Interlinked regulatory loops of ABA catabolism and biosynthesis coordinate fruit growth and ripening in woodland strawberry

Liao, Xiudong; Li, Meng-Si; Liu, Bin; Yan, Miaoling; Yu, Xiaomin; Zi, Hailing; Liu, Renyi; Yamamuro, Chizuko

doi:10.1073/pnas.1812575115

Cited by 164 publications

(192 citation statements)

References 65 publications

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“…Unlike uniformity the mechanism controlling fruit shape has been studied extensively in the wild strawberry; Fragaria vesca and may act as a surrogate model for the cultivated octoploid strawberry Fragaria × ananassa . In F. vesca , fruit shape is primarily controlled by phytohormones 49,50 . Auxin increases the width of fruit and by contrast gibberellic acid (GA) increases the length of a strawberry whereas Abscisic acid (ABA) down regulates both Auxin and GA and thus reduces fruit expansion 49,50 .…”

Section: Discussionmentioning

confidence: 99%

“…In F. vesca , fruit shape is primarily controlled by phytohormones 49,50 . Auxin increases the width of fruit and by contrast gibberellic acid (GA) increases the length of a strawberry whereas Abscisic acid (ABA) down regulates both Auxin and GA and thus reduces fruit expansion 49,50 . GA deficient Vesca mutants were found to have a “short” or globose berry shape, which, through the application of GA to the berry, could be restored to result in a “long” or long conic fruit shape 50 .…”

Section: Discussionmentioning

confidence: 99%

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Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Cockerton²,

Johnson³

et al. 2020

Preprint

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Strawberry uniformity is a complex trait, influenced by multiple genetic and environmental components. To complicate matters further, the phenotypic assessment of strawberry uniformity is confounded by the difficulty of quantifying geometric parameters ‘by eye’ and variation between assessors. An in-depth genetic analysis of strawberry uniformity has not been undertaken to date, due to the lack of accurate and objective data. Nonetheless, uniformity remains one of the most important fruit quality selection criteria for the development of a new variety. In this study, a 3D-imaging approach was developed to characterise berry uniformity. We show that circularity of the maximum circumference had the closest predictive relationship with the manual uniformity score. Combining five or six automated metrics provided the best predictive model, indicating that human assessment of uniformity is highly complex. Furthermore, visual assessment of strawberry fruit quality in a multi-parental QTL mapping population has allowed the identification of genetic components controlling uniformity. A “regular shape” QTL was identified and found to be associated with three uniformity metrics. The QTL was present across a wide array of germplasm, indicating a strong candidate for marker-assisted breeding. A greater understanding of berry uniformity has been achieved through the study of the relative impact of automated metrics on human perceived uniformity. Furthermore, the comprehensive definition of strawberry uniformity using 3D imaging tools has allowed precision phenotyping, which has improved the accuracy of trait quantification. This tool has allowed us to illustrate the use of advanced image analysis towards the breeding of greater uniformity in strawberry.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Cockerton²,

Johnson³

et al. 2020

Preprint

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“…Furthermore, quantitative pheno-types were linked to genetic features that interact with large-effect genes, i.e., suppressors of OVATE (sov) , through bulk segregant analysis and QTL mapping [80]. In woodland strawberry ( F. vesca ), fruit size and shape are linked to the accumulation and complex interaction of auxin, GA, and ABA, mediated by the expression and activity of FveCYP707 and FveNCED , as well as other genes [9]. Because of the high H 2 estimates for several of the newly created phenotypic variables 1, we hypothesize that quantitative shape phenotypes can yield a more comprehensive understanding of the underlying genetic mechanisms of fruit shape in garden strawberry ( F .…”

Section: Discussionmentioning

confidence: 99%

“…× ananassa was created in the early 1700s by interspecific hybridization between ecotypes of wild octoploid species ( F. virginiana and F. chiloensis ), multiple subsequent introgressions of genetic diversity from F. virginiana and F. chiloensis subspecies in subsequent generations, and artificial selection for horticulturally important traits among interspecific hybrid descendants. Domestication and breeding have altered the fruit morphology, development, and metabolome of garden strawberry, distancing modern cultivars from their wild progenitors [4, 5, 6, 7, 8, 9]. Approximately 300 years of breeding in the admixed hybrid population has led to the emergence of high yielding cultivars with large, firm, visually appealing, long shelf-life fruit that can withstand the rigors of harvest, handling, storage, and long-distance shipping [10].…”

Section: Introductionmentioning

confidence: 99%

“…The genetic determinants of fruit shape are unknown in strawberry, in part because researchers have not yet translated fruit shape attributes into quantitative phenotypic variables, which are essential for identifying the underlying genes or quantitative trait loci through genome-wide association studies (GWAS) and other forward-genetic approaches [34, 35, 36, 37]. Quantitative phenotypic measurements have allowed researchers to uncover some of the genetic basis of fruit shape in tomato [38, 39], pepper [40, 41], pear [42], melon [33], potato [43], and strawberry [9, 44]. These quantitative features often rely on linear metrics of distance (e.g., height, width, and perimeter) and are generally modified into compound descriptors that remove the effects of size (e.g., aspect ratio or roundness) [42, 44, 45].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Dimensional Machine Learning Approaches for Fruit Shape Recognition and Phenotyping in Strawberry

