Abstract

Plants produce a wide range of secondary metabolites that play important roles in plant defense and immunity, their interaction with the environment and symbiotic associations. Sulfur-containing compounds (SCCs) are a group of important secondary metabolites produced in members of the Brassicales order. SCCs constitute various groups of phytochemicals, but not much is known about them. Findings from previous studies on SCCs were scattered in published literatures, hence SuCComBase was developed to store all molecular information related to the biosynthesis of SCCs. Information that includes genes, proteins and compounds that are involved in the SCC biosynthetic pathway was manually identified from databases and published scientific literatures. Sets of co-expression data was analyzed to search for other possible (previously unknown) genes that might be involved in the biosynthesis of SCC. These genes were named as potential SCC-related encoding genes. A total of 147 known and 92 putative Arabidopsis thaliana SCC-related genes from literatures were used to identify other potential SCC-related encoding genes. We identified 778 potential SCC-related encoding genes, 4026 homologs to the SCC-related encoding genes and 116 SCCs as shown on SuCComBase homepage. Data entries are searchable from the Main page, Search, Browse and Datasets tabs. Users can easily download all data stored in SuCComBase. All publications related to SCCs are also indexed in SuCComBase, which is currently the first and only database dedicated to plant SCCs. SuCComBase aims to become a manually curated and au fait knowledge-based repository for plant SCCs.

Introduction

Secondary metabolites are metabolites synthesized by living cells that are not directly involved in cellular metabolism of organisms such as growth, development and reproduction (1, 2). In plants, secondary metabolites act as chemical defense against plant pests and pathogens. Many experimental findings revealed various important functions of secondary metabolites indicating their role and importance in defense response against pathogens (1, 3–6). Apart from being toxic or repellant to herbivores or pests and microbes, they also regulate beneficial interactions such as attracting pollinators or seed dispersal and modulation of abiotic stress responses (7–9).

Different characteristic plant species–specific mix of these chemicals can be used as taxonomic identifier in plant classification (10, 11). Secondary metabolites are extremely diverse and usually belong to one of the three major classes, namely terpenes, phenolics and alkaloids (5, 12). However, there is one unusual plant constituent that was found to be highly involved in plant defense system known as sulfur-containing compounds (SCCs) (11). SCCs are very important in plant–pest interaction in various types of plant families comprising species-specific defense chemicals such as camalexin in Brassicaceae, glucosinolates (GSLs) in Brassicales, alline in Alliaceae, thiopene in Asteraceae and defensins in other plant families (13).

Currently, there are almost 200 SCCs found in Brassicaceae (11, 14). Camalexin for example, is a major phytoalexin SCC found in Arabidopsis thaliana that plays an important role in deterring pathogens such as Botrytis cinera (15) and Alternaria brassicicola (16). Research on SCCs has contributed to new knowledge on their important biological induction: for example, antioxidant activity, chemoprevention effects and apoptosis (17). However, among known SCCs, only GSLs and isothiocyanates have become popular research subjects due to their anticancer activities (18–22). Isothiocyanates in cruciferous vegetables were shown to induce glutathione S-transferase and NAD(P)H:quinone oxidoreductase 1 that act as cell protectants by detoxifying against potential carcinogens and oxidants (23), which explain their effect in reducing the risk of bladder cancer in individuals who consume loads of these vegetables (24, 25). Besides, they also have broad antibiotics properties such as antimicrobial, nematocidal, antifungal and antiprotozoal (26, 27). Furthermore, sulforaphane is one of the most studied isothiocyanates in numerous animal studies [e.g. samples taken from lung (28), colon (29), breast (30), skin (31), stomach (32), small intestine (33) prostate (34), pancreas (35) and oral cells (36)]. The findings reported the ability of sulforaphane to inhibit the carcinogenic cells at either in the early or late stages of malignant tissues. Meanwhile, GSLs are potential putative repellents and attractants of diamondback moth (DBM), Plutella xylostella L., which is a widespread destructive pest on Brassicales crops (37, 38). A number of studies revealed the contribution of different GSL profiles toward the behavior of DBM feeding that serve as novel findings in plant–herbivore interactions (39–41).

