As shown in Figure 3.4, the expression level of CYP7A1 , a gene that controls bile acid synthesis rate from cholesterol, was increased by 2.1-folds in the HP diet, while that was reduced to 0.86-, 0.91- and 0.90- fold in LP, LE, and HE diets. HMG-CoAR and Cyp51 are two important genes in cholesterol biosynthesis. Compared with the control diet, LP, HP, LE, HE demonstrated the same up-regulating pattern in these two genes — 1.03-, 1.51-, 1.11- and 1.18-folds for HMG-CoAR , as well as 1.18-, 1.72-, 1.25- and 1.22-folds for Cyp51 , respectively. LDL receptor facilitates the hepatic LDL uptake from circulation. In the present study, LP and HP diets up-regulated LDLR expression by 1.18- and 1.38-folds , while that was slightly down-regulated by 0.95-fold in both LE and HE diets. These findings were in line with hepatic LDL cholesterol — all the reformulated diets prompted hepatic cholesterol synthesis. HP diet increased bile acid synthesis while other diets decreased it. Peel-formulated diets elevated LDL uptake and extract-formulated ones alleviated it. For peel-formulated diets, bile acid synthesis was dominating in lowering hepatic cholesterol, whereas for extract formulated diets it lowered LDL uptake. PPARα was an essential transcription factor regulating fatty acid β-oxidation. It was up-regulated in LP and HP diet-fed hamsters by 1.04- and 1.61- folds. In contrast, a lower expression of 0.90- and 0.93-fold of PPARα was observed in LE and HE diets . SREBP-1c is a gene encoding transcription factor for fatty acid synthesis . It targets SCD-1 to catalyze the synthesis of monounsaturated fatty acids, which is a substrate for TG synthesis and storage. LP diet slightly up-regulated the expression of SREBP-1c by 1.07-folds, while HP, LE, hydroponic nft gully and HE diet down regulated that by 0.74-, 0.85- and 0.92-fold . All the diets significantly reduced SCD-1 expression level by 0.42-, 0.24-, 0.73- and 0.61-fold . These results indicated that all the formulated diets induced lower uptake of fatty acids.

Peel-enhanced diet slightly promoted fatty acid β-oxidation in a dose-dependent manner, while extract-enhanced diet mitigated that, which was aligned with liver TG levels and hepatic lipid contents.To identify the microbiota-changing effects of different diets on lipid metabolism, relative abundance was assessed at the phylum level . Besides these, Proteobacteria is considered correlated for the variation of the functionality of gut microbiota . Compared to the control HF diet, HE and HP diets reduced the relative abundance of Firmicutes by 9.33% and 18.3% while increasing the RA of Bacteroidetes by 43.1% and 41.9% and nearly triplicated the RA of Proteobacteria. The corresponding ratio of Firmicutes/ Bacteroidetes dropped by 39.4% and 42.4%, with an increase of Proteobacteria/ Bacteroidetes ratio by89.6% and 105.1%, indicating that a shift of fecal microbiota towards leaner phenotypes. Verrucomicrobia was boosted to 4.0% and 8.5% for HP and HE diets, which was non-detectable for the HF diet. Similar observations of Verrucomicrobia increase were found in formulated diets with PPE , cranberry , and black raspberry , which could be attributed to the abundant polyphenol content. At the same time, Cyanobacteria was elevated from 0.3% in the HF diet to 2.3% in HE and shown of associated with improved gut health . To the best of our knowledge, no other research reported the change of Verrucomicrobia and Cyanobacteria after PPP incorporation in hypolipidemic diets. Further metagenomics studies are required to understand how Verrucomicrobia and Cyanobacteria modulate the lipid metabolism pathways.As shown in Figure 3.6, the expression of hepatic HMG-CoAR was significantly correlated with obesity-related indices, with a positive correlation with plasma concentrations of the total- and LDL-cholesterol , and a negative correlation with liver , adipose and body weight .

The expression of LDLR also exhibited a significant positive correlation with total cholesterol. This was consistent with previous research from Teh et al. , who concluded that HMG-GoAR and LDLR were the two major regulating factors in meditating hypo cholesterol effects of hamsters fed with fruit and vegetable seed meals. As for types of bacteria, total plasma cholesterol exhibited a significant positive correlation with the phylum Bacteroidetes, and a significant negative correlation with the F/B ratio in response, suggesting that the increase of Bacteroidetes attributed to the decrease of F/B ratio and played a role in elevating total cholesterol level.Pomegranate peel, a commonly underutilized by-product with high phenolic and fiber content, was incorporated into the hypolipidemic diet in the form of powder and extract to investigate its hypoalipidemic potential. PPP and PPE containing a rich mixture of phytonutrients demonstrated sufficient effects to suppress weight gain, hepatic lipid profile and ameliorate the symptoms of metabolic syndrome in Golden Lakeview Golden Syrian hamsters with HF diet-induced obesity. These observations can be at least partially explained by hepatic metabolism changes and changes in gut microbiota composition. In this study, PPP and PPE lowered Firmicutes and boosted Bacteroidetes, Verrucomicrobia, and Cyanobacteria to lower the F/B ratio, as well as increased microbiotadiversity. These indices were significantly correlated with obesity-related indices, indicating that microbiota might play an important role in the hypolipidemic effects of PPP and PPE. 2 hepatic genes were closely related to modulating the plasma and lipid profile, suggesting the ingested cholesterol and LDL uptake level were crucial metabolic changes. However, adverse plasma LDL-elevating effects were observed in a higher dose of PPP and PPE intake, which required further study on the potential toxicity.

Since the 21st century, society has an increasing awareness of health and is switching to healthier lifestyles and eating habits . Several sectors for product development are responsible for the change, such as food industries, researchers, health professionals, and regulatory authorities . In this context, functional foods have great potential. Functional foods represent the portion of the human diet that could provide health benefits and reduce the risk of chronic diseases beyond nutrition. Polyphenol-containing products are a common type of functional food with proven health benefits, such as protecting against certain cancers, cardiovascular diseases, type 2 diabetes, osteoporosis, pancreatitis, gastrointestinal problems, lung damage, and neurodegenerative diseases . According to Scalbert & Williamson , 1 g of daily consumption of polyphenols in long term is suggested to fulfill all the aforementioned health benefits of polyphenols. U.S. dietary guidelines recommended daily food intake to satisfy certain nutrient needs. However, polyphenols are not included and only 552 mg of polyphenol is satisfied through the recommended diet based on our calculation .Yogurt is a popular fermented dairy product known for its high nutritional value, especially the significant content of proteins and essential minerals, such as calcium. Greek Style Yogurt is a type of nutrient-dense yogurt with increasing popularity among consumers. According to Statista , from 2015 to 2020, the consumption of GSY in the U.S. significantly improved 50%, worth $3.7B and accounting for 52% of the U.S. yogurt market share. Compared to regular yogurts, GSY contains a higher solids content and is often perceived as being less acidic. The nutritional information commonly claims “twice the amount of protein as in regular yogurt” . However, they are never considered aYogurt is a popular fermented dairy product known for its high nutritional value, especially the significant content of proteins and essential minerals, such as calcium. Greek Style Yogurt is a type of nutrient-dense yogurt with increasing popularity among consumers. According to Statista , from 2015 to 2020, the consumption of GSY in the U.S. significantly improved 50%, worth $3.7B and accounting for 52% of the U.S. yogurt market share. Compared to regular yogurts, GSY contains a higher solids content and is often perceived as being less acidic. The nutritional information commonly claims “twice the amount of protein as in regular yogurt” . However, they are never considered a temperature pH at breaking, . cooling conditions and . handling of product post manufacture . Based on these considerations, this study aimed to investigate the effects of different contents of protein and PPE on the sensory, nutritional, dutch buckets for sale and functional attributes of GSY. Response surface methodology with the multi-response statistical technique was applied to optimize a yogurt formulation.To quantify the tannic acid equivalent, a 0.6 mL extract sample was mixed thoroughly with 2.5 mL of 10-fold diluted Folin-Ciocalteu reagent and 2 mL of 7.5% Na2CO3using a vortex mixer .

After 30 min of 25°C incubation of the mixed solution, the absorbance was measured at 760 nm using a UV spectrophotometer . To measure the DPPH scavenging activity, liquid extract or DI water was mixed thoroughly with 3 mL of DPPH solution in methanol using a vortex mixer and kept in a 25°C water bath for 20 min. Liquid extract was also mixed with 3 mL of methanol and used as a blank solution. Absorbance at 517 nm was noted. Three measurements were conducted for each liquid sample, and each test was replicated three times. For each liquid extract, the tests were conducted in triplicate, and the absorbance was read three times for each sample. A reference blank was prepared using the aforementioned procedure with DI water rather than liquid extract.For each response, linear, 2FI, and quadratic models were built. Models of the highest adjusted-R2 value without aliasing were selected, and all the responses could be extrapolated by linear models. Linear effects of protein content were significant on all responsevariables, while that of extract content were only related to TPC , firmness and DSA . The corresponding coefficients along with respective p-values were listed in Table 4.4. 3D response surface graphs were generated to visualize the interaction effects of protein and extract content on GSY characteristics . TPC increased with lower protein content and extract addition. This finding was in line with previous research. Trigueros et al., incorporated pomegranate juice into yogurt and observed the polyphenolprotein interaction. After formulation, their PGY contained 40% of juice and presented 241.44 mg GAE/L of TPC, which meant 85.35% of the theoretically expected. They also evaluated the TPC of PGY permeate after 1-day storage and concluded a TPC of 111.92 mg GAE/L. indicating nearly 54% of the TPC remained interacting with milk proteins. An increase in DSA was observed with higher protein and extract content. The same pattern was found in the study carried out by Jiménez et al., As expected, syneresis decreased with enhanced protein content, which led to a dense yogurt matrix microstructure and enhanced denaturation of whey protein . Usually, lower syneresis equals to longer shelf-life. However, a grainy texture should be avoided when enriching the milk base with high protein. pH and all the texture properties were positively correlated with protein content while not affected by the extracted content. S. thermophilus and L. bulgaricus in yogurt starter were able to produce exopolysaccharides during fermentation and improve yogurt texture. According to Sodini et al., , higher solid content was correlated with stronger EPS interaction with casein, therefore a stronger texture could be formed.In this study, a modified response surface methodology was applied to investigate the effects of protein and pomegranate peel extract on the physiochemical characteristics of Greek Style Yogurt, including total phenolic content, DPPH scavenger activity, pH, syneresis, firmness, cohesiveness, consistency, and viscosity. Enhanced protein content affects all the characteristics while extract content only affects TPC, DSA, and firmness. Based on product quality and visual appeals, the optimum formulation should consist of 8% of protein and 77g of extract for a 130 g yogurt in this study. Further research is needed to analyze the product cost and explore the bio-availability of polyphenol within the fortified GSY for better mass production guidance.Pectin is widely found in the middle lamella layers between plant cells , forming a primary cell wall during plant growth and development . It is a family of heterogeneous polysaccharides consisting of α-1,4-Dgalacturonic acid , L-rhamnose , D-galactose , L-arabinose , and other 13 different monosaccharides through 20 different linkages . The pectin backbone primarily consists of D-GalA residues linked at α-1,4 positions. Based on the abundance of side chains, pectin can be divided into “smooth”, homogalacturonan, and “hairy” regions, namely rhamnogalacturonan I, rhamnogalacturonan II, xylogalacturonan, and apio-galacturonan . A comparison of these regions is listed in Table 5.1 . The backbone unit, GalA, can be partially esterified with a methyl group or converted into the carboxylic acid amide with ammonia. . Based on the degree of methyl-esterification and acetylation, pectin can be divided into high-methoxyl pectin , low-methoxyl pectin , and amidated pectin as shown in Table 5.2.Besides the aforementioned significant industrial benefits, pectin has functional properties as dietary fiber, prebiotics, and fat replacer, as well as in antiglycation, antioxidant, and antibacterial.

