Cassava (Manihot esculenta
Crantz) is an important starchy root crop in the tropics. It is a common staple food in Africa, Asia, and Latin America [1
], and particularly of high importance in sub-Saharan African countries. Cassava is cultivated for its tolerance to stress and ability to grow under drought conditions [2
]. As a result, cassava serves as an important component for food security for low income farmers who rely on it as a major source of dietary energy. While cassava is rich in starch and serves as a good energy source, it is extremely low in protein and important micronutrients, such as iron, zinc, and provitamin A carotenoids [3
]. Therefore, a diet reliant on cassava predisposes one to malnutrition, especially provitamin A deficiency.
Carotenoids are C40 lipophilic isoprenoids synthesized in plants, algae, bacteria, and fungi [4
]. Carotenoids play essential roles in photosynthesis and photoprotection and provide precursors for phytohormones abscisic acid (ABA) and strigolactones (SLs) [5
]. They are also precursors of provitamin A, an essential component of human diet due to its roles in human health [6
Carotenoids are synthesized in plants via the methylerythritol 4-phosphate (MEP) pathway, localized in the plastids [4
] (Figure 1
). Carotenoid synthesis starts with the formation of the C5 prenyl phosphates; isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP) are formed when isomerized by isopentenyl diphosphate isomerase (IPI) [6
]. IPP and DMAPP then undergo reaction condensation by geranylgeranyl diphosphate synthase (GGPPS) to form geranylgeranyl diphosphate (GGPP) [4
]. Condensation of two molecules of GGPP by phytoene synthase (PSY) leads to the formation of phytoene, the first carotenoid product and known rate-limiting metabolite of carotenoid biosynthesis [4
]. Plant tissues exhibiting low levels of carotenoid production are thought to have a low expression level of PSY; additionally, PSY expression driven to high levels has resulted in accumulation of β-carotene crystals in Arabidopsis
and carrot [3
]. An overexpression of PSY in nonphotosynthetic storage roots such as cassava (Manihot esculenta
) and some nongreen plants like tomato fruits (Solanum lycopersicum
), canola seeds (Brassica napus
), and rice endosperm (Oryza sativa
) has also resulted in an increased flux through the biosynthetic pathway, further confirming that high carotenoid levels depend on PSY expression [3
]. Phytoene is converted into lycopene by a series of desaturation and isomerization reactions catalyzed by phytoene desaturase (PDS), ζ-carotene desaturase (ZDS), ζ-carotene isomerase (ZISO), and carotenoid isomerase (CRTISO) [4
]. Lycopene undergoes a cyclization reaction by lycopene ε-cyclase (LCYε) and/or lycopene β-cyclase (LCYβ) to produce α-carotene and β-carotene, representing the α- and β-branch of the pathway, respectively [5
]. Subsequently, α-carotene and β-carotene undergo hydroxylation by β-carotenoid hydroxylases (CHYβ) to form lutein in the α-branch and zeaxanthin in the β-branch of the pathway [4
]. Epoxidation and de-epoxidation of zeaxanthin by zeaxanthin epoxidase (ZEP) and violaxanthin de-epoxidase (VDE) form the xanthophyll cycle, a mechanism to protect plants against photodamage [5
]. The pathway concludes by the conversion of violaxanthin into neoxanthin by neoxanthin synthase (NXS). Furthermore, oxidative cleavage of carotenoids by carotenoid cleavage dioxygenases (CCDs) such as 9-cis-epoxycarotenoid dioxygenase (NCED), produces apocarotenoids, phytohormones, signaling molecules and volatiles, thus maintaining a steady-state level of carotenoids and curbing excessive accumulation [5
]. Figure 1
shows a simplified carotenoid pathway.
Food high in β-carotene is related to several health benefits because of its contribution to vitamin A in the body. Other carotenoids, such as β-cryptoxanthin and α-carotene can also be converted to vitamin A following cleavage by β-carotene oxygenase 1 (BCO1) [8
]. Vitamin A maintains good eyesight, keeps the skin and epithelial linings of the internal organs healthy, and maintains immunity against diseases [8
]. It is also involved in growth, development, and reproduction [9
]. Vitamin A deficiency (VAD) is a global health problem affecting mostly vulnerable groups, such as preschool children and pregnant women [10
], and health consequences include a weakened immune system, poor eyesight, and eventual blindness. Globally, 1–2.5 million child deaths are attributed to nutritional deficiencies; an estimated 761,000 of these deaths are caused by vitamin A, iron, and zinc deficiency [11
]. Due to these consequences, efforts are being made to improve the provitamin A carotenoid content of major staple crops such as cassava. These efforts include fortification, dietary diversification, supplementation, and biofortification of major staple crops such as rice and cassava. Of all these efforts, biofortification proves to be most sustainable [12
Biofortification is the use of breeding or genetic engineering to enhance nutritional content of crops and subsequently improve human nutritional value [7
]. Genetic engineering of cassava has the advantage in that conventional cassava breeding requires a lengthy process to breed for new traits, usually about 8–13 years [13
]. Studies have reported a negative correlation between dry matter and carotenoid content due to genetic linkage in African cassava germplasm [2
]. The observed negative correlation may pose a challenge to biofortification efforts for cassava.
