Horticulture

TA percentages were quantified with a Metrohm Robotic Titrosampler System from 1 to 5 ml of the defrosted homogenized fruit juice . SSC was measured from approximately 200 ml of juice on an RX-5000a-Bev Refractometer . Total AC was measured from a 25 ml sample of juice in 200 ml 1% HCl in methanol by reading absorption at a wavelength of 520 nm on a Synergy HTX platereader equipped with Gen5 software . A standard curve was calculated for quantifying AC using a dilution series of pelargonidin from 0 to 300 mg/ml in 50 mg/ml increments, where y was absorption readings for the perlagonin dilution series, s was the slope, x was the concentration of perlagonin inthe dilution series, and i was the intercept. AC was estimated by ðA iÞ=s, where A was the absorption reading.Gray mold incidence ranged from 0.0% to 2.7% among cultivars at 14 dph, a typical postharvest storage duration for LSL cultivars. The five day-neutral cultivars were screened out to 21 dph to develop insights into the post harvest storage limits for modern LSL cultivars . Although the fruit were still marketable at 14 dph, 30 litre plant pots they became marginally marketable or unmarketable by 17–18 dph .

We observed an exponential increase in gray mold incidence beyond 17–18 dph for every cultivar with means ranging from 10.3% to 36.7% among day-neutral cultivars at 21 dph. These studies showed that gray mold was ubiquitous and eventually rendered the fruit unmarketable but that the natural incidence of gray mold was negligible on fruit of LSL cultivars grown in coastal California within the 14 dph storage window . From common knowledge and earlier surveys of phenotypic diversity for resistance to gray mold , we hypothesized that the low incidence of gray mold on commercially produced fruit of LSL cultivars might be genetically correlated with fruit firmness and other fruit quality traits affecting shelf life . Although phenotypic correlations have been reported , genetic correlations have not. The fruit of LSL cultivars are typically much firmer than the fruit of SSL cultivars commonly grown for local or direct-market consumption, as exemplified by Earlimiss, Madame Moutot, and Primella in the present study . The latter are sweeter, softer, and perish more rapidly than “Royal Royce” and other LSL cultivars under normal post harvest storage conditions . To explore how these phenotypic differences affect resistance to gray mold, we developed a training population for GS studies by crossing “Royal Royce,” one of the LSL cultivars assessed for natural infections , with four SSL cultivars , in addition to crossing a pair of LSL parents with differences in fruit firmness and AC .

These full-sib families were phenotyped for resistance to gray mold using an artificial inoculation protocol and genotyped with a 50K Axiom SNP array .Natural infections are too inconsistent and unreliable for analyses of the genetics of resistance to gray mold in strawberry. To overcome this problem, we developed a highly repeatable artificial inoculation protocol for gray mold resistance phenotyping that involved puncturing fruit with a 3-mm probe, propagating spores of a single B. cinerea strain , introducing a known concentration of spores into the wound site, and monitoring disease development on individual fruit stored undisturbed under high humidity . Two quantitative B. cinerea disease symptoms were recorded on multiple fruits harvested from training population individuals: water-soaked LD in mm and the number of dpi when EM was observed on the surface of the fruit. We found that incubating artificially inoculated fruit at 10 C and 95% humidity in the dark yielded highly repeatable results with minimal contamination from other post harvest decay pathogens. LD and EM were recorded daily from 1 to 14 dpi . This protocol produced highly reproducible results with repeatability estimates in the 0.66–0.83 range for LD and 0.68–0.71 range for EM . Although critical for maximizing repeatability, this protocol produced more severe disease symptoms than those commonly observed from natural infection, especially on non-wounded fruit of firm-fruited LSL cultivars .

To study the genetics of resistance to gray mold in strawberry, artificially inoculated fruit of individuals in multifamily and Royal Royce  Tangi populations were phenotyped daily for LD and EM over 14 days in cold storage . The speed of fungal development and symptom severity differed among individuals in both populations . The phenotypic extremes we observed are illustrated in time-series photographs of four individuals from the upper and lower tails of the LD and EM distributions in the multifamily population . Lesions became visible and had enlarged to 10.0 mm by 5 dpi in one of the most susceptible individuals , whereas lesions were not visible until 8 dpi and developed the slowest in one of the least susceptible individuals . Lesions spanned the entire fruit surface of the most susceptible individuals by 8 dpi, thereby resulting in significant post harvest fruit deterioration and fungal decay . Consequently, our genetic analyses of LD were applied to phenotypes observed 8 dpi, the last day in the study that resistance phenotypes could be observed for every individual. As expected, our analyses confirmed that resistance to gray mold is genetically complex in strawberry, a finding consistent with observations in other hosts . Although statistically significant differences were observed among individuals for LD and EM in both populations , every individual was susceptible and the phenotypic ranges were comparatively narrow . LDs were approximately normally distributed and ranged from 7.0 to 33.5 mm at 8 dpi in the multifamily population and 7.0 to34.0 mm at 8 dpi in the Royal Royce  Tangi population , a few in Supplementary Figure S2. Similarly, the speed of appearance of teresting candidate gene associations were identified when short mycelium on the surface of the fruit was approximately nor- QTL-associated haploblocks were searched in the reference gemally distributed and ranged from 4.0 to 12.5 dpi in the multifam- nome for genes with biotic stress and disease resistance annotaily population and 5.5 to 12.5 dpi in the Royal Royce Tangi tions . A cluster of 11 tandemly population . The repeatabil- duplicated genes encoding pathogenesis-related proteins ities for LD and EM among individuals in these populations sug- were found in close proximity to the most significant SNP associated with a QTL on chromosome 4A . observed for gray mold resistance was genetically caused These genes share sequence homology to FcPR10, an Fragaria chi- . Narrow-sense genomic heritability estimates ranged loensis ribonuclease encoding gene previously predicted to reduce from 0.38 to 0.71 for LD and 0.39 to 0.44 for EM, 25 liter pot plastic which suggested the severity of gray mold disease in strawberry . The other QTL-associated candidate genes thatmight warrant further study encode peroxidases reported to modulate reactive oxygen species levels and inhibit fungal growth during B. cinerea infections and transcription factors reported to signal pathogen-triggered immunity, e.g., WRKY and AP2/ERF , that might target pathogenicity factors, e.g., chitinases and protease inhibitors . While these genes are worthwhile candidates for further study , the effects of the associated QTL were too small and insignificant for direct selection . This was, nevertheless, a first attempt to identify loci underlying resistance to B. cinerea in strawberry through a genome-wide search for genotype-to-phenotype associations in the octoploid genome . Royal Royce, the firm-fruited LSL parent, was more resistant to gray mold than the soft-fruited SSL parents .

Royal Royce was the more resistant parent for both traits in the four full-sib families with that parent . For the 05C197P002 16C108P065 full-sib family, 05C197P002 was more resistant than 16C108P065 for LD and vice versa for EM. The LD and EM differences were highly significant with individuals transgressing the phenotypic ranges of the parents . Transgressive segregation was primarily bidirectional for both traits; however, the EM distributions for Royal Royce Tangi and 05C197P002 16C108P065 were right-skewed toward more resistance and lacked individuals in the lower tails distal to the more susceptible parent . These results suggested that favorable alleles were transmitted by both parents for both traits and that favorable alleles for different loci segregated in most of the families.Genomic prediction accuracies for different WGR methods ranged from 0.28 to 0.47 for LD and 0.37 to 0.59 for EM when estimated by cross-validation from 100% of the subsamples . The differences in accuracy among WGR methods for each trait-population combination ranged from 0.00 to 0.07. RKHS produced the highest accuracy for two of the trait-population combinations and was equal in accuracy to G-BLUP for the other two trait-population combinations. SVM often perfomed intermediate to both G-BLUP and RKHS. The prediction accuracy was greater for LD than EM in the Royal Royce Tangi population, whereas the reverse was observed in the multifamily population. Using cross-validation with 100% of the subsamples, clear differences in prediction accuracy and shrinkage were observed between disease symptoms within and between populations . The prediction accuracy for LD was markedly different between the multifamily and Royal Royce Tangi populations. The GEBV range for LD in the multifamily population was half as wide and the kernel density was flatter and more vertical than that observed in the Royal Royce Tangi population . Notably, the LD phenotypes of the most resistant individuals in the RR Tangi population were well predicted. Their EM phenotypes, however, were not as well predicted—the GEBV range for EM was half that of the phenotypic range and the kernel density distribution was flatter and more vertical . One of the challenges of breeding for resistance to gray mold and other postharvest traits is phenotyping throughput. Collectively, 2563 fruit were harvested and individually stored,tracked, and phenotyped in our study . Our expectation was that multiple fruit/individual was needed to more accurately estimate EMMs and GEBVs and nominally increase heritability. To assess the effect of subsamples on prediction accuracy and explore the feasibility of applying selection for resistance to gray mold from a single subsample/individual, GEBVs and prediction accuracies were estimated from a single randomly selected subsample/individual. We observed a significant decrease in narrowsense genomic heritability for LD and EM in the single subsample analyses, e.g., h ^2 decreased from 0.38 to 0.13 for LD and 0.39 to 0.16 for EM in the multifamily population . Naturally, prediction accuracies plummeted in the single subsample analyses too . This is clearly illustrated by the kernel density distributions for GS accuracy estimated for G-BLUP, RKHS, and SVM by cross-validation with a single subsample/individual . GEBV ranges were narrower and kernel density distributions were flatter and more vertical for the single subsample vs multiple subsample analyses for LD and EM in both populations . Hence, we concluded that breeding values cannot be accurately predicted without subsampling fruit.One of our working hypotheses was that selection for increased fruit firmness and other shelf life-associated fruit quality traits pleiotropically decreased susceptibility to gray mold in strawberry. The additive genetic correlations support this hypothesis and highlight between family differences driven by breeding history, the phenotypic diversity of the parents, and transgressive segregation . The pairwise breeding value distributions further highlight the family structure and phenotypic diversity within and among families. The fruit size, firmness, and TA by LD and EM breeding value distributions for the only elite  elite family in our study were distinct from the four elite  exotic families . LD and EM were weakly negatively genetically correlated and weakly to strongly genetically correlated with fruit quality traits in directions predicted by our hypotheses. Because gray mold resistance increases as LD decreases and EM increases, signs of the additive genetic correlations have different interpretations for LD and EM and can be antagonistic or synergistic. The interpretation depends on the specific phenotypes targeted for a particular market, e.g., SSL vs LSL. LD was negatively genetic correlated with titratable acidity and positively genetically correlated with pH ; hence, LD increased as titratable acidity decreased and pH increased . The effect of titratable acidity on resistance phenotypes was the motivation for screening additional individuals from the Royal Royce Tangi family, which had a significant genetic variation for TA and yielded more accurate genomic predictions for LD than were observed in the multifamily population . EM was more strongly positively genetically correlated with fruit size and firmness than LD and negatively genetically correlated with total soluble solids .

For whole genome sequencing a wild-type sugar apple fruit was purchased from a retail source in the United States, seeds from the fruit were planted, and one plant grown in the UC Davis Conservatory was sampled for sequencing with voucher herbarium samples stored as DAV225058 and DAV225059. The Hawaiian seedless line was obtained as budwood from Frankie’s Nursery , grafted to a wild-type A. squamosa rootstock and grown in the UC Davis Conservatory with a voucher sample stored as DAV225060.For genetic inheritance studies, three different wild types were used: M1, M2 and M3 and a seedless Bs line. The authors planned the crosses with different wild types for two propositians: inheritance studies and for initial steps in production of desirable seedless lines. Plants were grown at the Experimental Farm and molecular analysis was performed at Molecular Biology laboratory of the State University of Montes Claros, latitude 15º48′09’’S, longitude 43°18′32’’W and altitude 516 m. For phenotypic characterization of seedless versus seeded two strategies were applied: fruits were harvested, pulped, 30 plant pot and examined for the presence or absence of seeds ; or flowers either fresh or fixed in formalin acetic acid-alcohol were dissected to separate the ovules from the carpel tissue.

The wild-type ovules present a domed shape opposite the funiculs, while the mutant ovules come to a point at this position . Filial generations , self-fertilization , backcrosses with wild-types parents M1, M2, M3 , and backcrosses with mutant parent Bs were obtained. Segregations were evaluated for conformity to predicted ratios with the Chi-square test using the Genes statistical software .DNA was extracted from young leaf samples with hexadecyltrimethylammonium bromide buffer as described by Doyle and Doyle and separated from polysaccharides as described by Cheung et al. . Primers used in PCR are listed in Supplementary Table 1. PCR was performed with DreamTaq and the included reagents with an initial denaturation at 94 °C for 3 min; 35 cycles with denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s, and extension at 72 °C for 1.5 min; and a final extension of 72 °C for 4 min. For reactions using the AsINODel primers a 60° annealing temperature was used. PCR products were electrophoresed on 1.2% agarose buffered with 1×TBE or SB and DNA visualized by staining with ethidium bromide an illumination with ultraviolet light. For sequencing, PCR products were processed with ExoSAP-IT or Quiapure and sequenced using amplification primers on an ABI 3500 or 3730 genetic analyzer at Análises Moleculares Ltda. or the University of California Davis CBS DNA Sequencing Facility .Whole genome sequencing was performed by the North American author prior to initiating the current collaborative effort.

