sour phenotype only accounted for 0.06% of the variations in tannins among different cultivars . The quality of aril juice under deficit irrigation was investigated in Spain . Three water regimes were applied to pomegranate trees at 75% evapotranspiration , 43% ETo , and 12% ETo . Water stress drastically decreased punicalagins, causing 30 and 70% reduction in moderate and sever stresses, respectively, in aril juices of fruits harvested from the corresponding trees . This study provided valuable information on the implications of water stress on the hydrolyzable tannin metabolism and the nutritional value of aril juice.Plants have developed diverse mechanisms to regulate their biological and metabolic processes via transcription factor regulatory networks .

Among the TF families, the basic leucine zipper family is present in all eukaryotes and is one of the largest and most diverse TF groups in higher plants. There are about four times more bZIP genes in the Arabidopsis genome than in the genomes of other model organisms such as Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster . Large numbers of bZIP TF family members have been found in many plant species including rice , maize , tomato , common wheat , sorghum , soybean , banana , cassava , grape , peach , strawberry , apple , rapeseed , radish , cucumber , tea plant , sweet potato, watermelon/melon , Chinese jujube , pepper , Chinese pear , poplar , quinoa and plum . The bZIP family is phylogenetically categorized into different groups, with different species having various members of homologs. For example, the Arabidopsis AtbZIP family members were systematically classified into 10 groups based on conserved motifs . Subsequently, a more complete classification was expanded into 13 groups, designated as A-J, M, and S . The tomato SlbZIPs were classified as nine clades . The cucumber CsbZIPs and sorghum SbbZIPs were separately categorized into six and seven groups . The bZIP family in both rice and maize has 11 groups which are the same as castor bean . The plum PmbZIP proteins were divided into 12 groups . Chinese pear PbbZIPs were categorized into 13 groups . Several interspecies clustering studies indicate that the S group found in Arabidopsis has especially high homology across different species , although some clades might be specific to Arabidopsis compared to peach, strawberry, and apple . These classifications, phylogeny, and homology analyses define the possible biological roles of bZIPs in green plant evolution .

Basic leucine zipper TFs orchestrate a diverse array of functions in multiple biological processes including flower development and pollen development , seed maturation , senescence , light signaling , anthocyanin and chlorophyll biosynthesis , nutrient signaling , hormone signaling such as salicylic acid, ABA, ethylene, auxin, and cytokinin , sugar signaling , and abiotic/biotic stress signaling in plants. Group S is the largest bZIP subgroup in several species such as Arabidopsis and safflower and comprises three to four even smaller subgroups. In this review, we focus on the well-studied S1-bZIP subgroup, whose members contain unique conserved upstream open reading frames in the 50 region of their transcripts and play important regulatory roles in many metabolic processes relating to fruit quality and stress responses. Our review aims to provide perspectives for further surveying the biological function, exploring regulatory mechanisms, and genome engineering the S1-bZIPs to obtain desirable traits for quality improvement in horticultural plants.Of the AtbZIPs, the 17 members of the S group are further separated into three subgroups based on homology: S1, S2, and S3 . The S1 subgroup in Arabidopsis contains five members: AtbZIP1, −2, −11, −44, and −53. Recent studies indicate that other species, including many horticultural plants, also have multiple members of the S1- bZIP subgroup . Like other bZIP members, those in the S1 subgroup are characterized by a conserved bZIP domain, composed of two functionally distinct motifs located on a contiguous α-helix. The basic region of −18 amino acids contains, sequentially, a nuclear localization signal and an invariant N-x7-R/K-x9 motif for DNA binding. This motif preferentially binds to the A-box, C-box, and G-box of target promoters which contain DNA sequences with an ACGT core . The leucine zipper comprises a heptad repeat of leucines or other numerous hydrophobic amino acids . Compared to other groups, members of the S group include the extraordinarily high number of eight hydrophobic amino acid repeats . The two subunits form a zipper structure that binds DNA to form dimers through interactions with the hydrophobic sides of the helices . Of three S subgroups, only members of the S1 subgroup show specific heterodimerization with C group bZIP proteins , whereas weak homodimerization within members of the S1 subgroup is detected . Phylogenetic analysis between S1 and C group bZIPs from angiosperms, gymnosperms, mosses, and algae suggests that the S1 and C groups evolved from a proto-S/C bZIP in algae species that homodimerized, which has since diverged into heterodimerizing pairs prior to the evolution of seeds plants .Besides their common structural features, S1-bZIPs are unique in that they have an unusually long 50 -leader sequence in the upstream region of the main open reading frame of the mRNA. This leader sequence contains several upstream open reading frames that encode small peptides . Among those, the second uORF is conserved and encodes a Sucrose Control peptide of 28 residues, plastic plants pots which regulates the translation of the mORF and reduces protein expression through a mechanism known as Sucrose-Induced Repression of Translation , which contributes to sucrosehomeostasis in the cells . Here, we summarize uORFs of the S1-bZIP subgroup from different horticultural plants, including banana , grape , apple , peach , cucumber , strawberry , petunia, and white pear . The regulation of gene expression involves different layers, including transcriptional and translational controls . Compared with transcriptional regulation, translational control allows more immediate responses to adjust protein expression and reprogram metabolism upon cellular signals or environmental stimuli . The translation process of mRNA includes four major steps: initiation, elongation, termination, and ribosome re-initiation . Translation initiation is the major step that determines the rate of protein biosynthesis and is regulated by multiple mechanisms . uORFs have been suggested to play a critical role in regulating the translation of the mORF . uORFs of S1-bZIPs are involved in the translational regulation in a SIRT manner .

