The LowSugarBerry trait phenotyped at population and single fruit levels

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Berhe, Hernan Ojeda, Charles Romieu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3943674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The grapevine is one of the most widely grown perennial crops worldwide. As for other species displaying clusters of small fruits, the development of single berries within a bunch is asynchronous and heterogeneous. Because of this, the study of water and solute accumulation kinetics and balance at the organ level cannot be directly extrapolated from samples of populations of fruits. In order to have an original and reliable dataset on the accumulation kinetics of the main solutes (primary and secondary metabolites and cations) and water, we analyzed fruit populations sorted according to their density and also individual berries from the onset of sugar importation to the end of phloem unloading. The studies were performed on a panel of varieties representing different metabolic contexts for the relationship between the import of organic and inorganic solutes and fruit growth, including genotypes able to import water at low fruit osmotic potential. These original data sets are suitable to revisit some aspects of the accumulation of main solutes and water in grapevine fruit development and/or to perform comparative studies with other fleshy fruits. Fleshy fruits growth water import metabolites soluble solids Objective Despite a diversity in the ontogenesis of the pericarp [ 1 ], fleshy fruits share mechanisms of growth based on osmotic gradient-mediated water imports. The grapevine fruit displays a consistent varietal diversity of solute concentration at the physiological ripe stage [ 2 , 3 ], i.e., when the phloem unloading fruit stops [ 4 , 5 ]. The balance between the quantity of water imported into the fruit (i.e., the volume), which is driven by the osmotic force (i.e., the concentration in solutes) and the parameters regulating tissue turgidity (i.e. cell skin and flesh plasticity) are of special importance to determine the final soluble solids’ concentration in the fruit. To cope with climate warming, which increases the grape concentration in sugars and results in excessive alcoholic wines, a new target for genetic improvement is to identify a genotype able to grow berries at lower osmotic potential [ 2 ]. However, as for other small fleshy fruits, the quantification of water and solute imports in the grape is made difficult due to three factors: i) the asynchrony and heterogeneity of single berry development, which prevent the data extrapolation from the grape to single fruits [ 6 ], ii) the interplay of water budget and solute net accumulation mechanisms to determine the final metabolite concentrations and iii) the difficulty of pointing final ripening stage. This data yielded during four years of experiments designed to improve the accuracy of the quantification of solute and water accumulation during ripening. The originality of this data set is about the panel of varieties, which includes 3 phenotypically-contrasted Vitis vinifera varieties (Grenache, Merlot and Morrastel) and 3 new Muscadinia rotundifolia x V. vinifera fungus-tolerant genotypes (G5, G7 and G14) displaying low sugar fruits. This phenotype, known as Sugarless or LowSugarBerry [ 3 ] is a promising option to mitigate excessive sugar concentration in juice or wine grapes under warming [ 7 , 8 ]. Data description Plant material and sampling strategy Fruit samples were collected to cover the entire ripening development, from the onset of ripening when the berries are green and hard, at their maximum content and concentration in organic acids, until the phloem discharge stops, when water and solute quantities are at maximum level, just before shriveling. The first set of data (Table 1 ), which corresponds to analyses of batches of 600 berries of G5, G14 and Merlot from 2 plots sorted through their firmness and apparent density, was partially obtained in 2015 and partially reported [ 4 ]. The second set of data was obtained in 2015, with the same protocol but slight modifications adding Grenache and Morrastel varieties. Sampling was divided into three triplicates of 200 berries for some analyses (Tables 2 and 3). Then, the berries of each replicate were screened for their apparent density and the osmotica analysis was performed on the major density groups of berries pooled together to represent at least 80 out of the 200 berries and on the remaining pooled samples (Table 4). A third set of data (table 5) was obtained with the six genotypes through single berry analyses in 2016 and 2017, as described in [ 5 ]. Collected variables Weight and volume - For each experiment, berries were weighed (Tables 1 , 2 and 5) and their volume measured using the Dyostem® or through the Archimedes method [ 10 ] (Table 3). Firmness monitoring - Firmness was appreciated by hand as explained in [ 2 ] (Tables 1 , 2 and 3). In Table 5, firmness was monitored with a digital penetrometer [ 5 ]. Osmolality assessment and dry matter content (DMC) - Osmolality was assessed using a Gonotec™ Osmomat 3000 ( osmometers.com ) giving a value in milliosmol to represent a solute concentration in osmotic potential (Table 1 ). DMC was performed on fresh juice and obtained as the sample weight after desiccation divided by the sample weight before desiccation times 100. Total