The limited number of studies on blueberry phytochemicals and cell culture models of intestinal inflammation, the diversity of cell lines used, and parameters measured speak to the need for more studies to determine how blueberries modulate gut function and health.Because both inflammation and oxidative stress are frequently associated with the development of chronic diseases , it is important to understand how dietary factors impact these outcomes. Here, we have reviewed studies reporting the effects of blueberry phytochemicals on cell culture models of inflammation and/or oxidative stress . Blueberry phenolic compounds and more broadly, phytochemicals, exert regulatory effects including a decrease in proinflammatory gene expression/production in part through the modulation of the NF-κB pathway. A modulation of the MAPK pathway by blueberry phytochemicals is less evident with contradictory observations reported but may also play a role. Blueberry phytochemicals decreased DNA damage in cells in vitro, via the reduction of ROS production, lipid peroxidation, and an increase in antioxidant enzyme activities. Despite many in vitro studies on blueberry extracts, no specific compounds have emerged as singly responsible for the regulatory effects on inflammation and oxidative stress. Virtually all studies have focused on blueberry phenolic extracts or fractions, with a large emphasis on anthocyanins. Health effects of dietary anthocyanins have been extensively reported and discussed , and berries provide an excellent vector for anthocyanin consumption. Blueberries have a complex anthocyanin profile and both major anthocyanidin derivatives, planting gutter malvidin and delphinidin, have demonstrated a reduction of inflammatory markers in different in vitro models of intestinal inflammation and endothelial dysfunction .

Although it is highly likely that anthocyanins largely contribute to the health benefits provided by blueberries, as supported by the number of studies focusing on those compounds, it is doubtful that they are entirely responsible for the bioactivities. Several in vitro studies compared different fractions of blueberry phytochemicals, with reports of similar or better effects by other phenolic fractions and/or whole blueberry extract compared with anthocyanins . These different studies highlight that mechanisms of action of individual blueberry compounds and fractions are context and/or model specific. More studies comparing the effect on individual compounds and well-defined combinations of molecules in different systems are needed to investigate the impact of a system’s environment or system-specific regulation on the bioactivity of blueberries. Although the amplitude of the effect of individual compounds appears to be widely specific to the model studied, the use of whole fractions of the fruits seems to alleviate inflammation and/or oxidative stress more consistently across models, despite not always demonstrating the strongest effects compared with specific blueberry fractions. As the health effects of polyphenols have been extensively described, more data on other phytochemicals should be gathered as they may also exert health benefits. Other notable phytochemicals in blueberries include ascorbic acid , polysaccharides , and volatile compounds and could contribute to inflammatory or oxidative responses of cells to stimuli. A blueberry volatile extract, high in monoterpenes , modulated the inflammatory response in LPS-induced RAW 264.7 cells through inhibition of the NF-κB pathway . Phenolic compounds, although carrying anti-inflammatory and antioxidant modulatory effects, may not be solely responsible for the health benefits of blueberries. Whether the phytochemicals act in synergy or target different molecular pathways remains to be elucidated.

Although the scope of this review is limited to blueberries, the anti-inflammatory and antioxidant effects and mechanisms are likely applicable to other commonly consumed berries. Berries are generally rich in polyphenols, particularly anthocyanins, flavonols, and proanthocyanidins, but the profile of each berry species, and even within varieties, harbors differences in terms of the individual compounds present and their respective concentration . Gasparrini et al. reviewed in detail the anti-inflammatory effects of several berries in cellular models using LPSinduced inflammation, and consistently report alleviation of inflammation by berry phytochemicals through inhibition of NF-κB and MAPK pathways. Other reviews also discuss and compare the anti-inflammatory properties of berries, in preclinical and human models . Moore et al. and Gu et al. have reported similar anti-inflammatory effects of berry volatiles compared with phenolic extracts for cranberries, blackberries, blueberries, red and black raspberries, and strawberries. Notably, the bioactivities of berry polyphenol extracts do not always explain the overall anti-inflammatory effects observed with whole berries , highlighting that potential health effects of berries as a group derived from highly diverse phytomolecules. After consumption, blueberries and their phytochemicals undergo metabolism through phase II enzymatic reactions in the enterocytes and hepatocytes or microbial metabolism in the gut . Evidence of the role of blueberry metabolites in the modulation of inflammation and/or oxidative stress has also been established . Metabolites of elderberry were tested in RAW 264.7 and dendritic cells, and p-coumaric, homovanillic, 4- hydroxybenzoic, ferulic, protocatechuic, caffeic, and vanillic acids [also reported to be blueberry metabolites ], exerted a dose-response inhibitory effect on NO . Studies regarding berry catabolites are less abundant than studies on berry parent phytochemicals but have gained interest in more recent literature. These studies of microbeand host-modified phytochemicals are extremely important to fully understand the potential anti-inflammatory effects of blueberry consumption. Although most of the evidence focuses on the effect of individual compounds, it is essential to consider the potency of these metabolites in profiles similar to what occurs physiologically.

To take the compound profile and physiologically available doses into account, Rutledge et al. treated LPS-induced rat microglial cells with serum from subjects having regularly consumed blueberry, strawberry, or a placebo powder blends over 90 d. The blueberry consumption decreased NO production, TNF-α secretion, iNOS expression, and moderately modulated COX-2 protein expression in the cells . This type of design allows the integration of a more realistic profile of parent compounds and metabolites from blueberry consumption, at physiological doses, within a cell-culturebased model. The current review summarizes the extensive amount of literature available on blueberry phytochemicals and inflammation using cell-based models. This choice comes with limitations, since it can be challenging to interpret results using specific concentrations of berry-derived molecules on cells when concentrations of these metabolites at the site of the target organs may not be established. There have been major differences in concentrations used to treat the cells, ranging anywhere from tens of μg/mL to mg/mL for total polyphenols and from tens of ng/mL to ≤1.2 mg/mL for anthocyanin fractions. Some of these concentrations are much higher than the blood concentrations that would be present in the body after consumption, as bio-availability of anthocyanins in the body is estimated to be lower than 2%, and peaking at 100 nmol/L after consumption of grape/blueberry juice . The relevance of the findings of cell-culture-based studies in complex human systems needs further investigation. These studies should comprise of well-controlled clinical trials, with the relevant choice of placebo controls and inclusion criteria depending on the specific blueberry phytochemical and physiological condition investigated. Future studies should also quantify the entire suite of berry-derived molecules and derivatives in key pools such as the blood, concurrently with physiologic indices of inflammation and oxidative stress.General anti-inflammatory and antioxidant outcomes are consistently reported for blueberry extracts or derivatives across many studies. However, gutter berries results observed in diverse cell culture studies from different investigators are challenging to interpret due to the differences in protocol, treatment, cell line, and analyzed markers. More studies investigating the effects of blueberry extracts on different systems and using comparable conditions would be valuable. Cellculture-based models are not suitable to draw definitive conclusions on the effects of blueberry compounds on complex physiological processes occurring in the human body. Limitations include the compartmentalization of the observations in space and time: the compounds are only available in the form they are distributed and to the type of cells tested, outside of any regulatory processes by surrounding local tissues or on the whole-body scale, and tested on a one-time, acute, and usually high-dose treatment. Thus, precautions should be taken when drawing conclusions from simplified models, especially when using pharmacological doses of compounds. Despite the limitations, cell-culturebased studies have yielded critical information regarding mechanisms of action of blueberry phytochemicals, and have provided consistent evidence that components of blueberries have anti-inflammation and antioxidant properties, which likely contribute to health and functional benefits attributed to blueberries.The gastrointestinal tract, especially the large intestine, houses the most abundant and complex microbiota in humans. Most of intestinal bacteria belong to the phylum Firmicutes and Bacteroidetes , which make up more than 90% of known phylogenetic categories and dominate the distal gut microbiota. Other lower abundance bacteria include Actinobacteria, Fusobacteria, Proteobacteria, and Verrucomicrobia.

Diet is one of the important factors contributing to the gut microbial composition that ultimately affects human health. Obesity and associated metabolic diseases, including type 2 diabetes, are intimately linked to diet . A number of recent in vitro, in vivo, and human studies showed that polyphenols or polyphenol-rich dietary sources, particularly tea, wine, cocoa, fruits, and fruit juices, influence the relative abundance of different bacterial groups within the gut microbiota byreducing the numbers of potential pathogens and certain gramnegative Bacteroides spp. and enhance beneficial bifidobacteria and lactobacilli . Spices are derived from bark, fruit, seeds, or leaves of plants and often contain spice-specific phytochemicals. Spices have been used not only for seasoning of foods but also for medicinal purposes, and have a number of demonstrated disease preventive functions such as antimicrobial, antiinflammatory, antimutagenic activities, and are known to reduce the risk of cancer, heart disease, and diabetes . They are best known for their strong antioxidant properties that exceed most foods. It was reported that of the 50 food products highest in antioxidant concentrations among 1113 U.S. food samples, 13 were spices. Among them, oregano, ginger, cinnamon, and turmeric ranked #2, 3, 4, and 5, respectively . Previous research from our group reported that consumption of hamburger meat with spice mix added prior to cooking resulted in a reduction in the concentration of malondialdehyde, a lipid peroxidation marker, in the meat and in plasma and urine of healthy volunteers, and improved postprandial endothelial dysfunction in men with Type 2 diabetes . Subsequent study reported that commercial spices in dry or fresh form exhibited significant antioxidant capacity that correlated with total phenolic content butnot with the concentration of chemical biomarker . There is limited amount of information regarding the activity of culinary spice extracts against clinical isolated intestinal bacteria, and a limited number of bacterial strains have been assessed for their susceptibility or antimicrobial activity against spices. Gunes and colleagues reported that minimum inhibitory concentration of curcumin against 7 standard bacterial strains is in the range of 129 to 293 µg/mL . Cinnamaldehyde, a bio-active component of cinnamon, was shown to exhibit more potent in vitro antibacterial properties against 5 common foodborne pathogenic bacteria with MIC being 125 to 500 µg/mL as compared to crude cinnamon stick extract , but cinnamaldehyde did not modulate the population of selected Lactobacillus and Bifidobacterium counts in mouse cecal content . Supplementation of rosemary extract was reported to increase Bacteroides/Prevotella groups and reduce the Lactobacillus/Leuconostoc/Pediococcus group in the caecum of both obese and lean rats . Based on potential health benefits demonstrated from our group, this study investigated major chemical constituents, antioxidant activity, and in vitro effect of 7 spice extracts on the growth of 33 beneficial Bifidobacterium spp. and Lactobacillus spp., and established their antimicrobial activity against 88 intestinal, pathogenic, and toxigenic bacterial strains.Plants are some of the greatest chemists on our planet. They offer a vast, barely tapped repository of potentially bio-active compounds, with current estimates predicting over 200,000 unique specialized metabolites across the plant kingdom . Many of these metabolites act as therapeutic phytochemicals and essential nutrients in humans, making plants an invaluable source of bio-active compounds. However, barriers, such as the lack of access to healthy foods, limit the availability of these essential nutrients for human consumption . Plants also produce a wealth of therapeutic phytochemicals, both pharmaceuticals and nutraceuticals , that are difficult to chemically synthesize, leaving consumption of medicinal plants or plant extracts as the sole source of these important chemicals . Additionally, many important phytochemicals are expressed in plants that are difficult to cultivate or produce insignificant amounts of the desired phytochemical .

Protein sequences from Arabidopsis thaliana, Actinidia chinensis, and UniprotKB plant database were also used as evidence for genome annotation. We predicted a total of 128,559 protein-coding genes. Benchmarking Universal SingleCopy Orthologs analysis v.3 was performed to assess the completeness of the assembly and qual-ity of the genome annotation. The annotated gene set contains 1,394 out of 1,440 BUSCO genes . Functional annotation was assigned using Basic Local Alignment Search Tool 2GO to reference pathways in the Kyoto Encyclopedia of Genes and Genomes database. Comparative genomic analyses assigned genes to 16,909 orthogroups shared by six phylogenetically diverse plant species including five eudicots , each with distinct fruit types, and Zea mays as the outgroup. Transposable elements , both Class I and II, were identified and classified in the genome using the protocol described by Campbell et al.. Overall, 44.3% of the blueberry genome is composed of TEs . Consistent with previous reports, the most abundant Class I TEs were long terminal repeat retrotransposons , specifically the superfamily LTR/Gypsy followed by LTR/Copia, while for Class II transposons, the miniature inverted repeat superfamily hAT was the most abundant. The quality of the genome was further assessed by examining the assembly continuity of repeat space using the LTR Assembly Index deployed in the LTR retriever package. The adjusted LAI score of this blueberry genome is 14, and based on the LAI classification, dutch buckets system this score is within the range of ”reference” quality . Estimation of the regional LAI in 3 Mb sliding windows also showed that assembly continuity is uniform and of high quality across the entire genome.

