Corresponding author: Patrick Xuechun Zhao Plant Biology ...Nov 25, 2009 · important roles in protecting the plant against insect predation and other biotic challenges (Peter and

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Running head: TrichOME: A Comparative Omics Database for Plant Trichomes

Corresponding author: Patrick Xuechun Zhao

Plant Biology Division, The Samuel Roberts Noble Foundation, 2510 Sam Noble

Parkway, Ardmore, OK 73401, USA

E-mail: [email protected]

Phone: +1-580-224-6725

Fax: +1-580-224-6692

Research area: Bioinformatics and Plant Genomics

Plant Physiology Preview. Published on November 25, 2009, as DOI:10.1104/pp.109.145813

Copyright 2009 by the American Society of Plant Biologists

https://plantphysiol.orgDownloaded on January 1, 2021. - Published by Copyright (c) 2020 American Society of Plant Biologists. All rights reserved.

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TrichOME: A Comparative Omics Database for Plant Trichomes Xinbin Dai1, Guodong Wang2, Dong Sik Yang1, Yuhong Tang1, Pierre Broun3, M. David Marks4, Lloyd W. Sumner1, Richard A. Dixon1 and Patrick Xuechun Zhao1*

1. Plant Biology Division, The Samuel Roberts Noble Foundation, Ardmore, OK, USA 2. Institute of Genetics and Developmental Biology, Chinese Academy of Sciences,

Beijing, China 3. Nestlé R & D Center Tours, Plant Science & Technology, 101 Avenue Gustave Eiffel,

7390 Notre-Same D'Oé, France 4. Department of Plant Biology, University of Minnesota, St Paul, MN, USA * Corresponding author



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Financial source: Financial support for this project was provided by the National

Science Foundation Plant Genome Program (Award #0605033) and the Samuel Roberts

Noble Foundation.

Corresponding author: Patrick Xuechun Zhao ([email protected])



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TrichOME: A Comparative Omics Database for Plant Trichomes

Abstract

Plant secretory trichomes have a unique capacity for chemical synthesis and secretion,

and have been described as biofactories for the production of natural products. However,

until recently, most trichome-specific metabolic pathways and genes involved in various

trichome developmental stages have remained unknown. Furthermore, only a very

limited amount of plant trichome genomics information is available in scattered databases.

We present an integrated ‘omics’ database, TrichOME, to facilitate the study of plant

trichomes. The database hosts a large volume of functional ‘omics’ data, including

EST/Unigene sequences, microarray hybridizations from both trichome and control

tissues, mass spectrometry-based trichome metabolite profiles, and trichome-related

genes curated from published literature. The EST/Unigene sequences have been

annotated based upon sequence similarity with popular databases, e.g. Gene Ontology,

KEGG, and TCDB. The Unigenes, metabolites, curated genes and probe-sets have been

mapped against each other to enable comparative analysis. The database also integrates

bioinformatics tools with a focus on the mining of trichome-specific genes in Unigenes

and microarray-based gene expression profiles.

TrichOME is a valuable and unique resource for plant trichome research since the genes

and metabolites expressed in trichomes are often under-represented in regular non-tissue-

targeted cDNA libraries. TrichOME is freely available at http://www.planttrichome.org/.



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Introduction

Plant trichomes are epidermal tissues located on the surfaces of leaves, petals, stems,

petioles, peduncles, and seed coats depending on species. By virtue of their physical

properties (size, density), trichome hairs can directly serve to protect buds of plants from

insect damage, reduce leaf temperature, increase light reflectance, prevent loss of water,

and reduce leaf abrasion (Wagner, 1991; Wagner et al., 2004).

Although the morphology of trichomes varies greatly, they can be generally classified

into two types: simple trichomes (STs) and glandular secreting trichomes (GSTs)

(Wagner et al., 2004). STs of Arabidopsis thaliana have been chosen as models for

studying cell fate and differentiation (Wagner, 1991; Breuer et al., 2009; Marks et al.,

2009). In Arabidopsis, simple trichomes on leaves consist of a uni-cellular structure with

a stalk and three to four branches (Fig. 1b). Although the STs are referred to as “non-

glandular” (presumably non-secreting), expression of genes involved in anthocyanin,

flavonoid, and glucosinolate pathways can nevertheless be detected in STs, indicating the

roles of STs in the biosynthesis of secondary compounds and defense (Wang et al., 2002;

Jakoby et al., 2008). GSTs are found on about one third of vascular plants. GSTs have a

multi-cellular structure with a stalk terminating in a glandular head [Fig 1a-1g, but not

1b]. GSTs are initiated from a single protodermal cell which undergoes vertical

enlargement and multiple divisions to give rise to fully developed trichomes. GSTs often

produce and accumulate terpenoid and phenylpropanoid oils (Wagner et al., 2004).