acidity, pH, Brix, primary metabolite and cation analyses - Total acidity converted in meq.L − 1 [ 9 ] and pH were obtained using a TitroMatic KF 2S 2B ( crisoninstruments.com ) by directly injecting berry juice for both 2014 (Table 1 ) and 2015 (Table 2) data sets. Brix and stored Brix (each sample was frozen, then a few days later defrosted, heated, vortexed and reanalysed) was measured using a refractometer (Tables 1 and 2). Sugars (glucose and fructose), organic acids (malic and tartaric) and major cations (potassium, calcium, magnesium and ammonium) were measured using HPLC as described in [ 4 ] for 2014 data set (Table 1 ) and as described in [ 5 ] for 2016 and 2017 data sets (Table 5). Anthocyanins and Total Polyphenol Index (TPI) - To obtain anthocyanin content (Tables 1 and 2), the same protocol as in [ 4 ] was performed with all genotypes. For polyphenol contents (Tables 1 and 2), the value at 280 nm obtained by the spectrometer was used as TPI = dilution × OD 280 nm. Table 1 Overview of data files Label Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number) Table 1 Table_1_2014_Full . xlsx 10.6084/m9.figshare.25197050 Table 2 Table_2_2015_Set1/3 . xlsx 10.6084/m9.figshare.25197056 Table 3 Table_3_2015_Set2/3 . xlsx 10.6084/m9.figshare.25197059 Table 4 Table_4_2015_Set3/3 . xlsx 10.6084/m9.figshare.25197047 Table 5 Table_5_2016–2017_Full . xlsx 10.6084/m9.figshare.25197053 Limitations It should also be pointed out that the genotype panel contains varieties that are in the process of being deployed and on which very little data exists. The data produced here, corresponding to four years of experiments, enable assessing the actual contribution of genotype to the berry phenotypic variations including some environmental effects. Another potential limitation of the data concerns the first data set, plot 2, which was obtained from an experimental plot contaminated by the Grapevine Leafroll Virus, known to limit sugar accumulation in grapes. In spite of this, the data are potentially valuable to study the effect of this virus on the composition of grapes, not only in terms of sugars but also of other major osmotica, organic acids, cations and secondary metabolites. A methodological limitation may also exist when comparing 2014–2015 (preparation from the whole berry) and 2016–2017 (seedless samples). Even these differences did not affect the accuracy of the analyses carried out on primary and secondary metabolites, but may have led to some differences in the quantification of cations. Declarations Ethics approval and consent to participate: N/A Consent for publication: All authors agree to publish these data and to make them publicly accessible Availability of data and materials: The data described in this data note are freely and openly accessed on Figshare repository (https://figshare.com). Please see table 1 and references [Reference numbers] for details and links to the data. To be determined after acceptance. Competing interests: Authors declare non-competing interests. Funding: This work was funded by the CIVB ( Comité Interprofessionnel des Vins de Bordeaux ), the Jean Poupelain foundation, the local government of Occitanie, INRAE and the Institut Agro de Montpellier. Authors’ contributions: L.T., H.O., A.B. and C.R. designed and supervised sampling collection and analysis. A.B., D.T. collected and prepared the samples for HPLC analyses. A.B. managed the assessments of berry firmness. LT and AB conceived and drafted the manuscript. Acknowledgements: Mélanie Veyret, Marc Farnos, Yannick Sire, Eleonora Maoddi and Philippe Abbal for relevant advice and valuable technical help at several stages of the experiments. References Coombe BG. The development of fleshy fruits. Ann Rev Plant Physiol. 1976;27:507–28. Bigard A, Berhe DT, Maoddi E, Sire Y, Boursiquot JM, Ojeda H, Péros JP, Doligez A, Romieu C, Torregrosa L. Vitis vinifera L. fruit diversity to breed varieties anticipating climate changes. Front Plant Sci. 2018. 10.3389/fpls.2018.00455 . Bigard A, Romieu C, Sire Y, Torregrosa L. ( Vitis vinifera L. diversity for cations and acidity is suitable for breeding fruits coping with climate warming. Frontiers Plant Sci. 2020. DOI: 202010.3389/fpls.2020.01175. Bigard A, Romieu C, Sire Y, Veyret M, Ojeda H, Torregrosa L. Grape ripening revisited through berry density sorting. OenoOne 2019. 10.20870/oeno-one.2019.53.4.2224 . Bigard A, Romieu C, Ojeda H, Torregrosa L. The sugarless grape trait characterized by single berry phenotyping. OenoOne 2022. 10.1101/2022.03.29.486323 . Shahood R, Torregrosa L, Savoi S, Romieu C. Berry development hidden by its non-synchronous population. OenoOne. 2020. https://doi.org/10.20870/oeno-one.2020.54.4.3787 . Escudier H, Bigard A, Ojeda H, Samson A, Caillé S, Romieu C, Torregrosa L. De la vigne au vin: des créations variétales adaptées au changement climatique et résistant aux maladies cryptogamiques 1/2 : La résistance variétale. Revue des Oenologues 2017. 16–8. Ojeda H, Bigard A, Escudier JL, Samson A, Caillé S, Romieu C, Torregrosa L. De la vigne au vin: des créations variétales adaptées au changement climatique et résistant aux maladies cryptogamiques 2/2 : Approche viticole pour des vins de type VDQA. Revue des Oenologues 2017. 