Feldmann

Hardigan

Famula

et al. 2019

Preprint

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Background: Shape is a critical element of the visual appeal of strawberry fruit and determined by both genetic and non-genetic factors. Current fruit phenotyping approaches for external characteristics in strawberry rely on the human eye to make categorical assessments. However, fruit shape is multi-dimensional, continuously variable, and not adequately described by a single quantitative variable. Morphometric approaches enable the study of complex forms but are often abstract and difficult to interpret. In this study, we developed a mathematical approach for transforming fruit shape classifications from digital images onto an ordinal scale called the principal progression of k clusters (PPKC). We use these human-recognizable shape categories to select features extracted from multiple morphometric analyses that are best fit for genome-wide and forward genetic analyses. Results: We transformed images of strawberry fruit into human-recognizable categories using unsupervised machine learning, discovered four principal shape categories, and inferred progression using PPKC. We extracted 67 quantitative features from digital images of strawberries using a suite of morphometric analyses and multi-variate approaches. These analyses defined informative feature sets that effectively captured quantitative differences between shape classes. Classification accuracy ranged from 68.9 -99.3% for the newly created, genetically correlated phenotypic variables describing a shape. Conclusions: Our results demonstrated that strawberry fruit shapes could be robustly quantified, accurately classified, and empirically ordered using image analyses, machine learning, and PPKC. We generated a dictionary of quantitative traits for studying and predicting shape classes and identifying genetic factors underlying phenotypic variability for fruit shape in strawberry. The methods and approaches we applied in strawberry should apply to other fruits, vegetables, and specialty crops.

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Genetic, Epigenetic, and Hormonal Regulation of Fruit Development and Ripening inCapsicumL. Species

Kumar

Kumar²,

Anju

et al. 2021

Annual Plant Reviews Online

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Fruits are not only important for the plants but also for ensuring sustainable food security of the burgeoning global population. Considering the huge nutritional and ecological importance of fruits, investigation of mechanisms, and factors governing their development and ripening is crucial. The investigation of fruit development in several economically important crops such as tomato, strawberry, grape, and banana includingCapsicumsuggest that early fruit development is largely dependent on auxins and gibberellins (GAs) whereas later ripening is dependent on ethylene or abscisic acid (ABA). The initial development is relatively conserved whereas the ripening of fruits can be either climacteric or non‐climacteric depending on the presence or absence of respiratory burst and ethylene production respectively. Climacteric fruit ripening is mediated by ethylene, and it is well studied in tomato and banana, whereas non‐climacteric fruit ripening requires ABA and much of the information on it comes from strawberry and grape. The genusCapsicumL. shows rich diversity in fruit traits such as shape, colour, size, and even in its ripening behaviour. Early fruit development in different species ofCapsicumappears similar to other fruit crops, whereas later fruit ripening behaviour in different species ofCapsicumcan be either climacteric or non‐climacteric depending upon evolution of ethylene and respiratory burst. It has been found that, even different cultivars of the same species ofCapsicumshow variation in ripening behaviour suggesting interspecific and intraspecific complexities in fruit ripening responses. Until a few years, a majority of the papers have focussed on the non‐climacteric nature of fruit ripening inCapsicum. However, recent studies have shown substantial involvement of ethylene and ethylene related genes in fruit ripening ofCapsicumsuggesting the existence of extensive common regulons between climacteric and non‐climacteric fruits. Recent studies have also suggested the involvement of several epigenetic mechanisms such as non‐coding RNAs (ncRNAs) and cytosine methylation in regulating fruit development and ripening. With the advent of new tools in the omics field such as genomics, transcriptomics, metabolomics, and epigenomics, there is considerable progress in understanding the fruit development and complex nature of ripening inCapsicum. Nevertheless, consolidated information onCapsicumfruit development and its complex nature of ripening is not available. In this article, an attempt is made to present up to date consolidated information on the development and ripening ofCapsicumfruit with an emphasis on its genetic, epigenetic, and hormonal regulation.

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Interlinked regulatory loops of ABA catabolism and biosynthesis coordinate fruit growth and ripening in woodland strawberry

Cited by 164 publications

References 65 publications

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Defining Strawberry Uniformity using 3D Imaging and Genetic Mapping

Multi-Dimensional Machine Learning Approaches for Fruit Shape Recognition and Phenotyping in Strawberry

Genetic, Epigenetic, and Hormonal Regulation of Fruit Development and Ripening inCapsicumL. Species

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