However, alkaloids, the third major class of plant secondary compounds, are not common

in GST exudates (Laue et al., 2000). The amount of exudates produced by GSTs may

reach 30% of mature leaf dry weight, as found in certain Australian desert plants (Dell

and McComb, 1978). Plant GSTs can impact pathogen defense, pest resistance, pollinator

attraction and water retention based on the phytochemicals they secrete.

GSTs on the aerial organ surfaces have a unique capacity for synthesis and secretion of

chemicals (largely plant secondary metabolites), and they have been described as

“chemical factories” for the production of high-value natural products (Mahmoud and



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Croteau, 2002; Wagner et al., 2004; Schilmiller et al., 2008). Secondary metabolites play

important roles in protecting the plant against insect predation and other biotic challenges

(Peter and Shanower, 1998), and they are potential sources for pharmaceutical and

nutraceutical product development. For example, the trichome-borne artemisinin from

Artemisia annua is still the most effective drug against malaria, and the early steps of its

biosynthetic pathway have been extensively studied (Duke et al., 1994; Arsenault et al.,

2008). Recently, the mechanisms by which plant glandular trichomes make, transport,

store and secrete a great variety of unique compounds, especially terpenoids and

flavoniods, are receiving extended research interest because of the potential use of these

compounds in pharmaceutical and nutraceutical applications. Seminal studies have

reported the assignation of gene functions to specific metabolic pathways in glandular

trichomes of several plant species including mint (Alonso et al., 1992; Rajaonarivony et

al., 1992; Lange et al., 2000), basil (Gang et al., 2002; Iijima et al., 2004; Xie et al., 2008),

Artemisia (Teoh et al., 2006; Zhang et al., 2008), tomato (Fridman et al., 2005; Besser et

al., 2009; Schilmiller et al., 2009) and hops (Nagel et al., 2008; Wang et al., 2008). Table

1 summarizes the trichome structure, classification and secondary metabolites in these

species.

‘Omics’ approaches are being extensively employed for systematically studying the

molecular aspects of trichome development, metabolism and secretion at the “systems

biology” level, and data are being rapidly generated; however, currently only limited

‘omics’ data for plant trichomes are publically available in the literature and scattered

databases. For example, NCBI dbEST (Boguski et al., 1993) hosts approximately

130,000 ESTs sampled from trichome cDNA libraries of 13 species (as of Feb. 2009) or

mixed tissues including trichomes. The public MIAMI-compliant (Hatada et al., 2006)

repository ArrayExpress (Parkinson et al., 2009) hosts 16 microarray hybridization

experiments for Arabidopsis non-glandular trichomes, but there are no glandular

trichome microarray data available in public databases so far. Lastly, we could not find

large-scale metabolite profile data for plant trichomes from public ‘omics’ databases,

such as AraCyc and MedicCyc databases (Mueller et al., 2003; Urbanczyk-Wochniak and

Sumner, 2007).



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The ‘omics’ data in publicly available databases are usually far from comprehensive and

not integrated as most of these data are in raw format. For example, EST /Unigene and

microarray datasets are not inter-linked, and comparative analysis between trichome and

non-trichome tissues for the identification of trichome-specific genes is absent. For

example, to mine useful information such as trichome-specificity of gene expression,

signal strengths need to be normalized among hybridization experiments from both

trichome and non-trichome tissues; to search a specific gene and deduce its potential

function(s), sequences from cDNA libraries need to be annotated based on metabolic

pathways and further cross-linked to metabolite profiles by catalytic reaction. Such data

collection, integration and mining processes pose great challenges to biologists interested

in trichomes.

To overcome the above limitations, we have developed TrichOME, an integrated

genomic database for plant trichomes. TrichOME integrates ‘omics’ data, such as EST /

Unigene sequences, microarray gene expression profiles, metabolite profiles and

literature mining results. To assemble Unigenes and pursue comparative analysis of gene

expression, the database has integrated a large amount of EST sequence and gene

expression profile data from non-trichome tissues as well. We processed, curated and

annotated the hosted data by referring to multiple popular biological databases to ensure

the highest quality. TrichOME provides an interactive website

(http://www.planttrichome.org/) for searching trichome-related ESTs/Unigenes,

microarray hybridizations, metabolites and literature information.

Data Compilation, Integration and Mining

EST Sequences

Plant trichome-related EST sequences were downloaded from NCBI dbEST and were

grouped by cDNA libraries and species using an in-house Perl script. The cDNA libraries

from non-trichome tissues and full length cDNAs were collected for the purpose of



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Unigene assembly and in-silico expression analysis. The cDNA libraries of low quality,

smaller libraries (<500 EST members) and non-trichome cDNA libraries (due to some

errors in the dbEST database) were manually removed.