Information regarding SCCs genes, proteins and compounds that are involved in SCC biosynthetic pathway is abundant due to their potential contribution in pharmaceutical and agricultural industries. The availability of co-expression studies has significantly contributed to the search of new or potential SCC-related encoding genes. However, this information was distributed in various literature and biological databases, causing difficulties in finding all genes and molecular information of SCCs. This limitation has prompted the need to develop a digital repository that provides a platform for easy access of comprehensive information on SCCs, hence the development of a manually curated database called SuCComBase. The collective data in this database aims to provide valuable resources for genomic studies on potential SCC-related genes that might be involved in the SCC biosynthetic pathway. Herein, the development and current status of SuCComBase were described with the web interface systematically elaborated. SuCComBase is accessible at http://plant-scc.org (SuCComBase ver1.2, last updated on 4 January 2019).

Materials and methods

Data collection

Extensive bibliomic (all related publications published between early 2001 and 2017) and database searching from publication and biological databases were carried out to identify all genes responsible in encoding the SCCs related proteins in A. thaliana. Known SCC-related genes and compounds were identified using various keywords such as ‘sulfur containing compounds’, ‘sulfur containing secondary metabolites’, ‘sulphur containing compounds’, ‘sulphur containing secondary metabolites’, ‘glucosinolate’, ‘phytoalexin’, ‘camalexin’ and ‘Brassicaceae’, ‘Arabidopsis thaliana’, ‘Brassica rapa’, ‘cabbage’, ‘Brassica oleracea’, ‘broccoli’, ‘Carica papaya’ and ‘papaya’. These keywords were searched in publication databases such as PubMed, ScienceDirect and Scopus and biological databases, e.g. AraCyc v8.0 (42), KEGG v88.2 (43), KNApSAcK (last updated on 29 June 2018) (44) and PubChem v1.6.2 beta (45). Putative SCC-related genes were also identified using the keyword search in AraCyc and KEGG; however, no experimental evidence can be found to support their contribution in SCC biosynthesis.

SuCComBase schema contains 12 tables with the connections from table to table.
Figure 1

SuCComBase schema contains 12 tables with the connections from table to table.

Functional annotation

Genes identified from database searching were manually curated using all relevant information obtained from various databases such as NCBI Gene (last updated 4 December 2018) (46), UniProt (last updated 5 December 2018) (47), AraCyc (42), KEGG (43), Ensembl Plants v41 (48), KNApSAcK (44) and PubChem (45). Three co-expression databases, i.e. ATTED v9 (49), AraNet v2 (50) and GeneMANIA (last updated on 14 March 2017) (51), were used to identify potential SCC-related encoding genes. The protein sequence of known SCC encoding genes of A. thaliana were retrieved from Phytozome v12.1.6, which is a Plant Comparative Genomics Portal containing 93 plant genomes (52). SCC homologs were identified via BLAST (53) against B. rapa, B. oleracea and C. papaya in Phytozome database using the known SCC protein sequences as queries. Gene Ontology Consortium (last updated on 26 October 2018) (54) was used to identify gene ontology (GO) in known SCC-related genes, potential SCC-related genes and putative SCC-related genes to provide a clearer understanding of SCC biosynthesis in A. thaliana.

Database organization and architecture

SuCComBase consisted of 12 linked tables (Figure 1) with information on the SCC encoding genes and compounds, their functional information and references. MySQL Server 5.0.11 was used to host SuCComBase relational database. The SuCComBase web interfaces were developed using Laravel 5.3.31 (PHP web framework), HTML and JavaScript.

SuCComBase data types structure organization. These data types are tables that can be found in ‘Browse’ and ‘Datasets’ menu.
Figure 2

SuCComBase data types structure organization. These data types are tables that can be found in ‘Browse’ and ‘Datasets’ menu.