The origin of highbush blueberry from either a single or multiple diploid progenitor species is a long-standing question. Previous reports have suggested that highbush blueberry may be an autotetraploid based on the segregation ratios of certain traits. However, an analysis of chromosome pairing among different cultivars revealed largely bivalent pairing during metaphase I, similar to patterns observed in known allopolyploids. To gain further insights into the polyploid history of highbush blueberry, we calculated sequence similarity and synonymous substitution rates between genes in homoeologous regions across the genome. The average sequence similarity is ∼96.3% among syntenic homoeologous genes. The average Ks divergence between syntenic homoeologous genes is ∼0.036 per synonymous site. The average Ks divergence between homoeologous genes can be used to not only identify polyploid events but also to estimate the divergence of the diploid progenitors from their most recent common ancestor. The Ks divergence between homoeologs in highbush blueberry is six times higher than that between orthologs of two A. thaliana lines that diverged roughly 200,000 years ago. Based on the relatively high Ks rate between homoeologous regions across the genome, this suggests that tetraploid blueberry is unlikely an autopolyploid that was formed from somatic doubling or failure during meiosis involving a single individual . Furthermore, comparative genomics revealed that homoeologous regions are highly collinear, except a few notable chromosome-level translocations . Rapid changes among homoeologous chromosomes is known to occur in newly formed allopolyploids. We also assessed the level of similarity and content of LTR transposable elements among the four haplotypes.

As the most prevalent transposable elements in plants, LTR-RTs undergo continual ”bloat and purge” cycles within most plant genomes, resulting in a unique signature that may distinguish subgenomes in an allopolyploid. To examine the evolutionary history of LTR-RTs in the highbush blueberry genome, we calculated the mean sequence identity of LTR sequences among each of the four haplotypes . This analysis revealed that the majority of more recent LTRs are subgenome specific in highbush blueberry. In other words, the data suggest that LTRs proliferated independently in the genomes of each diploid progenitor , following the divergence from their MRCA, but prior to polyploidy. The pair-wise LTR difference of the two ancestors is 2.4%–2.6%. With Jukes-Cantor correction and synonymous substitution rate of , the estimated time of divergence for the diploid progenitors from their MRCA is between 0.94 to 1.02 million years ago. These date estimates and the average speciation rate for temperate angiosperms suggests that highbush blueberry is either an allopolyploid derived from two closely related species or an autopolyploid derived from the hybridization of two highly divergent populations of a single species. To date the most recent polyploid event in highbush blueberry, we analyzed the unique LTR insertions present in each haplotype. Based on the pair-wise LTR difference between the four haplotypes, which is of 0.81%–0.89%, the polyploid event occurred approximately 313 to 344 thousand years ago. The substitution rate of LTR sequences is likely different from that of protein coding genes. Thus, more accurate date estimates will be possible once the LTR substition rate in highbush blueberry becomes available from future studies. After allopolyploidization, one of the parental genomes often emerges with significantly greater gene content and a greater number of more highly expressed genes.

The emergence of a dominant subgenome in an allopolyploid is hypothesized to resolve genetic and epigenetic conflicts that may arise from the merger of highly divergent subgenomes into a single nucleus. However, classic autopolyploids, formed by somatic doubling, are not expected to face these challenges or exhibit subgenome dominance since all genomic copies were contributed by a single parent. This was recently supported by genome-wide analyses of a putative ancient autopolyploid . It’s important to note that subgenome expression dominance could still be observed in intraspecific hybrids and autopolyploids formed by parents with highly differentiated genomes. To explore this in highbush blueberry, we compared gene content and expression-level patterns between homoeologous chromosomes . While gene content levels were largely similar among homoeologous chromosomes, with a few notable exceptions , gene expression levels were highest for one of the four chromosome copies in the majority of gene expression libraries . Noteworthy, in the three fruit libraries, the most dominantly expressed often became the least expressed among the four homoeologous chromosomes or among the two lowest expressed copies . The most dominantly expressed in other tissues remained so in developing fruit for only two of the chromosomes . These homoeologous chromosome sets have undergone the most structural variation, which may have modified gene expression patterns . These analyses are based on a single biological replicate from a plant grown in a growth chamber. Thus, the findings reported here should be considered as preliminary. Future studies should further explore subgenome expression dominance in highbush blueberry, including at the individual homoeolog level, with additional biological replicates and across multiple environments.The progression of fruit development in blueberry is marked with visible external and internal morphological changes including in size and color . We profiled gene expression in fruit across seven developmental stages from the earliest stage through the final stage to identify genes differentially expressed during fruit development. Distinctive transitions in gene expression were observed between early fruit growth to start of color development and complete color change to ripened fruit. We found that the majority of genes upregulated during early fruit development were involved in phenylpropanoid biosynthesis, nitrogen metabolism, as well as cutin, suberin, and wax biosynthesis . In contrast, genes involved in starch and sugar metabolism were highly expressed at the onset of and during fruit ripening . Moreover, principal component analysis showed the first two components accounted for 84% of the variation and separated the developmental stages into three groups: early developmental stages, petal fall and small green fruit; middle developmental stages, expanding green and pink fruit; and ,late developmental stages, complete fruit color change, unripe and ripe fruit . Genes associated with cell division, cell wall synthesis, and transport were found to be expressed the highest during the earliest developmental stages , which is consistent with previous work on other fruit species. In addition to genes regulating cell proliferation, defense response-related genes were also highly upregulated during the earliest developmental stages. During the middle developmental stages, genes regulating cell expansion, seed development, and secondary metabolite biosynthesis were highly expressed. During late developmental stages and as the berry transitions to ripening, late embryogenesis, transmembrane transport, defense, secondary metabolite biosynthesis, and abscisic acidrelated genes were highly over represented. Blueberry is considered a climacteric fruit; however,unlike the ethylene-driven fruit ripening in other climacteric species, dutch buckets abscisic acid has been demonstrated to regulate fruit ripening in blueberry. In summary, global gene expression patterns mirror the morphological and physiological changes observed during blueberry development .

The economic value of blueberry is largely determined by its fruit quality and nutritional value. We assessed the total antioxidant capacity in mature fruit across a blueberry diversity panel and the abundance of secondary metabolites responsible for its antioxidant activity in developing fruit. A diversity panel, composed of 71 highbush blueberry cultivars and 13 wild Vaccinium species, was evaluated for total antioxidant capacity in mature fruit using the oxygen radical absorbance capacity assay. Similar to previous reports, we observed a wide range in antioxidant capacity across cultivars, with ”Draper” having the highest levels of antioxidants . The observed variation in antioxidants among highbush blueberry, consistent with our results, were previously shown not to correlate with fruit weight or size. However, in another study, a correlation between fruit size and total anthocyanin levels was identified within a few select highbush blueberry cultivars but not across other Vaccinium species or blackberry. This inconsistency is likely due to sample size differences between studies. To further examine the antioxidant capacity in ”Draper” during fruit development, fruits from the seven aforementioned fruit developmental stages were assayed for antioxidant levels . The highest level of antioxidants was observed at the earliest ”petal fall” stage after which, the level of antioxidants declined during the middle and late developmental stages. This is consistent with previous reports on the antioxidant activity in blueberry during fruit maturation and similar to observations in blackberry and strawberry, wherein green fruit have the highest ORAC values. The antioxidant capacity in blueberry is influenced by various metabolites including anthocyanins. Using the same fruit development series, we quantified anthocyanin and flavonol aglycones in ”Draper” using liquid chromatography-mass spectrometry . Overall, as the fruit changed its exocarp color from pink to dark blue during ripening, delphinidine-type anthocyanins started to accumulate and were the most abundant compound in ripe fruit followed by cyanidin, malvidin, and petuni-din . Flavonols were also detected in all developmental stages, with quercetin glycoside being the most abundant , while myricetin glycoside and rutin were present at very low levels. Blueberry also has high levels of phenolic acids; among phenolics, chlorogenic acid was the most abundant. High levels of CGA were observed throughout fruit development, with the highest accumulation detected in young fruits . This correlates with the pattern of antioxidant capacity across different fruit stages, suggesting that CGA is one of the major metabolites contributing to high ORAC values in young developing fruit. CGA is derived from caffeic acid and quinic acid and has vicinal hydroxyl groups that are associated with scavenging reactive oxygen species. The antioxidant properties of CGA have been associated with preventing various chronic diseases.To better understand the biosynthesis of antioxidants in blueberry fruit, we identified homologs of previously characterized genes in other species involved in ascorbate, flavonols, chlorogenic acid, and anthocyanin biosynthesis. The key biosynthetic genes for these compounds exhibited a distinct developmental-specific pattern of expression . For example, genes involved in the conversion of leucoanthocyanidins to proanthocyanidins are highly expressed in the earliest and middle developmental fruit stages but not in ripening fruit . Conversely, genes involved in the conversion of leucoanthocyanidins to anthocyanins were highly expressed in mature and ripe fruit but not during early fruit developmental stages . Additionally, paralogs encoding the same anthocyanin pathway enzymes and genes involved in vacuolar localization of proanthcyanidins and aldehydes -2-hexenal, -2-hexenol, -3-hexenol. Both linalool and geraniol are associated with sweet floral flavor. However, linalool was reported to largely impart the characteristic blueberry flavor when combined with certain aldehydes. Here, we also identified and examined the expression of genes involved in the biosynthesis of linalool. Four of the linalool synthase homologs in tetraploid blueberry are highly expressed during late fruit development . This pattern of expression coincides with previous reports of linalool accumulation in ripened blueberry fruit. On the other hand, one homolog of linalool synthase, although it was expressed during fruit growth, did not show a clear fruit development-specific pattern. Investigating the underlying factors regulating these enzymes will facilitate genetic manipulations that may lead to further improving blueberry flavor in the future.

Some volatiles have been lost during domestication and breeding as a combined result of negative selection and linkage drag in tomato and watermelon . Likewise, gain and loss of terpene compounds during strawberry domestication and its genetic causes have been investigated . Recent advances in sequencing technology and analytical approaches have opened new opportunities to understand the chemistry and genetics of fruit flavor. Genome-wide association studies have revealed loci for flavor in a variety of fruit crops . Meanwhile, genomes-wide expression quantitative trait loci studies have the capability to bridge the gaps between GWAS signals and their underlying causative genes. Integration of GWAS and eQTL studies has led to discovery of a master metabolite regulator in tomato and a flesh-color-determining gene in melon . Long-read sequencing now allows assembly of genomes with high contiguity, and when coupled with parental short-read data , the two haplotypes of a heterozygous individual can be fully resolved. Phased assemblies have improved variant discovery, especially for large structural variants . The extent, diversity and impact of SVs increasingly are being studied in horticultural crops and have been shown to alter fruit flavor, nft hydroponic fruit shape and sex determination . Garden strawberry is an allo-octoploid species with highly palatable non-climacteric fruit . It increasingly has been utilized as a model for Rosaceae fruit crops genomics and flavor research as a result of its short generation time, wide cultivation and high value.