22–7. Ribéreau-Gayon P, Dubourdieu D, Donèche B, Lonvaud A. (2006) Handbook of Enology Volume 1. The Microbiology of Wine and Vinifications, John Wiley & Sons: Chichester 2006. UK. Tesniere C, Torregrosa L, Pradal M, Souquet JM, Gilles C, Dos Santos K, Chatelet P, Gunata Z. Effects of genetic manipulation of alcohol dehydrogenase levels on the response to stress and the synthesis of secondary metabolites in grapevine leaves. J Exp Bot. 2006. 91–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3943674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":272252043,"identity":"ec0ea825-65b7-494f-88a4-948865c165f9","order_by":0,"name":"Antoine Bigard","email":"","orcid":"","institution":"AGAP, University of Montpellier, CIRAD, INRAE, Institut Agro","correspondingAuthor":false,"prefix":"","firstName":"Antoine","middleName":"","lastName":"Bigard","suffix":""},{"id":272252044,"identity":"87f31396-1db4-4ca8-8b44-fe10ac0c348a","order_by":1,"name":"Dargie T. 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The grapevine fruit displays a consistent varietal diversity of solute concentration at the physiological ripe stage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], i.e., when the phloem unloading fruit stops [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The balance between the quantity of water imported into the fruit (i.e., the volume), which is driven by the osmotic force (i.e., the concentration in solutes) and the parameters regulating tissue turgidity (i.e. cell skin and flesh plasticity) are of special importance to determine the final soluble solids\u0026rsquo; concentration in the fruit. To cope with climate warming, which increases the grape concentration in sugars and results in excessive alcoholic wines, a new target for genetic improvement is to identify a genotype able to grow berries at lower osmotic potential [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, as for other small fleshy fruits, the quantification of water and solute imports in the grape is made difficult due to three factors: i) the asynchrony and heterogeneity of single berry development, which prevent the data extrapolation from the grape to single fruits [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], ii) the interplay of water budget and solute net accumulation mechanisms to determine the final metabolite concentrations and iii) the difficulty of pointing final ripening stage.\u003c/p\u003e \u003cp\u003eThis data yielded during four years of experiments designed to improve the accuracy of the quantification of solute and water accumulation during ripening. The originality of this data set is about the panel of varieties, which includes 3 phenotypically-contrasted \u003cem\u003eVitis vinifera\u003c/em\u003e varieties (Grenache, Merlot and Morrastel) and 3 new \u003cem\u003eMuscadinia rotundifolia\u003c/em\u003e x \u003cem\u003eV. vinifera\u003c/em\u003e fungus-tolerant genotypes (G5, G7 and G14) displaying low sugar fruits. This phenotype, known as Sugarless or LowSugarBerry [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] is a promising option to mitigate excessive sugar concentration in juice or wine grapes under warming [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e"},{"header":"Data description","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and sampling strategy\u003c/h2\u003e \u003cp\u003eFruit samples were collected to cover the entire ripening development, from the onset of ripening when the berries are green and hard, at their maximum content and concentration in organic acids, until the phloem discharge stops, when water and solute quantities are at maximum level, just before shriveling.\u003c/p\u003e \u003cp\u003eThe first set of data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which corresponds to analyses of batches of 600 berries of G5, G14 and Merlot from 2 plots sorted through their firmness and apparent density, was partially obtained in 2015 and partially reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The second set of data was obtained in 2015, with the same protocol but slight modifications adding Grenache and Morrastel varieties. Sampling was divided into three triplicates of 200 berries for some analyses (Tables\u0026nbsp;2 and 3). Then, the berries of each replicate were screened for their apparent density and the osmotica analysis was performed on the major density groups of berries pooled together to represent at least 80 out of the 200 berries and on the remaining pooled samples (Table\u0026nbsp;4). A third set of data (table 5) was obtained with the six genotypes through single berry analyses in 2016 and 2017, as described in [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Collected variables","content":"\u003cp\u003e \u003cb\u003eWeight and volume -\u003c/b\u003e For each experiment, berries were weighed (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2 and 5) and their volume measured using the Dyostem\u0026reg; or through the Archimedes method [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirmness monitoring -\u003c/b\u003e Firmness was appreciated by hand as explained in [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, 2 and 3). In Table\u0026nbsp;5, firmness was monitored with a digital penetrometer [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eOsmolality assessment and dry matter content (DMC) -\u003c/b\u003e Osmolality was assessed using a Gonotec\u0026trade; Osmomat 3000 (\u003cem\u003eosmometers.com\u003c/em\u003e) giving a value in milliosmol to represent a solute concentration in osmotic potential (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). DMC was performed on fresh juice and obtained as the sample weight after desiccation divided by the sample weight before desiccation times 100.