The collected ESTs were further processed prior to assembly in order to remove

remaining cloning vectors, adaptors and contaminating sequences (Lee et al., 2005). For

each cDNA library, the cross_match (Ewing and Green, 1998) program was used to

search against corresponding vector sequence. Subsequently, the rigorous Smith-

Waterman SSearch program (Pearson and Lipman, 1988) was employed to clean short

adapters, restriction endonuclease sites and PCR primers provided in the original cDNA

library descriptions. After the above procedures, TrichOME currently hosts about 1

million cleansed EST sequences from both trichome and corresponding non-trichome

control tissues of 13 species (Artemisia annua, Cistus creticus, Humulus lupulus,

Medicago sativa, Medicago truncatula, Mentha x piperita, Nicotiana benthamiana,

Nicotiana tabacum, Ocimum basilicum, Salvia fruticosa, Solanum habrochaites, Solanum

lycopersicum and Solanum pennellii).

The cleansed EST sequences were further assembled into Unigenes, which consist of

singletons and tentative consensuses (TC) by species, using the TGI clustering tools (Lee

et al., 2005). Some Unigenes were further refined by referring to the available full length

cDNA from the same species in order to improve assembly quality.

The Unigene and EST sequences were annotated using BLASTX queries against

UniProtKB and Swiss-Prot databases with a cut-off of E-value < 1e-04. The top five

query results were listed as valid annotations. Using the same protocol, the Unigene/EST

sequences were further annotated using the Gene Ontology (GO) database (Ashburner et

al., 2000), KEGG gene and metabolic pathway database (Kanehisa et al., 2008),

transporter classification database (TCDB) (Saier et al., 2006) and plant transcription

factor database (PlantTFDB) (Guo et al., 2008). The conserved domains of sequences

were searched by InterProScan with its default cutoff of E-value (Hunter et al., 2009).



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Microarray Hybridizations

The microarray datasets in TrichOME were collected from three different sources;

ArrayExpress (Parkinson et al., 2009), Arabidopsis Gene Atlas (Schmid et al., 2005) and

our internal experiments. TrichOME currently hosts microarray-based expression profiles

for two model plants, Arabidopsis thaliana (using the Affymetrix ATH1 GeneChip) and

Medicago truncatula, and the forage crop Medicago sativa (alfalfa) (using the Affymetrix

Medicago GeneChip). Our expression analyses with three biological replicates were

performed on glandular and non-glandular trichome tissues as well as non-trichome

tissues (as control), e.g. stem, flower, root and nodule etc. (Schmid et al., 2005; Benedito

VA, 2008; Wang et al., 2008).

For each Affymetrix array hybridization, the resultant image .cel file was exported using

the GeneChip Operating Software Version 1.4 (Affymetrix) and then imported into

Robust Multiarray Average (RMA) software for global normalization (Irizarry et al.,

2003). Presence/absence call for each probe set was analyzed using dCHIP software (Li

and Wong, 2001) for its high reliability.

To annotate the Unigenes and array data, the trichome-related Unigenes were mapped to

Affymetrix GeneChip probesets using a probe-sets remapping PERL script developed by

Affymetrix, Inc. Meanwhile, the Affymetrix probeset target sequences were mapped to

the Gene Ontology (GO) database (Ashburner et al., 2000), KEGG gene and metabolic

pathway database (Kanehisa et al., 2008), transporter classification database (TCDB)

(Saier et al., 2006) and plant transcription factor database (PlantTFDB) (Guo et al., 2008)

by performing BLASTX searching against the reference sequences with a cut-off of E-

value < 1e-04.

Mass Spectrometry-based Metabolite Profiles

TrichOME hosts mass spectrometry-based metabolite profiles for plant trichomes.

Currently, the database hosts gas chromatography-mass spectrometry (GC-MS) data



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obtained for potato leafhopper susceptible and resistant lines of Medicago sativa. The

data are composed of triplicate profiles obtained by multiple published methods

(Broeckling et al., 2005). These methods included GC-MS of a polar extract, GC-MS of a

non-polar extract with lipid hydrolysis, and GC-MS of non-polar extracts without lipid

hydrolysis. Metabolite profiling methods have been described in the details page of each

experiment. Polar GC-MS metabolite profiles include over 300 components composed of

amino acids, organic acids, alcohols, nucleotides, sugars, and sugar phosphates. Non-

polar, hydrolyzed GC-MS metabolite profiles include over 300 components composed of

fatty acids, long chain alcohols, waxes, terpenoids, and sterols. In particular, fatty acid

amides were reported as these have been recently suggested to contribute to leafhopper

resistance (Ranger et al., 2005). Additional experimental data including SPME-GC-MS

of volatiles and UPLC/Q-TOF-MS for profiling secondary metabolites have been

collected and will be added to the database in the near future.