Identification of 92 putative A. thaliana SCC-related genes, 778 potential A. thaliana SCC-related genes and 4026 SCC homologs using 147 known A. thaliana SCC-related genes as queries.
Figure 3

Identification of 92 putative A. thaliana SCC-related genes, 778 potential A. thaliana SCC-related genes and 4026 SCC homologs using 147 known A. thaliana SCC-related genes as queries.

Detailed overview on the approaches used to identify and annotate GO terms to each SCC-related genes entry. Known, potential and putative A. thaliana SCC-related genes were used to search for the GO annotation information.
Figure 4

Detailed overview on the approaches used to identify and annotate GO terms to each SCC-related genes entry. Known, potential and putative A. thaliana SCC-related genes were used to search for the GO annotation information.

Table 1

Number of entries in SuCComBase

Data setEntries
Known A. thaliana SCC-related genes147
Putative A. thaliana SCC-related genes92
KEGG putative A. thaliana SCC-related genes3
AraCyc putative A. thaliana SCC-related genes89
Potential A. thaliana SCC-related genes778
SCC homologs4026
B. rapa SCC homologs1970
B. oleracea SCC homologs1319
C. papaya SCC homologs737
Compounds116
A. thaliana SCCs47
B. rapa SCCs28
B. oleracea SCCs40
C. papaya SCCs1
Publications206
Data setEntries
Known A. thaliana SCC-related genes147
Putative A. thaliana SCC-related genes92
KEGG putative A. thaliana SCC-related genes3
AraCyc putative A. thaliana SCC-related genes89
Potential A. thaliana SCC-related genes778
SCC homologs4026
B. rapa SCC homologs1970
B. oleracea SCC homologs1319
C. papaya SCC homologs737
Compounds116
A. thaliana SCCs47
B. rapa SCCs28
B. oleracea SCCs40
C. papaya SCCs1
Publications206
Table 1

Number of entries in SuCComBase

Data setEntries
Known A. thaliana SCC-related genes147
Putative A. thaliana SCC-related genes92
KEGG putative A. thaliana SCC-related genes3
AraCyc putative A. thaliana SCC-related genes89
Potential A. thaliana SCC-related genes778
SCC homologs4026
B. rapa SCC homologs1970
B. oleracea SCC homologs1319
C. papaya SCC homologs737
Compounds116
A. thaliana SCCs47
B. rapa SCCs28
B. oleracea SCCs40
C. papaya SCCs1
Publications206
Data setEntries
Known A. thaliana SCC-related genes147
Putative A. thaliana SCC-related genes92
KEGG putative A. thaliana SCC-related genes3
AraCyc putative A. thaliana SCC-related genes89
Potential A. thaliana SCC-related genes778
SCC homologs4026
B. rapa SCC homologs1970
B. oleracea SCC homologs1319
C. papaya SCC homologs737
Compounds116
A. thaliana SCCs47
B. rapa SCCs28
B. oleracea SCCs40
C. papaya SCCs1
Publications206
The integration of three co-expression gene networks reveals potential SCC-related genes. Different colors refer to the function of known SCC-related genes in GSL biosynthesis: yellow (transcription factor), blue (core structure synthesis), green (side-chain elongation), purple (side-chain modification) and red (GSL degradation). Known SCC-related genes were used as query to identify the co-expressed genes to be classified as potential SCC-related genes.
Figure 5

The integration of three co-expression gene networks reveals potential SCC-related genes. Different colors refer to the function of known SCC-related genes in GSL biosynthesis: yellow (transcription factor), blue (core structure synthesis), green (side-chain elongation), purple (side-chain modification) and red (GSL degradation). Known SCC-related genes were used as query to identify the co-expressed genes to be classified as potential SCC-related genes.