The SC-peptide encoded by the uORF in the 50 leader region of AtbZIP11 is capable of repressing translation of the subsequent mORF in the presence of sucrose . High sucrose levels enhance ribosome stalling on the uORF, which results in poor translation of the mORF . The members of the Arabidopsis S1-bZIP subfamily show similar responses to sucrose. Translation of AtbZIP1, AtbZIP2, AtbZIP11, AtbZIP44 and AtbZIP53 is down regulated by sucrose . Transgenic seedlings with 35S:bZIP11 50 leader::LUC show significantly reduced luciferase activities when treated with sucrose while those incubated in media lacking sucrose show two- to three-fold higher luciferase activities . SIRT-mediating S1-bZIP orthologs exist in all seed plants .Previous research showed that amino acids such as serine, leucine, and tyrosine in the conserved peptide of uORF are essential for SIRT . However, it has been shown that expressing the gymnosperm 50 uORF sequence, which only contains the conserved leucine and tyrosine in Arabidopsis cells efficiently mediates the translational repression of the LUC reporter gene in response to sucrose . This study suggests that the SIRT mechanism most likely depends on structural conformation, but not on recognition of specific sequence motifs . Recently, interesting research conducted using gene-editing technology in strawberry demonstrated that uORFs are involved in regulating protein translation efficiency and sucrose content . In the study, to manipulate the SC-uORF of FvebZIPs1.1, the start codons of the uORF and the codons encoding a conserved pair of amino acid arginine within the SCpeptide were edited using the CRISPR/Cas9 system. Mutations in the start codons and the conserved C-terminal region of the SC-peptide significantly reduced translation of the SC-uORF. This consequently enhanced the translation efficiency of the downstream mORF. Seven novel alleles with C-to-T substitutions and small deletions within the uORF were identified. To test if phenotypic effects were additive in heterozygous and biallelic plants, 4000 T1 seedlings were generated by crossing the biallelic and homozygous T0 mutants to each other and to wild type. 35 novel genotypes were obtained in T1 and inherited in T2 generation. In comparison with wild-type fruits, the mutants had significantly higher levels of fructose, glucose, and total sugar contents, demonstrating that engineering the conserved SCuORF of FvebZIPs1.1 can increase the sugar content in strawberry . In addition, the citric acid content was slightly lower in the homozygous mutants than that in wild type. A continuum of gradual increase of sugar contents was generated in T1 by combining heterozygous, homozygous, and biallelic mutants, and inherited in T2 generation by propagating stolons of these T1 mutants, therefore confirming the transmissibility of novel genotypes and phenotypes from T1 to T2 by asexual propagation . Given that sugars can modulate multiple growth and development processes, the agricultural traits including leaf shapes, leaf areas, plant height, growth rates, pollination, fruit size and fruit weight were further evaluated in FvebZIPs1.1 uORF mutants. Remarkably, editing SC-uORF does not severely impair plant growth. The agricultural traits in FvebZIPs1.1 uORF mutants were similar to wild-type , whereas impaired phenotypes and retarded growth are observed in transgenic lines with the overexpression of AtbZIP11, tbz17, and FvbZIP11 mORF . Taken together, this suggests a broad application of editing uORFs of S1-bZIPs for quality improvement in horticultural plants.Amino acids are not only involved in plant response to stress but also influence fruit flavor . For example, asparagine is present in almost all fruits and determines fruit flavor and quality in a concentration dependent manner . Glutamate is responsible for “umami” or savory taste . Glycine, alanine, serine, threonine, proline, glutamine, and lysine are highly correlated with sweetness , while phenylalanine and tyrosine are bitter .