Capturing biochemical diversity in cassava (Manihot esculenta Crantz

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Omics Technologies Applied to Agriculture and Food

Capturing biochemical diversity in cassava (Manihot esculenta Crantz) through the application of metabolite profiling Margit Drapal, Elisabete Barros de Carvalho, Tatiana M. Ovalle Rivera, Luis Augusto Becerra Lopez-Lavalle, and Paul D. Fraser J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b04769 • Publication Date (Web): 17 Dec 2018 Downloaded from http://pubs.acs.org on December 18, 2018

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Journal of Agricultural and Food Chemistry

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Capturing biochemical diversity in cassava (Manihot esculenta Crantz)

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through the application of metabolite profiling

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Margit Drapal1, Elisabete Barros de Carvalho1, Tatiana M. Ovalle Rivera2, Luis

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Augusto Becerra Lopez-Lavalle2 and Paul D. Fraser1*

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Egham, Surrey, TW20 0EX, UK.

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2 International

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Corresponding author: [email protected], Tel: +44 1784 443894

School of Biological Sciences, Royal Holloway, University of London, Egham Hill,

Center for Tropical Agriculture (CIAT), Cali, Columbia.

ACS Paragon Plus Environment

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Abstract (100-150 words)

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Cassava (Manihot esculenta Crantz) is the predominant staple food in Sub-Saharan

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Africa (SSA) and an industrial crop in South East Asia. Despite focused breeding

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efforts for increased yield, resistance and nutritional value, cassava breeding has not

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advanced at the same rapidity as other staple crops. In the present study,

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metabolomic techniques were implemented to characterize the chemotypes of

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selected cassava accessions and assess potential resources for the breeding

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program. The metabolite data analysed was applied to describe the biochemical

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diversity available in the panel, identifying South American accessions as the most

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diverse. Genotypes with distinct phenotypic traits showed a representative

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metabolite profile and could be clearly identified, even if the phenotypic trait was a

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root characteristic e.g. high amylose content.

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Keywords

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Cassava, characteristic traits, chemotypes, diversity, metabolite profiling, modern

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breeding approach,

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Introduction

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Cassava (Manihot esculenta Crantz) is a woody perennial shrub with edible storage

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roots (further referred to as roots) which provide a major source of calories for many

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populations, especially those in Sub-Saharan Africa 1. Cassava plants are able to

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grow on marginal soils and provide feasible yields under drought and other stresses

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2.

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and more recently biofortification has been ongoing since 1937 3. Despite these

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attempts to deliver new improved varieties, progress has been limited in comparison

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to other global staple crops. This is in part due to the disadvantages associated with

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clonal propagation, the common cultivation practice for cassava, which limits

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germplasm diversity 4. In addition, improvements in one trait often adversely affect

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other traits, for example higher yield has been shown to decrease protein content

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and high carotenoid to lower starch content - the most important bio product of the

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crop 5-6.

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A focus of the CGIAR Research Program is to combine generic tools and resources

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that facilitate the implication of modern breeding techniques, which is a limiting factor

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in the development of better root, tuber and banana bearing plants. For example,

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new breeding strategies, combining next-generation sequencing techniques and

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metabolite profiling, have been shown to increase the breeding efficiency and

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identification of candidate genes and marker defined regions for varieties with

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multiple traits

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wild/diverse genetic resources and incorporation of these plants into the breeding

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strategies, increases the genetic gain and enriches the diversity of currently favoured

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varieties 9. This approach was successfully applied in other crops, such as tomato,

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resulting in identification of quantitative trait loci 10.

Breeding for cassava varieties with improved yield, biotic and abiotic resistance

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Furthermore, the screening of available germplasm and


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The organism’s chemical phenotype, regulated by its genetic background, comprises

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all present chemical end-products associated with cellular processes and can be

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measured with metabolomics techniques

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present in an organism and even a specific tissue can vary distinctly and demands

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the use of several analytical platforms for a broad view of the metabolome.