Through exploration of spatiotemporal changes in gene expression and homolog search, several flavor genes have been cloned and validated, including an alcohol dehydrogenase and several alcohol acyltransferases for esters, a nerolidol synthase 1 for terpenes and a quinone oxidoreductase for furaneol. Recently, QTL studies and transcriptome data analyses for strawberry volatiles using biparental crosses have detected QTL and causative genes for mesifurane and gamma-decalactone . Nevertheless, low mapping resolution and a lack of subgenome-specific markers have hampered further characterization of causal genes underlying other QTL. This problem recently was addressed by the development of 50K Fana SNP array using probe DNA sequences physically anchored to the octoploid ‘Camarosa’ genome . High heterozygosity combined with an allopolyploid genome presents difficulties for resolving causative genes and their haplotypes. To further the goal of discovering causative genes affecting flavor in strawberry, association studies with larger sample sizes and additional genetic resources such as eQTL and additional genomes are required. Furthermore, these resources must span the breadth of natural variation in breeding germplasm. Here we present multi-omics resources consisting of an eQTL study representing the genetic diversity of strawberry breeding programs in the US, phased genome assemblies of a highly- flavored University of Florida breeding selection, a structural variation map in octoploid strawberry and a volatile GWAS of 305 individuals. These are combined to leverage the extensive metabolomic, genomic and regulatory complexity in strawberry for the discovery of natural variation in genes affecting flavor. Ultimately, the functional alleles identified will be selected in breeding to achieve superior flavor.The eQTL population consisted of 196 genotypes including 133 newly sequenced accessions . The University of Florida genotypes were grown at GCREC and collected in the spring of 2020 and 2021.

The University of California-Davis collection of diverse selections from multiple breeding programs were grown at either Santa Maria CA or Oxnard CA, for day-neutral and short-day accessions, respectively, and collected in the spring of 2021. Four UC genotypes were collected at both sites to ensure sequencing and SNP quality. Total RNA was extracted from a bulk of three fully ripe fruits using a Spectrum™ Plant Total RNA Kit , after flash freezing in liquid nitrogen. Illumina 150-bp pair-end sequencing was performed on the Illumina NovoSeq platform by Novogene Co. . On average, 6.9 Gb of sequence data were obtained for each sample. Raw RNA-Seq data of 63 samples from previous published studies were retrieved from the NCBI SRA database . In order to quantify gene expression, short reads were trimmed for adapter sequences and low-quality reads with TRIMMOMATIC v.0.39 and aligned against the reference genome using STAR v.2.7.6a in the two-pass mode . Only unique aligned reads were scored by HTSEQ v.0.11.2 in the union mode with the ‘–nonunique none’ flag supplied with the latest Fragaria_ananassa_v1.0.a2 annotation . All count files were compiled in R and normalized with the DESEQ package . To generate the marker dataset for eQTL mapping, SNPs and InDels were called using the mpileup and call commands. Markers were further hard-filtered using BCFTOOLS with the following steps: individual calls with lower than sequencing depth of three were set to missing using + setGT plugin; marker sites with quality < 30, missing rate > 0.3, heterozygous call rate > 0.98, minor allele frequency < 0.05, or number of alternative alleles > 1 were purged; the filtered markers were imported and analyzed in R, and only markers showing more than three matched calls in four duplicated sample pairs were retained. A total of 491 896 markers passed the three stages of filtering.

The missing calls were imputed, and all calls were phased using BEAGLE v.5.2 using the default settings . The eQTL mapping was performed for 62 181 fruit expressed genes using the filtered markers. Linear mixed models implemented in GEMMA were used for association analysis . The relationship matrix was computed in GEMMA and supplied to explain relationship within populations, and the top five principal components with a total of 25.0% variance explained were imported as covariates to reduce effects from population stratifi- cation to signify the genetic variance underlying the target traits. The Bonferroni corrected 5% significance threshold was used, determined the by number of LD-pruned markers . The approach to define an eQTL was similar to that used in previous studies . Briefly, we first clustered all significant markers with distance < 100 kb and purged clusters with fewer than three markers. The lead marker with lowest P-value was used to identify the eQTL, and boundaries of eQTL were defined as the furthest flanking significant markers. Clusters in LD were merged and boundaries were updated. The longest distance between cis-eQTL boundaries and eGene boundaries was limited to 500 kb.Because a substantial number of regulatory elements were found for fruit-expressed genes, a structural variant map would greatly facilitate the identification of potential causative SVs underlying the regulatory elements. To construct an SV map, we first assembled a phased genome of an UF accession. The genome of FL 15.89-25 was assembled into 1480 and 672 phased contigs with N50 of 12.8 and 12.4 Mb, respectively , with similar contiguity to other recent high-quality octoploid strawberry genomes . A Kmer-based approach revealed 97.1% and 99.2% completeness for the haploid assemblies based on parental Illumina short reads, which were corroborated by 98.1% and 98% completeness of the BUSCO eudicots odb10 genes . Phasing quality was evaluated by parent-specific Kmers; the average switching error and hamming error were 0.19% and 0.18% for the F12 haploid assembly , comparable to phased genomes in other species . The phased contigs were scaffolded into pseudochromosomes based on alignment to the ‘Camarosa’ reference genome, with 96.0% and 92.8% of phased contigs placed on 28 pseudochromosomes for the respective F12 and Bea haploid assemblies , consistent with previous flow cytometry estimations . There were only 88 and 79 gaps in the final scaffolds, averaging 3.14 and 2.82 per chromosome for the respective F12 and Bea assemblies . Scaffolding quality was evaluated by a linkage map and public Hi-C data . High collinearity was observed between haplotypes . The FL 15.89-25 assemblies and three additional haploid assemblies were utilized to explore SV diversity in garden strawberry. These geographically and genetically diverse accessions empowered the discovery of SVs across all chromosomes except for a large portion of Chr 4B which may be under strong purifying selection . Individual haplotypes had between 31 574 and 60 453 SVs relative to the PHASE1 assembly of ‘Royal Royce’ , hydroponic gutter with the WONG haplotype harboring the most SVs, consistent with the larger genetic distance of Asian populations to North American populations .

Insertions and deletions were the most common SV types, together consisting of 88.3– 94.1% of SVs. All SVs across haplotypes were then merged into a non-redundant set of SVs . In total, 56 342 deletions, 60 983 insertions, 12 016 translocations, 166 interspersed duplications, 236 tandem duplications and 137 inversions were identified. Unlike the SV composition of a tomato population in which the majority of SVs were singletons , an average of 62.6% strawberry SVs were shared by at least two haplotypes . We observed a gradually reduced number of new SVs every time a new haplotype was merged , suggesting this SV map surveys a substantial portion of SV diversity in cultivated strawberry. The majority of SVs were < 1 kb , whereas only 3.3% were > 10 kb . Structural variations were present extensively in exons , introns and promoter regions . Transposable elements were rich resources of SVs. We identified 34 379 deletions overlapped with TEs, especially inverted tandem repeats and long terminal repeats , consisting of 61.0% of total deletions, significantly higher than the genome-wide TE percentage of 38.42% . In order to investigate whether SVs were related to allele-specific expression in fruit, we performed total RNA sequencing for four biological replicates of FL 15.89-25 ripe fruit . Consistent density distributions of allelic expression ratios were observed across replicates . A total of 12 503 genes exhibited significant ASE. Extreme expression ratios were inflated, with 3415 genes showing extremely imbalanced expression in which the dominant allele contributed to > 90% of gene expression .In order to investigate the genetic control of fruit volatiles, we performed volatile phenotyping and SNP array genotyping with 49 330 markers on a panel of 305 accessions from the UF strawberry breeding program, with 59 individuals overlapped with the eQTL panel . A total of 97 volatiles including esters, terpenes, aldehydes, alcohols, acids, ketones and lactones were quantified . Based on relationships among volatiles, we identified at least five clusters belonging to the same chemical class or biosynthetic pathway, including clusters of eight aldehydes, three ethyl esters, three hexanoic acid derivatives, seven medium-chain esters and three terpenes . Generally high narrow-sense heritability was observed across volatiles , ranging from 0.212 to 0.916, with a mean of 0.660. The highest value of h2 was found for mesifurane and the lowest for octanoic acid, ethyl ester . Genome-wide association study identified 62 signals for 35 volatiles . The lead SNP effects varied from 0.27 to 2.44 , with the largest effect for methyl anthranilate . Two hotspots which contained multiple signals of volatiles belonging to the same class or pathway were found for medium chain esters and for terpenes , which also were detected to in previous studies and reflected in chemical relationships . Our GWAS results confirmed previous homoeologous group assignments for these volatile QTL and clarified their subgenome and physical positions. The SNP AX-166515537 was the lead SNP for three esters, and a 14 Mb region on Chr 6A shared signals for six medium-chain esters. An LD analysis revealed three linkage blocks . The distal region of Chr 3C was associated with six volatiles including five terpenes . This 3.1-Mb region did not display clear LD block separation . Two significant markers for medium-chain ester hotspot and methyl thiolacetate were tested for their predictability of flavor characteristics . Some abundant volatiles including: 2-hexenal, -; butanoic acid, 2-methyl-; and pentanal were associated with multiple DNA variants , suggesting polygenic inheritance. Pentanal was associated with threeloci, together explaining 30.7% of phenotypic variation in a GLM model. Significantly higher pentanal content was observed in genotypes with three doses of the alternative allele at two loci .In this study we leveraged eQTL, GWAS and haplotype-resolved genome assemblies of a heterozygous octoploid to identify allelic variation in flavor genes and their regulatory elements. Finetuning of metabolomic traits such as amylose content in rice and sugar content in wild strawberry recently were made possible via CRISPR-Cas9 gene-editing technology. Similar approaches can be taken in cultivated strawberry for flavor improvement, but not before thebiosynthetic genes responsible for metabolites production and their regulatory elements are identified. Our pipeline has proven to be effective in identification of novel causal mutations for flavor genes responsible for natural variation in volatile content and can be further applied to various metabolomic and morphological aspects of strawberry fruit such as anthocyanin biosynthesis , sugar content and fruit firmness.

Multiple diseases occur in a plant is more common in the real field condition. But the combinations of different diseases are too many to collect sufficient samples for each category from classification perspective . The current researches prefer to solve this problem by semantic segmentation. We do not cover this challenging problem due to limitations of data resources in this work. 2. Formulation of meta-learning data. The samples of PV were taken under controlled condition , which have a clean board as the unified background, the illumination is under controlled, only single leaf in per image, only single disease occurs in per leaf. The settings are simple and very different from the in-wild conditions. That is the reason many researches already achieved high accuracy by using deep learning CNNs on PV . But the samples of AFD were taken under in-wild condition, which have complex surroundings. When testing with AFD, we use PV in meta learning, mainly considering that both datasets are about plant diseases. Since we did not find any other appropriate dataset, the degree of similarity of the data used in training and test was not taken in account. According to our hypothesis, the degree of similarity of data used in meta-learning and test is higher, the adapting is easier, and the result would be better. It is demonstrated that the selection of meta-learning data is critical in this pipeline. The data used in meta-learning stage should be determined by the target. When the application scenarios cannot be predicted, how to formulate an appropriate meta-learning dataset is worthy to study. Inspired by Nuthalapati and Tunga and Li and Yang , round pot the effectiveness of a mixed dataset for meta learning will be considered. 3. Sub-class classification. For the application of plant disease recognition, it is more meaningful to distinguish the diseases belonging to the same species.

What farmers need more than anything else is a diagnostic assistant that can identify similar diseases belonging to the same plant. Although sub-class classification is difficult , it is an inescapable work in plant disease recognition and the performance is needed to be improved urgently. Fine-grained features of the lesions being the distinguishable features to solve this issue. In this direction, lesion detection and segmentation, fine-grained visual classification are involved. 4. The quality and quantity of training data. Most of the current researches of FSL deal with the configuration of data used in test, but very little work has concerned the data used in training. The common sense is that deep learning networks rely on large-scale data. However, a new direction is discussing the quality and quantity of training data recently . These works indicate that part of data can achieve at the same performance as full data. Date quality can be assessed, which can guide to establish a dataset with enough diversity data while without redundant samples. The networks of appropriate depth using good data can achieve optimal results in many traditional CNN classification tasks. In this work, we use large-scale data in base-training and meta-learning. The quantity of data follows the conventional settings for comparison purposes. The data quality assessment work is not involved in this work. For the specific topic of plant disease, the data quality is very important. We know that at different stages of development of plants and diseases, the symptom appearances are very different. How to construct a comprehensive set without redundant data to represent a disease is a valuable work in the future . 5. Cross-domain. The significance of cross-domain has been introduced in prior sections. We emphasize cross-domain again because it is common when we cannot predict the species, surroundings, and photo conditions in test. In this work, we consider it from training strategies.