Results and Discussion

Database summary

Figure 2 shows the organization of data types whereby SCC-related genes in A. thaliana are assigned as the main data in SuCComBase. A total of 147 known SCC-related genes were manually curated and supported with added information obtained from KEGG and AraCyc. We have identified 92 computationally predicted genes that might be involved in the production of SCCs and classified them as putative SCC-related genes. The known SCC-related genes were used as queries in identifying potential SCC-related genes from the co-expression data, and 778 potential SCC-related genes were successfully identified from three co-expression databases: e.g. ATTED (49), AraNet (50) and GeneMANIA (51). BLAST analysis against Phytozome database has identified 4026 SCC homologs from three Brassicales plant genomes, i.e. 1970 SCC homologs in B. rapa, 1319 SCC homologs in B. oleracea and 737 SCC homologs in C. papaya.Figure 3 summarizes the steps involved in identifying putative A. thaliana SCC-related genes, potential A. thaliana SCC-related genes and the SCC homologs. Furthermore, we have included GO terms of the SCC-related genes where a total of 800 biological process, 656 molecular functions and 119 cellular components were included in this database. Figure 4 shows the steps performed in identifying the GO terms and the results obtained from the analysis. We have also identified 116 SCCs in A. thaliana, B. rapa, B. oleracea and C. papaya and included them in SuCComBase. Table 1 shows the summary of each data set in SuCComBase.

Database interface and access

The interface of SuCComBase contains a homepage and seven main menus, i.e. About, Browse, Search, Datasets, Download, Help and Contact. These menus are used to facilitate the users in navigating the pages effortlessly.

  • (i) SuCComBase Homepage displays data statistics in each table and provides a brief overview of this database.

  • (ii) The background of SuCComBase can be viewed in the ‘About’ page.

  • (iii) The ‘Browse’ page allows users to assess all six data sets in SuCComBase. Each data set varied based on their biological information as described below:

    • (a) Known A. thaliana SCC-related gene data set: contains SCC-related encoding genes in A. thaliana including protein-encoding genes that involve in the GSL and camalexin biosynthetic pathways based on experiments reported in various publications and pathway databases (KEGG and AraCyc).

    • (b) Putative A. thaliana SCC-related gene data set: contains computationally predicted GSL and camalexin genes from KEGG and AraCyc databases.

    • (c) Potential A. thaliana SCC-related gene data set: contains 778 A. thaliana genes that might be involved in the SCC biosynthetic pathway based on the bioinformatic analysis using co-expression data retrieved from AraNet, GeneMANIA and ATTED. Figure 5 shows an example of the network constructed between the potential SCC-related encoding genes.

    • (d) SCC homolog data set: contains SCC homologs in C. papaya, B. rapa and B. oleracea.

    • (e) Compound data set: contains SCCs produced in A. thaliana, C. papaya, B. rapa and B. oleracea. All information was obtained from extensive literature search and from KNApSAcK as well as PubChem databases.

    • (f) Publication data set: contains 206 published articles that were used in identifying SCCs and all SCC-related encoding genes.

  • (iv) The ‘Search’ page is used to search for genes, compounds or any biological term that match to a particular keyword. For example, if ‘sulfur’ keyword is searched, all entries (Genes, Putative genes, Potential genes, SCC homologs, Compounds, Publications) in SuCComBase that contain ‘sulfur’ will appear.

  • (v) The ‘Datasets’ dropdown menu provides links to all data sets in SuCComBase. These Dataset tabs are located at the header of SuCComBase to help users navigate the database.

  • (vi) The ‘Download’ page provides access to current and archived data sources in SuCComBase.

  • (vii) The ‘Help’ menu contains manual of SuCComBase, database schema, data sources and all references used to retrieve the information in SuCComBase. All scientific terms, definition and FAQs that are related to SuCComBase were also provided in the ‘Help’ page.

  • (viii) The ‘Contact’ menu provides information on the SuCComBase developer contacts and email address.