Carotenoid content and expression of key transcript in carotenoid biosynthesis were studied previously in cassava landraces of storage roots with varying color. Analysis of 19 carotenoid types by high performance liquid chromatography with diode array detection (HPLC–DAD) detected a complex carotenoid composition as well as a significant association between lycopene β-cyclase (LYCβ) expression and total β-carotene content [15
Our study compares the variations in carotenoid profiles of thirteen Nigerian cassava landraces as well as the expression levels of carotenoid biosynthesis genes in a select subset of these landraces. These landraces are screened in an effort to introduce more cassava lines to tissue culture, thereby widening the genetic base with desirable traits such as high dry matter, high starch and high micronutrient content, for further improvement by genetic transformation or gene editing. The aim of this study is also to better understand the relationship between agronomic traits, carotenoids, and expression of genes in the carotenoid biosynthesis pathway in cassava.
2. Materials and Methods
2.1. Plant Material
Thirteen cassava landraces were selected from the collection of the International Institute of Tropical Agriculture (IITA), Nigeria, based on variance in starch and β-carotenoid accumulation.
2.2. Field Trial and Layout
Trials were conducted over two seasons between July 2017 and August 2019, each lasting for 13 months. Trials for the two seasons were carried out at the same location at IITA Ibadan, Nigeria (N 7.490250,E 3.884143). Temperature and rainfall data were collected from the IITA weather station over the two-season period to monitor the potential impact of seasonal changes of weather conditions on detected trait variations. For the agronomic measurements, trials were laid out in a completely randomized block design with two replications. There were twenty plants per plot, each plot had four rows with five plants in each row; two replications provided forty plants per genotype. Plants from both replication plots were harvested both seasons to confirm agronomic parameters and performance of all genotypes in the field.
Planting was done with a 1 m spacing between each plant and adjacent plots were separated by 1 m alleys. All the stakes used for planting were generated from fresh portions of mature stems. Hand weeding was done when needed. Since both field trials started in July (during the rainy season), plants received adequate water from rainfall during the initial growth stage, and there was no need for irrigation.
2.3. Sample Collection for Gene Expression and Metabolite Analyses
Source leaves and storage roots were collected for gene expression and metabolite analyses. Leaves and roots were immediately snap frozen in liquid nitrogen. Root samples were collected and prepared under a canopy tent to shield them from sunlight as much as possible. For molecular and metabolic analysis, three roots were randomly selected from the three plants harvested from the same plot to ensure homogeneity and minimize differences in soil characteristics. Selected roots were peeled, cleaned, and portions of the central part were chopped into small cubes and mixed together. The roots were then placed in aluminum foil and teabags, snap frozen in liquid nitrogen and stored at −80 °C.
2.4. Agronomic Data and Dry Matter Content (DMC)
For each trial, dry matter content (DMC) and fresh root weight (FRW) were measured at 4, 8, and 13 months after planting. Three plants per genotype were uprooted at random from the same plot at every sampling time to measure traits. iCheck Carotene by BioAnalyt (Teltow, Germany) a portable spectrophotometer, was used to measure total carotenoids. This tool determines the total carotenoid concentration based on absorbance at 450 and 525 nm [14
]. At harvest, biomass from plants was used to estimate yield by weighing fresh roots and foliage separately. Harvest index (HI) was computed following the formula by Esuma et al. [2
], for the calculation of harvest index: HI = FRW/(FRW + FSW), where HI is harvest index, FRW is fresh root weight, and FSW is fresh shoot weight. To measure dry matter content, roots were peeled, cleaned, and chopped. Three hundred grams of homogeneously mixed roots were collected into paper bags and oven dried at 60−80 °C for a minimum of 48 h. Samples were weighed after drying and the percentage DMC calculated as follows: DMC = (DSW/FSW) × 100 (where DSW is dry sample weight and FSW is fresh sample weight).
2.5. Metabolite Analysis
At eight months after planting, samples were collected for metabolite analysis. Thirty grams of root cubes were snap frozen and lyophilized prior to analysis. Lyophilized root samples were analyzed for starch content using a Total Starch Assay kit by Megazyme (Bray, Ireland). The standard protocol, which excludes D-glucose rinsing and measurement of the resistant starch of samples, was followed. Using a glucose standard, absorbance was measured at 510 nm and starch values were calculated using the weight and the absorbance value. Carotenoid content and composition were measured for the same samples by ultra-high performance liquid chromatography coupled with diode array detection and time-of-flight mass spectrometry (UHPLC–DAD–ToF–MS) at the Leibniz Institute of Vegetable and Ornamental Crops, Plant Quality and Food Security, Germany. Extraction and analysis of carotenoids and chlorophyll were conducted according to Frede et al. [16
] using an Agilent Technologies 1290 Infinity II UHPLC (Agilent Technologies Sales and Services GmbH and Co. KG, Waldbronn, Germany) coupled to Agilent Technologies 6230 TOF LC/MS.