There are many aspects to explore in future work, such as network architecture, feature distribution calibration etc.In response to the two problems when using FSL for plant disease recognition, we propose a network based on the MB approach that merges CMSFF and CA to obtain a richer feature representation. From experiments, we found that the CMSFF is effective to obtain richer feature representation, especially under the few-shot condition. The CA is an important compensation to the CMSFF, which helps to focus on these meaningful channels. Our method outperforms the existing related works, which indicates that our method is highly robust. The CMSFF+CA is an appropriate combination that fits for any algorithm that needs enhance the feature representation. In addition, a group of training strategies is proposed to meet requirements of different generalization situations. Many factors are discussed in this work, such as backbone networks, distance metrics etc. The limitations of this work and some new related research directions are discussed.Insect pollination is important to commercial blueberry production, and farmers usually rent honeybee hives and, when possible, also buy Bombus terrestris L. colonies and place them in the field during the flowering season. Bumblebees are very effective pollinators for this crop compared to other bees . They can work relatively early in the season when most blueberries flower, at cool temperatures unfavorable to honeybee pollination .According to a review by Garibaldi et al. the proximity of natural areas can increase bumblebee density in crop areas since it can provide floral resources and/or undisturbed nesting/overwintering habitat. Floral resources provided by crop and non-crop areas can also increase bumblebee densities , but the temporal dynamics of flowering crops alters bumblebee densities as well. Previous studies reported a ‘transient dilution effect’ in which bumblebee densities decrease with increasing area of oilseed rape fields during flowering, both in this crop area and in nearby grassland areas .

Only after the flowering events of oilseed rape bumblebee densities increased in nearby areas . Based on these observations, it is possible that bumblebee density in blueberry crops is positively influenced by natural areas and negatively by simultaneously flowering crops in the surrounding landscape. Research questions of this study were: Are there any effects of surrounding natural areas and flowering crop areas on wild bumblebee abundance associated with blueberry fields? What is the relevant spatial scale for these effects? Increasing the understanding of landscape effects on important wild pollinators can provide cues on possible management strategies to increase pollination services for blueberry production.Eight blueberry farms in the central valley of the Region La Araucanía, Chile, were studied . Samples were collected from ‘Briggita’ cultivar of highbush blueberry . Distance between farms ranged from 7.4 to 97.8 km . Blueberry fields varied in size from 0.5 to 120 ha . Commercial honeybee colonies were employed in 6 farms while 4 of those also had B. terrestris colonies. None of the surveyed farms applied pesticides during flowering. Weeds were suppressed in and around all of the fields. Sampling of pollinators was performed on two different dates within the flowering season, between October 13th and November 5 th , 2011, between 11:00 to 17:00 on days with favorable weather conditions . All sampled fields had the same plant density . Flower density was assumed to be relatively constant as blueberry variety was the same and plant age was relatively homogeneous . Depending on the field size, 4, 5 or 6 sites per farm were sampled; in one case only 1 site was sampled because of the limited field size . Sampled sites were placed at different locations within the fields including field edges and field centers, round planter pot in order to capture possible variation of pollinator densities. Each sampling event consisted of separate counts of 4 successive rows in which a person walked through 10 consecutive plants in a row for 5 minutes, recording all insects that visited flowers. All row counts were summed to produce a site-level estimate of abundance.Sampling was performed when the proportion of open flowers on 10 randomly chosen branches was ≥ 0.2. Bumblebees and honeybees were visually identified to the species level and other pollinators were recorded as other hymenopterans, syrphids, or other. During sampling wild bumblebee workers were not yet active and thus all sampled workers were assumed to be from commercial colonies placed by farmers. At the time of sampling no workers were observed in the area, except for those provided by commercial colonies.Natural forest areas and high-food-resources areas surrounding the blueberry fields were mapped in a radius of 3.50 km from the center of the fields, based on orthorectified aerial photos acquired between 2008 and 2010 depending on the location, and high resolution imagery available from Google Earth . The software ArcGIS 9.3 was used for this purpose. Natural forest areas correspond to unmanaged secondary forests and don’t include exotic pine and eucalyptus plantations. High-food resources areas were mapped using the imagery and identified via visual inspection as food resource if flowering in the mapped area at the time of the surveys. The influence of landscape context on pollinator abundance was analyzed at different spatial scales using circular neighborhoods centered on the central point of sampled sites within the farm with radii of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, and 3.5 km. To explore larger landscape effects circular areas of 5 and 8 km radius were used from on a land cover map of 15 m spatial resolution. This map was generated by classification of an ASTER satellite image from 2008 and was used to measure only natural forest areas at these scales. One farm at these scales of analysis was excluded because its buffer overlapped with neighboring farms. The proportion of natural forest and high-food-resources areas varied across farms and both tended to decrease with increasing spatial scale .Analysis of landscape effects focused on the relationship between the proportion of forest area and high-food-resources area with the abundance of wild B. terrestris queens. This group was the most abundant among sampled pollinators. Bumblebee abundances were averaged for each farm and across sampling dates. Linear regression models to predict farm-level bumblebee abundance were fitted as a function of the proportion of forest area and high-food-resources area, accounting for unequal number of sites per farm weighting each observation by the number of sites of that farm. Models with two covariates were tested at each scale of analysis considering also all combinations of different spatial scales. In all models loge-loge transformation of the data was used to improve linearity. Correlation between forest and high-food resources areas at each spatial scale was tested using Pearson correlation coefficient. R v.2.15.0 was used for statistical analysis. These colonies are placed in the field only during blueberry flowering and competition for food resources is unlikely given the over-abundance of this resource.Honeybees were the most abundant pollinator sampled, followed by naturally occurring B. terrestris queens and syrphids . Few individuals of B. ruderatus and B. dahlbomii, or other hymenopterans were found. In farms stocked with commercial bumblebee colonies B. terrestris workers were also observed. The abundance of wild B. terrestris queens was positively associated with the area of natural forest and negatively associated with areas of high-food resources. These associations were significant at various spatial scales and peaked at 1 and 3.5 km radii for forest and high-food resources respectively . No correlation was found between natural forest area and high-food resources area at any spatial scale. However positive correlation of the proportion of natural forest and high-food resources areas between similar spatial scales was found.Among wild pollinators, B. terrestris queens were most abundant reflecting a successful spread of this exotic species in the study area. The only native bumblebee was almost absent which is consistent by previous reports on the decline of this species after the introduction of B. ruderatus and B. terrestris in 1982-1983 and 1997-1998, respectively . The positive relationship between natural forest area and bumblebee abundance might reflect nesting suitability and/or hibernation habitat requirements of queen bumblebees. Undisturbed areas such as forests and forest edges can be suitable nesting habitat for bumblebees . These areas might be a source of continuous pollen and nectar resources throughout the foraging stage of bumblebees. While food supply in early spring can favor bumblebee colony establishment and initial growth , the reproductive success of the colony seems to be determined by late-season food availability provided by surrounding natural areas .. Late-season food supply is crucial for hibernating queens since Beekman et al. found that body weight at the start of diapause positively affects its success, while environmental temperature has no effect.

In ecosystems impacted by anthropogenic disturbance, honey bees may help fill the pollination void left by declines in non-honey bee pollinators . Lastly, where honey bees reach high densities, as reported in some areas of their introduced range , they may exploit enough food resources to compete with other pollinators . These phenomena are of broad ecological, evolutionary, and conservation interest, but to our knowledge, there currently exists no quantitative synthesis on the numerical importance of honey bees as floral visitors in natural ecosystems worldwide, either in their native or introduced range.Here, we use a meta-analysis to address the question of honey bee importance by taking advantage of a recent trend in pollination research—the documentation of community-level plant-pollinator interaction networks . Pollination network studies match the identity and frequency of each type of pollinator visiting each plant species within a locality . While these studies are performed to investigate a variety of questions , data from pollination networks provide an excellent opportunity to investigate the importance of honey bees in natural habitats, not the least because the role of honey bees has rarely been their focus . Here, we compile a database of 80 pollination networks from natural and semi-natural habitats from all continents except Antarctica, as well as several oceanic islands, including regions where honey bees are native and places where they have been introduced. These networks allow us to address four questions regarding the ecological importance of honey bees in natural habitats. What proportion of floral visits in natural habitats are due to honey bees? Do honey bees reach higher numerical dominance in their non-native range? How are honey bee visits distributed among plant species?

For instance, plant plastic pot what proportion of plant species is not visited by honey bees, and for what proportion do honey bees contribute the majority of visits? Finally, network studies often use visitation frequency as a proxy for pollinator importance . To further assess the value of honey bees as pollinators, we compile data on per-visit pollination efficiency of honey bees relative to other floral visitors from studies on 35 plant species. Using these data, we address a fifth research question: How does the per-visit pollination efficiency of honey bees compare to the average non-honey bee pollinator?We used two approaches to compile our data set of pollination networks. First, we performed a literature search using the ISI Web of Science database with the search terms [pollinat* network], [pollinat* web], and [pollinat* visit* community] from October 2014 to August 2016. Second, we downloaded pollination network data from the Interaction Web DataBase of the National Center for Ecological Analysis and Synthesis website and the Web of Life Ecological Networks Database . From the latter two databases, we downloaded all plant-pollinator interaction network datasets available as of December 2014 that reported visitation frequency in addition to the presence / absence of interaction between plant and pollinator taxa. Each data point in our study consists of a weighted pollination network in which the set of interactions between each plant and pollinator pair is weighted by a measure of visitation frequency . We defined a network as the sum of recorded plant-pollinator interactions in all study sites from a single study that fell within a 50-km diameter circle, regardless of the number of study plots that constitute the network. Sites within the same study that are separated by more than 50 km were treated as separate networks. When we encountered networks from different studies that were less than 50km apart, we excluded those studies that sampled a smaller number of plants or pollinators, or documented fewer interactions.

All networks retained for analyses met the following criteria. The data were collected in natural or semi-natural habitats; agricultural, urban, experimental, or otherwise managed habitats were excluded. Each included network consisted of observations on five or more plant species when pooled across study sites; networks that focused only on select plant taxa with specialist pollination syndromes were excluded from analyses. Included networks documented a broad range of pollinators; studies that had a narrow taxonomic scope or that explicitly excluded honey bees from data recording were excluded. Because we are primarily interested in quantifying the importance of honey bees in natural areas free of human interference, we excluded data from study sites that are known to be heavily influenced by honey bee colonies stocked for adjacent agricultural pollination. Thus, our estimates of honey bee numerical importance may be conservative with respect to mosaic landscapes where natural habitats are intermixed with agriculture, but achieve a closer representation of the role of honey bees in natural areas worldwide, overall. We also did not exclude networks from localities outside of the honey bee’s climatic niche, or where honey bees have never been introduced. In all, we obtained 80 networks from 60 peer-reviewed studies, two graduate theses , and our own study of plant-pollinator interactions in San Diego’s scrub habitats .For each network, we obtained the following data from their associated publications or from study authors when data were not available from publications: latitude, longitude, and final year of data collection. When these data were not available and authors could not be reached, we used the approximate geographical center of the study locality listed in the publication, and the year of publication as the last year of data collection.

We defined the native status of honey bees based on ; in Great Britain , where the native status of honey bees is uncertain, we treated honey bees as native rather than introduced, but classifying honey bees there as introduced in that location did not substantially alter our results. We also extracted the following information from each study, when available: the proportion of total floral visits contributed by honey bees, the proportion of plant species receiving at least one visit by honey bees, and the rank of honey bees with respect to both the total number of interactions and the proportion of plant species visited. Additionally, we used geographic information system analysis to obtain elevation data and bioclimatic variables for each network based on its GPS coordinates. We also assigned each network as being on an island or a mainland; the latter category includes all continents as well as large islands > 200,000 km2 , namely Great Britain , Honshu , and Greenland. For relevant studies for which raw data were not available, we contacted the corresponding authors to request data, or, in cases where data could not be shared, requested summary statistics on plant-pollinator interactions. When raw numeric data were unavailable from the publication or from authors, we used ImageJ to extract data from figures, where possible . Due to the different methodologies and data-reporting requirements of each study, not all of the above mentioned variables were extracted from all networks. Comparison of honey bees and bumble bees in pollination networks: Because studies vary in the level of detail with which individual species of floral visitors other than Apis mellifera are reported, we cannot reasonably compare frequencies of honey bee visitation with those of other single species across all of our networks. However, data are sufficiently detailed in 66 of our 80 networks to enable comparison of honey bees and bumble bees ; the latter are the only other pollinator group with a similar pattern of local numerical abundance and widespread introduction compared to honey bees . We compared the network-level relative visitation frequency of honey bees with that of all bumble bee species combined using a paired t-test. Since our goal was to compare global patterns of numerical importance, this analysis did not exclude networks in which honey bees, bumble bees, or both taxa were absent. It is worth noting that the leaf cutter bee Megachile rotundata , another widely introduced pollinator , was not reported in any of our 80 networks. Drivers of honey bee visitation frequency among pollination networks worldwide: We used multiple linear regression models to examine environmental factors that may contribute to variation in the network-level frequency of floral visits by honey bees. Networks where honey bees were not recorded were excluded from this analysis because of the variety of reasons that could explain their absence, black plastic plant pots ranging from studies that were outside the geographical or altitudinal range of the honey bee , to studies where honey bees were undetected despite being present in the ecosystem .