Conclusion and future work

SuCComBase is publicly available online at http://plant-scc.org and will be periodically updated. Currently, SuCComBase is the first and only database that provides the information on SCCs that are related to plant defense system in Brassicales. All information provided in this database is important to plant scientists, synthetic biologists, systems biologists, chemists or anyone who are interested working on the secondary metabolites or potential compounds, as well as those who study plant–host interactions specifically in Brassicales crops, hence continuous comprehensive cataloguing and curation is a priority.

Acknowledgements

This research was carried out in the Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia.

Funding

Institut Biologi Sistem (DPP-2018-010) Universiti Kebangsaan Malaysia under Research University Grants; Malaysian Ministry of Higher Education and Ministry of Science, Technology and Innovation (UKM-MGI-NBD0005-2007, ERGS/1/2013/STG07/UKM/02/3 to Z.-A.M.-H.); MyBrain15 and SLAB programs from Malaysia Ministry of Higher Education (to S.H).

Conflict of interest. None declared.

Database URL: http://plant-scc.org

References

1.

Scharf
,
D.H.
,
Heinekamp
,
T.
and
Brakhage
,
A.A.
(
2014
)
Human and plant fungal pathogens: the role of secondary metabolites
.
PLoS Pathog.
,
10
,
10
12
.

2.

Hartmann
,
T.
(
2008
)
The lost origin of chemical ecology in the late 19th century
.
Proc. Natl. Acad. Sci. U.S.A.
,
105
,
4541
4546
.

3.

Miedes
,
E.
,
Vanholme
,
R.
,
Boerjan
,
W.
et al.  (
2014
)
The role of the secondary cell wall in plant resistance to pathogens
.
Front. Plant Sci.
,
5
,
1
14
.

4.

Bednarek
,
P.
(
2012
)
Chemical warfare or modulators of defence responses—the function of secondary metabolites in plant immunity
.
Curr. Opin. Plant Biol.
,
15
,
407
414
.

5.

Piasecka
,
A.
,
Jedrzejczak-Rey
,
N.
and
Bednarek
,
P.
(
2015
)
Secondary metabolites in plant innate immunity: conserved function of divergent chemicals
.
New Phytol.
,
206
,
948
964
.

6.

Voigt
,
C.A.
(
2014
)
Callose-mediated resistance to pathogenic intruders in plant defense-related papillae
.
Front. Plant Sci.
,
5
,
1
6
.

7.

Moghe
,
G.
and
Last
,
R.L.
(
2015
)
Something old, something new: conserved enzymes and the evolution of novelty in plant specialized metabolism
.
Plant Physiol.
,
169
,
1512
1523
.

8.

Guerriero
,
G.
,
Berni
,
R.
,
Muñoz-Sanchez
,
J.A.
et al.  (
2018
)
Production of plant secondary metabolites: examples, tips and suggestions for biotechnologists
.
Genes (Basel)
,
9
,
1
22
.

9.

Wink
,
M.
(
2018
)
Plant secondary metabolites modulate insect behavior-steps toward addiction?
Front. Physiol.
,
9
,
1
9
.

10.

Noel
,
J.P.
,
Austin
,
M.B.
and
Bomati
,
E.K.
(
2005
)
Structure-function relationships in plant phenylpropanoid biosynthesis
.
Curr. Opin. Plant Biol.
,
8
,
249
253
.

11.

Bednarek
,
P.
(
2012
)
Sulfur-containing secondary metabolites from Arabidopsis thaliana and other Brassicaceae with function in plant immunity
.
ChemBioChem
,
13
,
1846
1859
.

12.

Patra
,
B.
,
Schluttenhofer
,
C.
,
Wu
,
Y.
et al.  (
2013
)
Transcriptional regulation of secondary metabolite biosynthesis in plants
.
Biochim. Biophys. Acta
,
1829
,
1236
1247
.

13.

Burow
,
M.
,
Wittstock
,
U.
and
Gershenzon
,
J.
(
2008
) In:
Hell
,
R.
;
Dahl
,
C.
;
Knaff
,
D.
;
Leustek
,
T.
(eds).
Sulfur Metabolism in Phototrophic Organisms
, Vol.
27
.
Switzerland
:
SpringerLink
, pp.
201
222
.