Raw GC-MS metabolite profiles were deconvoluted using AMDIS (the Automated Mass

Spectral Deconvolution and Identification System from National Institute of Standards

and Technology, Gaithersburg, MD, USA, http://www.amdis.net/) software.

Deconvoluted peaks were identified using AMDIS spectral matching with a custom

spectral database generated from authentic standards (Broeckling et al., 2005). The

unidentified peaks were further analyzed through spectral comparisons with data in

MassBank (http://www.massbank.jp/) with a similarity score cutoff >= 0.85. The relative

peak areas of metabolites were extracted using custom MET-IDEA software (Broeckling

et al., 2006) and normalized against the peak areas of the internal standard. Normalization

allows for the quantitatively comparison of accumulated metabolites or tentative peaks.

The annotated peaks were linked to corresponding compounds in the PubChem database

(http://pubchem.ncbi.nlm.nih.gov/) and KEGG compound database by manually

matching compound names. We further mapped the identified compounds to trichome-

related Unigenes and probe-set targets. This was performed by downloading the KEGG

mapping files of compounds, reactions and enzymes, as well as standard KEGG E.C. or

KEGG Orthology (KO) sequence datasets from the KEGG ftp site. By referring to these



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mapping files, compounds were mapped to EC/KO number through reaction numbers.

The compounds were further associated with Unigenes and probeset target sequences by

BLASTX searching these sequences against the downloaded standard EC/KO reference

sequences (E-value<=1e-04). TrichOME allows the user to download raw data by

experiments or explore details of the peak data [Fig 2].

Literature Mining of Trichome Related Genes and Proteins

TrichOME hosts trichome-related genes and proteins curated from the published

literature. The genes were initially searched using information such as gene / protein

name, alias names, and annotations, from the NCBI Gene Database

(http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene), followed by extensive human

curation. Hosted genes were further associated with trichome-related Unigene/EST

sequences as well as microarray probe-set target sequences by BLASTX queries of the

standard protein sequences of hosted genes downloaded from the NCBI Gene Database

with a cutoff of e-value < 1e-6.

System Implementation and Web Interfaces

The TrichOME system consists of a backend database and a frontend website developed

using Java. The frontend website interfaces integrated AJAX technology powered by

YUI libraries (http://developer.yahoo.com/yui/) to improve user-server interactions.

The ESTs / Unigenes and associated cDNA library information are available for batch

download by species and libraries [Fig 3a]. Web treeviews and comprehensive search

interfaces were also implemented to facilitate the interactive queries. For example, the

KEGG tree view allows users to explore trichome-related ESTs/Unigenes and present the

sequences by metabolic pathways [Fig 3b]. A BLAST interface enables users to search

user-submitted sequences against the trichome-related sequences in the database.

TrichOME allows users to download the sequences filtered by query individually or in



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batch. It also includes an online data submission page to collect trichome related ‘Omics’

data from the research communities.

TrichOME integrates an in-silico gene expression analysis algorithm to search the unique

or highly expressed Unigenes in trichome tissue [Fig 4]. The algorithm counts EST

members of each Unigene across multiple cDNA libraries to calculate a R-value (Stekel

et al., 2000), which represents the library-specificity of the Unigene.

For each hybridization experiment, TrichOME hosts both raw and normalized data, and it

provides a description page with links for batch downloading. In addition, the database

provides a series of “Compare by …” functions for the query of hybridization signals by

probe-set IDs, annotation of probe-set target sequences, associated trichome-related

Unigenes or the ratio of signal strength between trichome and non-trichome tissues.

The trichome-related Unigenes, microarray probe-sets, metabolites and curated trichome-

related genes have been mapped to each other to allow users to gain an overall systems

understanding of trichome biology and biochemistry.

Case Studies

Case Study #1: Mining of Putative Trichome-specific Genes by in silico and

Microarray Gene Expression Analysis

Because Medicago truncatula has the most (52 in total) comprehensive non-trichome

cDNA libraries available as controls, this organism was chosen as an example to search

for putative trichome-specific or highly preferentially expressed sequences. In the ‘in-

silico expression’ of the ‘EST analysis’ section, we selected ‘MT_TRI’ as trichome

cDNA library and all of the non-trichome cDNA libraries as control. After clicking the

Analyze button, the server calculates R-values of all Unigenes by comparing the

abundance of gene transcripts among cDNA libraries and returns 111 unigenes with

significantly higher expression level in the trichome cDNA library than in the non-



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trichome cDNA libraries (R-value>=4) (see supplementary file #1). The sequences are

available for batch download by clicking the ‘Download All’ link on the same page.