The lines available for sequencing were a wildtype North American commercial line and the Hs line, and these were used for this part of the analysis. DNA for whole genome sequencing was isolated from young leaves by grinding in 100 mM TRIS–Cl, 20 mM EDTA, 1.4 M NaCl, 2% CTAB, 1% each polyvinylpyrrolidone and sodium metabisulfte pH 8.0. Samples were treated with 70 µg/ml RNAaseA , extracted with 1:24 mixture of isoamyl alcohol and chloroform, and precipitated with isopropanol. Samples were dissolved in 10 mM TRIS pH 8.0, 1 mM EDTA, adjusted to 0.3 M Na Acetate, pH 4.8, precipitated with 2 volumes of ethanol and dissolved in 10 mM TRIS pH 8. Wild-type A. squamosa DNA was processed and sequenced at the University of California, Davis Genome Center . For PacBio sequencing, DNA fragments greater than 10 kb were selected by BluePippin electrophoresis and were sequenced on a PacBio RSII or Sequel Single Molecule, Real-time device. This resulted in 2.46 million reads with an average read length of 8 kb comprising more than 29 Gbases, or approximately 37 X genome representation. For Illumina sequencing the DNA was sheared and fragments of an average size of 400 bp were selected and sequenced on a HiSeq 4000 apparatus by the paired-end 150 bp method resulting in approximately 390 million sequences. The sequences were trimmed of poor quality regions and primer sequences with Sickle and Scythe , respectively, resulting in 229 Gbases or approximately 124 X genome representation. Hs DNA was similarly processed and sequenced by QuickBiology resulting in 408 million sequences and approximately 130 X genome representation.

The PacBio reads of wild-type DNA were assembled using Canu with default settings, producing 3519 contigs. The wild-type Illumina reads were aligned with the assembly using BWA MEM with default settings, and Pilon used the alignment to correct the contigs, changing 148 k single nucleotide errors and adding a net of more than 1.8 Mbases of insertions for a final assembly of 707.7 Mbases with an average contig length of 201 kb and an N90 of 93.9 kb. A BLAST search with the known A. squamosa INO gene sequence identifed a 587 kb contig containing the INO gene . BWA MEM aligned the Hs Illumina readswith the assembly and Tablet was used to examine the alignment with the 587 kb contig containing INO. BLAST was used to search one half of the set of Hs Illumina sequence reads for those extending across a detected deletion and the resulting sequences were aligned and assembled using Sequencher 5.4.1 .Genetic diversity was assessed among varieties of seedless sugar apple Bs, Ts, and Hs, with the fertile parent M2 as a contrasting control. Sixty-seven pairs of SSR microsatellite markers, described for A. cherimola were used, with fifteen having been described by Escribano et al. and fifty-two by Escribano et al. . DNA extraction was performed as described above for the markers association of seedless trait with INO deletion. Amplification utilized an initial denaturation at 94 °C for 1 min; 35 cycles at 94 °C denaturation for 30 s, annealing at 48–57 °C depending on the primer; and extension at 72 °C for 1 min; and a final extension of 72 °C for 7 min. The amplification products were separated by 3.0% agarose gel electrophoresis bufered, stained and visualized as above. To calculate diversity, the amplification data of the SSR primers were converted into numerical code per locus for each allele. The presence of a band was designated by 1 and the absence by 0. Although the microsatellite markers can be codominant, grow raspberries in a pot molecular analyses of the locus were performed based on the presence/absence of each amplified fragment. The established binary matrix was used to obtain estimates of genetic similarities between genotype pairs, based on the Jaccard coefficient. The Genes statistical program was used for data processing.The results of the phenotypic analysis of the parents M2 and Bs, F1, F2, backcrosses with the wild-type parent M2 and with mutant parent Bs are displayed in Table 1. In generation F1, all individuals presented fruits with seeds. In the F2 population, among the plants in reproductive stage during the evaluation period, 48 formed fruits with fully developed seeds and ten presented only seed rudiments, characterized by the absence of seeds . Considering segregation hypotheses expected for one, two and three genes , the Chi-square test revealed that the trait under study segregated at a 3:1 ratio , consistent with a monogenic inheritance. These results were corroborated by data from backcrosses with the parent M2 , where all plants that produced fruits had seeds, consistent with the 1:0 ratio, while the plants evaluated through back crossing with the mutant parent Bs , showed segregation of 1:1 for presence and absence of seeds. Taken together, these results corroborate the monogenic inheritance found in the analyses of F2 generations, indicating that a single recessive locus controls the seedless trait in Bs A. squamosa.

Previously described molecular markers for the presence of the INO gene were tested on parents M1, M2, M3, and Bs and displayed the expected band patterns. These markers generated amplification products only in the three wild-type parents, with no amplification of any fragment in Bs for any of the primer pairs used . The dominant marker LMINO primer-set was also used to amplify DNA from F1 plants obtained from crosses between genotypes of A. squamosa with the mutant Bs . All evaluated F1 individuals produced fruits with seeds in the feld and amplified the products with all primer pairs, as shown in the Supplementary Fig. 3B. The same procedure was applied in order to genotype segregating generations in seedling stage in the nursery . Figure 2 shows a sample of individuals amplified with the LMINO1/2 primers and the results confirm the discriminatory capacity of those genetic markers . The field confirmation of presence/absence of seeds in the fruits in these generations F2, BCM and BCBs was obtained later . In the F2 generations of the three crosses , there was a segregation of the products of the amplification of the LMINO markers that correlated exactly with the presence/absence of seeds. Fertile plants in this generation uniformly produced an amplification product with the LMINO1/2 primer set, while plants producing no product produced only seedless fruit . The same complete cosegregation pattern seen in F2 individuals for the presence/absence of seeds and PCR product was also observed in backcross populations of BCBs . For BCM backcross plants, the formation of INO amplification products was observed in all DNA samples tested for these uniformly fertile/seed bearing plants. The χ2 test was performed with the data generated in the molecular analysis to confirm the segregation of the dominant amplification . F1 plants displayed the expected genotypic ratio of 1:0 that had been linked to the trait of seeded fruits. In F2 generations, six segregation hypotheses expected for one, two and three genes were tested . Considering a signifcance of 5% probability, the frequencies of genotypes ft a ratio of 3:1, but allowed rejection of the other predicted ratios, confirming the hypothesis that a single locus confers the phenotype for the trait under study, with the dominant allele responsible for the presence and the recessive allele for the absence of the amplification product. To identify the homogeneity between the F2 crossings , statistical techniques were applied to verify whether the differences observed in the results could be explained by chance or not. The heterogeneity test was not significant and indicated, with a 55% likelihood, that the results of the χ2 were consistent for the populations of the three families studied, confirming the expected segregation . To further support the hypothesis of segregation in F2 generations, BCM and BCBs backcrosses were used. Similarly, the heterogeneity of segregation between the families of the BCBs backcrossing was not significant . BCM and BCBs progenies analyzed separately displayed segregation in a manner consistent with the hypothesis of a single gene. In BCBs backcrossing, carried out between generations F1 and the parent Bs, the proportion was close to 1:1 presence/absence of seeds in the fruits. χ2 test was applied and the deviations between the observed and expected frequencies were not significant. In the BCM backcrossing between generations F1 and parents , the proportion was 1:0 presence/absence of seeds in the fruits. These results confirmed the monogenic inheritance found in the analyses of F2 generations consistent with a single recessive allele being responsible for the seedless trait in A. squamosa considering the 3:1 segregation hypothesis.Whole genome shotgun sequencing was used to determine the characteristics of the INO gene deletion event. A draft wild-type A. squamosa genome was assembled through sequencing of total DNA isolated from a plant grown from seed derived from commercially available A. squamosa fruit. Genomic DNA was sequenced by both long-read Single Molecule, Real-Time sequencing and short read paired-end 150 base methods. The long reads were assembled into a draft sequence that was corrected with the higher coverage short read sequences. The resulting assembly comprised 707 Mbases of DNA in 3,519 contigs, with average contig length of 201 kb. A BLAST search with a previously published A. squamosa INO gene sequence was used to identify a 587 kb contig that included the INO gene . Total Hs A. squamosa DNA was used to produce a second short-read sequence set and this was aligned with the assembled wild-type sequence. Visualization of the alignment of the Hs sequences with the 587 kb contig including INO revealed a clear absence of reads over a region of 16,020 bp indicating a 16 kb deletion that included the INO gene . The alignment program truncates read sequences where they do not align with the reference sequence, so a deletion or a deletion with a heterologous insertion would appear similar in this visualization.

These cultivars were selected based on leaf shape as described in Tatiana’s TOMATObase and The Heirloom Tomato . Tomato seeds were treated, germinated, and field planted as previously described . In both the 2014 and 2015 seasons, plants were laid out in a randomized block design and were planted and grown in soil, with furrow irrigation once weekly. Gas exchange and intercepted PAR measurements Gas exchange measurements were done in the field on attached leaves after the plants had recovered from transplanting. Measurements were made weekly from week 10 to week 15 , on week 17 , and weeks 18– 21 , on c. 60 plants each week, on three plants per cultivar wk–1 . Measurements were made on leaves fromthe upper and lower portions of the plants to eliminate positional bias within the plant, and measured for three leaves per plant. The A , gst , transpiration, and ɸPS2 of a 6 cm2 area of the leaflet were measured using the LI-6400 XT infrared gas exchange system , and a fluorescence head . The chamber was positioned on terminal leaflets such that the midvein was not within the measured area. Light within the chamber was provided by the fluorescence head at 1500 µmol m2 s 1 photosynthetically active radiation , raspberry plant container and the chamber air flow volume was 400 µmols s1 with the chamber atmosphere mixed by a fan.

CO2 concentration within the chamber was set at 400 µmols mol1 . Humidity, leaf and chamber temperature were allowed to adjust to ambient conditions; however, the chamber block temperature was not allowed to exceed 36°C. Measured leaflets were allowed to equilibrate for 2–3 min before measurements were taken, allowing sufficient time for photosynthetic rates to stabilize with only marginal variation. The amount of intercepted PAR was measured in four orientations per plant and an average PARi calculated. PARi was measured by placing a Line Quantum Sensor onto a base made from ¼” PVC piping, and a Quantum Sensor approximately 1 m above the plant on the PVC rig. Measurements from both sensors were taken simultaneously for each sample using a Light Sensor Logger . This allowed variation in overall light intensities such as cloud movement to be measured and accounted for in the total PARi.After gas exchange measurements, three plants per cultivar were destructively harvested each week. The final yield and fresh vegetative weight of each plant harvested was measured using a hanging scale in the field. Five leaves were collected at random from the bottom and top of the plant to capture all canopy levels, and approximately nine fruit were collected for BRIX measurements. FW was used owing to the large number of plants and measurements being done in situ in the field setting.

All measurements were made in kg. To measure the BRIX value of the tomatoes, the collected fruit was taken to the laboratory where the juice was collected and measured on a refractometer . The yield and BRIX for each plant were multiplied together to get the BRIX 9 yield index , which gives an overall fruit quality measure, accounting for variations and extreme values in either measurement. It should be noted that while BRIX is used as a standard quality measure, BY is a composite value that folds in yield to assess weight of soluble solids per plant and is being used to measure commercial quality and not consumer quality . BY measurements were done for both the 2014 and the more detailed 2015 fields. These data were compared to test for reproducibility of results . Subsequently, primary leaflets were used for imaging and analysis of shape and size as previously described , and the images then processed in IMAGEJ . The images were cropped to individual leaflets maintaining the exact pixel ratio of the original image, and then cropped again to only include the single leaflet using a custom Java script written for FIJI. Single leaflet images converted to a binary image as black on a white background, and smoothed to allow for the exclusion of any particulates in the image were then processed in R using MOMOCS , a shape analysis package. Leaflet images were imported and then aligned along their axes so that all images faced the same direction. They were then processed using elliptical Fourier analysis based on the calculated number of harmonics from the MOMOCS package. Principal component analysis was performed on the resulting eFourier analysis and the principal components were used for subsequent analysis. Traditional shape measures such as leaflet area, circularity, solidity, and roundness were done with the area measurement based on pixel density. These measures were compared with the PCs to determine the characteristics captured by each PC.