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Therefore, the primary objective of this study was to establish chemical screening

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methods for primary and secondary metabolites that can be widely applied to classify

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and assess cassava accessions. The second objective was to identify potential

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biomarker metabolites from the screening methods and link them to characteristic

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traits. In effect, elucidating the relationship between genotype and phenotype/traits.

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For this purpose, cassava varieties originating in the native regions Central

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America/Caribbean and South America as well as improved varieties from Africa,

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were included in a panel. The plants were analyzed as in vitro plantlets and the

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chemotypic differences elucidated. A comparison between five cassava accessions

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highlighted the difficulties comparing plants grown in vitro and cultivated using field

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practices.

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The chemical properties of metabolites

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Material and methods

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Plant cultivation and material generation

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Twenty-three cassava varieties (Table 1) were harvested twice from in vitro stocks,

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maintained at the cassava genetics laboratory at CIAT. The plantlets were grown on

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4E medium, which included Murashige and Skoog (MS) salts

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benzylaminopurine (BAP), 0.05 mg/l 4-gibberelic acid (GA3), 0.02mg/l α-

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naphthaleneacetic acid (NAA), 1mg/l thiamine, 100 mg/l myo-inositol and 2%

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sucrose, at pH 5.7–5.8

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harvested and individual meristems placed back on 4E medium (150ml) in a 500ml

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0.04mg/l 6-

Six meristem apices per cassava accession were


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glass jar. A total of 138 jars (6 x 23) were placed in a complete randomized designed

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on a 12h photoperiod for a twelve weeks growth period. The in vitro plantlets were

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carefully removed from the medium, frozen in liquid nitrogen and lyophilized.

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Five cassava landraces (BRA1A, COL22, COL638, CUB23 and PER183) were taken

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to the field and grown under CIAT’s standard field conditions. Ten months after

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planting, leaves, stems and roots were harvested, immediately frozen in liquid

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nitrogen and lyophilized.

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Extraction of metabolites

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Freeze-dried tissue (approx. 200g) was ground into a fine powder. Samples,

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including quality controls (pool of all samples, QC), were weighed (10±0.5 mg) into

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plastic tubes and extracted as described previously 14. Metabolites, according to their

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hydrophilic properties, were separated in a polar and non-polar phase and aliquots of

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each phase were immediately dried down after extraction.

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GC-MS analysis of polar extracts

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An aliquot of the polar phase (200µl) was removed and internal standard

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([D4]Succinic acid, 10µg) added before dry down. Dried samples were derivatized as

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previously described with methoxymation and silylation derivatisation

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analyzed by GC-MS based on literature

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gradient 70 to 325°C. Metabolites were identified with respect to an in-house library

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(Supplementary Table S1) based on retention time, retention indices and mass

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spectrum 14 and quantified relatively to the internal standard.

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15,

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and

using a 10:1 split mode and a heat

LC-MS analysis of polar extracts

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Each dried aliquot of the polar phase (700µl) was resuspended in methanol/water

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(1:1, 100µl) and internal standard (homogentisic acid, 5µg) added. Samples were

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filtered using syringe filter (nylon, 0.45µm) before analysis based on a previously


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published method 15. All solvents for analysis were of LC-MS grade. Solvent A (water

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and 0.1% formic acid) was held at 100% for 1 min, followed by a gradient up to 35%

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solvent B (acetonitrile and 0.1% formic acid) until 18 min and to 95% B until 19 min.

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Solvent B was then held at 95% for 4 min and the column returned to the initial

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conditions (100% A) within 1 min and equilibrated for 5 min. Detection of eluting

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compounds was performed in a high resolution ESI-q-TOF Bruker maXis mass

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spectrometer. MS data was collected in negative centroid mode, from 50 to 1200 m/z

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for 0.5 sec. Source settings were set as follows: end plate offset and capillary

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voltages at -500 and 3500 V respectively, nebulizer gas (nitrogen) at 1.3 bar, dry gas

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to 8 l/min and dry temperature at 195 ºC. Transfer settings were set as follows:

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ISCID, quadrupole and collision cell energies at 0, 5 and 5 eV respectively; funnel,

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multipole and collision RF at 200 Vpp; ion cooler RF at 40 Vpp; transfer time at 40 µs

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and prepulse storage at 1µs. Calibration was conducted at the end of each run. Data

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analysis was performed based on R package metaMS

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with a retention time window match set to 0.5min.