Users may also select ‘show unigenes only detected in trichome’ option to view the

Unigenes only present in the trichome library; the R-value statistical result will be

ignored under this option.

To cross-validate the above in silico expression analysis results, the 111 Unigene IDs

were copied into the ‘compare by unigene’ section under the ‘Microarray Analysis’ menu

to retrieve expression signals of corresponding probe-sets from all tissue-specific

microarray hybridization experiments (mean values). Among the 111 Unigenes, 82

unigenes, for which corresponding probe-set targets were expressed at least two times

higher in signal strength in trichome tissue than in non-trichome tissue (see

supplementary file #2), were identified as putative trichome related genes in Medicago

truncatula for future experimental analysis.

Remarkably, among the 20 genes that were between 10 and 758-fold preferentially

expressed in trichomes compared to non-trichome tissue, 16 were annotated as being

involved in biotic or abiotic stress responses (Table 2). These include pathogenesis-

related proteins of the PR1a, 4 and 5a (thaumatin) classes, and chitinases and

endoglucanases, all of which are associated with induced responses to pathogen attack

(Rigden and Coutts, 1988). Chitinases and glucanases likely modify the cell walls of

invading pathogens and are often potent inhibitors of fungal growth (Mauch et al., 1988;

Zhu et al., 1994). Two genes annotated as germin-like proteins were preferentially

expressed 72- and 78-fold, respectively, in Medicago trichomes. Germ and germin-like

proteins may exhibit oxalate oxidase or superoxide disumutase activity, have been

implicated in the generation of hydroxgen peroxide during plant defense responses, and

have been shown genetically to play important roles in plant defense (Lou and Baldwin,

2006; Godfrey et al., 2007). Two lipid transfer protein genes were preferentially

expressed by 32- and 37-fold in the trichomes. These genes are often among the most

highly expressed in plant trichomes (Lange et al., 2000; Aziz et al., 2005; Wang et al.,

2008). Although they may play roles in transfer of lipid metabolites between cell



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compartments, they have also been shown to act in plant defense, either through direct

antimicrobial activity (Kader, 1997) or as signal transduction components (Maldonado et

al., 2002). The serine protease inhibitor gene that was preferentially expressed over 750-

fold in the Medicago trichomes is likely involved in defense against herbivory, whereas

the gene annotated as encoding a dessication-related protein may help protect against

abiotic stress. Genes with similar potential functions (e.g. dehydrins) are also expressed

in trichomes of other species (Aziz et al., 2005).

On the basis of this preliminary analysis, the M. truncatula glandular trichomes appear to

provide the plant with a defensive barrier through primary expression of high levels of

proteins with direct defensive properties. This is in contrast to the trichomes from other

species which, although expressing some of the same types of defense genes (see

supplementary file #3), primarily appear to harness their gene expression towards the

biosynthesis and accumulation of antimicrobial secondary metabolites. For example,

genes associated with terpene metabolism are among the most highly expressed in

trichomes of mint, hops and tomato (Lange et al., 2000; Wang et al., 2008; Besser et al.,

2009).

We also compared the transcriptional profiles in non-glandular trichomes (of Arabidopsis)

and leaves from which trichomes had been removed using the Arabidopsis Ath1

GeneChip. Six transcription factor genes (three Myb genes, two WRKY genes and one

HD-ZIP gene) were found in the top 20 probesets/genes which were highly expressed in

trichome tissue (Table 3). Furthermore, a portion of these genes or their family members

are reportedly involved in trichome development (Jakoby et al., 2008). For example,

AT1G79840 (HD-ZIP, GL2 gene) and AT2G37260 (WRKY, TTG2 gene) are well-

studied regulators of leaf trichome formation (Rerie et al., 1994; Johnson et al., 2002).

Interestingly, the top 20 most highly-expressed genes in non-glandular trichomes are

quite different from the genes that are highly expressed in glandular trichomes (shown in

Table 2). The latter include many more genes that are involved in biotic or abiotic stress

responses, and no transcription factor was found in the latter list.



