Phenotypic plasticity of two M. oleifera ecotypes from different climatic zones under water stress and re-watering

Abstract

Moringa oleifera is a fast-growing hygrophilic tree native to a humid sub-tropical region of India, now widely planted in many regions of the Southern Hemisphere characterized by low soil water availability. The widespread cultivation of this plant worldwide may have led to populations with different physiological and biochemical traits. In this work, the impact of water stress on the physiology and biochemistry of two M. oleifera populations, one from Chaco Paraguayo (PY) and one from Indian Andhra Pradesh (IA) region, was studied in a screenhouse experiment where the water stress treatment was followed by re-watering. Through transcriptome sequencing, 2201 potential genic simple sequence repeats were identified and used to confirm the genetic differentiation of the two populations. Both populations of M. oleifera reduced photosynthesis, water potential, relative water content and growth under drought, compared to control well-watered plants. A complete recovery of photosynthesis after re-watering was observed in both populations, but growth parameters recovered better in PY than in IA plants. During water stress, PY plants accumulated more secondary metabolites, especially β-carotene and phenylpropanoids, than IA plants, but IA plants invested more into xanthophylls and showed a higher de-epoxidation state of xanthophylls cycle that contributed to protect the photosynthetic apparatus. M. oleifera demonstrated a high genetic variability and phenotypic plasticity, which are key factors for adaptation to dry environments. A higher plasticity (e.g. in PY plants adapted to wet environments) will be a useful trait to endure recurrent but brief water stress episodes, whereas long-term investment of resources into secondary metabolism (e.g. in IA plants adapted to drier environments) will be a successful strategy to cope with prolonged periods of drought. This makes M. oleifera an important resource for agro-forestry in a climate change scenario.

Keywords: Ecotypes; Moringa oleifera; phenotypic plasticity; secondary metabolites; water stress.

Figure 1

Net photosynthesis (P_n), stomatal conductance (g_s), apparent maximum rate of carboxylation (Vc_max), maximal efficiency of PSII photochemistry (F_v/F_m), actual efficiency of PSII (Φ_PSII), ETR, water potential (Ψ_w, MPa), osmotic potential (Ψ_π, MPa) and RWC in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) comparing well-watered plants (WW) with plants at the end of water stress (WS) and well-watered plants (R-WW) with plants subjected to re-watering (R-WS). Data are means ± standard deviation (n = 4). Different letters represent significant differences between ecotypes and treatments (P < 0.05).

Figure 2

Shoot RGR and LAER in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) at the end of water stress (WS) and subsequent re-watering (R-WS) period reported as percentage of the corresponding well-watered plants (WW and R-WW, respectively). Data are means ± standard deviation (n = 4). Different letters represent significant differences between ecotypes and treatments (P < 0.05).

Figure 3

Content of soluble carbohydrates (sucrose, glucose, fructose and galactose) in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) comparing well-watered plants (WW) with plants at the end of water stress (WS) and well-watered plants (R-WW) with plants subjected to re-watering (R-WS). Data are means ± standard deviation (n = 4). Different letters represent significant differences between ecotypes and treatments (P < 0.05).

Figure 4

Content of phenylpropanoids (caffeic acid derivatives, apigenin derivatives, quercetin derivatives and kaempferol derivatives) in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) comparing well-watered plants (WW) with plants at the end of water stress (WS) and well-watered plants (R-WW) with plants subjected to re-watering (R-WS). Data are means ± standard deviation (n = 4). Different letters represent significant differences between ecotypes and treatments (P < 0.05).

Figure 5

Photosynthetic pigments measured at (A–I) pre-dawn and (J–R) mid-day: lutein (A, J), β-carotene (B, K), zeaxanthin (C, L), anteraxanthin (D,M), violaxanthin (E, N), neoxanthin (F, O), xanthophyll cycle pigments (violaxanthin (V), anteraxanthin (A), zeaxanthin (Z), collectively named VAZ, G, P), de-epoxidation state of xanthophylls (DES, H, Q), total chlorophyll (Chl_Tot, I, R) and the ratio VAZ on total chlorophyll (VAZ/Chl_Tot, I,R) in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) comparing well-watered plants (WW) with plants at the end of water stress (WS) and well-watered plants (R-WW) with plants subjected to re-watering (R-WS). Data are means ± standard deviation (n = 4). Different letters represent significant differences between genotypes and treatments (P < 0.05).

Figure 6

Content of ABA and ABA-GE in two M. oleifera ecotypes (PY ecotype from Paraguay and IA ecotype from India) comparing well-watered plants (WW) with plants at the end of water stress (WS) and well-watered plants (R-WW) with plants subjected to re-watering (R-WS). Data are means ± standard deviation (n = 4). Different letters represent significant differences between ecotypes and treatments (P < 0.05).