metabolic profiling of ripening strawberry (fragaria x ananassa)

Strawberries are regularly consumed for the unique flavor and nutritional value. A greater understanding of the biochemistry of fruit ripening in different strawberry cultivars can help deliver higher quality produce to consumers. Botanically the strawberry fruit are actually the achenes or seeds that cover the surface of a modified shoot or flower receptacle. Different cultivars and physiological changes that occur throughout fruit growth and ripening influence the metabolic profile. Fruit quality characteristics are based on the metabolites present at the ripe harvest stage. Metabolite profiling explores both core (primary) and specialized (secondary) metabolite compounds. This comprehensive study integrates metabolites detected in four strawberry (F. x ananassa) cultivars (Festival, Sensation, Winterstar, and Radiance) by separating receptacle (flesh) and achene (seed) tissue samples during six ripening stages (ripe, turning, white, large green, medium green, small green). Metabolites were investigated using non-targeted GC-MS, and targeted UPLC-qTOF-MS profiling. Correlation analysis was used to differentiate cluster patterns of metabolite profiles and ANOVA statistical significance was used to identify metabolites of interest.

Ashlyn Wedde*1, 2, Mahmoud Gargouri1, Jeong-Jin Park1, Michael Schwieterman3, Thomas Colquhoun3, David R. Gang1 1Institute of Biological Chemistry, Washington State University, Pullman, WA, USA

2Molecular Plant Science Graduate Program, Washington State University, Pullman, WA 3 IFAS Plant Innovation Program , Gainesville, FL

METABOLIC PROFILING OF RIPENING STRAWBERRY (Fragaria x ananassa)

Abstract Strawberry Metabolic Correlation ‘Ripe’ Metabolite Comparison

Research Fellowship: NIH Protein Biotechnology Training Program, WSU Instrumentation: NSF MRI grant #1229749

Institute of Biological Chemistry & Dr. David Gang’s Laboratory for Cellular Metabolism and Engineering for experimental equipment support.

Overall Experimental workflow. Strawberry cultivars including Festival, Winterstar, Radiance, and Sensation were assayed to generate a metabolite profile model to determine correlations at time points through development. Proteomic studies are underway to identify unique expressed proteins. Transcriptome data has already been published by other scientists.

References & Acknowledgements

Research Plan

Data Acquisition, Processing, Analysis & Interpretation

Highlights & Conclusions Metabolomics enables better understanding and characterization of fruit across ripening. •  Experimentally, we determine valuable

information for localization of specific metabolites in different tissues, and at different abundances through development.

Future Direction •  Plant and human health are important focuses

for trait enhancement and clinical studies. •  Metabolic correlation studies will supply gene

candidates for quality fruit, flavor & health targets.

Winterstar™

Overall Likability

Sweetness Intensity

Texture Liking

Highest Sucrose Content

‘Strawberry Festival’

Overall Strawberry Flavor

Total Volatiles

Diversity of Volatiles

Sensation™

Total Sugar Content (Suc, Fru,

Glc)

Diversity of Volatiles

‘Florida Radiance’

Transcriptome F. vesca genome, RNA-seq, microarrays etc.

Identify differentially expressed genes

Metabolome Metabolite profiling

Identify metabolites with modeling

Proteome Proteomic Profiling

Identify expressed proteins

Network Analysis Determine overall network correlation

I f strawberry CULTIVARS di f fer in CONSUMER ‘LIKABILITY’, then they must differ in the makeup of a strawberry.

(Schwieterman et al., 2014)

*[email protected]

Achene Receptacle

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Ellagic acid

Cv: Fest-RcptCv: Rad-RcptCv: Sens-RcptCv: Wint-RcptSG MG LG W T R

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Heat map displays the abundance values of metabolites identified in the ‘ripe’ stage for the four cultivars of receptacle and achene tissue. The top axis, groups samples by respective cultivars in the achene and receptacle tissue. The y-axis displays the compounds within their respective compound classes. The heat map uses a multi-fold color change; blue corresponds to the bottom 10 percentile of data values, green is the median, and yellow represents the top 90 percentile of values. Each cell corresponds to the mean abundance intensity of a specific metabolite at a specific time point within a certain cultivar. Tissue is normalized per gram fresh weight and relative to actual berry composition.

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sveratrol glucuronide

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Specific Metabolite Plots during Development

Achene Receptacle

‘F. Festival’ Achene and Receptacle Network Correlation. Immature stages (small, medium, and large green), and mature (white, turning, ripe). Blue bubble designates primary metabolites, red bubble designates secondary metabolites, blue line is a negative correlation, and a red line is a positive correlation. Correlation cut off values 0.8 and -0.8.

Specialized metabolite abundance in receptacle (green background) and achene tissue (blue background) displayed using line graphs across the six developmental stages. All 4 cultivars are represented, ‘Florida Festival’ (blue squares), ‘Florida Radiance’ (orange circles), Sensation™ (green diamonds), and Winterstar™ (gray triangles). Replicate error is taken into account with the vertical line at each stage. The metabolite is labeled on the left y-axis along with its respective abundance values.

Experimental Process. Metabolite Extraction. Achene and receptacle tissue was separated using needle-nose forceps on liquid nitrogen. Frozen tissue (50-500 mg) was extracted using MeOH:IPA:H2O for primary metabolites. Samples were vortexed, sonicated, and centrifuged. Then dried in-vacuum. Derivatization was performed by dissolving dried sample in MeOX HCl /pyridine for 90 minutes at 30˚C. Then MSTFA was added and incubated 30-min at 37˚C. GC-MS Analysis was performed using hot needle injection technique at 230 ˚C. Mass Spectra was collected between 50-1000 m/z. Primary metabolites were identified across development using the NIST Library for GC-MS metabolite identification. Pegasus software was used to process spectra, align samples based on retention times and identify metabolites. Secondary metabolites were extracted with MeOH vortexed, sonicated, and centrifuged. UPLC acquisition was performed with full loop injection into a C18 column using UV detector, mobile phase 0.1% FA in H2O: ACN; 9:1. Spectra was collected between 50-1000 m/z. MS/MS was performed using ramp collision energy between 15-90V. Progenesis QI Metabolomics software was used to identify secondary metabolites by precursor mass accuracy, retention times, MS/MS fragments, and isotope distribution. All experiments were completed in triplicate.

metabolic profiling of ripening strawberry (fragaria x ananassa)

Data & Analytics