Regulation of Carotenoid Biosynthesis During Fruit Development

Abstract

Carotenoids are recognized as the main pigments in most fruit crops, providing colours that range from yellow and pink to deep orange and red. Moreover, the edible portion of widely consumed fruits or their derived products represent a major dietary source of carotenoids for animals and humans. Therefore, these pigments are crucial compounds contributing to fruit aesthetic and nutritional quality but may also have protecting and ecophysiological functions in coloured fruits. Among plant organs, fruits display one of the most heterogeneous carotenoids patterns in terms of diversity and abundance. In this chapter a comprehensive list of the carotenoid content and profile in the most commonly cultivated fleshy fruits is reported. The proposed fruit classification systems attending to carotenoid composition are revised and discussed. The regulation of carotenoids in fruits can be rather complex due to the dramatic changes in content and composition during ripening, which are also dependent on the fruit tissue and the developmental stage. In addition, carotenoid accumulation is a dynamic process, associated with the development of chromoplasts during ripening. As a general rule, carotenoid accumulation is highly controlled at the transcriptional level of the structural and accessory proteins of the biosynthetic and degradation pathways, but other mechanisms such as post-transcriptional modifications or the development of sink structures have been recently revealed as crucial factors in determining the levels and stability of these pigments. In this chapter common key metabolic reactions regulating carotenoid composition in fruit tissues are described in addition to others that are restricted to certain species and generate unique carotenoids patterns. The existence of fruit-specific isoforms for key steps such as the phytoene synthase, lycopene β-cyclases or catabolic carotenoid cleavage dioxygenases has allowed an independent regulation of the pathway in fruit tissues and a source of variability to create novel activities or different catalytic properties. Besides key genes of the carotenoid pathway, changes in carotenoid accumulation could be also directly influenced by differences in gene expression or protein activity in the pathway of carotenoid precursors and some relevant examples are discussed. The objective of this chapter is to provide an updated review of the main carotenoid profiles in fleshy fruits, their pattern of changes during ripening and our current understanding of the different regulatory levels responsible for the diversity of carotenoid accumulation in fruit tissues.

Synthetic biogenesis of chromoplasts from leaf chloroplasts

“…1A). These plastids were devoid of organized photosynthetic thylakoid membranes and grana but contained tightly packed membrane stacks and a proliferation of small electron-dense (i.e., lipidcontaining) plastoglobules typically observed in chromoplasts Egea et al, 2010;Lado et al, 2016). Very similar structures were observed when the crtB gene was virally expressed in Arabidopsis leaves (Fig.…”

“…The most prominent changes during chloroplast-to-chromoplast differentiation are the reorganization of the internal plastid structures, together with a concurrent loss of photosynthetic competence and overaccumulation of carotenoid pigments (Egea et al, 2010;Jarvis and Lopez-Juez, 2013;Lado et al, 2016;Llorente et al, 2017;Sun et al, 2018;Sadali et al, 2019). The remodeling of the internal plastid structures generates an increased metabolic sink capacity but it also promotes carotenoid biosynthesis.…”

“…The remodeling of the internal plastid structures generates an increased metabolic sink capacity but it also promotes carotenoid biosynthesis. The control of chromoplast differentiation appears as a very promising strategy for improving the nutritional and health benefits of crops (Lado et al, 2016;Giuliano, 2017;Llorente et al, 2017;Sun et al, 2018;Wurtzel, 2019). However, very few inducers of chromoplast development have been identified to date.…”

A New Feature Ensemble with a Multistage Classification Scheme for Breast Cancer Diagnosis

“…ROI detection in the past decade was generally performed using wavelet transforms [16], segmentation algorithms [17, 18], and edge operators [19]. …”

“…The authors extracted statistical features on manually detected ROIs and implemented feature selection and classification using a multilayer perceptron neural network [27]. Lado et al developed an extended generalized additive model (GAM) involving interaction of breast tissue factors to reduce the false-positive rate for microcalcification detection [16]. The authors stated that the false-positive rate has decreased to 0.74 per image from 1.46 when the breast tissue type is integrated into the GAM.…”