The PC values were used for all subsequent leaflet shape and size analyses. Total leaf area for each plant was measured by imaging the whole plant and a 4 cm2 red square and then processed in the EASY LEAF AREA software .Five plants per line were used to analyze leaflet sugar content. The plants were grown under the same conditions as field plants with the following exceptions. Plants remained in the glasshouse after transfer to 1 gallon pots. All plants were watered with nutrient solution and grown until mature leaves could be sampled. Using a hole punch, a disk with an area of 0.28 cm2 was taken from the leaflets and extracted from the disks using a modified extraction method from the Ainsworth laboratory . Leaf disks were placed in 2 mM HEPES in 80% EtOH and heated to 80°C for 20 min and the liquid collected and stored at 20°C. The entire process was repeated twice. They were then placedin 2 mM HEPES in 50% EtOH and heated, collecting the liquid and storing at 20°C followed by another 2 mM HEPES in 80% treatment. The collected liquid was then used to measure the amount of sugar present per area of disk. To measure leaf sugar content a working solution of 100 mM HEPES , 6.3 mM MgCl2 , and 3 mM ATP and NADP at pH 7 was prepared. From the working solution, an assay buffer was made adding 50 U of glucose-6- phosphate dehydrogenase , and 295 or 280 µl of the working solution was added to a 96-well plate for sucrose standards or samples, respectively. Standards were added at a 60-fold dilution and samples were added at a 15-fold dilution. Then 0.5 U of hexokinase , 0.21 U of phosphoglucoisomerase , and 20 U of invertase were added to each well and the plates allowed to sit overnight to reach equilibrium. The plates were measured on a UV spectrometer at 340 nm, followed by analysis in JMP .All statistical analyses were performed using JMP software. To determine statistical significance, measurements were modeled using general linear regression model and tested by a one-way ANOVA followed by Tukey’s honestly significant difference, if necessary. These modeled data for all measured values were compiled into a table and used to create a model using partial least-squares path modeling in SMARTPLS 3.0 . Modeled data were used for the statistical analyses as many measurement types varied in number of data points, and therefore a set of generated predicted values of equal size was used to make an equal data matrix . Partial least squares-PM was used to explore the cause-and-effect relationships between the measured variables through latent values. PLS-PM is effective in both exploring unknown relationships and combining large-scale data, such as field, physiological, container raspberries and morphological data, that otherwise are not well described together . In addition to running the PLSPM, 1000 bootstraps were performed to obtain statistical significance and confidence intervals of the path coefficients and the R 2 values of each latent variable. The path coefficients are the standardized partial regression coefficients , and represent the direction and strength of causal relationships of direct effects. Indirect effects are the multiplied coefficients between the predictor variable and the response variable of all possible paths other than the direct effect . To determine the best path model, the latent variables were combined using our best understanding of biological relationships, and a general model using all data was generated. The paths between LVs were altered until a best-fit model was found. PLSPredict was then used on the dataset to ensure that the model did not over or under fit the data, and for predictive performance of each manifest variable . This structural model, and not the fit values, was retained for use in predictive modeling of a separate dataset. PLSPREDICT, with the structural model developed as described earlier, was used on a separate dataset to determine the efficacy of the model.

Two commercial cultivars, M82 and ‘Lukullus’, were used and only the leaf shape values were entered as exogenous variables. The predicted values for each output variable were compared with the actual measured values to determine how well the model predicted these variables.To perform phylogenetic analysis, all single nucleotide polymorphisms detected by CLC Genomics Workbench 11.0 from whole genome sequencing were exported as a vcf file. The SNPRELATE package for R was used to determine the variant positions that overlapped between cultivars and then all sequences combined into a single gds file . This file was run through SNPhylo with the following parameters: the linkage disequilibrium was set to 1.0, as we wanted to exclude as few variants as possible based on this factor, the minor allele frequency was set to 0.05, and the missing rate was set to 0.1. In all, 1000 bootstraps were performed for confidence intervals and significance. Solanum pimpinellifolium was used as the outgroup. The bootstrapped output tree was displayed in MEGA7 . Analysis of c gene flow was performed using PHYLONETWORKS . All common SNPs from chromosome 6were run through the TICR pipeline and then analyzed using PHYLONETWORKS with default settings, except for the number of runs which was set to 20. After the hybrid network for chromosome 6 was obtained, bootstrap analysis was done in PHYLONETWORKS using default settings with the following exceptions: ftolRel was set to 0.01, ftolAbs was set to 0.001, liktolAbs was set to 0.0001, and Nfail was set to 5. These adjustments were made to decrease processing time. The bootstrapped tree was output in DENDROSCOPE .Tomato is one of the highest-value and most extensively used vegetable crops worldwide. However, to meet increasing demand, modern tomato cultivars have been selected for qualities such as size and firmness instead of taste . Consequently, most of modern commercial varieties have lost their flavor and are often tasteless . Flavor of fruit is the sum of interactions between taste and aroma, whereas sugars and acids are the two of primarily components to activate taste receptors and aroma components such asvolatile compounds activate olfactory receptors . Though the relative contribution of taste and aroma to fruit flavor has not been clearly defined , plenty of studies have shown the importance of sugars and acids in determining fresh fruit flavor . For tomato, the levels of sugars and acids not only contribute to tomato taste , but also are major factors affecting tomato overall flavor intensity , and increasing sugar content of the fruit will enhance tomato flavor . Recent studies have shown that fruit sugar accumulation in modern tomato is two to three-fold less than that in wild species , which can account for the decline in flavor quality of tomato fruit. Fruits are the primary photosynthetic sinks and over 80% of sugars in the fruit are produced in the leaf through photosynthesis and subsequently translocated through the phloem . Therefore, factors involved in regulating leaf photosynthesis, as well as sugar biosynthesis and sugar transport would influence sugar levels in fruit. Leaves are the principle site of plant photosynthesis, and leaf traits directly impact the efficiency of light capture and photosynthetic carbon fixation Thus, changes in leaf traits could have an effect on fruit yield and quality. Studies evaluating the influence of leaf area on tomato yield have shown high leaf area index can lead to an increase in tomato yield as a result of better light interception . Recently, leaf shape was shown to be strongly correlated with fruit sugar levels in tomato, with rounder and more circular leaves having higher sugar content in their fruit .

Consistently, six genes, CrPORA, CrCAO, CrCHIH, CrCHLM, CrGGDR and CrMPEC, involved in the process of chlorophyll biosynthesis were more highly expressed, while two genes, CrCLH1 and CrRCCR, involved in chlorophyll degradation were less expressed in WT than in MT . Similarly, auxin could also affect the gene expression of carotenoid and chlorophyll metabolism during tomato fruit ripening . In this study, we did not measure the IAA content in fruit peels for technical reasons. Although peel coloration and pulp maturation are two different processes in the same ripening fruit, they are normally synchronized in the majority of the world citrus growing areas. Similarly, in our case, the changes in peel color were closely coupled with the changes in pulp sugar and acid content. It is therefore relatively safe to assume that the changes in IAA content in pulps should similarly occur in peels, plastic gardening pots and no fruit peel IAA data should interfere with drawing a reliable conclusion.

It must be pointed out that the citrus fruit may delay or stop CC during fruit ripening in rare cases, such as in some very early satsuma mandarins, and in hot, tropical regions.Organisms attain their form and function by readouts from an intricate web of regulatory relationships between DNA, RNA, proteins and metabolites. The era of large-scale biology promises to provide insights into this web of regulation at the whole genome level and has spurred growth of computational methods that allow us to look at diverse readouts and generate a comprehensive frame-work for how molecules generate morphological phenotypes. The number of sequenced genomes grows apace; NCBI currently lists >2500 genomes , and the number of plant genomes is currently listed at >100. However, the challenges associated with whole genome sequencing and assembly have caused many researchers to turn to other types of genome scale data. Since 2009, when RNAseq was described as a recently developed technology that had the potential to revolutionize our understanding of the complexities of eukaryotic transcriptomes, the technology has evolved and proved useful for identifying links between transcription factor activity and transcript abundance, for the generation of transcriptomes in non-model species through de novo assembly methods, for detection of genomic variants, and for identification of splicing variants.

Continued improvements in efficiency combined with reduction in cost of sequencing have made sequencing technology available even to fields that traditionally did not rely on them. In a recent review Alvarez and co-authors reported on >500 studies that relied on either microarray or RNAseq methods in the last 10 years and present the potential to analyze gene expression in an ecological context across multiple taxa. In the field of evolutionary developmental biology the numbers are even more staggering. How can this explosion of genome scale data be leveraged to better understand how organisms develop, evolve, respond to biotic and abiotic stimuli and function in the context of their environment? Network analysis, an offshoot of graph theory used in mathematics and computer science to model relationships between objects, was utilized extensively in the social sciences and has become a method of choice for identifying relationships between units of biological data.Early gene networks, such as those produced by Davidson et al., were generated using perturbation assays and direct experimental data to create a directed network of developmental regulatory control. While these networks were small they were a large step in advancing our understanding of developmental processes that was not obtainable by analysis of just one or two genes at a time.

Many early network models consisted of one of two types of mathematical analysis, the Logical or Boolean model , and the dynamic systems model. The Switch model consisted of genes being either in an ‘On’ or ‘Off’ state, which could be regulated by other genes. This method demonstrated feedback loops as well with genes regulating the activity of other genes , but could not allow for variable states of expression. Dynamic systems utilizing differential equations allowed for variable expression states of genes and nonlinear interactions, but were limited by computational power and the lack of large-scale transcriptomic data availability. Transcription factor– promoter interaction studies provided an additional method of gene regulatory network construction. These interactions showed a multi-tiered, or hierarchical, structure to gene regulation in networks, with a top, core, and bottom tier of transcription factors and their targets. This tiered structure revealed an interesting aspect of gene networks and biological processes, as the top tiered transcription factors and their targets tended to be noisy or have a high degree of variability in expression, while the bottom tier showed very low noise and stricter regulation of expressional states. Jothi et al. hypothesized that the increased variability in top-level gene regulation allowed for greater adaptability, while low variability in the bot-tom tier acted as a buffer against inadvertent changes in the higher tiers that could be detrimental.In the post-genomic era, and with the large volume of whole genome transcriptional data available, gene net-work construction has become readily available to most researchers. Figure 1 represents a flowchart of the potential analyses discussed below. Before constructing a network, genes often need to be subset into interest groups in order to facilitate data visualization and focus analysis on specific biological questions. This involves differential expression analysis in conjunction with dimensionality reduction and clustering methods such as PCA, k-means, hierarchical, self-organizing maps, and t-distributed stochastic neighbor embedding. Each of these methods attempts to reduce high dimensionality data such as gene expression patterns, either over time, different tissue types or treatments, into a representative and more easily interpretable two dimensional structure.

PCA has been the most utilized dimensionality reduction method, using Euclidean distances to measure dissimilarity and determine placement within a two dimensional space. However, the output often does not represent the actual relation- ship of objects from higher dimensional space as it measures distinct orthogonal components within each PC representing the greatest amount of variance without regard to overall gene-to-gene correlations. With k-means, a user-defined number of clusters are used, and a mean vector is calculated for a cluster to assign new members, and then the mean recalculated. This iterative process reduces the level of dissimilarity of objects within the cluster, thus giving a better representation of object relationships in higher dimensional space within the limitation of the defined number of clusters . Despite this improvement, k-means is highly susceptible to noise distortion, or the influence of outliers on the overall mean and structure of a cluster. Hierarchical clustering builds a tree with nodes that represent clusters through multiple different methods including matrix construction by gene pair similarity measures, blueberry pot size and then identifying those genes with the highest degree of similarity. While hierarchical clustering provides a more informed output, merging errors or smaller cluster merging can result in the loss of more interesting local groups of genes. Each of the previous methods relies on Euclidean distances for similarity/ dissimilarity measures between genes within higher dimensional space, which does not conform to a linear relationship by its nature. SOM and t-SNE employ non-linear distance measures to ap-proximate the relationship between genes within higher dimensional space, often providing a much more realistic representation of gene similarity in two dimensions. Once distinct clusters of genes with similar expression patterns have been identified gene ontology or gene set enrichment, tests can be performed to identify the nature of genes within clusters. Networks can then be constructed from an individual cluster or multiple clusters sharing similar biological functions. There are three primary network types; gene regulatory networks which give directionality to the interaction between nodes or genes, association networks which are non- directional but show direct interaction between associated genes, and gene coexpression networks which are non-directional and can show direct or indirect interactions between associated genes. With transcriptomic datasets GCNs offer the most versatile gene interaction exploratory tool, using gene expression patterns to deter- mine potential associations and modularity. This is especially useful in non-model organisms where the function of most or many genes has not been determined, and regulatory interactions remain unknown. Of the four primary network construction methods, the two most commonly utilized are correlation and supervised networks. Correlation network construction consists of determining a correlation between two genes based on expressional changes, with Pearson’s moment correlation coefficient being the most common method . PMCC identifies linear correlations, but suffers from the inability to deal with outliers or genes which may have a nonlinear relationship. Spearman’s rank correlation coefficient deals with both of these issues, as it is more robust to outliers and accommodates non-linear relationships. Maximum information correlation allows detection of the strength of any type of linear or nonlinear correlations between genes, and Partial correlation coefficient can be employed to quantify the association between two genes when conditioning on other genes to infer direct dependencies among variables in a network. In addition, it has been reported that Network deconvolution can allow one to infer direct effects from an observed correlation matrix containing both direct and indirect effects. On the other hand, supervised network construction utilizes regression models , which deal with the response of genes to a set of predictor genes. Supervised network construction deals well with cascade expression changes, but is less reliable when dealing with feedback loops, a feature of the regression analysis where response and predictor variables are set and not necessarily interchangeable during construction. A combination of several mathematical techniques is preferable to obtain a more accurate representation of the gene associations.

