Innovative breeding technologies in lettuce for improved post-harvest quality

Abstract

Societal awareness of healthy eating is increasing alongside the market for processed bagged salads, which remain as one of the strongest growing food sectors internationally, including most recently from indoor growing systems. Lettuce represents a significant proportion of this ready-to-eat salad market. However, such products typically have a short shelf life, with decay of post-harvest quality occurring through complex biochemical and physiological changes in leaves and resulting in spoilage, food waste and risks to health. We review the functional and quantitative genetic understanding of lettuce post-harvest quality, revealing that few findings have translated into improved cultivar development. We identify (i) phytonutrient status (for enhanced antioxidant and vitamin status, aroma and flavour) (ii) leaf biophysical, cell wall and water relations traits (for longer shelf life) (iii) leaf surface traits (for enhanced food safety and reduced spoilage) and (iv) chlorophyll, other pigments and developmental senescence traits (for appearance and colour), as key targets for future post-harvest breeding. Lettuce is well-placed for rapid future exploitation to address postharvest quality traits with extensive genomic resources including the recent release of the lettuce genome and the development of innovative breeding technologies. Although technologies such as CRISPR/Cas genome editing are paving the way for accelerated crop improvement, other equally important resources available for lettuce include extensive germplasm collections, bi-parental mapping and wide populations with genotyping for genomic selection strategies and extensive multiomic datasets for candidate gene discovery. We discuss current progress towards post-harvest quality breeding for lettuce and how such resources may be utilised for future crop improvement.

Keywords: Food safety; Gene editing; Lactuca sativa; Plant breeding.

Fig. 1

Decline in visual quality throughout the shelf life of romaine (A to B) and lollo rosso (C to D) lettuce. Adapted from Wagstaff et al. (2010).

Fig. 2

Timeline for the commercialisation of Beneforté, a broccoli variety with 2-3 higher glucoraphanin content (Traka et al., 2013), developed by exploiting the genetic variation in wild brassica germplasm (adapted from: http://www.beneforte.com/).

Fig. 3

The basic premise of CRISPR/Cas genome editing (A) is the use of RNA-directed nucleases to make targeted double stranded DNA (dsDNA) breaks at specific sites in the genome, as directed by a short guide RNA (gRNA) sequence, which are repaired by endogenous DNA repair mechanisms inherent to all living organisms (Gaj et al., 2013). A dsDNA break is repaired via two major pathways (B): non-homologous end joining (NHEJ) or homology-directed repair (HDR). NHEJ, in which blunt DNA ends are stitched directly back together, is typically error-prone, often resulting in the incorporation or deletion of base pairs and leading to indels and frame shift mutations, which can be exploited for targeted gene knockout (Rodgers and Mcvey, 2016). Alternatively, if an appropriate template is provided, breaks can be repaired via HDR, which occurs by homologous recombination with the template, leading to gene insertion (Rodgers and Mcvey, 2016). “DNA-free” genome editing involves delivering the CRISPR reagents into target cells as pre-assembled ribonucleotide-protein complexes, without exogenous gene or vector backbone integration (Woo et al., 2015).

Fig. 4

Unravelling the genetic basis of complex quantitative traits using a multitude of tools. Those which have been applied to post-harvest quality improvement of lettuce (in the public domain) are highlighted in blue, those which have been applied in lettuce, but not for post-harvest quality traits are in light blue and those which have not been exploited in lettuce are in red. Numbers indicate key references; [1] Landry et al., 1987, [2] https://cgngenis.wur.nl, [3] Stoffel et al., 2012, [4] Simko, 2016, [5] Reyes-Chin-Wo et al., 2017, [6] Zhang et al., 2017, [7] Damerum et al., 2015, [8] see Table 2,[9] Sthapit Kandel et al., 2020, [10] Ripoll et al., 2019, [11] Jeuken and Lindhout, 2004, [12] Michelmore et al., 1991, [13] Damerum, 2017, [14] Su et al., 2020, [15] Wagstaff et al., 2010, [16] Zhang et al., 2018a, [17] Simko et al., 2018, [18] Zhang et al., 2009b (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).