The gene expression patterns of CNR, NOR, and RIN across ripening stages were decreased or delayed in each of the single ripening mutants.The most substantial variation in gene expression was the downregulation of NOR and RIN expression across all stages in the Cnr mutant . We present for the first time double ripening mutants, homozygous for both loci, that can be used to see the combined effects of each mutation on fruit development and quality traits. We successfully generated the double mutants by establishing reliable and high throughput genotyping protocols for each mutation and evaluating segregation of the mutant phenotypes in field trials across multiple growing seasons. We obtained double mutants from both reciprocal crosses but saw no fruit phenotypic differences between them, suggesting that the ripening mutations are not influenced by maternal or paternal effects . Because the nor and rin mutants look so similar, it was hard to visually determine the individual effects of each mutation on the appearance of rin/nor fruit. However, when specific fruit traits were measured, we could detect additive or intermediate fruit phenotypes in this double mutant, supporting the proposed relationship in Wang et al. ; Figure 2.5. Thus, nor and rin appear to influence similar fruit traits and act in coordination. The Cnr mutation had a significant effect on the Cnr/nor and Cnr/rin mutants resulting in fruit with similar appearance and ethylene production to the Cnr fruit . When analyzing the gene expression profiles of the Cnr/nor fruit, round nursery pots we also observed multiple similarities to the Cnr parent, but also several deviations .
Surprisingly, Cnr/nor was also reminiscent of nor, as it displayed few ripening-related gene expression changes, suggesting the inhibition or delay of specific ripening events in nor carried over to the double mutant. Here, we proposed that the Cnr mutation causes defects throughout fruit development while the nor mutation causes defects predominantly in ripening. However, the Cnr/nor double mutant showed additional phenotypic and transcriptional defects before ripening than both mutant parents . These observations indicate that in combination with Cnr, nor may contribute to alterations in early fruit development and the inhibition of ripening progression.Pistachio world production has more than doubled over the past two decades, with over 1 million metric tons of pistachio fruits produced in 2021 . The United States produced 47% of the world’s pistachios in 2021, with California comprising 99% of the national total . Pistachios provide numerous health benefits to humans including decreased risk of coronary heart disease and can aid in weight loss . In addition to its economic and nutritional importance, pistachio is highly adaptive to stress and grows well in arid climates, making it a sustainable option for fruit production with continued climate changes . Pistachio belongs to the Anacariaceae family, which includes mango, cashew, and sumac from other genera. Among the 11 species in the Pistacia genus, P. vera is the only edible and commercially used species. The Kerman cultivar has been considered the industry standard in the United States with the highest proportion of acreage in California . Pistachio fruits are dehiscent drupes composed of three main tissues: a leathery exo-mesocarp , a stony endocarp , which encloses a seed . Three stages of fruit growth have been previously proposed . Cell division and expansion of the hull and shell was reported to occur rapidly from April to late-May. As soon as the fruits reach the final size, fruit growth appeared to be reduced from late-May to early-July and lignin accumulates in the shell . The remaining part of the season was believed to be dominated by the growth of the kernel .
While this understanding of pistachio growth has guided research and production in the past, it was defined using a few parameters, like fruit and embryo size. Thus, to date, a comprehensive analysis of the dynamics of the pistachio fruit and embryo development is lacking. The coordinated development of pistachio fruit tissues is required for high-quality, marketable fruits. Quality attributes include a soft detachable hull without signs of deterioration, hard and split shells, and a flavorful kernel free from damage. Pistachio fruit physiological and compositional studies have described aspects of pistachio quality with particular emphasis on mature fruits at or near the time of harvest . Like many fruit, color changes in the hull can serve as an index of harvest, changing from green-yellow to shades of pink at the end of the growing season . Pistachio shell quality relies upon the endocarp hardening and splitting before harvest, as well as being free from stains caused by the deteriorating hull. Pistachio kernels are naturally rich in antioxidants, vitamins, and unsaturated fatty acids essential for the human diet . Both mono- and poly-unsaturated fatty acids accumulate in the kernel and are important for the human diet, as polyunsaturated fats cannot be synthesized by the body. To date, there is no knowledge of the genetic basis of these quality traits in the fruit and how these compounds are accumulated in the kernel. While the pistachio industry has grown exponentially over the past 50 years in the US, there is a limited body of research in fruit development and the mechanisms behind key quality traits to inform breeding, production and management. Here, we present a study integrating physiological, genomic, transcriptomic, and biochemical data to define pista-chio fruit development and identify biological processes that contribute to fruit quality, such as hull softening, shell hardening, and kernel fatty acid composition. A high-quality genome and annotation were fundamental for analyses in this study; however, while improvements have been made over the past three years the existing genome is incomplete spanning . We, therefore, assembled a reference-quality chromosome-scale genome for P. vera cv. Kerman using PacBio HiFi long reads and chromatin interaction information with Dovetail Genomics Omni-C data. We integrated Isoform sequencing data from multiple tissue types and RNA sequencing data from two fruit tissues across multiple time points to obtain the most complete genome annotation for pistachio to date. We assessed physiological and metabolic changes in the hull, shell, and kernel tissues with a comprehensive 24-week study encompassing the entire growing season and generated gene expression data for hull, shell, and kernel tissues for 15 of those weeks. The datasets produced allowed us to construct networks of coexpressed genes for each developmental stage and connect genes and molecular pathways to specific fruit traits. Overall, our study provides the first understanding of genetic networks governing pistachio fruit development and defines the processes occurring during fruit ripening that determine fruit quality traits.High molecular weight genomic DNA was extracted from young leaves of ‘Kerman’ tree using Circulomics Nanobind Plant nuclei kit .