There are two types of PGM methods, Bayesian and Markov, with the former providing interaction directionality of gene relationships, and the latter using neighborhood selection methods similar to linear regression in supervised learning. Bayesian PGM is highly sensitive to experimental design and requires computationally intensive methods for interpreting Bayesian networks. The possibility of misinterpreted causal relationships among genes from gene expression data makes this method less appealing. However, when applied correctly the method can provide gene relationship information not obtained with some other methods with large scale, high dimensionality data. Meta-prediction includes meta-analysis and ensemble learning, however each utilizes multiple methods of network construction, and then creates a consensus relationship among gene expression patterns. Meta- Prediction methods, through the use of multiple methods, may provide a more robust network than any one method on its own.Once the GCN has been constructed, the interaction among genes can be determined, and other information such as gene function and biological processes regulated can be obtained. Since transcriptionally coordinated genes are often functionally related, GCN can be used for gene function prediction. Especially a comparative GCN analysis across species can yield more accurate gene function predictions because conserved gene modules are more likely to be functionally relevant. Hub genes, modularity and network restructuring are discussed in the following section. One of the more appealing aspects of GCNs is that whole transcriptome data can be combined with other large scale networks such as metabolic or protein–protein interactions to give a wider view of the biological processes to which specific clusters of genes belong. Interestingly, the transcriptomic data avail-able is outstripping computational capabilities of many researchers, creating a technological bottleneck rather than a biological one.A major challenge in biology is to understand the genetic basis of morphological evolution. Evo-devo studies aim to understand the developmental mechanisms that are modulated over time to give diverse phenotypic outputs. Most evo-devo studies, even though pursued on a gene-bygene level, have underscored the importance of gene expression regulation, suggesting that rewiring of developmental GRNs should be a crucial factor driving morphological evolution. Large-scale genomics tools can be used to investigate rewiring of developmental GRNs as crucial factors driving morphological evolution. Studies determining GRNs within an evo-devo context help us determine how developmental GRNs are reorganized to generate morphological diversity. Recent interaction mapping studies have showed the ability of differential analysis to reveal massive rewiring in the architecture of an interactome during cellular or adaptive responses.Our previous GCN analysis using cross-species and tissue-specific RNA-seq data had revealed the modular structure of the GRN controlling leaf development in the domesticated tomato and its wild relatives. Comparisons of the networks among species with experimental data showed that changes in a module regulating the key KNOX1 TFs made a significant contribution to the variation in leaf complexity.

Bornean forests contain some of the highest levels of vascular plant diversity in the world, and although many plant stems in the plots were identified to species, the inclusion of stems identified only to genus meant that all analyses were done at this higher taxonomic level. Previous work on a variety of taxa has shown that lower taxonomic resolution is appropriate when identification to the species level is not possible or feasible.We used rarefied species accumulation curves to assess the dietary richness of gibbon and leaf monkey diets, both overall and for the frugivorous portion of the diet. We report dietary richness as the observed cumulative number of plant genera consumed given the number of months in which feeding observations were recorded for each primate species. We constructed diet richness curves using the “specaccum” function from the “vegan” package in R 3.0.0 statistical software. We described diet breadth of gibbons and leaf monkeys both in terms of the number plant taxa eaten and the relative proportion of feeding observations on each of five food classes .

We exclude the synconia of Ficus from the category of fruits and treat them separately because their reproductive parts are not true fruits, they are important foods for many vertebrates , square pot and they have unusual phenological behavior rendering them qualitatively distinct from other fruiting plants considered herein.Our study focused on the fruit portion of the diets, because fruits are expected to be the most limiting class of resources on which these species feed. Forest productivity was defined for gibbons as the proportion of stems bearing fruit that were mature or ripe; for leaf monkeys stems bearing immature and mature fruits were also included, reflecting the fact that gibbons avoid unripe fruits whereas leaf monkeys consume them frequently and tend to avoid ripe fruits because foods rich in starch or sugar can disrupt the fore stomach pH and cause acidosis. Despite this fact, we included ripe fruits in the estimate of food availability for leaf monkeys as our phenology categories were based on the most advanced stage, meaning that trees with one ripe fruit were scored as ripe, even if most of the fruits on the tree were still mature, so plants scored as ripe often still contained some food for leaf monkeys. We followed the Design I Protocol for calculating selectivity ratios, the appropriate approach when animals are not individually identified, the availability of a given resource is known, and resource use is sampled randomly. We calculated Manly Selectivity Ratios using the “widesI” function in the “adehabitat” package in R.

Genus use is simply the total number of independent feeding observations recorded on a genus, i. We followed the general convention for MSRs and calculated availability as the total number of stems of genus i that were observed to fruit during the study period. We calculated selection ratios for each genus that was observed to have been fed upon at least once by either gibbons or leaf monkeys. We report MSRs that are standardized so that they add to 1; these values can be interpreted as the probability that for a selection event the primate would choose the genus of interest over other available genera. Values close to zero indicate “avoidance”, meaning the genus was eaten less than would be expected based on its availability. Thus, in the MSR context, the term “avoidance” of an item does not necessarily indicate the item is never consumed . Large values indicate “preference” wherein genera were selected more than predicted based on availability. We conducted a chi-squared test of the null hypothesis that the animals were randomly feeding . The chi-squared test was significant for both animals, so we computed 95% confidence intervals for proportions of used and available resources. If there are fewer than five resource units per category , the corresponding confidence intervals should be interpreted with caution. Relatively few of the genera in our study had five or more feeding observations , so we caution against strong interpretations based on the calculated confidence intervals of the selectivity values.We calculated the proportion of the diet comprising each of the five food classes in three month blocks. We combined data into three-month periods in order to increase sample size per period and thereby improve the reliability of estimates. We then compared the number of feeding observations for each food class in each three-month block to the corresponding average fruit availability values during the same period.

Availability values were calculated as the proportion of the overall stems in the forest that were bearing fruit. Availability differed for gibbons and leaf monkeys because immature fruits were included in calculations for the latter but not the former . We fit linear models using ordinary least squares regression that predicted use based on the following predictors, calculated for a three-month block: fruit availability , seed availability , flower availability , and fig availability . We also included the following environmental predictors: minimumtemperature , maximum temperature , and rainfall . We then compared each of these models and a null intercept model using biased-corrected Akaike’s Information Criterion using the “AICctab” function in the R package “bbmle”. We obtained estimates of the 95% confidence bands for the regressions by simulation using the R package “rethinking”. Due to random sampling variation, some three-month blocks had observations of leaf monkeys but none for gibbons, so we fit the models using 15 three-month blocks for gibbons and 20 three-month blocks for leaf monkeys.This research complied with all applicable laws of the Republic of Indonesia and the United States of America. Per regulations of the Institutional Animal Care and Use Committee at the University of California-Davis, as our research entailed solely non-invasive observation of wild animals, no formal IACUC review was required. Permission to conduct research at Gunung Palung National Park was kindly granted by the Indonesian Institute of Sciences , the State Ministry of Research, Technology, and Higher Education , the Directorate General for Nature Conservation and the Gunung Palung National Park Bureau .We collected 145 feeding observations from gibbons and 219 feeding observations from leaf monkeys . Mean monthly survey effort across the five forest types sampled was 61.45 km/month . Approximately 41% of feeding observations were recorded on census routes , and the remainder were made during focal follows. Most feeding observations were made in the morning. There was no systematic bias towards males or females in either species, nor any difference in the proportion between them. Our observations were mostly of adults , but the proportion of adults and sub-adults did not differ between species. The sampling period included a mast fruiting event from December 2009 though February 2010, during which plant reproductive output spiked. The proportion of plant stems fruiting in our plots during these three months ranged from 0.08 to 0.15 and averaged over 0.10, blueberries in containers more than three times higher than lowest levels of productivity. This resulted in substantial variation in availability of food for both gibbons and leaf monkeys over the course of our study. Lumping of months to ensure adequate sample sizes for statistical analyses meant that variation in the proportion of fruiting stems in our 3-mo sampling blocks was more muted. For gibbons, the proportion of stems with mature and ripe fruit ranged from 0.026–0.077; for leaf monkeys the proportion of stems with immature, mature and ripe fruit ranged from 0.04–0.11 . The species accumulation curves illustrate that leaf monkey diets had higher overall taxonomic richness than gibbon diets, when considering all food classes .

Neither curve reached an asymptote, reflecting that additional richness would be predicted with additional sampling.Nevertheless, the gibbon curve clearly separates from the leaf monkey curve and begins to flatten earlier, indicating that the differences in dietary diversity between the taxa are not the result of different sample sizes. In contrast to the overall diet, gibbons and leaf monkeys exhibited high similarity in the richness of the frugivorous portion of the diet . Therefore, the higher overall dietary richness of leaf monkey diets is due to food classes other than fruit. Leaf monkey diets were more diverse than gibbon diets both in terms of the total number of taxa fed upon and the distribution of feeding observations across food classes . Leaf monkey diets were composed of 31.1% leaves , 25.6% fruits , 26% seeds , 8.2% figs , 6.4% flowers , and 2.7% unidentified items, whereas gibbon diets were composed of 50% fruits , 20% figs , 9.7% flowers , 9.7% leaves , and 6.9% unidentified items. Leaf monkeys consumed food sources from 69 genera in 37 families while gibbons consumed them from 46 genera in 40 families. Gibbons and leaf monkeys used different food classes from different genera. Leaf monkeys ate 28 genera of leaves, 13 genera of flowers, 32 genera of seeds, and 29 genera of fruits. Gibbons had a narrower dietary breadth, consuming 9 genera of leaves, 9 genera of flowers, 5 genera of seeds, and 31 genera of fruits.Our measure of dietary overlap included the number of genera consumed in common and the relative importance of each item in the diet, producing an index that permits asymmetry in dietary overlap measures. From the gibbon’s perspective, there was higher overall dietary overlap than from the leaf monkey’s perspective . This asymmetry is exemplified in the dietary overlap measures for the food class figs. From the leaf monkey perspective, the fig dietary overlap was 1; from the gibbon’s perspective it was 0.62. This asymmetry reflects differences in the importance of figs for the two primate species. In the fruit portion of the diet, leaf monkeys exhibited higher dietary overlap with gibbons then vice versa , a pattern that was reversed in the leaf portion of the diet, with leaf monkeys exhibiting lower dietary overlap with gibbons than gibbons with leaf monkeys . Leaf monkeys also had lower dietary overlap with gibbons in terms of seeds consumed than gibbons had with leaf monkeys .Fruit availability was in the top model as a predictor of resource use for all classes of foods for both primates, with the exception of leaves and flowers . The responses of gibbons and leaf monkeys to variation in fruit availability were qualitatively similar within food classes. We found that as overall fruit availability increased, the consumption of leaves and figs declined, while the consumption of fruit and seeds increased. For gibbons, the intercept only null model was the top model for leaf consumption , and this was also the case for flowers . For leaf monkeys, the strongest predictor of flower consumption was rainfall . For the other food classes, fruit availability was the only predictor included in the top models. The slope estimate for the model of leaf consumption was negative , meaning as the percentage of trees in the forest with fruit increased there was a decrease in the proportion of leaves in the diet. The proportion of figs in the diet also decreased in the diets of leaf monkeys and gibbons as fruit availability increased. The pattern was reversed in the models of seed consumption, with fruit availability having a positive slope in models for both leaf monkeys and gibbons . We also found a positive effect of fruit availability on fruit consumption for leaf monkeys . But for gibbons, the best predictor of fruit consumption was availability of flowers . The proportion of fruit in the diet for gibbons was generally greater than 50% even during periods of low availability ,while this proportion rarely exceeded 50% in leaf monkey diets and only then during periods of high availability .We report the results of a long-term comparative study of the feeding ecology of two sympatric primates. In the masting Dipterocarp forests of Gunung Palung National Park in Indonesia, leaf monkeys had higher dietary richness and diversity than gibbons, which is likely due to leaf monkeys’ physiological ability to process and digest a broader range of foods. We analyzed resource selection from two perspectives: 1) by genus in the selectivity analyses, and 2) and by food class to investigate how these primates alter their diets in response to variation in fruit availability.

Climatic change could lead to expanded ranges for plant pathogens due to favorable wet and warm conditions in higher latitudes and altered dispersal patterns influenced by intensifed rain and wind, and shifting vector habitats. Many factors associated with climate change, including elevated temperatures, increased periods of drought, intensifted storms and elevated CO2 will likely influence the health of our crops directly, but also indirectly by shifting the balance of microbes that may inhabit them, including plant pathogens, human pathogens and beneficial or commensal organisms. For some plant-pathogen pairs, weather-based forecasting models are already in use, helping growers time pesticide applications efficiently for the highest effectiveness and lowest environmental impact. Similar decision support systems could be implemented for use in food safety, growing blueberries in containers providing recommendations to growers on safest harvest times following single and repeated rain events. The first step in achieving this goal is amassing an understanding of the community-level dynamics on harvestable fresh produce preceding and following rain events.

Pomegranate is a fruit tree grown today in a wide range of subtropical and tropical geographical locations spread all over the globe; these locations include many countries in Asia, Europe, South and North America, Africa, and Australia . Pomegranate is considered a minor fruit and is far from the top of the list of consumed fruits, such as apple, banana, grapes, and citrus; however, it is one of the most interesting fruits in terms of cultural, traditional, and potential therapeutic usage.The pomegranate fruit is a fleshy berry with a nearly round shape, crowned by a prominent calyx. Its relatively thick peel has an outer colored skin and the fruit’s inner structure contains multi-arils chambers separated by membranous walls . The edible part of the pomegranate fruit, the arils, contains seeds and a special layer of cells that are of epidermal origin and protrude from the outer epidermal cells of the seed . The external fruit color ranges from yellow, green or pink overlaid with pink to deep red or deep purple. The color of the juicy layer can vary from white to deep red . Various parts of the pomegranate fruit were traditionally used as treatments against various ailments including stomachaches and bacterial infections . The traditional usages were strengthened by modern scientific studies focused on health beneficial metabolites and their therapeutic effects and mechanisms of action on human and animal health.