2.6. DNA and RNA Extraction and Amplification
Total nucleic acids (TNA) was extracted from cassava roots using a modified cetyltrimethylammonium bromide (CTAB) extraction protocol [16
], followed by a clean-up step using a DNA clean and concentrator kit (ZYMO Research, Irvine, CA USA). Total RNA was extracted in biological and technical triplicates from TNA using RNA clean and concentrator with DNase I (ZYMO research, Irvine, CA USA) treatment to eliminate genomic DNA. The quantity of RNA was assessed using a NANODROP 8000 spectrophotometer (Thermo Scientific, Waltham, MA USA) and quality was further assessed by agarose gel electrophoresis. RNA was reverse-transcribed into cDNA using M-MuLV Reverse Transcriptase (New England Biolabs, Ipswich, MA United States) according to manufacturer’s instructions. The cDNA products were then used in quantitative real-time polymerase chain reaction (qPCR) experiment.
Using the Applied Biosystems Veriti Thermal Cycler, cDNA and gDNA samples were amplified with Phusion proofreading DNA polymerase (Thermo Scientific) and primers complementary to the selected carotenoid genes, CHYβ, LCYε, and NCED1 under the following thermal cycling conditions: 94 °C for 30 s (1 cycle), 94 °C for 10 s, 60 °C for 30 s, 72 °C for 1.45 min (35 cycles), 72 °C for 10 min. The qPCR products were run on 2% agarose gel with 1× Tris base, Acetic acid and EDTA (TAE) buffer (Sigma-Aldrich, St. Louis, MO, USA), at 100 V for 2 h to confirm PCR products. To confirm product sizes match those of the selected genes, a 100 bp DNA ladder (New England Biolabs) was used.
2.7. Primer Design and RT–qPCR Conditions
The expression levels of carotenoid genes, phytoene synthase 1 and 2 (PSY1
and 2), β-carotenoid hydroxylase (CHYβ
), lycopene-ε-cyclase (LCYε
), and 9-cis-epoxycarotenoid dioxygenase (NCED1
) were investigated in roots of selected genotypes by qPCR. Using a standard curve, four reference genes: Actin, protein phosphatase 2 (PP2A), ubiquitin (UBQ), and G/T binding protein (GTBP), were tested across all samples. Since Actin gave a higher and relatively stable expression than the other reference genes, it was selected as the reference gene for the qPCR experiment. The primers for RT–PCR were designed using Integrated DNA Technologies (IDT) primer design tool (PrimerQuest® Tool, Integrated DNA Technologies, Coralville, Iowa, USA), using the following parameters: annealing temperature of 60–65 °C, GC content of 40–60%, and amplicon size of 50–150 bp (Table S1
). The qPCR was performed in three biological and three technical replicates in a Roche LightCycler 480 II Instrument (Roche Diagnostics, Indianapolis, IN, USA) using the Luna Universal qPCR Master Mix according to the manufacturer’s instructions (New England Biolabs). Primer specificity was confirmed by the presence of a single melting peak in the dissociation curves. Each PCR reaction mix was prepared according to manufacturer’s instructions. PCR cycling was performed as follows: 5 min at 94 °C followed by 40 rounds of 15 s at 94 °C, 10 s at 60 °C, 15 s at 72 °C, and finally 1 round of 35 s at 60 °C. Melting curve cycling consisted of: 15 s at 95 °C, 1 min at 60 °C, 30 s at 95° C, and 15 s at 60 °C. All primers used in the RT–qPCR reaction had an efficiency greater than 90%. The comparative ΔCt method was used to determine the standard curve Actin as reference gene for relative quantification. The relative expression data were calculated according to the Livak (2-ΔΔCt) method [17
]. Primer sequences are included in Table S1
2.8. Statistical Analysis
To distinguish groups of common genotypes, ANOVA tests were performed on β-carotene measurements from the UHPLC–DAD–ToF–MS analysis of the root tissues, percent starch measurements, and dry matter content from both 2018 and 2019 seasons using R. Tukey groupings were then generated using the agricolae library [18
To further understand the results presented by UHPLC–DAD–ToF–MS analyses, principal component analysis was done to show the patterns of variation of metabolites. Principal component analyses (PCAs) were constructed for leaf and root data. For each test, data were first normalized in the following way: (1) for each sample, the total sum of all metabolites was found, (2) the average of all the samples’ sums was calculated, (3) a scaling ratio for each sample was determined by dividing the average of all sums by each sample’s total sum, and (4) all of the metabolites in each sample were multiplied by that sample’s scaling ratio. This normalization method scaled measurements up if the total amount of metabolites found in the sample was less than average, and vice versa. These datasets were then input into the prcomp function in R. Visualizations were created using the ggbiplot library [19
Spearman correlations were found for pairs of elements between datasets. Elements are defined as the following: various agronomic factors from the agronomic data, amount of metabolites from the metabolite data, and relative expression from the qPCR data. This determined if any elements correlated or anticorrelated across all genotypes. For each comparison, calculations were performed using only shared genotypes. Comparisons between agronomic factors and amount of metabolites were visualized in a correlation network using Cytoscape [20
]. Only comparisons with a Spearman index greater than 0.5 and less than −0.5 were included in the network visualization.