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal acidity, pH, Brix, primary metabolite and cation analyses -\u003c/b\u003e Total acidity converted in meq.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and pH were obtained using a TitroMatic KF 2S 2B (\u003cem\u003ecrisoninstruments.com\u003c/em\u003e) by directly injecting berry juice for both 2014 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 2015 (Table\u0026nbsp;2) data sets. Brix and stored Brix (each sample was frozen, then a few days later defrosted, heated, vortexed and reanalysed) was measured using a refractometer (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2). Sugars (glucose and fructose), organic acids (malic and tartaric) and major cations (potassium, calcium, magnesium and ammonium) were measured using HPLC as described in [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] for 2014 data set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and as described in [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] for 2016 and 2017 data sets (Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnthocyanins and Total Polyphenol Index (TPI) -\u003c/b\u003e To obtain anthocyanin content (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2), the same protocol as in [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] was performed with all genotypes. For polyphenol contents (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 2), the value at 280 nm obtained by the spectrometer was used as TPI\u0026thinsp;=\u0026thinsp;dilution \u0026times; OD 280 nm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of data files\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName of data file/data set\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFile types\u003c/p\u003e \u003cp\u003e(file extension)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData repository and identifier (DOI or accession number)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTable_1_2014_Full\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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class=\"RefSource\"\u003e10.6084/m9.figshare.25197056\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.25197056\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTable\u0026nbsp;3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTable_3_2015_Set2/3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003cem\u003exlsx\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.25197059\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.25197059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e 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\u003cp\u003e\u003cem\u003eTable_5_2016\u0026ndash;2017_Full\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.\u003cem\u003exlsx\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6084/m9.figshare.25197053\u003c/span\u003e\u003cspan address=\"10.6084/m9.figshare.25197053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eIt should also be pointed out that the genotype panel contains varieties that are in the process of being deployed and on which very little data exists. The data produced here, corresponding to four years of experiments, enable assessing the actual contribution of genotype to the berry phenotypic variations including some environmental effects. Another potential limitation of the data concerns the first data set, plot 2, which was obtained from an experimental plot contaminated by the Grapevine Leafroll Virus, known to limit sugar accumulation in grapes. In spite of this, the data are potentially valuable to study the effect of this virus on the composition of grapes, not only in terms of sugars but also of other major osmotica, organic acids, cations and secondary metabolites. A methodological limitation may also exist when comparing 2014\u0026ndash;2015 (preparation from the whole berry) and 2016\u0026ndash;2017 (seedless samples). Even these differences did not affect the accuracy of the analyses carried out on primary and secondary metabolites, but may have led to some differences in the quantification of cations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eN/A\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors agree to publish these data and to make them publicly accessible\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data described in this data note are freely and openly accessed on Figshare repository (https://figshare.com). Please see table 1 and references [Reference numbers] for details and links to the data. To be determined after acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAuthors declare non-competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was funded by the CIVB (\u003cem\u003eComit\u0026eacute; Interprofessionnel des Vins de Bordeaux\u003c/em\u003e), the Jean Poupelain foundation, the local government of Occitanie, INRAE and the Institut Agro de Montpellier.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u0026nbsp;\u003c/strong\u003eL.T., H.O., A.B. and C.R. designed and supervised sampling collection and analysis. A.B., D.T. collected and prepared the samples for HPLC analyses. A.B. managed the assessments of berry firmness. LT and AB conceived and drafted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eM\u0026eacute;lanie Veyret, Marc Farnos, Yannick Sire, Eleonora Maoddi and Philippe Abbal \u0026nbsp; for relevant advice and valuable technical help at several stages of the experiments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCoombe BG. The development of fleshy fruits. Ann Rev Plant Physiol. 