These studies were thoroughly reviewed in recent years. Most of the therapeutic effects of the pomegranate fruit were attributed to its secondary and primary metabolites, such as polyphenols, including flavonoids, anthocynains and hydrolizable tannins, fatty acids, and lipids . These metabolites were found in all fruit parts, including the fruit peel , arils , seeds , and membranous walls . Anthocyanin biosynthesis occurs in parallel in the arils and in the fruit peel. These two tissues are not necessarily correlated in their activity with respect to color production, and often, the two tissues display different colors . The same situation could appear in other biochemical pathways responsible for other important metabolites. High variability was reported for pomegranate fruit that manifests, among other phenomena, considerable differences in size, shape, color, date of ripening, and taste. This external variability is interesting in view of the fact that the only edible species among the Punica, which include only two species, is the cultivated pomegranate . The only other pomegranate known to science is the non-edible species Punica protopunica, endemic to Socotra . The fruit of this species is small and not colorful and no biochemical, genetic, or molecular studies of its fruit were published. This high variability is also reflected in the content of primary and secondary metabolites.

Quite substantial work has been devoted in recent years to determining primary metabolites in the pomegranate fruit. These efforts include studies of sugar, organic acids, protein, amino acids, and lipid content and composition. In general, the pomegranate fruit consists of 50% peel, 40% arils, and 10% seeds . The arils contain 85% water, 10% total sugars, 1.5% metabolites and bioactive compounds such as organic acids, phenolics, and flavonoids . The seeds are a rich source of lipids; pomegranate seed oil comprises 12–20% of total seed weight . It appears that primary and secondary metabolites showed extensive variability due to the fact that the fruit used for the various studies originated from different varieties and highly variable climatic conditions and taken from trees grown under different agro-technical methods. While pomegranate reviews published up until now focused mainly on secondary metabolites , there are only few that focused on primary metabolites, despite their great importance to taste attributes and to the nutritional index of the fruit. In this review, we focus on primary metabolites and on secondary metabolites, anthocyanins and hydrolizable tannins, with special attention to the variability of their content and composition. A special effort was aimed at the developmental, genetic, and environmental effects on the content and composition of primary metabolites. Whenever available, primary metabolites in each of the fruit organ, peel, arils, and seeds, were specified.The pomegranate fruit is a rich source of sugars. The level of the sugars in pomegranate juice is highly correlated with the level of total soluble solids . Shwartz et al. and Dafny-Yalin et al. calculated a value of R 2 = 0.89, P < 0.01. The TSS level in the juice ranges from 4.2 to 8.5 g/100 g depending on cultivars, climatic conditions, and cultural techniques ; Amir et al.. Pomegranate juice contains a high amount of polyphenols such as flavonoids, ellgitannins, and the color molecules anthocyanins. A substantial fraction of these molecules are known to be conjugated to sugars, mostly glucose. The taste of arils from various pomegranate varieties is significantly variable, ranging from sour to sweet . Sugar content is an important parameter influencing taste, although it is highly influenced by organic acid content as well. Many studies examined the sugars in the pomegranate fruit, mainly in the juice, revealing glucose, and fructose as the main component of the juice sugars . Sugars found in the fruit peel were in some controversy among studies from different countries. It should be noted that those studies were done for different purposes and therefore followed different procedures of extraction and detection that might explain this disagreement. Some of the studies indicated glucose and fructose as the main sugars while others found that xylose and arabinose are the main sugars .Arils are a rich source of sugars. Studies obtained from different countries have shown that the composition of sugars among pomegranate varieties might differ. Analyses of the sugars in pomegranate aril juice from 29 worldwide varieties grown in Israel and 19 cultivars from Spain have shown that fructose and glucose were the major sugars found in the arils, square pots while sucrose and maltose were detected in lesser amounts. In some varieties, these two former sugars are the only sugars that were detected . In many studies the levels of fructose were similar to those of glucose in pomegranate juices, and both varied in different varieties by a factor of up to two-fold ranging from 4.2 to 8.5 g/100 g . In 76 Turkish varieties glucose levels showed a range of 4.2–8.3 g/100 g juice . Forty Spanish varieties showed a range of 5.5–7.8 g/100 g , 29 Israeli varieties showed a range of 4.8–6.6 g/100 g , and 30 Tunisian varieties showed a range of 5.7–8.5 g/100 g . In addition to the main fructose and glucose sugars, some other sugars were also detected in several varieties at a relatively negligible level .Determination of sugar contents in 6 Spanish varieties has shown that they all contain maltose and sucrose, but possess 45- and 70- fold lower quantities of glucose, respectively . Sucrose was also found in an about 33-fold lower quantity than glucose in 6 Turkish varieties and an up to 13- fold lower quantity in 53 out of 76 varieties . Maltose was only detected in one of 29 Israeli varieties . Several studies specifically measured the level of sugars and TSS in the fruit peel . Notably, the major sugars that were detected in the peels differed between the varieties grown in Tunisia, Iran and Israel. In the 12 Tunisian varieties, xylose and arabinose represented more than 60% of the total content, followed by galactose , glucose , mannose , rhamnose , and fucose .

Peels from one Iranian variety showed that the main sugar is glucose , followed by galactose , mannose , arabinose , and rhamnose . However, in the 29 worldwide varieties grown in Israel, the major sugars were glucose and fructose. The level of glucose varied in the range of 0.9–4.8 g/100 g , and that of fructose in 0.9–6.6 g/100 g . The level of fructose was higher than that of glucose in most of the varieties. Maltose was found at an about 50-fold lower concentration than that of glucose and fructose, in a range of 0.8–48.9 mg/100 g, while sucrose was detected in only 6 varieties at relatively low levels . Mannitol was also detected in all the varieties, ranging from 10 to 300 mg/100 g . As expected from these results, the TSS varied between the different collections. In 12 Tunisian cultivars, it ranged from 16.8 to 19.6 g/100 g , which was more than the range of three Indian varieties that show a range of 13.7– 14.5 g/100 g . However, these values were much higher than the results reported for 4 Turkish varieties that ranged from 3.8 to 6.4 g/100 g , and the 29 varieties grown in Israel, which showed a range of 5.2–11.3 g/100 g. In these varieties, the peels had a 2- to 3-fold lower level of TSS compared with the aril juice .Several studies followed the changes in the levels of sugars and TSS in aril juice during fruit development. The results taken from three cultivars in South Africa , and two cultivars from Israel have shown that the level of TSS rose during the development process in accordance with the levels of glucose and fructose. The increase shown in the two Israeli grown varieties and in “Wonderful” in South Africa was significant . The presented data indicate that the developmental stage of the pomegranate fruit is associated with sugar accumulation.Analysis of TSS and sugar content in different collections revealed that their values depended on climate and growth conditions. To gain more knowledge on the effect of the environmental conditions on the levels of sugars, 11 varieties from the Israeli collection in the Jezreel Valley were planted in Israel’s southern Arava Valley . Arils from both habitats were analyzed. The varieties grown in Mediterranean climate showed significantly higher levels of glucose and fructose in the juice than those grown in a hotter habitat . Similar results were also reported from the analyses of the 10 Chinese cultivars that grew in four different habitats . “Wonderful,” which was grown in Israel in two habitats , as well as in three habitats in South Africa , showed that relatively higher temperatures can decrease sugar content, whereas cooler temperatures apparently promoted the increase in glucose and fructose. Thus, temperatures appear to play an important role in the sugar content of pomegranate juice.Total titratable acidity values were shown to be affected by climate and growth conditions. Arils from 11 varieties grown in the Jezreel Valley and in the Southern Arava Valley were analyzed to study the effect of environmental and climatic conditions on the arils’ acid content. The cultivars grown in Mediterranean climate had higher acidity levels compared to the acidity levels found in desert climate. This was in accordance with the higher contents of citric and malic acids, the two main organic acids in the arils . Generally sour cultivars are mostly grown in northern cold regions, while sweet cultivars with low acidity values are mostly found in regions having hot dry conditions. In Southern Spain and North Africa most of the commercialized cultivars have a sweet taste , while in North Turkey and Russia sour cultivars are commercialized . In northern regions such as Russia, Macedonia, Georgia, and Turkey, the total acidity ranged from 0.5 to 2.3% , 0.6 to 2.2% , 0.4 to 2.8% , and 1.7 to 4.6% , respectively. However, in hot climates such as in India, Egypt, and Saudi Arabia, total acidity values dropped to 0.12–0.13% , 0.03–0.10%, and 0.02–0.14%, respectively .

As a reduction of water and nutrients increased leaf loss and changed the colour of leaves in the same year when stress was imposed, future studies should investigate plant performance of almond experiencing pollination limitation in subsequent years after stress was imposed and after long-term limitations to plant resources.Worldwide, tephritid fruit flies are among the world’s most economically important crop pests, with at least 200 pest species. In sub-Saharan Africa, several highly polyphagous Africa-native species—belonging to the genera Ceratitis Macleay and Dacus Fabricius have been recognized as economically important pests of several cultivated and wild fruit species, particularly mango , guava , citrus , and several cucurbit and solanaceous vegetables. The traditional problems with tephritid fruit flies have been aggravated in recent years by the invasion of the African continent by the Oriental fruit fly Bactrocera dorsalis , gallon pot which was first detected in coastal Kenya in 2003 and has spread to at least 32 countries in continental Africa and adjacent island countries.

Since the detection of B. dorsalis in Africa, several studies have established the African host range of this species and quantified crop losses due to its infestations. At present, B. dorsalis has been found infesting fruits of 40 host plant species, with mango, guava, citrus, and loquat Lindl. being the major infested cultivated hosts. In addition to causing extensive fruit losses in the field, fruit flies greatly restrict mango and other host fruit exports from Africa, particularly to the European Union , which, for example, intercepted and rejected more than 141 shipments of Cameroonian mango from 2011 to 2018, resulting in substantial financial losses. While several control methods have been developed and deployed across the continent, the large majority of fresh fruit producers in Cameroon and throughout Central Africa continue to experience substantial yield losses caused by fruit flies and they do not yet have the necessary resources and knowledge to successfully use available and new fruit fly control methods. The prevailing agronomic and plant protection practices are of very low or no input type. Apart from the common occasional weeding, pesticide and fertilizer inputs are rare. Knowledge of fruit fly species composition and their respective seasonal abundance using complementary monitoring tools in relation to host plant phenology under different environments is crucial to the understanding of population dynamics of these insects and the subsequent development and implementation of interventions to limit their infestations and damage.

Such knowledge is predicated on proper fruit fly species identification and quantification of the levels of host infestation which are fundamental for establishing the economic status of the pests and ultimately for developing and adopting effective pest control interventions. Two approaches have been traditionally used to provide the aforementioned needed information: effective tools based on food baits and male lures for monitoring and estimating the abundance of adult fruit flies, and systematic fruit samplings to determine host range and quantify the levels and rates of fruit infestations by the various fruit fly species present in the systems. The latter is often complemented with random fruit sampling from areas outside the targeted cultivated fields to determine the fruit fly host range. Ideally, monitoring tools and host fruit infestations should be tested and used over several years and in multiple environments to establish sufficient details of the bio-ecological context where management options will be developed and implemented. Several commercially available male lures and food baits have been developed and used widely for fruit fly monitoring, but their performance has been shown to vary with factors such as climate, fruit fly species, and other factors that affect fruit fly populations. All available studies in Africa are from several agro-ecologies, but none are from the midaltitude, high rainfall agro-ecologies that are prevalent in much of the Congo Basin of Central Africa.