Inclusion of networks that documented no honey bee visits using a zero-inflated multiple beta regression model in Program R v.3.3.1 did not qualitatively alter our results . The response variable in these regression models was the proportion of all floral visits in each network contributed by honey bees, logit-transformed to improve normality . To identify the environmental model that best explains network-level honey bee visitation frequency, we generated models containing all possible combinations of the following explanatory variables : latitude, longitude, altitude, land category , and bioclimatic variables relating to temperature and precipitation . To incorporate bioclimatic variables, we first performed Principal Components Analysis to avoid constructing models with highly collinear terms. We performed one PCA for the 11 variables measuring temperature , and a separate PCA for the eight bioclimatic variables measuring precipitation ; these analyses enabled us to reduce bioclimatic variables to the first two principal components of the temperature variables and the first two principal components of the precipitation variables . We used R package glmulti to generate the candidate models and to select the best model using corrected Akaike’s Information Criterion scores. We also used the resulting “best” environmental model to address the questions of whether or not the network-level frequency of honey bee visits depends on their native status and the year of data collection, by adding these two variables to the “best” environmental model, both individually and together. Distribution of honey bee visitation frequency across plant species: We examined the distribution of honey bee relative visitation frequency across plant species as measured by the proportion of visits to each plant species contributed by honey bees. In this analysis, we included 46 networks in which at least one visit by a honey bee was recorded, and data on the proportion of total visits contributed by honey bees were available for each studied plant species. We pooled all plant species from all networks, and did not correct for cases in which the same plant species occurs in more than one network. Given the breadth of geographical areas and ecological contexts represented by networks in our study, the same plant species is expected to be served by different pollinator assemblages in distinct networks. Because plant species receiving few visits overall may tend to have extreme values of proportion of visits by honey bees, we also repeated this analysis after restricting the dataset to plant species with 10 visits recorded. Pollination efficiency of honey bees: We used two approaches to compile our data set. Second, we examined the literature cited sections of each of the studies found through the first approach for additional studies that were not captured in the literature search. Data points in this analysis consist of studies of focal plant species that compared honey bees and at least one other pollinator taxon with respect to pollen deposition, seed set, or fruit set resulting from a single visit by an individual floral visitor . In a small number of cases, we used ImageJ to extract data from figures when raw data were not available. In all, we obtained 33 studies reporting single-visit pollination efficiency data for 35 plant species, spanning 23 plant families . Of these, 19 plant species in 16 families were undomesticated, and 16 plant species in 7 families were grown in agricultural settings. Multiple metrics of per-visit efficiency were available from some studies. We used or calculated seed set data whenever available since it is the most closely related to plant reproductive fitness , fruit set when no seed counts were available, and pollen deposition when measures of seed and fruit set were unavailable. For each plant species in each study, we calculated the average single-visit pollination efficiency of non-honey bee pollinators as the numerical mean efficiency metric of all non-honey bee visitors studied. Then, we calculated the relative single-visit pollination efficiency of honey bees by dividing honey bee pollination efficiency by the average efficiency of non-honey bee floral visitors studied.

Lastly, temporal beta diversity measures the degree to which individual temporal samples at a study site differ from one another with respect to the composition of taxa present, providing insight into the temporal turnover of the taxa that make up an assemblage . While some popular indices of beta diversity are mathematically derived from measures of alpha and gamma diversity , recent advancements in the field of statistics have enabled additional measures of beta diversity, such as multivariate dispersion , that are mathematically independent of measures of alpha and gamma diversity. Impacts of anthropogenic disturbance on temporal gamma diversity always result from changes in temporal alpha diversity, beta diversity, or both . Decreases in temporal alpha and beta diversity may be driven by different aspects of disturbance , and may have different implications for biological interactions and ecosystem function even if different patterns of temporal alpha and beta diversity loss lead to the same net change in temporal gamma diversity . Trends in temporal alpha and beta diversity may also act in opposition such that temporal gamma diversity remains unchanged in spite of the profound alteration to temporal assemblage structure . Thus, isolating the mechanisms through which disturbance impacts an assemblage requires an examination of all three components of temporal diversity . Such approaches may also serve to identify the ecological effects that result from disturbance .In this study, square pots plastic we investigated the impacts of urbanization-induced habitat fragmentation on the seasonal dynamics of a diverse native bee assemblage over a two-year period.

Bees represent an appropriate taxonomic group for studying how habitat fragmentation affects temporal dynamics because, like many other organisms that occupy seasonal environments, bees exhibit distinct periods of activity that differ among species with respect to both duration and timing of onset . Previous research has demonstrated that anthropogenic disturbance may differentially impact bee species active in different seasons , and that temporal turnover in bee assemblages can contribute to among-habitat differences in site-level bee species richness . Additionally, the key ecosystem function that bees perform is influenced by the season specific pollination effectiveness and temporal complementarity of individual bee species. An explicit consideration of temporal diversity patterns is thus necessary to assess how anthropogenic disturbance affects bee assemblage structure and to identify potential consequences for ecosystem function. Here, we explicitly examined the seasonal dynamics of our focal bee assemblages by simultaneously evaluating their temporal gamma, alpha, and beta diversity. Our use of linear mixed-effects models and analyses of multivariate dispersion distinguishes our study from previous work on temporal patterns in pollinator diversity, the majority of which has focused on quantifying the relative contributions of spatial versus temporal variation in structuring pollinator assemblages . Our approach enabled us to address the following research questions: does habitat fragmentation affect all three components of bee temporal diversity similarly? And how do the effects of habitat fragmentation vary with time? Addressing these research questions allowed us to scrutinize the impacts of habitat fragmentation with a temporal resolution that would be unachievable by pooling temporal samples within study sites.Study System: Between April and August of 2011 and 2012, we documented bee assemblages in the coastal sage scrub ecosystems of San Diego County, California, USA, a global hotspot of bee biodiversity with over 500 bee species documented in the surrounding areas .

We established 1-ha study plots in CSS habitat situated in large natural reserves , and well-preserved habitat fragments embedded within the residential, urban matrix. In 2011, we surveyed four study plots in reserves and four study plots in fragments. In 2012, we surveyed seven study plots in reserves and 11 study plots in fragments. Details regarding the location and treatment classification of each plot are provided in the Table 1-S1. Many of our study plots are located in the same system of reserves and fragments included in earlier studies on the ecological effects of urbanization-induced habitat fragmentation , including bees sampled incidentally in pitfall traps . Permission to conduct field research was obtained from the University of California, San Diego; the Otay-Sweetwater Unit and Tijuana River National Estuarine Research Reserve Unit of the US National Wildlife Refuge; the City of San Diego Open Space Parks Division and Real Estate Division; the City of La Mesa Open Space Division; and the City of Chula Vista Open Space Division. Data collection: We employed bowl trapping and aerial netting to sample bees at all study plots, on sunny days with light wind. Bowl traps consisted of plastic bowls 7 cm in diameter that were white or painted fluorescent blue or fluorescent yellow and filled with ca. 60 ml of unscented detergent solution. During each survey, 30 bowl traps were placed at a study plot before 0900 h and collected after 1500 h. Traps were placed on level ground in an alternating sequence of colors, deployed in two roughly linear transects originating from the corners of each plot and forming an “X” formation near the plot’s center. Traps were placed 5-10 m apart from one another and at least 1 m from the canopy of large shrubs to avoid being shaded. During aerial netting, one researcher walked throughout the study plot and examined blooming plants as well as presumed nesting substrates for bees. Non-Apis bee species were collected regardless of whether they were on flowers, in flight, or in the vicinity of presumed nesting substrates.

In 2011, surveys were performed ca. every 2-3 weeks at each study plot , during which time, 60-min bouts of netting were performed once between 0900 h and 1200 h and once between 1200 h and 1500 h . In 2012, in order to accommodate a larger number of study plots, surveys were performed ca. every 3-5 weeks and included only a single 60-min bout of netting at each plot during each survey. Although seven sites were sampled in both years , the level of sampling employed here seems unlikely to have altered bee assemblages during our study .All collected bees were individually mounted and identified to species or morphospecies within genus using taxonomic keys and the reference collections of the American Museum of Natural History, UC Riverside Entomology Research Museum, California Academy of Sciences, UC Berkeley Essig Museum of Entomology, and UC Davis Bohart Museum of Entomology. Additionally, we also categorized each bee species as a pollen generalist or a pollen specialist based on whether it is documented to exclusively collect pollen from a single plant family. Data used to classify bees as generalists or specialists come from literature accounts for the species  and its subgenus , as well as our own field observations. Bee assemblages often reflect the richness, abundance, and temporal dynamics of their host plant assemblages . Thus, concurrently with the bee sampling, we documented the identities of insect-pollinated native plant species present in each plot in each year; in 2012 we also counted the number of blooming individuals of each plant species in each plot during each survey. We documented blooming plants by walking through pre-planned paths that allowed the observer’s field of view to cover the entirety of the study plot, as in , because many key plant species in our system are patchily distributed and because the thick growth of large, woody shrubs prohibited the use of random linear transects at many of our plots. Statistical analyses: We compared native bee assemblages in reserve versus fragment plots with respect to their temporal gamma, alpha, and beta diversity. In order to avoid human biases associated with aerial netting , our analyses include only bee specimens collected by bowl traps; however, plastic grow pots inclusion of netted specimens in our analyses yielded qualitatively similar results. For analyses requiring species-level identification, we excluded 78 bee individuals not identifiable beyond genus. We also repeated all analyses at the genus level to ensure that particularly species-rich genera did not disproportionately influence our findings; the results of these additional analyses did not alter our main conclusions. Lastly, we verified that reserve and fragment plots did not differ with respect to the composition and temporal dynamics of insect-pollinated native plant assemblages, and that the plot-level compositions of bee assemblages were not spatially autocorrelated . All analyses were conducted in R version 3.3.1 ; packages vegan , MASS , car , and nlme were used in visualizing and analyzing data. Temporal gamma diversity: We define temporal gamma diversity as the diversity of bees at a single study plot, pooled across all temporal samples , with each sample representing the bee specimens collected at one study plot during a single day of data collection. We considered both species richness and assemblage evenness .