14.

Pedras
,
M.S.C.
,
Yaya
,
E.E.
and
Glawischnig
,
E.
The phytoalexins from cultivated and wild crucifers: chemistry and biology
.
Nat. Prod. Rep.
,
28
,
1381
1405
.

15.

Cargnel
,
M.D.
,
Demkura
,
P.V.
and
Ballaré
,
C.L.
(
2014
)
Linking phytochrome to plant immunity: low red: far-red ratios increase Arabidopsis susceptibility to Botrytis cinerea by reducing the biosynthesis of indolic glucosinolates and camalexin
.
New Phytol.
,
204
,
342
354
.

16.

Pedras
,
M.S.C.
,
Minic
,
Z.
and
Abdoli
,
A.
(
2014
)
The phytoalexin camalexin induces fundamental changes in the proteome of Alternaria brassicicola different from those caused by brassinin
.
Fungal Biol.
,
118
,
83
93
.

17.

Wu
,
X.
,
Kassie
,
F.
and
Mersch-Sundermann
,
V.
(
2005
)
Induction of apoptosis in tumor cells by naturally occurring sulfur-containing compounds
.
Mutat. Res.
,
589
,
81
102
.

18.

Warin
,
R.
,
Xiao
,
D.
,
Arlotti
,
J.A.
et al.  (
2010
)
Inhibition of human breast cancer xenograft growth by cruciferous vegetable constituent benzyl isothiocyanate
.
Mol. Carcinog.
,
49
,
500
507
.

19.

Warin
,
R.
,
Chambers
,
W.H.
,
Potter
,
D.M.
et al.  (
2009
)
Prevention of mammary carcinogenesis in MMTV-neu mice by cruciferous vegetable constituent benzyl isothiocyanate
.
Cancer Res.
,
69
,
9473
9480
.

20.

Pintão
,
A.M.
,
Pais
,
M.S.
,
Coley
,
H.
et al.  (
1995
)
In vitro and in vivo antitumor activity of benzyl isothiocyanate: a natural product from Tropaeolum majus
.
Planta Med.
,
61
,
233
236
.

21.

Wattenberg
,
L.W.
(
1981
)
Inhibition of carcinogen-induced neoplasia by sodium cyanate, tert-butyl isocyanate, and benzyl isothiocyanate administered subsequent to carcinogen exposure
.
Cancer Res.
,
41
,
2991
2994
.

22.

Rao
,
C.V.
(
2013
)
Benzyl isothiocyanate: double trouble for breast cancer cells
.
Cancer Prev. Res.
,
6
,
760
763
.

23.

Zhang
,
Y.
,
Munday
,
R.
,
Jobson
,
H.E.
et al.  (
2006
)
Induction of GST and NQO1 in cultured bladder cells and in the urinary bladders of rats by an extract of broccoli (Brassica oleracea italica) sprouts
.
J. Agric. Food Chem.
,
54
,
9370
9376
.

24.

Tang
,
L.
,
Zirpoli
,
G.R.
,
Guru
,
K.
et al.  (
2010
)
Intake of cruciferous vegetables modifies bladder cancer survival
.
Cancer Epidemiol. Biomarkers Prev.
,
19
,
1806
1811
.

25.

Tang
,
L.
,
Zirpoli
,
G.R.
,
Guru
,
K.
et al.  (
2008
)
Consumption of raw cruciferous vegetables is inversely associated with bladder cancer risk
.
Cancer Epidemiol. Biomarkers Prev.
,
17
,
938
944
.

26.

Assayed
,
M.E.
and
Abd El-Aty
,
A.M.
(
2009
)
Cruciferous plants: phytochemical toxicity versus cancer chemoprotection
.
Mini Rev. Med. Chem.
,
9
,
1470
1478
.

