Multisite evaluation of phenotypic plasticity for specialized metabolites, some involved in carrot quality and disease resistance

Abstract

Renewed consumer demand motivates the nutritional and sensory quality improvement of fruits and vegetables. Specialized metabolites being largely involved in nutritional and sensory quality of carrot, a better knowledge of their phenotypic variability is required. A metabolomic approach was used to evaluate phenotypic plasticity level of carrot commercial varieties, over three years and a wide range of cropping environments spread over several geographical areas in France. Seven groups of metabolites have been quantified by HPLC or GC methods: sugars, carotenoids, terpenes, phenolic compounds, phenylpropanoids and polyacetylenes. A large variation in root metabolic profiles was observed, in relation with environment, variety and variety by environment interaction effects in decreasing order of importance. Our results show a clear diversity structuration based on metabolite content. Polyacetylenes, β-pinene and α-carotene were identified mostly as relatively stable varietal markers, exhibiting static stability. Nevertheless, environment effect was substantial for a large part of carrot metabolic profile and various levels of phenotypic plasticity were observed depending on metabolites and varieties. A strong difference of environmental sensitivity between varieties was observed for several compounds, particularly myristicin, 6MM and D-germacrene, known to be involved in responses to biotic and abiotic stress. This work provides useful information about plasticity in the perspective of carrot breeding and production. A balance between constitutive content and environmental sensitivity for key metabolites should be reached for quality improvement in carrot and other vegetables.

Fig 1. Distribution of varieties by Principal Component Analysis (PCA) based on 86 metabolites content on 3 environments.

The 3 first components explain 35.89% of total inertia. For variety code, see Table 1.

Fig 2. Significant variables in decreasing importance for varietal clustering based on Random forest method.

For compound identification, see Tables 5 and S4 for polyphenol molecules characteristics.

Fig 3. Variety hierarchical clustering on the 20 most discriminating metabolites according to Random forest model over 3 environments.

Distances are computed according to euclidean distance and dendogram is built according to ward method. Colored rectangle at the bottom of the dendogram highlights the carrot type.

Fig 4. Variety heatmap on 20 metabolites selected according to Random forest model.

Couples variety/location are in column and metabolites in lines. In red, high content compounds and in blue, low content compounds. Compounds codes are presented in Table 5 (P compounds are unknown polyphenols. For polyphenol information, see S4 Table).

Fig 5. Environmental sensitivity of five varieties for six compounds over 20 environments.

(A) PP2 myristicin, (B) 6MM 6-methoxymellein, (C) S6 D-germacrene, (D) FaDOAc falcarindiol acetate, (E) T2 β-pinene and (F) acar α-carotene. X-axis represents environmental index for compounds synthesis all varieties combined, and Y-axis level of predicted synthesis for each variety. Slope is traced according to regression coefficient bi (S3 Table).

Fig 6. Identification of favorable environments for five varieties and six compounds exhibiting phenotypic plasticity, based on the AMMI model.

(A) PP2 myristicin, (B) 6MM 6-methoxymellein, (C) S6 D-germacrene, (D) FaDOAc falcarindiol acetate, (E) T2 β-pinene and (F) acar α-carotene. Varieties near to the center of biplot have no specific location to enhance metabolite content, contrary to the most external varieties which have predilection location for accumulation. Thus, a variety very close to environment vector highlights high metabolite enhancement of this variety on this environment compared to other varieties. Besides, varieties on right border are varieties with high accumulation potential.