The library construction and PacBio Hifi sequencing were completed on a Sequel II system at Gentyane facilities in the French National Institute of Agronomy . For Omni-C data, plastic flower pot young leaf tissue samples were collected, directly snap-frozen in liquid nitrogen, stored at -80℃, and sent to Dovetail genomics for the Omni-C library construction and sequencing. For Iso-seq sequencing, samples of fruits, leaves, inflorescences, and dormant and developing buds were collected and immediately stored in liquid nitrogen. 100 mg of material for each sample was pulverized with a pestle and mortar in liquid nitrogen. Total RNAwas extracted by using the Spectrum Plant Total RNA Kit, according to the protocol recommended for “difficult” species. The quantity and purity of the total RNA were checked with the Nanodrop . Iso-seq sequencing was performed using RNA samples with 400 ng/ul at Gentyane facilities in INRAE.A de novo assembly of PacBio HiFi reads into contigs was performed using Hifiasm v0.16.0 with default parameters . We then manually filtered out organelle origin contigs from the primary contig assembly using Kerman plastid and mitochondrial sequences in Geneious Prime . The high-coverage Omni-C data was first quality-checked with FastQC toolkit and aligned to filtered primary contig assembly using Juicer v1.6 . These aligned read pairs were utilized to scaffold the assembly into 15 chromosomes based on the Omni-C chromatin interaction data with 3D-DNA and manual correction on Juicebox . We scaffolded filtered primary contig assembly again with 3D-DNA scaffolds as a reference using RagTag v2.1.0 followed by manual correction by comparing both RagTag and 3D-DNA scaffolds . The completeness of assemblies, scaffolds, and 15 chromosomes was assessed using BUSCO v5.4.4 with the embryophyta-db10 database .Pistachio physiological data collected across three field seasons were collected for evaluation from an experimental pistachio orchard with 30 year old trees at the Kearney Agricultural Research and Extension Center in 2019. The results were validated in a commercial orchard with 10 years old trees of the same cultivar in Woodland, CA in 2020 and again in 2021 in a separate commercial orchard with trees 10 years old located in Three Rocks, CA . In each orchard the Kerman female was cross pollinated by the Peters variety male trees. In each study, trees were randomly selected across the orchard and were continuously sampled throughout the season. Four whole rachis clusters yielding about 50 fruits of uniform maturity were collected per tree at each sampling. To assess fruit area and color, fruits were imaged longitudinally using a VideometerLab 3 facilitated by Aginnovation LLC. VideometerLab 3 software was utilized for image analysis in both 2019 and 2020. Color measurements were taken on the L*a*b* color scale as an average across the entire fruit area. For both color and area, 10-30 fruits per tree were sampled weekly for 25 weeks. Growth was determined through fresh weight and dry weight measurements of the whole fruit and the kernels. Fresh weight was taken the day of harvest of 10 fruits per tree and the average per fruit weight was calculated. Fruits were cut open and separated into kernels and the remaining tissues . Fruits were put in a drying oven at 80℃ for 2 days until all moisture was evaporated and measured. The average per whole fruit and per kernel weights per cluster were calculated from the total weight and total number of fruits. Shell split was measured as the incidence of fruits with any degree of separation between sides and taken as a proportion of the total fruits. Destructive texture measurements were obtained with the use of a TA.XT2i Texture Analyzer using a TA52 2mm probe with a trigger force of 5 g and test speed of 2.00 mm/sec with Exponent software . The probe punctured through the hull, shell and kernel tissues and peaks of each tissue were distinguished and recorded by the software. Measurements were reported as kilograms of force. 20 to 60 fruits were assessed for each sampling for 25 weeks. Fat content was obtained from oven-dried kernels, as described in . Briefly, dried pistachio kernels were ground in a coffee grinder , weighed into a cellulose extraction thimble, placed in the Soxhlet extractor, and extracted using n- hexane for 6 h. The solvent was distilled and residual solvent eliminated in an oven at 105 ℃ for 3 h. Fat content was expressed as grams of fat per 100 grams.Physiological parameters including kernel and fruit dry weights , kernel and fruit areas , fruit colors , and kernel, shell, and fruit textures were modeled against heat accumulation . Various Box-Cox transformations on traits data using MASS package in R, were made before modeling to ensure an approximate normal distribution of traits and roughly equal variance of the error terms. Outliers were removed prior to model fitting for trait nut area. Initial model fitting started with a linear model of polynomial 3 of heat accumulation, and stepAIC function was used in R with criterion BIC . The upper and lower bounds of the model were polynomial 7 and 1, respectively. We chose the model with the lowest possible BIC value. The final model selection results were capped at polynomial 3 to avoid model complexity, overfitting, and difficulty for interpretation. As a result, linear models and linear mixed models were fitted for each trait with a polynomial of 2 or 3 as a function of heat accumulation, using lme4 and lmerTest packages in R . Random intercepts were added in linear mixed models. Random effects included cluster, tree, and year depending on the models .Fruit tissues from 15 of the 24 weeks assessed for physiological data were separated into the hull, shell and kernel and were each flash frozen on the day of sampling.