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16-17

using the default settings

Chromatographic analysis with UPLC-DAD

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For analysis of carotenoids and chlorophylls, an aliquot of the non-polar phase

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(350µl) was dried, resuspended in ethyl acetate/acetonitrile (1:9) and analyzed as

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previously described

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and UV/visible light spectrum and quantified from dose-response curves 18.

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14.

Metabolites were identified through specific retention time

Data processing and statistical analysis

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Principal component analysis (PCA) with pareto scaling was performed with Simca P

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13.0.3.0 (Umetrics, Sweden). All other statistical analysis was performed using

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XLSTAT 2017 (Addinsoft, Paris, France). For all statistical analysis the number of

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biological replicates varied between three and nine depending on the material


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provided (Supplementary File 1). Discriminant analysis was based on traits as

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dependent variables and metabolites as explanatory variables and included

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validation with half the data set. Additional settings for discriminant analysis included:

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within-class covariance matrices are assumed to be equal and prior probabilities

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were used to describe the classification functions. Partial least squares (PLS)

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regression was used to correlate traits and metabolites. This included the cross

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validation: Jackknife (LOO) and validation with random selection of half the data set.

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The correlation between five selected varieties, grown both in vitro and in the field,

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was confirmed with RV coefficient using P-value computation Extrapolation. For one-

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to-one comparison between varieties, significant metabolite changes in leaf and root

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were established through pairwise comparison with Student’s t test (P < 0.05) and

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overlaid with biochemical pathways constructed specifically with BioSynLab© (Royal

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Holloway University of London).

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Results and Discussion

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The metabolite composition of various cassava varieties (Table 1) was analyzed with

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three different platforms in order to provide a comprehensive range of metabolites.

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All 23 varieties were analyzed at the in vitro plantlet stage. Five varieties were

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chosen for analysis of leaf, stem and root material of cassava plants grown under

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field conditions. The methods for LC-MS, GC-MS and UPLC-DAD analysis were

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adapted for cassava samples (extract volume dried, injection volumes and dilutions)

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to create a metabolite profiling approach amenable for cassava plants. Targeted

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analysis included identified metabolites from all three platforms used.

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Diversity detected in in vitro plantlets

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Over 9000 features were detected in the untargeted analysis by LC-MS of in vitro

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plantlet samples (Supplementary Table S2) and were scaled to the internal standard


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and the quality controls. The combined untargeted data was displayed by PCA

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analysis (Figure 1) and showed a trend of the varieties on the basis of geographical

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origin. African varieties were located towards the centre surrounded by varieties from

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Central America/Caribbean and South America. The varieties from South America

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showed the widest distribution in the score plot which indicates a higher variance of

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metabolites and corresponds with several studies describing the Amazon basin as

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the origin of cassava and crop domestication patterns

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BRA488, PER496 and VEN77, were excluded from the combined untargeted

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analysis as their data was only available from one in vitro harvest, impeding

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confirmation of detected metabolite features as part of the chemotype or the growth

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condition.

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Targeted analysis included analysis by GC-MS and UPLC-DAD as well as

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identification of metabolites from untargeted LC-MS analysis. The comprised data

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set included over 100 metabolites representing amino acids, sugars, organic acids of

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the TCA cycle, phenylpropanoids, isopentenyl pyrophosphate derived pigments

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(IPP) and metabolites of other chemical groups (e.g. linamarin) (Supplementary

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Table S3). The data set was subjected to (i) PCA analysis to show the

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similarity/differences between cassava varieties and (ii) discriminant analysis with

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PLS regression to link the traits of varieties to metabolites/ metabolite groups. The

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PCA score plot indicated that some traits have a more similar metabolite composition

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than others (Figure 2). Discriminant analysis showed that 76% of the identified

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metabolites were significantly different (P

Capturing biochemical diversity in cassava (Manihot esculenta Crantz

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