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Case Study #2: Comparative analysis of monoterpenoid biosynthesis genes in hop

(Humulus lupulus L.) trichomes

Hop (Humulus lupulus L.) is a perennial, dioecious plant that belongs to the Cannabaceae

family. Hop trichomes produce and accumulate large amounts of terpenoids (the

monoterpene-pinene, and the sesquiterpenes-humulene and -caryophyllene),

prenylflavonoids and two important classes of bitter acids (humulone and lupulone

derivatives), all of which give additive flavor to beer (Stevens et al., 1998; Wang et al.,

2008). To study the monoterpenoid biosynthesis pathway, we firstly searched the hop

trichome-related Unigenes involved in the pathway by exploring the KEGG classification

tree in the ‘EST Analysis section’. Some of returned Unigenes, such as TCHL10609 and

TCHL10281, are annotated as encoding pinene synthase (monoterpene synthase) and

given the KEGG orthology term K07384. K07384 is located under the node representing

monoterpenoid biosynthesis pathway (‘Metabolism → Biosynthesis of Secondary

Metabolites → Monoterpenoid biosynthesis’). We then designed primers targeting these

Unigenes and successfully cloned the full-length cDNAs of the monoterpene synthases

HlMTS1 and HlMTS2. The detailed comparative analysis of the monoterpenoid

biosynthesis genes/enzymes in hop trichomes has been published (Wang et al., 2008).

Discussion

The genes expressed in trichomes may be under-represented in non-tissue-targeted

libraries due to the distinct features of trichome tissue in secondary metabolism. That is

to say, many of the trichome-related transcripts may not appear in regular EST databases

because of their relative low expression levels in whole plant tissue. For example, 68% of

ESTs in the Medicago truncatula trichome cDNA library are singletons or belong to

trichome-specific Unigenes, whereas the average value is 15% for all 52 non-trichome

cDNA libraries. Therefore, the unique data in TrichOME are a resource to the trichome

biology and plant defense research communities, and are also expected to be a valuable

resource for the annotation / re-annotation of “model” plant genomes, especially for the

model legume Medicago truncatula.



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Since EST sequences are often short and of low quality, we have included additional

sequences from ESTs libraries of non-trichome tissues as well as individual cDNA

sequences to generate longer and more credible Unigene sequences. These non-trichome

EST libraries were also used as control libraries for in silico analysis of trichome-specific

genes. We will routinely update the TrichOME database when more trichome or non-

trichome EST sequences are available in public repositories aiming to further improve the

quality of Unigene assembly and comparative analysis of trichome genes.

TrichOME hosts Metabolomics Standards Initiative (Sansone et al., 2007) compliant data.

The GC-MS based metabolite profiles are much more reproducible than LC/MS in

respect of retention time and spectrum of peaks. The retention time index of each peak

can be calibrated based on reference compounds and therefore enables comparison across

experiments. Meanwhile, the spectrum data of each peak from GC-MS are less variable

and are comparable across laboratories because the common electrical ionization (EI)

protocol (70eV) is applied for spectrum generation. In the TrichOME database, some of

the peaks were identified based on the standard EI spectrum libraries, for example, our

internal library and the MassBank (http://www.massbank.jp/).

TrichOME also provides tools for the analysis of microarray data and Unigenes in

addition to hosting sequences, gene expression profiles, metabolite profiles and curated

trichome-related genes. The data from different sections have been carefully cross-

mapped to each other to enable users to cross reference the data in different sections. For

example, TrichOME hosts the Affymetrix Medicago GeneChip hybridization results from

two organisms. The probe-sets are mapped with the curated trichome-related genes from

the literature and are also linked to Unigenes. These data and analytical functions enable

users to identify the differentially expressed genes between trichome and non-trichome

tissues.

Transcription factors and transporters are receiving extensive interest from the trichome

research community as the former may regulate secondary metabolic pathways and the



17

latter function to transfer natural products across the plasma membrane and tonoplast.

TrichOME annotates Unigenes based on the published plant transcription factor database

and transporter classification databases (Saier et al., 2006; Guo et al., 2008). These in-

depth annotations facilitate the further study of genes involved in secondary metabolic

pathways.

Acknowledgements

The authors thank Drs Haiquan Li, Mohamed Bedair and Zhentian Lei for their

comments on mass spectral data analysis, Joshua Smith and Zhaohong Zhuang for

literature curation, and Kevin Staggs and Shane Porter for web interface improvement.

Financial support for this project was provided by the National Science Foundation Plant

Genome Program (Award #0605033) and the Samuel Roberts Noble Foundation.

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Figure legends

Fig. 1 Representative Scanning Electron Microscope (SEM) images of trichomes on

plants. A) Erect glandular trichome on the stem of Medicago sativa, B) Non-glandular

trichome on a rosette leaf of Arabidopsis thaliana, C) Procumbent trichome on the petiole

of Medicago truncatula, D) Field of glandular trichomes on female bract of Cannabis

sativa, E) Glandular trichomes on the bract of Humulus lupulus. F) Non-glandular

trichome on the leaf of Medicago truncatula, G) Types VI (small arrow) and I (larger

arrow) on the leaf of Solanum lycopersicum. All the SEM images were generated as

previously described (Ahlstrand, 1996; Esch et al., 2004) using an Emitech K1150 Cyro-

preparation system and a Hitachi S3500N Scanning Electron Microscope (Emitech

Technologies Ltd., www.emitech.co.uk; Hitachi High Technologies, Inc., www.hitachi-

hhta.com). All white size bars equal 100 microns.