In addition, we examined the proportion of bee individuals represented by generalist species , as generalist bees can exhibit higher tolerance to anthropogenic disturbance compared to their specialist counterparts . Lastly, we also examined the temporal gamma component of bee abundance. We used rarefaction in our analyses of species richness and assemblage evenness to account for among-plot variation in the number of bees sampled. We used the lowest plot-level bee abundance recorded each year as the number of individuals to subsample in our rarefactions. Bee abundance was calculated as the total number of bee individuals collected at each plot averaged across the number of temporal samples. Assemblage evenness and generalist proportion were logit-transformed prior to analysis as recommended by , and bee abundance was cube root-transformed to improve normality. We used Welch’s two sample t-tests to compare fragment and reserve plots for all dependent variables listed above. Given the dependence of bee diversity on the diversity and assemblage composition of their host plant assemblages , we also repeated each analysis with the temporal gamma richness of native plants as an added independent variable . We then compared the corrected Akaike Information Criterion scores of each pair of models with or without plant richness added. Compared to original models that did not include plant richness, models that included plant richness yielded qualitatively similar results in all cases but had poorer or equivalent AIC scores; thus, we did not include plant richness in our final models. Temporal alpha diversity: We define temporal alpha diversity as the diversity of bees collected in a single temporal sample . As in our analyses of temporal gamma diversity, we examined species richness, logit-transformed assemblage evenness, logit-transformed generalist proportion, and cube root-transformed bee abundance. In our analyses of species richness and assemblage evenness, we rarefied each temporal sample to 20 bee individuals to allow for unbiased comparisons between treatments and across temporal samples. In analyses requiring rarefaction, we excluded one sample from the 2011 dataset and nine samples from the 2012 dataset . We chose to rarefy to 20 individuals in order to minimize the number of data points to exclude while retaining sufficient resolution in our data. To examine how bee assemblages in reserves and fragments differ over the course of the study period, we constructed linear mixed-effects models. This approach allowed us to quantify the direction of seasonal trends and to detect treatment-by-sample interactions, neither of which is possible for the additive diversity partitioning approach used by most published studies that examined bee temporal alpha diversity . In each model, treatment , temporal sample , and their interaction were included as fixed effects, and study plot identity was included as a random effect to control for repeated sampling as in . To account for possible non-linear relationships between dependent variables and Julian dates of temporal samples, we constructed second- and third-degree orthogonal polynomial models in addition to first-degree linear models for each dependent variable, and selected the model with the lowest corrected AIC score. When alternative models yielded equivalent AICc scores , the model with the lowest degree was chosen. Lastly, as with our analyses of temporalgamma diversity, we repeated all analyses with the temporal alpha richness of native plants as an added independent variable . Models that included plant richness yielded poorer AIC scores in all cases; thus, we did not include plant richness in our final models. Temporal beta diversity: We define temporal beta diversity as the multivariate dispersion of bee assemblages in distinct temporal samples from the same study plot. We chose this index because of its relative mathematical independence from measures of alpha and gamma diversity , as well as its capability to detect differences among assemblages in both species identity and relative abundance .

The Dothiorella clade and Neos. dimidiatum show no specific pattern. The expanded secondary metabolite proteins were specifically abundant in L. missouriana, L. exigua, B. dothidea, Do. sarmentorum, and Neos. dimidiatum. The P450 family was expanded mostly in Lasiodiplodia species and B. dothidea but contracted in Diplodia species and Neos. dimidiatum. Neofusicoccum species and B. dothidea have an important representation of expanded secreted CAZymes, Diplodia and Dothiorella represent several expanded proteins, however, the numbers in Lasiodiplodia are extremely low. Transporter related genes in the Major Facilitator Superfamily were the most enriched in all the clades analyzed . The secondary metabolite related proteins type 1 Polyketide Synthases were expanded in Neos. dimidiatum, L. missouriana, L. exigua, and Do. sarmentorum, whereas Non-Ribosomal Peptide Synthetases were expanded in B. dothidea and Neos. dimidiatum. For the secreted CAZymes, Neof. nonquaesitum, Neofusicoccum hellenicum, and B. dothidea show an enrichment of the Auxiliary Activity family 3. Also, Do. iberica and Diplodia mutila show an enrichment of the Glycoside Hydrolase Family 3.To identify similarities between species in the Botryosphaeriaceae family, a phylogenetically informed-principal component analysis was applied to the significantly expanded families of virulence functions. These gene families were grouped into the functional categories based on the specialized databases, square pots plastic and the PCA was carried out using the Phyl.PCA . Phyl.PCA considers correlations among species due to phylogenetic relatedness, while correcting the matrices for nonindependence among observations .

Two separate analyses were conducted using the clock-calibrated tree presented previously and the tables of the number of genes classified as secreted CAZymes and Secondary Metabolism . Due to the close phylogenetic relationship of the Botryosphaeriaceae family, the set of secreted CAZymes is remarkably similar. However, there is a clear separation of the species that are considered to be more virulent , those belonging to the genera Neofusicoccum, Lasiodiplodia, and Botryosphaeria . At the same time, we observe a close cluster of Neofusicoccum species which are separated from the other groups mostly by the abundance of AA1, AA3, and GH5. In addition, the genus Lasiodiplodia is tightly clustered together with B. dothidea. This is driven by the abundance of AA9, GH28, and GH3, with the last family being more abundant in Lasiodiplodia species. The close clustering of Neofusicoccum, Botryosphaeria, and Lasiodiplodia is driven mostly by their similar profile of GH16 and AA3. Neoscytalidium dimidiatum is well separated from the rest of the species by the higher presence of GH76 and PL3 proteins. The PCA on secondary metabolite genes shows a similar separation of the most virulent genera from the others . Lasiodiplodia species are grouped together by similarly high profiles of T1PKS, Beta-lactone and T1PKS/NRPS clusters. Neofusicoccum species are grouped due to high numbers of terpene synthases and NRPS-like clusters. Botryosphaeria dothidea is separated because of its high abundance of NRPS, T1PKS, Terpenes, Beta-lactone, and NRPS-like clusters.In this study, we describe the genome sequences of seventeen well-known canker-causing fungal species in the Botryosphaeriaceae. The genomes assembled coupled with in-planta experiments allowed us to start analyzing the pathogenicity levels and the virulence factor profiles within this important fungal family. The level of completeness of the assembled genomes is consistent across all the drafts based on the expected and assembled genome sizes. This behavior is also confirmed by the high representation of conserved genes .

The completeness of the genomes, as well as the protein-coding genes and the repetitive DNA content, are similar to those of other woodcolonizing fungi of grape, such as Diaporthe ampelina DA912 , Di. seriata DS831 , and L. theobromae LA-SOL3 . Apart from the estimated completeness of the genomes, it is necessary to understand some of the limitations of the short reads technology, like copy number errors, chimeric contigs, and under-representation of repetitive regions . The functional annotation of the seventeen Botryosphaeriaceae species presents a broad and variableprofile of virulence factors that are used in different ways by fungi to colonize and survive in their hosts . The results show a great variation in the number of genes identified with a functional category, and these differences were usually associated with the genus of each species like those observed by Baroncelli et al. in Colletotrichum and Morales-Cruz et al. in other grapevine trunk pathogens. Researchers are inclined to think that the gene content is associated with the lifestyle and the variety of hosts . The expansion or contraction of a gene family usually occurs on functions that are under positive or negative selection. For instance, the genes related to host colonization and defense are under high pressure, therefore, it is common to encounter duplications or even losses. On the other hand, genes related to growth are more conserved and usually selected against these changes . Gene duplication events are crucial as they are considered to be one of the main processes that generate functional innovation . This process plays one of the most important roles in fungal adaptation and divergence . Host colonization during infection is mostly driven by gene expression of some groups of well-known proteins, namely, the secreted CAZymes, cytochrome P450 monooxygenases, peroxidases, and secondary metabolite-producing proteins .

The Botryosphaeriaceae family has a variable profile of these sets of genes, with the most virulent and aggressive species having, on average, greater numbers of annotated genes in these categories . In grape and pistachio, species in the genera Neofusicoccum and Lasiodiplodia, are typically more virulent than species in the genera Diplodia and Dothiorella . GH functions of β-glucosidases, β-xylosidases, glucanases, L-arabinofuranosidase, and galactanase were present in all the pathogens in this study and significantly more in Neofusicoccum and Lasiodiplodia. In the same way as the GH, AA functions like cellobiose dehydrogenases, alcohol oxidases, pyranoseoxidase were more abundant among Neofusicoccum species and B. dothidea. GH and AA play a critical role in the degradation of the host cell wall compounds , which is involved with the degree of pathogenicity within these genera, albeit on grape, the host we examined. Marsberg et al. ; Massonnet et al. , and Félix et al. , found similar numbers of CAZymes in Neof. parvum, L. theobromae, and B. dothidea, respectively. P450s are instrumental to the development of all organisms. These enzymes are involved in many aspects of primary and secondary metabolisms and are responsible for xenobiotic detoxification and degradation . Virulence may in part reflect the ability of some species to better tolerate and, further, to metabolize phenolic compounds produced by the host. Both Neof. parvum and Di. seriata can eliminate the stilbene piceid and its derivative resveratrol in vitro , but the former is better able to tolerate resveratrol derivatives ampelopsin A, hopeaphenol, isohopeaphenol, miyabenol C, and ε-viniferin, which are produced at higher levels in planta in response to Neof. parvum versus Di. seriata infection . Therefore, it is not unexpected to see a variable profile amongst genera in the Botryosphaeriaceae family and even within a single genus. As presented in Figure 2, some superfamilies are abundant in Neofusicoccum, Lasiodiplodia and Botryosphaeria genera, but other superfamilies are especially more numerous in the Basidiomycetes species included in this study. On the other hand, for most of the superfamilies presented, Sa. cerevisiae shows a considerable lack of such annotated genes, but CYP53 and CYP578 the counts are comparable with the rest of the species. This variation is sourced by the constant evolution and adaptation of the microorganism and hosts to their specific environment . As plants evolve new defense mechanisms and compounds against pathogens, plastic grow pots the fungi diversify their methods to degrade these compounds or generate new metabolites to attack their hosts . The Botryosphaeriaceae species in this study and the two Basidiomycetes present a set of fungal peroxidases that range from 41 to 62. As for the previous putative virulence factors, Neofusicoccum, Lasiodiplodia, and Botryosphaeria genera have the most annotated peroxidases, however, in this case, Diplodia also showed a comparable amount. Manganese peroxidase was only found in the two basidiomycetes. This enzyme has a critical role in the degradation of lignocellulose compounds by basidiomycetes , therefore it is very common in white-rot fungi such as F. mediterranea and St. hirsutum . Ascomycetes that rot wood are characterized as soft-rot fungi, which do not degrade lignin by producing manganese peroxidase, but instead “alter” lignin by producing lignin peroxidases, peroxidases, polyphenol oxidases, and laccases .

The former enzyme was found in higher numbers in the genus Neofusicoccum compared to other genera within the family. The hybrid ascorbate-cytochrome C peroxidase was over represented in the genera Neofusicoccum, Lasiodiplodia, and Botryosphaeria and is associated directly with the detoxification of ROS . The wide array of transporters annotated in this study suggests a high adaptation to toxic compounds, either produced by other microorganisms, the host, or potentially chemical synthesized fungicides . The number of proteins in the Major Facilitator Superfamily and Superfamily in Neofusicoccum, Lasiodiplodia, and Botryosphaeria were more numerous than the other Botryosphaeriaceae species. Protein members of the MFS family may have different functions in the influx/efflux of molecules between cells and the exterior environment, and several cases of fungicide resistances have been associated with the overexpression of certain MFS channels . The former genera have been reported to have lower sensitivities to almost full resistance to different synthetic fungicides . Similar behavior was observed in Do. sarmentorum, were the ATP-binding Cassette ishighly represented. The ABC superfamily plays different roles in fungicide resistance, mycelial growth, and overall pathogenicity . In addition, the array of secondary metabolite gene clusters is more expanded in the Botryosphaeriaceae family than in the Basidiomycetes except for terpene synthase gene clusters. T1PKS, NRPS, and hybrids of T1PKS-NRPS produce toxic polyketides and toxic polypeptides, which kill host cells and leads to disease development . To evaluate the potential differences in virulence within the Botryosphaeriaceae family in more detail, we executed a Computational Analysis of gene Family Evolution . By identifying species and gene families with higher rates of gain and loss can help us to better understand the differences in pathogenicity as it relates to the numbers of copies of virulence genes . Six hundred and sixty-six gene families of the proteins analyzed in this study have a significantly higher than expected rate of gain/loss. The annotation of putative virulence factors in Neofusicoccum, Lasiodiplodia, and Botryosphaeria shows an average expansion of these gene families, even if some of the species shows a contraction, the overall clade rate is positive. Among those expanded or contracted families there is a set of functions that are over represented. The secreted CAZymes seem to be expanding in Neof. hellenicum, Neof. nonquaesitum, B. dothidea, Di. mutila, Do. iberica, and Do. sarmentorum, whereas the Dothiorella species show contractions in some families. However, almost no significant gain/loss of secreted CAZymes appears to be occurring in the genomes of Lasiodiplodia species. The opposite scenario is observed for the P450s, where Lasiodiplodia appears to be actively evolving, showing major expansions in three of the four species in this study. Also, B. dothidea and three Neofusicoccum species show an expansion of these families. On the other side, Neos. dimidiatum, B. dothidea, Do. sarmentorum, L. exigua, and L. missouriana are actively expanding their secondary metabolite gene clusters. Finally, the wide variety of transporters present in fungi, is the result of the positive selection pressure over them. The need of the fungi to adapt to new environments and hosts had selected for multiple mutations that diversifies the transporters functions . The MFS displays the largest effect of expansion and contraction among all the species. Botryosphaeria dothidea, L. missouriana, L. exigua, and Di. mutila appear to be actively expanding the MFS transporters. However, Neos. dimidiatum, Di. seriata, Neofusicoccum vitifusiforme, Neof. australe, and Neof. mediterraneum are contracting MFS transporters. Phylo PCAs results support the idea that within the Botryosphaeriaceae family, Neofusicoccum, Lasiodiplodia, and Botryosphaeria genera are the most virulent . There was a very clear separation of these species from the Diplodia, Dothiorella, and Neoscytalidium. The secreted CAZymes that cause the clustering of the Neofusicoccum species are usually associated with laccases, cellobiose dehydrogenases, and cellulase activities. These enzymes usually target components of the plant cell wall such as lignin, cellulose, cellobiose . Among the functions driving the clustering of Lasiodiplodia and Botryosphaeria, the lytic polysaccharide monooxygenases are one of the most important. They have a role in the oxidative degradation of various biopolymers such as cellulose and chitin.