The male lures methyl eugenol and terpinyl acetate are known to, respectively, attract Bactrocera and Ceratitis species, while Culure is known to attract various Dacus species. For principally females, several food baits including Torula yeast, Mazoferm, and Nulure have been developed and used to attract and monitor several fruitfly species. To our knowledge, monitoring the performance of food baits and male lures on fruit flies under the various environments that are prevalent in Central Africa, is lacking. Similarly, compared with other regions of Africa, information on fruit fly species composition, host range, crop losses, and seasonality, as well as various trapping approaches and control measures in Central Africa, are scarce. The Congo Basin of Central Africa harbours a rich humid forest with a high diversity of wild fruit trees that could, at the same time, represent a reservoir for fruit flies and their natural enemies. Central Africa further includes the five key agro-ecologies encountered across the African continent, from desert and arid agro-ecologies to dense high-rainfall and humid forest zones. Preliminary information from fruit collections in Cameroon revealed the presence of several species including B. dorsalis, Ceratitis cosyra , Ceratitis anonae , Ceratitis capitata , Ceratitis quinaria , Dacus punctatifrons Karschand Dacus bivittatus. Continuing to be scarce, however, is multi-year quantitative information on fruit fly’s species composition and their seasonal dynamics, host utilization, and fruit infestation levels, particularly from the principal commercial fruit species mango and guava, and the performance of different monitoring tools in mid-altitude humid and high rainfall agro-ecologies from Central Africa. The broad objective of this study is to establish and validate basic multi-year data necessary for the development of integrated pest management programs of fruit flies across two agro-ecological zones in Cameroon with contrasting climate and farming systems, representing a cross-section of the mid-altitude agro-ecologies of Central Africa. The study has the following specific objectives: determine the diversity of fruit fly species and the level of their infestation of mango, guava and other common fruit hosts; compare the performance of male lures and food baits for monitoring the abundance and seasonality of fruit flies in mango and mixed fruit orchards; and determine the contribution of temperature, relative humidity, and rainfall amount to the variation in fruit fly abundance. The results from this Cameroon study can possibly be extended to the rest of the Congo Basin, gallon nursery pot as the southern half of Cameroon is widely considered agro-ecologically a close representative of much of the rest of Central Africa.The study was conducted over 5–6 years in 2 agro-ecological zones of Cameroon as delimited by the Cameroon Institute of Agricultural Research for Development. The two target AEZs included the western highlands, with a mono-modal rainfall pattern , and humid forest, with bimodal rainfall . Both AEZs differed in their topography, climate characteristics, and cropping systems. The choice of the two zones was based on the richness, diversity and availability of fruit tree species. One experimental site was established in each AEZ for fruit fly trapping using food baits and male lures, and for evaluation of fruit infestations by fruit flies. Because of the long-term nature of the experiments and the need to secure traps for continuous monitoring over a period of 6 years, the traps were installed in the experimental orchard of the IRAD research station in Foumbot, for the WH-MR, and at the International Institute of Tropical Agriculture in Nkolbisson, for the HF-BR . Each orchard was characterized according to the description of the area, climatic conditions, fruit species present and management options . A homogenous hectare of mango was used in Foumbot, while a mixed hectare of fruit species was selected in Nkolbisson .All traps were suspended from tree branches with a galvanized steel wire at ~2 m above ground and at least 20 m apart.

The wire was coated at its middle length with a thick layer of Tanglefoot to prevent cursorial access to the traps by predators, particularly the common weaver ant Oecophylla longinoda . The number of traps varied according to the number of attractants used. For this purpose, 2 and 4 traps of each attractant were installed, respectively, at the Nkolbisson and Foumbot sites, for a total of 10 traps in Nkolbisson and 12 traps in Foumbot. For male lure-based traps, a dental cotton roll soaked with 2 mL of either methyl eugenol or terpinyl acetate was suspended from the centre of the trap lid. Two, 5 cm strips impregnated with 2, 2-Dimethyl dichlorovinyl phosphate  were placed at the bottom of the trap as the killing agent. For food-bait traps, BioLure’s 3 components, packed individually in a sachet containing either ammonium acetate, trimethylamine, or putrescine, were adhered to the inside of the Multilure trap. Torula yeast was used as a liquid bait consisting of 2 pellets dissolved in 350 mL of water per trap. Similarly, the commercial product Mazoferm was diluted in 350 mL of water to obtain a 6% concentration, with 2 g of borax added to the solution as a preservative.Food bait and male lure traps were inspected in both orchards at weekly intervals. Torula yeast and Mazoferm baits were replaced weekly, while BioLure, male lures, DDVP strips, and cotton rolls were renewed monthly. Trap servicing techniques and regular rotation among trees followed those of. All the specimens were transferred and preserved in vials containing 70% ethanol. All samples were brought to the Entomology Laboratory of IITA in Yaoundé for identification. Meteorological data were collected at each location with a Hobo Pro v2 data logger for temperature and RH , and a Tru-Chek® DirectReading rain gauge . Temperature and RH were recorded at hourly intervals and the data were retrieved at monthly intervals, while rain amounts were collected between 7 and 8 am daily throughout the study periods.Fruit sampling was carried out from 2011 to 2015. Systematic random sampling was used in the two AEZs to determine the diversity of fruit flies associated with mango and guava fruits in orchards and home gardens. The mango variety “Camerounaise” and two varieties of guava were available at Nkolbisson orchard. At Foumbot orchard, mango varieties included Ruby, Zill, Irwin, Julie Nyombe, Palmer, and “Camerounaise”, and as in the Nkolbisson orchard, there were local and improved guava varieties of unknown names. Twenty mature fruits each of mango and guava—based on the varieties’ maturity status—were harvested randomly from all sampling sites, and up to 10 fallen mature fruits were collected from the ground at 2-week intervals from five trees of each fruit species. Fruits from other cultivated and wild plants were also collected during their fruiting periods from orchards, home gardens, and natural vegetation within a 70 km radius of each of the two experimental sites in WH-MR and HR-BR to determine the host range of fruit flies and the infestation levels. The number and size of fruit samples from the various plant species were primarily determined by the availability of fruits. Efforts were made to ensure a minimum collection of 20 fruits per sample at each location. Collected fruits were classified by species, known variety, date, and sampling area, then counted and weighed. All fruits were incubated in a screenhouse at the IITA station in Yaoundé. Incubation units consisted of 450 mL plastic containers and 1.5 L circular plastic basins. Owing to their larger size, fruits of mango, papaya, and Annona spp. Were individually incubated in plastic boxes. The other fruit species were incubated in the circular plastic basins, but in groups of 3–5 depending on their size. Fruits were placed on a dome-shaped galvanized steel wire grid which rested on a 2–3 cm layer of moist heat-pasteurized Sanaga river sand as fruit flies pupariating medium. Each incubation unit was then covered with a fine-mesh cloth and secured to prevent larval escape. The incubation units were arranged on metallic shelves. The supports of each shelf were placed inside pint-size containers which were maintained at full capacity with soapy water as barriers against ants and other cursorial insects. Fruit samples were incubated for up to 4 weeks to ensure that all live fruit fly larvae exited the fruits. Sand in each incubation unit was sieved twice at 12 days after the start of incubation, and at the end of incubation for the collection of fruit fly puparia. Collected puparia from each container were placed in 9 cm Petri dishes and transferred to an insectarium maintained at 25 ◦C, 70 ± 5% RH, and photoperiod of 12L:12D for adult emergence. Emergence dishes contained a wet mixture of table sugar and enzymatic yeast as food for full wing development of emerging adults.

However, the underlyinggenetic mechanism has not been revealed for Z. cucurbitae. In fact, nonchemical stimuli, such as color, are associated with vision-related genes that allow the identifcation of different hosts . The genes responsible for color discrimination in Diptera are primarily related to opsin proteins in the photoreceptor cells of the eye . Six types of Rh opsin-expressed genes have been identified as major genes involved in color recognition and photoreception in Diptera insects. The Rh1 and Rh2 opsin genes are associated with motion detection and direction, respectively . Rh3 and Rh4 are UV-sensitive opsin genes, Rh5 is a blue-sensitive gene and Rh6 is a green opsin gene . These opsin genes lead the photoreceptor of eyes to receive various chromophore pigments and then activate a series of visual transduction cascades to launch corresponding color identification behavior. In the genome of polyphagous C. capitata, the long wavelength sensitive genes Rh1, Rh2, and Rh6 and the UV-sensitive genes Rh3 and Rh4 were found, black plastic pots for plants while Rh2-4 and Rh6 were found in the phototransduction pathway of oligophagous B. minax . Moreover, the role of Rh6 in modulating green color discrimination was reported in C. capitata and B. minax .

In B. minax, the function of Rh6, which is responsible for green spectral sensitivity, has been identifed by knockdown of the gene B. minax in female adults, and B. minax flies significantly reduced their preference for green fruit after cutting Rh6 . Absence of a member of the blue sensitive opsin subfamily was found in both tephritid species C. capitata and B. minax, but Rh5 can be specifically expressed in Drosophila . Reports about vision-related genes directly involved in the host expansion of tephritids are still very few.Tephritid fruit fy hosts expand to other new host plants, and the phenology of the new host is another nonchemical stimulus that affects fly adaptation. The phenology of the host plant fruits includes the time of flowering, fruiting, or maturation . Many studies have revealed that dormancy plays a crucial role in assisting insects in responding to various phenological environments, including the phenology of different host fruits . Therefore, genes associated with development are crucial factors that regulate the adaptation of phenology of various hosts. For example, genes related to sensing daylength or photoperiodism and the central nervous system regulate chronic adaptation . Under the regulation of related genes, diapause may involve the deceleration of the developmental progress of tephritids to synchronize the phenological environment . R. pomonella of tephritids is a typical case.

The ancestral host of R. pomonell is the hawthorn Crataegus mollis, but its species host expanded to the domestic apple Malus domestica and subsequently formed a new apple race . Apple fruits ripen earlier than hawthorn. The flies that infest apples and hawthorns must differentially time their life rhythms to match the differences in ripening times of their respective hosts . To realize this process, the flies of the two host races varied their time of overwintering pupal diapause. Under the pressure of different host fruit phenologies, many development-related genes are involved in regulating the adaptation to the different phenologies of two host plant fruits . Functional genes associated with cell/tissue development , metabolism , translation , and cell division are highly enriched . By increasing the expression levels of these genes, the CNS development of apple flies was elevated during their diapausing period compared to that of hawthorn flies. Adult emergence-associated genes, including key hormone signaling genes, the ecdysone receptor partner usp, the ecdysone biosynthesis protein ecd, cell cycling genes Myb and rbf, genes coding Mediator complex proteins, and various genes in the Wnt signaling pathway , etc., were enriched to regulate adult fy eclosion to match their host fruit ripeness .Genes coding for ribosomal proteins are often associated with protein translation by stably expressing ‘housekeeping’ genes. This type of gene is involved in many basic biological processes, such as digestion, detoxification, growth, and development, in most organisms .

Therefore, ribosomal genes may also be involved in the host plant expansion of tephritids after receiving chemical and nonchemical stimuli. As mentioned above, ribosomal genes increased their expression level to regulate the growth of R. pomonella in response to the different phenology of its new host apple . The role of ribosomal genes involved in host expansion and new host adaptation of insects, including tephritid flies, is mainly related to the response of ribosome-inactivating proteins in host plants . RIPs have been found to have insecticidal functions in many insects, including beetles, mosquitoes, and moths . Ribosome genes can help insects such as tephritids realize host shifting by regulating their expression levels to counteract the RIPs of various host plants . In addition, ribosome genes interact with some epigenetic factors, which leads to chromatin remodeling to change gene expression and regulate different biological processes, including host plant adaptation . In response to different secondary chemicals, ribosomal genes were also involved in host detoxification of different species of the R.pomonella complex. R. zephyria and R. pomonella are sister species in the R. pomonella complex that specialize in snowberry and domestic apple plants, respectively . In reciprocal transplant tests of these two Rhagoletis taxa, microarray data indicated signifcant enrichment of mitochondrial ribosomal proteins when the two fly species fed on their new hosts, which contain different complements of phenolic and glycosidic in laboratory studies . Several studies on lepidopteran species revealed the role of ribosomal genes in response to host expansion . For example, ribosomal genes were downregulated in C. suppressalis when extended to the novel host water oats , which may be a more suitable host for C. suppressalis than its native host, rice. In contrast, ribosomal genes were upregulated in H. armigera when shifting to unsuitable novel hosts .The role of genes associated with the oxidative phosphorylation pathway is primarily involved in energy metabolism and provides energy in the form of ATP for most organisms and most biological actions . The OXPHOS pathway is coupled with the mitochondrial electron transport chain, and mitochondria are major sites of reactive oxygen species production in the majority of eukaryotic cells . The level of mitochondrial oxygen fow through the OXPHOS pathway influences ROS homeostasis and regulates the energy supply in different biological processes . OXPHOS genes can take part in many biological activities, and therefore they may also be important in the regulation of the response to host plant expansion of tephritid flies. Research on Bactrocera tau reared on two native cucurbit hosts and a novel host showed a large number of upregulated NADH genes in the OXPHOS pathway in transcript data of B. tau when feeding on banana. These results suggest that OXPHOS genes play an important role in the process of novel host fruit use in B. tau . OXPHOS was also involved in the host expansion of R. pomonella in response to the different phenologies of various hosts, as mentioned above. Certain genes in the fat bodies of tephritids are also involved in the energy supply for many biological processes, including digestion, detoxification, drainage pot development, and immunity . Differentially expressed genes, such as the lipase gene, ATP synthase gene, and alpha-amylase genes , were documented in the tephritids B. dorsalis and P. utilis in response to different secondary chemical environments .The various types of genes summarized above led to multilevel responses in tephritids, including nervous-, behavioral-, chemical-, and physical-level responses, when the flies faced different host environments. These multilevel responses to host expansion result in multilevel adaptations in flies, which lead to successful expansion to a novel host . Adaptation to a novel host is a complex process. Multilevel adaptation in fruit flies results from multigene regulation rather than a single gene or several genes performing various regulatory roles. The transcriptome data revealed that olfactory-, digestion- and detoxifcation-related genes and ribosomal genes were all involved in novel host adaptation in R. pomonella . Laboratory strains of B. tau also had activated OXPHOS genes and digestive and detoxification genes when the fy responded to a novel host environment .