Fig. 2 The interface for metabolite profiles by GC-MS in TrichOME.

Fig. 3 The interface for searching trichome-related ESTs/Unigenes. a) List of species

with trichome-related ESTs/Unigenes and links for batch downloading; b) Trichome-

related Unigenes classified by KEGG metabolic pathways.

Fig 4 The interface for in-silico gene expression analysis. The function calculates R-

Values for the identification of trichome-specific or highly-expressed Unigenes by

comparing with ESTs members of Unigenes expressed in trichome library and non-

trichome control libraries.



24

Table 1. A summary of trichome structure, classification and accumulated metabolites in

representative plant species

Species Trichome structure and classification Metabolites Arabidopsis thaliana

Non-glandular trichome, large, single epidermal cells with a stalk and three or four branches on the surface of most shoot-derived organs.

Artemisia annua

Biseriate 10-celled glandular trichome, head including three apical cell pairs (Duke and Paul, 1993).

Artemisinin, an endoperoxide sesquiterpene lactone

Cistus creticus

Glandular trichome composes of a long multi-cellular stalk of over 200 μm topped by a small glandular head cell. Two types of non-glandular trichome: multi-cellular stellate and simple unicellular spike, respectively (Gülz et al., 1996).

Labdane-type components, e.g. ent-3'-acetoxy-13-epi-manoyl oxide, ent-13-epi-manoyl oxide(Falara et al., 2008 )

Humulus lupulus

Peltate glandular trichome, with a glandular head consisting of 30 to 72 cells, four stalk cells and four basal cells; Bulbous glandular trichome, consisting of four (occasionally eight) head glandular cells, two stalk cells and two basal cells; non-glandular trichome (cystolith hair), with a hard calcium carbonate structure at base of a hair (Oliveira et al., 1988; Nagel et al., 2008).

Essential oil, including Myrcene, humulene and caryophyllene; bitter acids, including humulones and lupulones; prenylfalvonoids, including Xanthohumol and desmethylxanthohumol (Wang et al., 2008)

Medicago sativa

Erect glandular trichome, containing multicellular stalk typically over 200 μm long topped by a glandular head composed of a few cells with a diameter of approximately 15 μm; non-glandular trichome, composed of a short base cell and a unicellular elongated shaft (Ranger and Hower, 2001).

N-(3-methylbutyl) amide of linoleic acid (Ranger et al., 2005)

Mentha x piperita

Peltate glandular trichome consisting of a basal cell, a stalk cell, and disc of 8 glandular cells approximately 60 μm in diameter (McCaskill et al., 1992).

Essential oils, such as p-menthanes; monoterpenes including menthone and menthol

Nicotiana tabacum

Tall glandular trichome, a multicellular stalk topped by unicellular or multicellular head; short glandular trichome, a unicellular stalk topped by multicellular head (Akers et al., 1978).

Labdene-diol diterpenes and amphipathic sugar esters (Lin and Wagner, 1994)

Ocimum basilicum

peltate glandular trichome, consisting of a base cell, stalk cell, and a 4 celled head; capitate glandular trichome, consisting of a single base and stock cell and one to two celled head; multicellular non-glandular spiked trichome (Werker et al., 1993).

Phenylpropanoid eugenol, monoterpanoid linalool and phenylpropanoid mehylcinnanmate (Xie et al., 2008)

Salvia Type I, consisting of 1-2 stalk cells and Essential oils, such as α-pinene,



25

fruticosa 1-2 enlarged, rounded to pear-shaped secretory head cells; Type II, consisting of 1-2 stalk cells and one elongated head cell as narrow as the stalk cells at its base and slightly enlarged above; Type III, consisting of 2-5 elongated stalk cells and rounded head in young leaves, which becomes cup-shaped in mature leaves (Werker et al., 1985).

1,8-cineole, camphor, and borneol (Arikat et al., 2004)

Solanum habrochaites

Type I glandular trichome, large in size with multicelluar base; Type III, intermediate in size with a single basal cell; Type IV, a short, multicellular stalk and secrete droplets of sticky exudate at the tip; Type V, short, slender, 1-4 celled; Type VI, short with a 2-4 celled glandular head and Type VII, 0.05-0.1 mm smaller glandular hair with a 4-8 celled glandular head. Type VI are particularly abundant (Reeves, 1977).