A STRUCUTRE analysis found that the optimal K was five . This is the same number of populations identified by Chacón-Sánchez and Martinez-Castillo . Rather than fitting neatly into the categories of MI, MII, AI, AII, and admixed, these samples were optimally divided into MII, AI, AII and two MI groups. The larger of the MI groups included a mixture of wild and domesticated lines while the smaller MI group consisted mainly of wild accessions collected in Mexico. Based on this analysis, 36 lines identified as belonging primarily to the two Andean gene pools were removed from the study. For future publication, a higher threshold of admixture may also be considered for removing some additional genotypes. With the Andean lines removed the remaining population showed significantly less population structure .A GWAS of volatile HCN production in the first 15 minutes of tissue rupture caused by thawing, identified several significant SNPs for flower tissue and one highly significant SNP for pod tissue . The most significant SNPs for flower tissue, on Chromosomes 2 and 4 are located near matches for the BLAST search of the white clover Li/li sequence. The SNP identified in pods is not near the significant alignment of the BLAST search against the Lima bean reference genome of the white clover sequence or the QTL identified in the biparental population. When considering cyanogenesis as a defense trait, the immediate release of HCN following tissue disruption deters an insect herbivore and therefore serves as a resistance trait . As such, it will be most successful against opportunistic, square plant pots generalist herbivores rather than specialist herbivores which would have experineced coevolution with the crop and had more opportunity to adapt to its defenses .

Additional study of these findings may yield great contributions to breeding efforst for L. hesperus resistance. Additional significant SNPs were found in the 15-30 minute exposure window . In flower tissue, SNPs on chromosomes 9, 5, and 7 were closely located to significant matches from the BLAST of the white clover Ac/ac gene sequence on the Lima bean reference genome. In pod tissue, a significant SNP on chromosome 6 was also closely located to a match for the Ac/ac sequence.Prior QTL analysis of HCN in floral buds, immature pods, and leaves of a RIL population identified significant loci for volatile HCN on chromosome 5 . This QTL is very close in position to one found by the GWAS analysis of HCN in flowers defrosting for 15-30 minutes, PL05_36471809. There is also a significant alignment with the white clover sequence for the Li/li gene in a nearby region of chromosome 5 . It is interesting to note that there is evidence of β-glucosidase activity being induced by the presence of insect herbivores . The greenhouse from which the samples in this study were collected had a stable infestation of thrips but was free of the larger herbivores typically found in field settings. It is therefore possible that if this study were repeated with field-collected samples, this locus would have a stronger effect.Cyanogenesis is a complex trait in Lima bean with multiple SNPs closely associated with the expression of cyanogenesis. Highly significant SNPs found in flowers during the first 15 minutes after tissue disruption are close matches for the white clover Li/li gene sequence. This could contribute to the effectiveness of cyanogenesis as a resistance trait that deters insect herbivores . Additional SNPS on chromosomes 9, 5, 7, and 6 found in the 15-30 minute exposure window may be associated with the biosynthesis of cyanogenic glucosides as they are close to matches of the white clover Ac/ac gene sequence.

Finally, a QTL on chromosome 5 was in close proximity to previously identified QTL for cyanogeneis in flowers, pods, and leaves as well as the white clover sequence for Li/li. Further analysis and research is needed to clarify the function and expression of genes located near the significant SNPs identified by this study and solidify understanding of the genetic architecture of cyanogenesis in Lima beans. Several additional steps will be take to advance this research prior to publicaiton. First, the STRUCTURE and GWAS analyses will be reexamined to consider higher thresholds of admixture. Next, confidence intervals and markers flanking the significant SNPS will be analyzed to increase certainty about the relationship between these findings and the BLAST search maches as well as previously identified QTL from the RIL population. A study of genome annotations and the expression atlas will also be undertaken to identify clues about the function of genes near these significant SNPs. The results from wild and domesticated accessions will also be compared to determine how the matches for Li/li and Ac/ac genes may have been affected by domestication. Lastly accessions with extreme phenotypes will be identified and their associated genotypes used for breeding, further mapping, and validation studies.Amplifying defense traits that protect plants from insect herbivores through plant breeding has the potential to increase yields while reducing pesticide use and associated concerns for human and environmental health . This is a particularly important strategy for organic systems in which conventional pesticides cannot be used. Lima beans are an important grain legume globally and the most economically important dry bean grown in California where their primary insect pest is the Western Tarnished Plant Bug . Lima beans are a model experimental organism for studying anti-herbivore defense traits . Within this body of literature, many studies have focused on the trait of cyanogenesis . Several experiments have been conducted in recent years to identify specific mechanisms that contribute to the tolerance or resistance traits that protect some Lima bean accessions from damage by L. hesperus .

One mechanism that has been considered is the production of various polygalacturonase inhibiting proteins in the cell walls of Lima bean that bind to L. hesperus salivary enzymes and mitigate attempted digestion of the cell wall . This trait was found to be strongly influenced by environmental variables such as pest pressure and insecticide treatments but the study design did not permit differentiation of these results as the primary goal was QTL mapping . Cyanogenesis is a trait of particular interest since it is known to be an effective anti-herbivore defense trait in wild Lima beans that has been selected against during domestication . Several QTL have been identified for cyanogenesis in flowers, immature pods, and leaves . However, these studies have not yet determined if cyanogenesis is an effective trait in the defense of Lima beans against L. hesperus specifically. L. hesperus predominantly feed on the flowers and immature pod tissue of Lima bean and if cyanide is an effective deterrent or toxin for L. hesperus then increased expression of cyanogenesis in these tissue types could be amplified through breeding without risk to the human consumers of mature seeds which are known to have low cyanogenic capacity . The final part of this study aims to determine how cyanogenesis affects L. hesperus survival and development as well as test if cyanogenic capacity can be induced by the presence of L. hesperus.Many economically important crops with high protein content and great importance for indigenous food systems are members of the legume family . Several of these legume crops are cyanogenic . It appears that this trait has evolved independently several times in the legume family. Within the legume genus Phaseolus, there are five domesticated crop species but only one, Lima bean, is cyanogenic . In addition to Lima bean, five other cross-compatible, Phaseolus species within the Polystachios group of section Paniculati are also cyanogenic . This and other evidence indicate that despite being a widespread trait, cyanogenesis evolved independently multiple times through the recruitment of similar genes .In an extensive screening of wild, weedy, garden pots square and cultivated forms of Lima bean, all were found to be cyanogenic. However, there is variability within and between populations . Domesticated forms typically have much less cyanogenic potential , the amount of stored cyanogenic glucosides, and cyanogenic capacity , the amount of cyanide released when damage occurs . Cyanogenic potential is determined by the biosynthesis and accumulation of cyanogenic glucosides . Cyanogenic capacity is primarily determined by genetic factors but there is also a significant influence of plant age and other environmental factors . Cyanogenesis is typically considered a constitutive trait with strong genetic control by two Mendelian genes . However, there is great variation in the trait within populations and even within an individual plant . Previous studies have found cyanogenic potential and capacity to vary based on the age and tissue type being measured . For example, in Lima bean, young leaves have higher cyanogenic potential than mature leaves .

Wild Lima bean seeds by contrast have very high cyanogenic potential but low cyanogenic capacity, likely due to the low moisture content inhibiting β-glucosidase activity . Additionally, there is evidence that cyanogenic capacity may be locally induced by the presence of insect herbivores even if cyanogenic potential is constitutive . Temperature, humidity, seasonal dynamics, water-stress, and nutrient availability may also affect cyanogenesis .Cyanogenesis is very nitrogen intensive with a one-to-one ratio of nitrogen and carbon in each molecule of hydrogen cyanide . The availability of nitrogen can be a limiting factor for plant growth . Therefore, it has been hypothesized that cyanogenic glucosides evolved first as an intermediate nitrogen storage compound and only later evolved into a defense compound . In the case of Lima beans, evidence supports the hypothesis that cyanogenic glucosides primarily serve as an anti-herbivore defense more so than a nitrogen storage mechanism . Lima bean plants with high cyanogenic glucoside content in leaves had lower above ground biomass than low cyanogenic glucoside content plants when no herbivores are present, but this difference was less in the presence of herbivores . This could indicate that there is a high cost to producing cyanogenic glucosides. Alternatively, these plants may be investing in a strong defense of their vegetative tissue so that a smaller above ground biomass can produce higher yield. Additionally, seeds of Lima bean with high cyanogenic glucoside content had lower germination rates but produce seedlings that had high cyanogenic glucoside content and supported lower growth rates of the generalist herbivore Spodoptera littoralis . In addition to having tradeoffs with growth and vigor, plants with high cyanogenic glucosides have lower investment in other defense mechanisms . In Lima bean, a negative correlation was found between cyanogenic glucosides and volatile organic compound emissions . This evidence indicates that in Lima bean, cyanogenicglucosides serve primarily as an antiherbivore defense compound rather than a nitrogen storage mechanism.Cyanogenesis is an anti-herbivore defense trait found in many plant families . It is especially common in crop plants. While an estimated 11% of all plant species are cyanogenic, the trait is present in approximately 21% of the major world food crops . Given that humans have long known of several effective methods of detoxifying cyanogenic foods, including leaching, cooking, and fermenting, it is possible that crops with this trait were specifically selected by early farmers for their superior defense against insect herbivores . Despite its value as an anti-herbivore defense trait, cyanogenesis has been selected against during the process of domestication. With the notable exception of sorghum, most crops have lower levels of pre-cyanogenic compounds than their wild relatives . This may be because, though satisfactory, our methods of detoxifying foods do not fully eliminate cyanide. Specifically, in the example of Lima bean, the enzyme linamarase rapidly hydrolyzes cyanogenic glucosides during cooking but becomes denatured at 141 °C . If cyanogenic glucosides remain unhydrolyzed when that cooking temperature is reached, their cyanide will be released within the consumers digestive track . This can be tolerated at low levels, but chronic cyanide intoxication can cause severe symptoms including degenerative neuropathy, paralysis, blindness, and premature death . Given the severe consequence of chronic or acute cyanide intoxication as well as the bitter taste, it is understandable that cyanogenesis was selected against in crop plants . The toxicity of HCN comes from asphyxiation when it binds to cytochrome oxidase, a key enzyme in the mitochondrial respiratory pathway . This chemistry makes it toxic to both animal and plant cells. It is therefore necessary for plants to store pre-cyanogenic compounds, typically cyanogenic glycosides, separately from enzymes that cleave the compound and form HCN . The result of this arrangement is a possible difference in the HCNp and the HCNc of a plant.