References

Methods

Liquid chromatography coupled to mass spectrometry (LCMS) has become a widely used technique in metabolomics research for differential profiling, the broad screening of biomolecular constituents across multiple samples to diagnose phenotypic differences and elucidate relevant features. However, a major bottleneck in non-targeted metabolomics studies is processing the large amounts of data produced by most metabolomics experiments to obtain useful biological information. Haystack is a novel web-based discovery tool that was designed to address this problem. Haystack offers a range of data visualization options for LCMS-based metabolomics data and supports non-biased differential profiling studies through a unique and flexible binning function. Binned mass data can be analyzed by exploratory methods such as principal component analysis (PCA) to model class assignment and identify discriminatory features.

The focus of this project was to compare metabolite profiles of a wild type tomato variety (‘Manapal’) and the hp-2dg mutant strain using non-targeted LCMS. hp-2dg plants carry a mutation in the tomato homolog of the DEETIOLATED-1 gene involved in various aspects of plant photomorphogenesis (Levin et al. 2003). Consistent with previous studies (Enfissi et al. 2010), we show that hp-2dg plants have higher phenylpropanoid and flavonoid levels than wild type at both ripe and unripe developmental stages. This could have important implications for developing tomato varieties with higher levels of health-promoting phytonutrients.

We found Haystack to be equally effective as the popular XCMS Online tool in modeling class assignment and identifying relevant features. XCMS Online uses a peak deconvolution procedure to process LCMS datasets and offers a range of statistical output functions. However, we found XCMS Online to be slower and considerably less flexible for data analysis than Haystack. Furthermore, XCMS Online does not offer the ability to produce custom visualizations of extracted ion chromatograms and other data outputs. We conclude that Haystack provides an alternative platform for processing non-targeted metabolomics data with several unique advantages, particularly the ability to process and visualize raw full-scan LCMS data rapidly and efficiently, and most importantly its unique bin analysis for identifying mass ranges of possible interesting features.

Analysis of tomato metabolite variations via liquid chromatography-mass spectrometry

Arthur Colvis and Stephen C. Grace

Biology Department, University of Arkansas at Little Rock, Little Rock, AR 72204

Numerous factors can influence metabolite profiles of tomato variants

and the fruits they produce. The focus of this project is a comparative

metabolomic analysis of the high pigment-2dg (hp-2dg) tomato mutant

with its wild type counterpart (‘Manapal’) at early and late developmental

stages. The goal is to determine if differences can be found using an

“untargeted” metabolomics approach.

Untargeted metabolomics was carried out by ESI-LCMS (Electrospray

Ionization-Liquid Chromatography Mass Spectrometry), a powerful tool

that is used to detect, separate, and identify particular chemicals in a

given sample. Differential profiling was carried out with the recently

developed Haystack software tool and its unique mass binning function

(Grace et al. 2014). Results were compared with the popular XCMS

Online tool (Tautenhahn et al. 2012).

These experiments provide a unique approach to diagnose phenotypic

differences and elucidate discriminatory features between tomato

genotypes that could be applied to other plant systems.

Seeds of Manapal and hp-2dg strains were obtained from the Tomato Genetics Resource Center (http://tgrc.ucdavis.edu). Plants were grown during summer 2013 in the UALR campus garden. Fruits at green and red stages were harvested, freeze dried, and extracted from a uniform mass (20mg). Samples were extracted with a bead beater, vortex, and sonication bath using cold 80% methanol as the extraction solvent. Samples were then dried and reconstituted, followed shortly by LCMS analysis Ten independent samples were analyzed from each experimental group. Files were saved in netCDF format and uploaded into Haystack.

Acknowledgements: Support for this project was provided by the Arkansas Center for Plant Powered Production with funding from the NSF EPSCoR ASSET II Program (EPS-1003970).

ResultsBPC Plots

LCMS Sample Analysis

Total on chromatogram Single bin analysis

Base peak chromatogram

Extracted on chromatogram

Mass spectrum (MS)

Principal Component Analysis

Volcano plot

Dendrogram

Group bin analysis

Mission Queuing

User Database

Display Functions Processing Functions Statistical Functions

Upload Data(netCDF, mzXML)

Create Project

Data Analysis in Haystack

Workflow in Haystack(http://binf-app.host.ualr.edu/haystack/)

Generate bins from TIC data

Import group bin data into MetaboAnalyst (http://

www.metaboanalyst.ca)

Data normalization and statistical analysis (PCA, ANOVA, Volcano plots)

Model class assignment and identify important features

hp-2dg

Manapal

Fig. 1. Base peak chromatograms of representative hp-2dg and Manapal fruits at green and red stages. The sample LCMS data reveal significant differences between the mutant and wild type as well between their ripe and unripe counterparts.

Fig. 2. Principal component analysis of LCMS data from ripe and unripe fruits of hp-2dg and Manapal. Raw data were processed in both Haystack (A) and XCMS online (B). Haystack performed better than XCMS Online in discriminating classes.

A B

Abstract Conclusions

Class Assignment

O H

O H

O

O

OO H

O H

O

O H

O

O H

OH

OH

O

O

Tricaffeoylquinic acidFW 678

CH 3 C H 3NC H3 O

O HO H

O HO

OH

O H

OO

OOH

O HO H

O HO

O

OH

OH

O HO

O

C H 3

α-TomatineFW 1078 (CO2 adduct)

O H

O HOH

OOH

OH

OH O HOH

O H

O H

O H

OH

O

O

OO

OO

OQuercetin Trisaccharide

FW 742

Feature Identification

Fig. 3. Volcano plot of sum normalized Haystack data for ripe fruits of Manapal and hp-2dg plants. Mass bins that show both large magnitude fold-changes and high statistical significance are shown as blue and red symbols. Yellow symbols indicate neutral variables.

m/z range Feature identificationhp-2

Greenhp-2Red

ManGreen

ManRed

[270,272] Naringenin[354,356] Chlorogenic acid[514,516] Dicaffeoylquinic acid[608,610] Rutin[676,678] Tricaffeoylquinic acid[740,742] Quercetin Trisaccharide-1[770,772] Quercetin Trisaccharide-2[1078,1080] α-Tomatine

Color Legend

Low High

O H

O H

OH

O

O

NaringeninFW 272

Fig. 5 (left) Extracted ion chromatograms of highly ranked features. Unripe fruits contain high levels of the alkaloid a-tomatine (A), whereas ripe fruits contain high levels of the phenylpropanoid tricaffeoylquinic acid (B). Ripe fruits of hp-2dg plants had higher levels of the flavonoids quercetin trisaccharide (C) and naringenin (D) than Manapal plants.

A

B D

C

Fig. 4 (above) Heatmap representation of discriminatory features of hp-2dg and Manapal fruits at green and red stages identified by Haystack.

Enfissi E.M. et al. “Integrated transcript and metabolite analysis of nutritionally enhanced DE-ETIOLATED1 downregulated tomato fruit.” Plant Cell 2010 22:1190-1215.

Grace S.C. et al. “Haystack, a web-based tool for metabolomics research.” BMC Bioinformatics 2014, in press.

Levin I. et al. “The tomato dark green mutation is a novel allele of the tomato homolog of the DEETIOLATED1 gene.” Theor Appl Genet 2003, 106:454-460.

Tautenhahn R. et al. “XCMS Online: a web-based platform to process untargeted metabolomic data.” Anal Chem 2012, 84:5035-5039.