27.

Nowicki
,
D.
,
Rodzik
,
O.
,
Herman-Antosiewicz
,
A.
et al.  (
2016
)
Isothiocyanates as effective agents against enterohemorrhagic Escherichia coli: insight to the mode of action
.
Sci. Rep.
,
6
,
1
12
.

28.

Conaway
,
C.C.
,
Wang
,
C.-X.
,
Pittman
,
B.
et al.  (
2005
)
Phenethyl isothiocyanate and sulforaphane and their N-acetylcysteine conjugates inhibit malignant progression of lung adenomas induced by tobacco carcinogens in A/J mice
.
Cancer Res.
,
65
,
8548
8557
.

29.

Chung
,
F.-L.
,
Conaway
,
C.C.
,
Rao
,
C.V.
et al.  (
2000
)
Chemoprevention of colonic aberrant crypt foci in Fischer rats by sulforaphane and phenethyl isothiocyanate
.
Carcinogenesis
,
21
,
2287
2291
.

30.

Zhang
,
Y.
,
Kensler
,
T.W.
,
Cho
,
C.-G.
et al.  (
1994
)
Anticarcinogenic activities of sulforaphane and structurally related synthetic norbornyl isothiocyanates
.
Proc. Natl. Acad. Sci. U.S.A.
,
91
,
3147
3150
.

31.

Gills
,
J.J.
,
Jeffery
,
E.H.
,
Matusheski
,
N.V.
et al.  (
2006
)
Sulforaphane prevents mouse skin tumorigenesis during the stage of promotion
.
Cancer Lett.
,
236
,
72
79
.

32.

Fahey
,
J.W.
,
Haristoy
,
X.
,
Dolan
,
P.M.
et al.  (
2002
)
Sulforaphane inhibits extracellular, intracellular, and antibiotic-resistant strains of Helicobacter pylori and prevents benzo[a]pyrene-induced stomach tumors
.
Proc. Natl. Acad. Sci. U.S.A.
,
99
,
7610
7615
.

33.

Myzak
,
M.C.
,
Dashwood
,
W.M.
,
Orner
,
G.A.
et al.  (
2006
)
Sulforaphane inhibits histone deacetylase in vivo and suppresses tumorigenesis in Apc-minus mice
.
FASEB J.
,
20
,
506
508
.

34.

Singh
,
A.V.
,
Xiao
,
D.
,
Lew
,
K.L.
et al.  (
2004
)
Sulforaphane induces caspase-mediated apoptosis in cultured PC-3 human prostate cancer cells and retards growth of PC-3 xenografts in vivo
.
Carcinogenesis
,
25
,
83
90
.

35.

Kallifatidis
,
G.
,
Rausch
,
V.
,
Baumann
,
B.
et al.  (
2009
)
Sulforaphane targets pancreatic tumour-initiating cells by NF- B-induced antiapoptotic signalling
.
Gut
,
58
,
949
963
.

36.

Liu
,
C.-M.
,
Peng
,
C.-Y.
,
Liao
,
Y.-W.
et al.  (
2017
)
Sulforaphane targets cancer stemness and tumor initiating properties in oral squamous cell carcinomas via miR-200c induction
.
J. Formos. Med. Assoc.
,
116
,
41
48
.

37.

Henniges-Janssen
,
K.
,
Heckel
,
D.G.
and
Groot
,
A.T.
(
2014
)
Preference of diamondback moth larvae for novel and original host plant after host range expansion
.
Insects
,
5
,
793
804
.

38.

Moreira
,
L.F.
,
Teixeira
,
N.C.
,
Santos
,
N.A.
et al.  (
2016
)
Diamondback moth performance and preference for leaves of Brassica oleracea of different ages and strata
.
J. Appl. Entomol.
,
140
,
627
635
.

39.