1976;27:507\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigard A, Berhe DT, Maoddi E, Sire Y, Boursiquot JM, Ojeda H, P\u0026eacute;ros JP, Doligez A, Romieu C, Torregrosa L. \u003cem\u003eVitis vinifera\u003c/em\u003e L. fruit diversity to breed varieties anticipating climate changes. Front Plant Sci. 2018. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpls.2018.00455\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2018.00455\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigard A, Romieu C, Sire Y, Torregrosa L. (\u003cem\u003eVitis vinifera\u003c/em\u003e L. diversity for cations and acidity is suitable for breeding fruits coping with climate warming. Frontiers Plant Sci. 2020. DOI: 202010.3389/fpls.2020.01175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigard A, Romieu C, Sire Y, Veyret M, Ojeda H, Torregrosa L. Grape ripening revisited through berry density sorting. OenoOne 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20870/oeno-one.2019.53.4.2224\u003c/span\u003e\u003cspan address=\"10.20870/oeno-one.2019.53.4.2224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigard A, Romieu C, Ojeda H, Torregrosa L. The sugarless grape trait characterized by single berry phenotyping. OenoOne 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2022.03.29.486323\u003c/span\u003e\u003cspan address=\"10.1101/2022.03.29.486323\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShahood R, Torregrosa L, Savoi S, Romieu C. Berry development hidden by its non-synchronous population. OenoOne. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20870/oeno-one.2020.54.4.3787\u003c/span\u003e\u003cspan address=\"10.20870/oeno-one.2020.54.4.3787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscudier H, Bigard A, Ojeda H, Samson A, Caill\u0026eacute; S, Romieu C, Torregrosa L. De la vigne au vin: des cr\u0026eacute;ations vari\u0026eacute;tales adapt\u0026eacute;es au changement climatique et r\u0026eacute;sistant aux maladies cryptogamiques 1/2 : La r\u0026eacute;sistance vari\u0026eacute;tale. Revue des Oenologues 2017. 16\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOjeda H, Bigard A, Escudier JL, Samson A, Caill\u0026eacute; S, Romieu C, Torregrosa L. De la vigne au vin: des cr\u0026eacute;ations vari\u0026eacute;tales adapt\u0026eacute;es au changement climatique et r\u0026eacute;sistant aux maladies cryptogamiques 2/2 : Approche viticole pour des vins de type VDQA. Revue des Oenologues 2017. 22\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRib\u0026eacute;reau-Gayon P, Dubourdieu D, Don\u0026egrave;che B, Lonvaud A. (2006) Handbook of Enology Volume 1. The Microbiology of Wine and Vinifications, John Wiley \u0026amp; Sons: Chichester 2006. UK.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesniere C, Torregrosa L, Pradal M, Souquet JM, Gilles C, Dos Santos K, Chatelet P, Gunata Z. Effects of genetic manipulation of alcohol dehydrogenase levels on the response to stress and the synthesis of secondary metabolites in grapevine leaves. J Exp Bot. 2006. 91\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fleshy fruits, growth, water import, metabolites, soluble solids","lastPublishedDoi":"10.21203/rs.3.rs-3943674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3943674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe grapevine is one of the most widely grown perennial crops worldwide. As for other species displaying clusters of small fruits, the development of single berries within a bunch is asynchronous and heterogeneous. Because of this, the study of water and solute accumulation kinetics and balance at the organ level cannot be directly extrapolated from samples of populations of fruits. In order to have an original and reliable dataset on the accumulation kinetics of the main solutes (primary and secondary metabolites and cations) and water, we analyzed fruit populations sorted according to their density and also individual berries from the onset of sugar importation to the end of phloem unloading. The studies were performed on a panel of varieties representing different metabolic contexts for the relationship between the import of organic and inorganic solutes and fruit growth, including genotypes able to import water at low fruit osmotic potential. These original data sets are suitable to revisit some aspects of the accumulation of main solutes and water in grapevine fruit development and/or to perform comparative studies with other fleshy fruits.\u003c/p\u003e","manuscriptTitle":"The LowSugarBerry trait phenotyped at population and single fruit levels","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-12 10:17:11","doi":"10.21203/rs.3.rs-3943674/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b230c0e8-79e0-4eeb-b29a-0ebc835fd280","owner":[],"postedDate":"February 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-19T12:57:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-12 10:17:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3943674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3943674","identity":"rs-3943674","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}