The multiple-gene regulation mechanism during host expansion to a novel host was also documented in other insects. For example, C. suppressalis launched three types of genes simultaneously to regulate adaptation to the new novel host water oat . S. yangi differentially expressed genes related to digestion, detoxification, oxidation–reduction, stress response, water deprivation, and osmoregulation during adaptation to the new host Ephedra lepidosperma . Various genes also regulate the adaptation of tephritids to new hosts via multiple mechanisms. As summarized above, the alteration of gene expression levels, gene family expansion, and the use of various gene types or subfamilies are the major mechanisms involved in novel host adaptation.Many tephritid species attack economically important crops, including vegetables and fruits. The economic losses caused by tephritids reach over US$2 billion annually . Control strategies for tephritids primarily involve chemical use in many countries, which may be harmful to the environment and human health. Therefore, more environmentally friendly control methods should be sought and recommended when possible. RNA interference is an effective method to safely control tephritid flies. RNAi control methods suppress the expression of certain target genes by importing dsRNA . Therefore, selecting the target genes to be ‘silenced’ is a key step in the RNAi control method . Some target genes are associated with functions such as temperature sensitivity and sex determination . For tephritid species, including Anastrepha suspense , Anastrepha fraterculus , B. dorsalis , B. minax , Bactrocera tryoni , and C. capitata , effective RNAi controls have been developed based on the suppression of functional genes associated with eye pigmentation, embryonic segmentation regulation, postembryonic growth/development, reproduction, embryonic temperaturesensitive lethality and sex determination . Based on these target genes, RNAi can be applied in pest control not only for tephritid species but also for some Coleopterans and Lepidoptera insects by foliar spays, ingested dsRNA or sterile insect technique application . However, functional genes related to host plant adaptation are also target genes in RNAi control methods for tephritids. For example, the vision-related gene R6 or gustation gene GR59f of B. minax , digestion-related genes try1, try2, try4, and try5 of B. dorsalis , olfactory Orco gene of B. oleae , CSP2 gene of B. dorsalis , and detoxification genes CYP6A41 and CYP6EK1 of B. dorsalis are associated with host adaptation functional genes, and all of these genes possess an exploitable potential as target genes to control fruit flies. More target genes related to host plant expansion for tephritids need to be identified for their major functions and implemented in pest management. Although RNAi is an effective and tractable genetic tool, other novel gene tools, such as clustered regularly interspaced short palindromic repeats and the CRISPR-associated protein 9 gene editing system, can also provide scalable pest control strategies . Compared with traditional RNAi, CRISPR‒Cas9 can knock down or modify the target gene precisely instead of just suppressing the expression of target gene . The target genes edited by the CRISPR‒Cas9 system can create stable and heritable strains, which can be applied in actual tephritid control. Applying the CRISPR‒Cas9-mediated editing system, some target genes in tephritid flies have been evaluated for their potential for functional application, such as the eye pigmentation gene we , embryonic segmentation gene prd , sex-determination gene Astra-2 , tra2 , and pupae color gene wp . CRISPR/Cas9-mediated precise editing is a process in whichCas9 endonuclease recognizes a specific genomic region under the leading of chimeric single guide RNA . The CRISPR/Cas9 system editing the functional target gene shibire, tsl in B. tryoni and the white pupae gene wt in B. dorsalis, C. capitate, and Z. cucurbitae have been applied in the development of genetic sexing strain application in SIT control. This gene tool also has broad application prospects in tephritid management based on host plant adaptation-related genes in the future. Regulation of host adaptation would be an important mechanism to target because this adaptation allows tephritids to expand in new habitats and change to new biotypes. Therefore, developing suitable novel host adaptation functional genes as target genes in genetic disruption control strategies could help prevent tephritids in an environmentally friendly manner.Fruit trees exhibit two major multiannual reproductive strategies . In the first, the amount of fruit produced allows a sufficient amount of vegetative growth to support production of an ample number of flowers during the following year . Such trees, including fig and some orange and grapefruit cultivars, are defined as regular bearers.

The lemon vacuolar H+-ATPase was purified and characterized by Taiz’s group . They revealed that, in fact, two tonoplast-bound ATPase activities exist, a nitrate-sensitive V-type ATPase that is partially inhibited by vanadate, and a vanadate-sensitive ATPase that is partially inhibited by nitrate . These results should be taken with caution because of the possible cross-contamination of the tonoplast vesicles with other membrane vesicles. Nitrate inhibition seemed to be dependent on the time of tonoplast vesicle preparation; for the same phenological stage, inhibition peaked during the spring and was minimal during the autumn–winter, suggesting an environmental effect resulting in seasonal changes in membrane lipid composition . Moreover, the H+/ATP coupling ratio varied between 1 to 2 as the DpH increased, displaying a pH-dependent slippage, where the hydrolytic activity and the H+ transport are partially uncoupled. Further, pot with drainage holes the fruit V-ATPase reconstituted into artificial proteoliposomes showed a steeper pH gradient than the corresponding reconstituted epicotyl enzyme .

Overall, the following characteristics seem to allow lemon fruit V-ATPase to generate a steep pH gradient: variable coupling, low pH-dependent slip rate, low proton permeability of the membrane, lower H+/ATP stoichiometry, and improved coupling by citrate, the major accumulated organic acid, which also enhance the enzyme’s ability to generate a pH gradient. The pyrophosphatase activity in acid lime fruit was much lower than that of H+-ATPase, suggesting the latter as the major mechanism for proton influx . Tonoplast vesicles isolated from juice cells of ‘Valencia’ oranges displayed similar V-type ATPase and V-PPiase activities, although a steady-state was reached faster with ATP as substrate. At a DpH of 3 units, V-PPiase synthesized PPi in the presence of Pi, indicating that mature orange juice cells acted as a source of PPi, providing a mechanism for recovery of stored energy in the form of the pH gradient across the vacuole during later stages of development and postharvest storage . In summary, in light of the possible presence of an additional tonoplastic H+ transport mechanism, P-ATPase, vacuolar proton homeostasis and transport across the tonoplast require further biochemical research. A vacuolar citrate/H+ symporter, CsCit1 , homologous to the Arabidopsis decarboxylate transporter, was characterized in orange fruit; its mRNA and protein levels coincided with the acid-decline stage, suggesting its role in citrate efflux .

Yeast cells expressing the CsCit1 displayed electroneutral coupled citrate–H+ cotransport with a stoichiometry of 1citrate/2H+.Amino acids have been studied in citrus fruit in relation to the nutritional value of the juice provided the motivation, mostly for early workers, to analyze the levels of free amino acids and their patterns of accumulation during fruit development and storage . The exposure of fruit to stress on-the tree and cold or heat treatments during storage was associated with the accumulation of several amino acids. Glycolysis and the tricarboxylic acid cycle are metabolically associated to amino acid metabolism , its relation to citrate decline and the induction of a γ-aminobutyric acid shunt during the second half of fruit development. Moreover, the possible relationships between amino acid accumulation and Huanglongbing resistance/tolerance mechanisms have been recently investigated .In general, all of the amino acids are detected in the juice of mature fruit, with aspartic acid, asparagine, serine, glutamic acid, proline and GABA being the more abundant . A gradual increase in most of the free amino acids was detected during fruit development and toward maturation of Valencia orange .

This increase is associated with citrate decline and it is common to all citrus cultivars . However, different trends were detected in Navel oranges , with most amino acids and their metabolites decreasing from stage II to III of fruit development . A comparative analysis of total amino acid contents among various citrus cultivars showed lemon and mandarin with overall higher contents of essential amino acids than pomelo, grapefruit or sweet orange . Moreover, lemon displayed higher levels of amino acids with bitter taste, such as histidine, phenylalanine and valine, as well as acidic amino acids, aspartic acid and glutamic acid. Following harvest, citrus fruit are usually subjected to relatively long storage periods at low temperatures. However, heat treatments, which vary from 37°C for 24 h to ~50°C for a few minutes, prior to storage, are common to reduce pathogenic agents, as well as to induce resistance to chilling and pathogens. The effects of such treatments on amino acid contents and metabolism were investigated, with conflicting results. In Satsuma mandarins, the contents of most amino acids were reduced or remained unchanged following heat treatment and only ornithine showed a consistent increase following the treatment . On the other hand, Matsumoto and Ikoma found that most Satsuma mandarin amino acids were heat-responsive, showing a remarkable contents increase during postharvest storage at 20°C or 30°C, but not at 5°C or 10°C. However, two amino acids, ornithine and glutamine, were cold-responsive, suggesting active metabolism during postharvest cold storage. Changes in amino acid metabolism during fruit development of various cultivars and in the presence of external stimuli have been studied mostly by transcriptomic and metabolomic analyses. The activation of the GABA shunt, a major route for citrate catabolism , was identified in a transcriptomic analysis and confirmed by proteomics ; these analyses identified an increase in the transcript of glutamate dehydrogenase, aspartate/alanine aminotransferase, glutamate dehydrogenase, glutamine synthase, GABA amino transferase and succinate semialdehyde dehydrogenase during fruit development, and the presence of their corresponding proteins during the declining-citrate stage of fruit development . Moreover, use of an aconitase inhibitor, which induces citrate accumulation, resulted in induced activities of some of the enzymes of the GABA shunt . In addition, proteins of most amino acid-synthesis enzymes were induced either from early stage II to stage II or from stage II to stage III of fruit development, including pathways leading to the synthesis of cysteine, glycine, serine, leucine, valine, asparagine, aspartate, alanine, ornithine and glutamine . Induction of amino acid metabolism was suggested to play a role in the accumulation flavor-associated volatiles . Comparative transcriptomic analysis of high- and low-citrate oranges showed elevated transcript levels of phenylalanine-, arginine-, proline-, cysteine- and methionine-metabolism genes in the high-citrate orange . Cold storage of mandarins resulted in major alterations in amino acid metabolism, including the biosynthesis of proline and arginine, and significant enhancement of the catabolism of branchedchain amino acids . Catabolism of the branched-chain amino acids leucine, isoleucine, and valine releases acetyl-CoA, providing a precursor for amino acid-derived volatiles that are associated with off-flavor development during fruit storage . Water stress also induced alterations in the amino acid metabolism suggested to be involved in defense mechanisms against stress .Citrus HLB, caused by the phloem sap-restricted bacterium Candidatus Liberibacter, is a serious production threat to the citrus industry in various regions of the world. The bacteria are transmitted by phloem sap-piercing citrus psyllids while they feed, mostly on young expanding vegetative shoots. Different citrus cultivars show varied susceptibility/tolerance to HLB. The differential response seems to be associated with psyllid feeding preferences and with plant tolerance to the bacteria. Based on controlled graft-inoculation experiments, cultivars were classified into three major groups, sensitive, moderately tolerant and tolerant, each showing different symptoms, from severe leaf chlorosis, large pot with drainage depressed growth and death in the sensitive cultivars, to fewer and lesser severe symptoms in the tolerant cultivars. The bacteria appeared to be auxotrophic for a few amino acids, supplied by their host. The bacteria were suggested to affect free amino acid availability by altering the expression of amino acid storage proteins, at least in the insect host.

