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.

1. 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).
Results
BPC 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
Tricaffeoylquinic acid
FW 678
α-Tomatine
FW 1078 (CO2 adduct)
Quercetin
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 identification
hp-2
Green
hp-2
Red
Man
Green
Man
Red
[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
Naringenin
FW 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.