Robin
,
A.H.K.
,
Hossain
,
M.R.
,
Park
,
J.-I.
et al.  (
2017
)
Glucosinolate profiles in cabbage genotypes influence the preferential feeding of diamondback moth (Plutella xylostella)
.
Front. Plant Sci.
,
8
,
1
13
.

40.

Santolamazza-Carbone
,
S.
,
Sotelo
,
T.
,
Velasco
,
P.
et al.  (
2016
)
Antibiotic properties of the glucosinolates of Brassica oleracea var. acephala similarly affect generalist and specialist larvae of two lepidopteran pests
.
J. Pest Sci.
,
89
,
195
206
.

41.

Santolamazza-Carbone
,
S.
,
Velasco
,
P.
,
Soengas
,
P.
et al.  (
2014
)
Bottom-up and top-down herbivore regulation mediated by glucosinolates in Brassica oleracea var. acephala
.
Oecologia
,
174
,
893
907
.

42.

Mueller
,
L.A.
,
Zhang
,
P.
and
Rhee
,
S.Y.
(
2003
)
AraCyc: a biochemical pathway database for Arabidopsis
.
Plant Physiol.
,
132
,
453
460
.

43.

Kanehisa
,
M.
,
Sato
,
Y.
,
Kawashima
,
M.
et al.  (
2016
)
KEGG as a reference resource for gene and protein annotation
.
Nucleic Acids Res.
,
44
,
D457
D462
.

44.

Nakamura
,
Y.
,
Mochamad Afendi
,
F.
,
Kawsar Parvin
,
A.
et al.  (
2014
)
KNApSAcK metabolite activity database for retrieving the relationships between metabolites and biological activities
.
Plant Cell Physiol.
,
55
,
1
9
.

45.

Kim
,
S.
,
Thiessen
,
P.A.
,
Bolton
,
E.E.
et al.  (
2016
)
PubChem substance and compound databases
.
Nucleic Acids Res.
,
44
,
D1202
D1213
.

46.

Brown
,
G.R.
,
Hem
,
V.
,
Katz
,
K.S.
et al.  (
2015
)
Gene: a gene-centered information resource at NCBI
.
Nucleic Acids Res.
,
43
,
D36
D42
.

47.

Bateman
,
A.
,
Martin
,
M.J.
,
O’Donovan
,
C.
et al.  (
2017
)
UniProt: the universal protein knowledgebase
.
Nucleic Acids Res.
,
45
,
D158
D169
.

48.

Kersey
,
P.J.
,
Allen
,
J.E.
,
Armean
,
I.
et al.  (
2016
)
Ensembl Genomes 2016: more genomes, more complexity
.
Nucleic Acids Res.
,
44
,
D574
D580
.

49.

Aoki
,
Y.
,
Okamura
,
Y.
,
Tadaka
,
S.
et al.  (
2016
)
ATTED-II in 2016: a plant coexpression database towards special online collection
.
Plant Cell Physiol.
,
57
,
1
9
.

50.

Lee
,
T.
,
Yang
,
S.
,
Kim
,
E.
et al.  (
2014
)
AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species
.
Nucleic Acids Res.
,
43
,
D996
D1002
.

51.

Warde-Farley
,
D.
,
Donaldson
,
S.L.
,
Comes
,
O.
et al.  (
2010
)
The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function
.
Nucleic Acids Res.
,
38
,
W214
W220
.

52.

Goodstein
,
D.M.
,
Shu
,
S.
,
Howson
,
R.
et al.  (
2012
)
Phytozome: a comparative platform for green plant genomics
.
Nucleic Acids Res.
,
40
,
1178
1186
.

53.

Altschul
,
S.F.
,
Gish
,
W.
,
Miller
,
W.
et al.  (
1990
)
Basic local alignment search tool
.
J. Mol. Biol.
,
215
,
403
410
.

54.

Blake
,
J.A.
,
Christie
,
K.R.
,
Dolan
,
M.E.
et al.  (
2015
)
Gene ontology consortium: going forward
.
Nucleic Acids Res.
,
43
,
D1049
D1056
.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.