Mainly α-santalene, α-bergamotene, and β-bergamotene; small amount of α-humulene and β-caryophyllene (Besser et al., 2009)

Solanum lycopersicum

Same as above. Monoterpenes (Besser et al., 2009)

Solanum pennellii

Same as above but possesses high density of type IV glandular trichome (Lemke and Mutschler, 1984).

2,3,4-triacylglucoses (Goffreda et al., 1989)



26

Table 2. Top 20 probesets /unigenes highly-expressed in trichome of Medicago

truncatula.

Probeset ID Unigene Acc. Annotation Ratio

Mtr.16277.1.S1_at TCMT54757 Serine proteinase inhibitor 758.5

Mtr.49787.1.S1_s_at TCMT59300 ES611317 Endo-1,3-beta-glucanase 323.5

Mtr.43770.1.S1_at TCMT49613 Pathogenesis-related protein 1a 242.7

Mtr.31496.1.S1_s_at TCMT40799 Putative arsenate reductase 153.6

Mtr.22721.1.S1_at TCMT48089 Foot protein-4 variant-2 153.28

Mtr.6179.1.S1_at TCMT56621 Desiccation-related protein PCC13-62 144.43

Mtr.34454.1.S1_at TCMT43273 Class Ib chitinase 128.25

Mtr.32629.1.S1_s_at TCMT42631 Thaumatin-like protein PR-5a 104.22

Mtr.22726.1.S1_at TCMT59229 unknown 94.63

Mtr.34878.1.S1_at TCMT60298 Germin-like protein 77.63

Msa.1226.1.S1_at CO513853 Germin-like protein 71.59

Mtr.44506.1.S1_x_at TCMT59300 Endo-1,3-beta-glucanase 66.05

Mtr.10325.1.S1_at TCMT41963 Chitinase-related agglutinin 63.20

Mtr.35661.1.S1_at TCMT59126 Probable lipid transfer protein family protein 54.78

MsaAffx.3538.1.S1_at TCMT42628 TCMT40025 Putative senescence-associated protein; 40.86

Mtr.8763.1.S1_at ES613671 Thaumatin-like protein PR-5a 38.56

Mtr.34695.1.S1_at TCMT55690 Plant lipid transfer/seed storage/trypsin-alpha amylase inhibitor 37.48

Mtr.34695.1.S1_s_at EX525955 Plant lipid transfer/seed storage/trypsin-alpha amylase inhibitor 32.14

Mtr.22715.1.S1_s_at TCMT43191 eIF4G eukaryotic initiation factor 4, Nic domain containing protein 27.21

Mtr.39139.1.S1_at TCMT52856 Pathogenesis-related protein 4A 19.37

* Ratio represents the ratio of average signal strength between trichome tissue and non-trichome tissues.



27

Table 3. The Top 20 probesets that are highly expressed in non-glandular trichome compared with leaf tissue in Arabidopsis thaliana.

ProbesetID Gene Acc. Annotation Signal Ratio (Trichome / Leaf)

249408_at At5g40330 myb-related protein 2.65

245181_at At5g12420 putative protein similarity to various predicted proteins, contains ATP synthase delta (OSCP) subunit signature AA211-230

2.61

257490_x_at At1g01380 Myb homolog 2.55

260166_at At1g79840 GLABRA2, HD-ZIP protein, GL2 gene 2.40

260386_at At1g74010 putative strictosidine synthase 2.36

267459_at At2g33850 unknown protein 2.31

256359_at At1g66460 protein kinase, Pfam profile: PF00069 2.31

265954_at At2g37260 putative WRKY-type DNA binding protein 2.31

264387_at At1g11990 putative growth regulator protein (axi 1 gene) 2.24

253392_at At4g32650 potassium channel protein AtKC potassium channel 2.24

253595_at At4g30830 M1 protein, Streptococcus pyogenes, PIR2:S46489 2.23

263775_at At2g46410 putative MYB family transcription factor 2.23

263480_at At2g04032 putative root iron transporter protein 2.23

265008_at At1g61560 Mlo protein 2.21

248236_at At5g53870 putative phytocyanin/early nodulin-like protein 2.19

248896_at At5g46350 WRKY-type DNA-binding protein 2.17

246000_at At5g20820 putative protein predicted proteins 2.15

259410_at At1g13340 hypothetical protein 2.14

266544_at At2g35300 late embryogenesis abundant proteins 2.13





Tentative Peak

Compound Name

Spectrum Searching

KEGG Compound ID

KEGG PATHWAY

Associated Unigenes

EC ID

Associated Probesets

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