To assess whether amino acid metabolism plays a role in the variable citrus tolerance to HLB, metabolomics analyses were performed in various cultivars on healthy and infected trees. Although most of the analyses were performed with phloem sap, and not the fruit, we include their brief description, as some fruit symptoms might also be associated with changes in amino acid metabolism. In a metabolic survey of phloem sap and leaves of citrus cultivars showing varied sensitivity/tolerance to HLB, the levels of all amino acids were elevated in the tolerant cultivars . Comparative analyses of amino acid contents in the phloem sap of bacterium-permissive and non-permissive hosts showed that seven amino acids, mostly of the glutamate family, were associated with susceptibility, whereas five amino acids, mostly of the serine family, were associated with tolerance/resistance . Moreover, high proline-to-glycine ratios were associated with bacterium-permissive hosts. Overall, the level of consistency in these studies in relation to amino acid composition in sensitive/tolerant plant species was not high. HLB-symptomatic Valencia orange fruits showed an overall increase in the level of most detected amino acids as compared to no symptomatic fruit, possibly due to protein degradation .Disease resistance or susceptibility of a plant depends not only on the specific plant–pathogen combination, but also on the developmental stage of the host tissues. The ripening process of fleshy fruit is an example of a developmental transition that coincides with increased susceptibility to pathogens. Ripening involves a complex network of regulatory and hormone-mediated pathways leading to significant changes in the physiological and biochemical properties of the fruit . Among the ripening events, modifications in cell wall structure and composition, conversion of starch into simple sugars, changes in apoplastic pH and redox state, and decline in the concentration of antimicrobial metabolites contribute to susceptibility of fruit to pathogens . The enhanced susceptibility of ripe fruit to pathogens could be a default outcome of ripening or, alternatively, could be promoted by some, but not all, ripening processes . Fruit pathogens exhibit necrotrophic, biotrophic, or hemibiotrophic lifestyles , categories that reflect different infection strategies . Necrotrophs, such as the ascomycete, Botrytis cinerea, cause necrosis by deploying hydrolytic enzymes , secreting toxins and/or hijacking the plant’s enzymatic machinery . Biotrophs depend on the integrity of plant host tissues and have developed strategies to deceive the host to obtain nutrients without inducing plant defenses or cell death . Hemibiotrophs are those pathogens that switch lifestyles at different developmental phases and/or in certain environmental conditions . Therefore, the infection strategies of different pathogens challenge the competency of the plant host to respond and deploy effective defense mechanisms. Tomato has served as a model organism to study fruit ripening and has emerged as an informative experimental system to characterize the molecular regulation of the ripening-related susceptibility to pathogens, in particular to necrotrophic fungi, such as B. cinerea . B. cinerea fails to develop in unripe tomato fruit, but as fruit start their ripening program and become ripe , concurrently they become more susceptible to infections, which lead to rapid breakdown of host tissues and extensive microbial colonization . The roles of the plant stress hormones, ethylene , salicylic acid , jasmonic acid , and abscisic acid , in the control of plant developmental processes and the initiation of defense mechanisms against necrotrophic, biotrophic, or hemibiotrophic pathogens have been documented mostly for vegetative tissues . However, our understanding of how these hormones influence plant–pathogen interactions in fruit is still limited. The gaseous hormone, ET, is involved in the control of terminal developmental programs, such as organ abscission, leaf and flower senescence, and fleshy fruit ripening . ET also modulates plant resistance and susceptibility to pathogens. Thus, from one point of view, ET controls a variety of immune responses in conjunction with other signaling networks; but from another perspective, it promotes senescence or ripening, processes which facilitate infection by pathogens . JA influences flower development and may be involved in some ripening processes, depending on the plant species . The best-known function of JA is to regulate plant immune responses against insects and pathogens, particularly necrotrophs . JA may also play a role in resistance against abiotic stresses, including mechanical stress, salinity, and UV irradiation . SA is a phenolic compound with hormonal features that is crucial for the establishment of basal defenses, effector-triggered immunity, and both local and systemic acquired resistance . SA is typically involved in the activation of plant defenses against biotrophs and hemibiotrophs, but it also appears to enhance susceptibility to necrotrophs by antagonizing the JA signaling pathway through the regulatory protein NPR1 and by inhibition of auxin signaling . ABA regulates many aspects of plant development, including seed dormancy and germination, and plays a significant role in tolerance to abiotic stress . Negative and positive roles have been described for this hormone depending on the pathosystem, developmental stage of the host, and/or the environmental conditions in which the plant–pathogen interaction occurs .

To evaluate the thermal stability of encapsulated bio-actives, the cells were heat-treated at 90 ◦C for 1, 2, 5, 10, 20, 40, and 60 min in a temperature-controlled water bath. The heating conditions were selected based on prior studies. After the heat treatment, the cell-encapsulated polyphenolics were extracted using the methods described in Section 3.10 of the material and methods section. The total antioxidant concentration of the extract was then measured using the FRAP assay. The control group of cells with encapsulated compounds but without the heat treatment were also extracted using the same approach and used for calculating the retention ratio during the treatment. The results in Figure 4 illustrate the percentage of total antioxidant capacity retained at each time point during the heating process. As observed in Figure 4, the bacterial carrier effectively protected the encapsulated compounds during thermal treatment. Approximately 93% of the antioxidant capacity for the encapsulated MG juice was retained after 1 h of heat treatment , square pot whereas only 74% of the initial antioxidants were preserved without using encapsulation after the heat treatment of juice for 1 h.

These observations indicated that cell carriers can effectively protect encapsulated antioxidant compounds against thermal stress. In addition to the total antioxidant capacity, the retention of anthocyanins was also monitored during the heating process at 90 ◦C. The cells encapsulated with polyphenols from the juice matrix were sampled at 1, 2, 5, 10, 20, 40, and 60 min, and compared to the non-heated polyphenols encapsulated in cells from the juice. As described previously, the anthocyanin content retained in the cells was extracted using methanol and measured using a UV-Vis spectrometer. Figure 5 shows similar patterns of enhanced stability of anthocyanin compounds on cell carriers similar to the results in Figure 4. Despite the fact that the MG juice contains more colored pigments , these compounds seemed to be more susceptible to heat. Only 61% of the anthocyanin pigments in the MG juice were retained after 60 min of heat treatment. In contrast, 90% of the encapsulated anthocyanin pigments were preserved in the cell carriers. These results demonstrated that the bacterial cell carriers effectively protected encapsulated anthocyanins from degradation caused by the thermal treatment.Overall, the results demonstrated that, after 60 min of heat treatment at 90 °C, more than 87% of the total antioxidant capacity and 90% of the anthocyanin content were recovered from the encapsulated MG as compared to the respective juice without encapsulation. The degradation of juice phenolics content including anthocyanin from heating were comparable with previous studies. The thermal stability of the encapsulated active compounds was significantly higher than non-encapsulated MG juice.

This protective effect of microcarriers has been observed in a range of encapsulation systems such as spray-drying particles and emulsions. However, comparable or higher percentages of antioxidant capacity and pigment content retention were observed using the cell carriers compared to the synthetic encapsulation carriers. For instance, more than 20% losses were observed for anthocyanins encapsulated in polymer matrices such as maltodextrin, mixture of maltodextrin and gum arabicadvantage of the cell carriers might be attributed to both the physical cellular structure and its complex chemical composition. As shown in Figure 1, the cell structures persisted through the encapsulation process, and literature has shown that some of the Lactobacillus strains can maintain structural integrity at elevated temperatures around 100–120 ◦C, for 30 to 60 min. The robust structure is essential for protecting encapsulated bio-actives, whereas colloidal encapsulation systems tend to destabilize both physically and chemically during encapsulation or in adverse environmental conditions. Besides the physical structure, the antioxidant property of intracellular content of L. casei has also been reported. Aguilar-Toalá et al. suggested that glutathione and other intracellular lipid and protein components might be involved in the antioxidant activities, which might in turn help stabilize and protect bio-active compounds encapsulated within the cell carrier.

Therefore, cell carriers are an efficient encapsulation material for preserving the bio-active functions of the extracted polyphenolics during thermal processing. In addition, encapsulation using cell carriers exhibits certain advantages in terms of the manufacturing process. In this study, we used L. casei cells to encapsulate a composite profile of polyphenolics with a basic temperature-controlled incubation. Currently, spray drying and freeze drying are the most commonly applied industrial techniques for microencapsulation and stabilization of plant polyphenolics from natural sources. Spray drying is a unit operation where liquid is atomized in a hot gas current to obtain a powder. While spray drying is prevalent with low cost, its limitations have also been extensively discussed. We observed 4 to 5 times higher amounts of anthocyanin content encapsulated in the cell carrier in this study when compared to spray-dried powder. Despite variations in the raw material, loss of heat in sensitive compounds during spray drying might be due to the exposure to oxygen and the thermal treatment . In addition, the drying process may cause the loss of dried material due to wall deposition, low thermal efficiency, broad size distribution, and irregular microstructures. Encapsulation using the preformed cellular structure of probiotic bacteria and passive incubation, on the other hand, significantly simplified the process with more uniform cellular size and microcellular structure.Total antioxidant capacity of juice matrix before and after encapsulation was quantified using the Ferric Reducing Antioxidant Power assay. Antioxidant activity was selected as a representation of the total bio-active compound concentration in the juice. The changes in the antioxidant content of the juice after encapsulation was evaluated to assess the encapsulation efficiency of diverse class of bio-active compounds. The protocol for measuring FRAP activity was adapted from Benzie and Strain. The stock solutions included 300 mM acetate buffer , 10 mM TPTZ solution in 40 mM HCl, and 20 mM FeCl3·6H2O solution. The fresh working solution was prepared by mixing 25 mL acetate buffer, 2.5 mL TPTZ solution, and 2.5 mL FeCl3·6H2O solution and then warmed at 37 ◦C before using. Fruit juice before and after encapsulation was allowed to react with 2850 µL of the FRAP solution for 30 min in the dark condition. Change in color of the solution was quantified using a UV-Vis measurement at 593 nm using a spectrometer. The standard curve was generated using a range of Trolox solutions between 25 and 800 µM. Results were expressed in µM T.E./mL fresh juice. The samples were diluted in case the absorbance value measured for the samples was over the linear range of the standard curve.Anthocyanin content in juice before and after encapsulation was also measured using a UV-Vis spectrometry. Grape juice is a significant source of plant anthocyanins and changes in the level of anthocyanins in a juice matrix before and after encapsulation also represent a measure of encapsulation of water-soluble pigments in bacterial cells. The absorbance value of the clarified samples was scanned from 250 nm to 600 nm, and a peak intensity was recorded at 530 nm for all the samples. The samples were diluted accordingly to avoid saturation in the absorbance signal. Standard curves were constructed using different concentrations of keracyanin chloride and anthocyanin content was represented as keracyanin equivalent content.Confocal Laser Scanning Microscopy images of bacterial cells after encapsulation with and without incubation with muscadine juice sample were collected using a Zeiss LSM 510 upright microscope with 40×/1.1 water objective.

Each sample was excited at 405 nm using an argon diode laser. Emission scans were acquired using a 500–550 nm bandpass emission filter. Lambda scans of each sample were collected over a range of 470–670 nm with 20 nm step size. The average intensity of the images acquired at different wavelengths during the lambda scan was measured using ImageJ software and plotted using an Origin 8.0 .Phenolic compounds were extracted by mixing 2 mL of the reconstituted MG juice sample with 13 mL of acidified methanol . After mixing using a vortexer, drainage collection pot the mixture was sonicated using a bath sonicator for 10 min and the extract was separated from the remaining juice solids by centrifugation at 5500 rpm for 5 min. The samples were then diluted 10-fold with milliQ water for HPLC-DAD analysis. To assess encapsulation efficiency and yield in cell-based carriers, phenolic content in the aqueous phase before and after encapsulation process was quantified. Chromatography separation and detection of phenolic compounds were performed on an Agilent 1260 Infinity reverse phase HPLC -D.A.D. system equipped with a thermostatic autosampler, thermostatic column compartment, and a diode array detector according to a method adapted from Plaza et al.. An Agilent PLRP-S 100  column with an Agilent 3 × 5 mm guard column was used at a temperature of 35 ◦C for all the analysis. Mobile phase A: 1.5% phosphoric acid solution. Mobile phase B: acetonitrile solution containing 20% mobile phase A. The gradient protocol for HPLC separation and analysis was as follows: 0 min, 94% solvent A; 73 min, 69% A; 78 min, 38% A; and 90 min, 94% A. The flow rate was 1 mL/min and the injection volume for all samples was 10 µL. Samples were filtered through 0.45 µm type H.A. Millipore filters prior to injection. Absorbance spectra were recorded from 250 nm to 600 nm. The eluted compounds were monitored and identified based on spectral and retention time comparisons with standards at multiple wavelengths, including 280 nm for Flavanol [gallic acid, -catechin and -epicatechin] and polymeric phenols, 320 nm for hydroxycinnamates , and 360 nm for flavonol and derivatives , respectively, using the D.A.D. detector. External calibration curves were constructed for gallic acid, -catechin, -epicatechin, caffeic acid, coutaric acid, quercetin, and myricetin glycosides were used for quantification of the target compounds. Polymeric phenols were quantified as catechin equivalents. Chromatograms were integrated using the Agilent CDSChemStation Software .The thermal stabilities of the encapsulated bio-active compounds were evaluated using a thermostatic water bath at 90 ◦C for up to 60 min. A 1 mL suspension of the cells with encapsulated compounds and 1 mL of juice alone were added to the prewarmed 20 mL glass vials and incubated in the dark for 1, 2, 5, 10, 20, 40, and 60 min. The concentration of the total antioxidant contents in the juice sample and the cell encapsulated sample were maintained the same. After the treatment, 1 mL acidified methanol was added to each vial. Bead-beating at 6.0 m/s for 30 s for 3 times was then carried out to facilitate thorough extraction. Finally, the homogenized samples were sonicated using a bath sonication device for 10 min. The methanolic extract was then centrifuged to remove cell Ecosystems are suffering from the pressures of on-going global change, including climate change and habitat loss. One of the main consequences of ecosystem disturbance is the local extinction of species, yet we have little understanding of the consequences of these extirpations for ecological interactions, community dynamics and ecosystem functions. To predict how ecosystems, which are naturally dynamic, will react to these pressures, we first need to understand how communities react to the natural dynamics that lead to changes in their composition of species, with special emphasis on changes in species interactions and the ability of the community to re-arrange itself and maintain its functioning. Up to now, understanding community-level rearrangements following changes in species composition has proved elusive, given the great complexity of ecological systems, which feature high levels of species diversity, interactions across species and environmental variability. The use of network analyses to represent some of the biotic interactions has allowed us to address part of this complexity. However, many network studies have used temporally and spatially aggregated data of observed interactions representing a snapshot of a community. Aggregating data omits important information regarding the dynamic nature of ecological interactions, and in particular concerning species functional roles, which can change due to competition for resources, the presence of parasites and pathogens or changes in species composition. These changes in species composition have been primarily assessed through studies focusing on species extinctions or invasions. Some of them have used experimental set-ups to explore community level dynamics following species extinctions. For example, Brosi & Briggs temporarily removed the most abundant bumblebee species and analysed how the rest of the pollinator community responded. They found that in manipulated sites, floral fidelity decreased, with consequences for plant reproductive success, and also that the loss of a single pollinator species changed pollination network structure.