“Non-food Crops-to-Industry schemes in EU27”
WP1. Non-food crops
D2.1 Genetic resources for the selected non-food
crops
Lead beneficiary: Agricultural University of Athens
Authors:
Dimitra Milioni
Theoni Margaritopoulou
May 2011
The project is a Coordinated Action supported by
Grant agreement no. 227299
Table of contents
OIL CROPS ...................................................................................... 3
Oilseed rape (Brassica napus) ......................................................... 3
References ................................................................................... 5
Sunflower (Helianthus annus) ......................................................... 7
References ................................................................................... 9
FIBRE CROPS ................................................................................. 11
Flax (Linum usitatissimum L) ........................................................ 11
References ................................................................................. 12
Hemp (Cannabis sativa) ............................................................... 13
References ................................................................................. 14
Kenaf (Hibiscus cannabinus L.) ...................................................... 15
References ................................................................................. 15
CARBOHYDRATE CROPS .................................................................. 16
Maize (Zea mays L) ...................................................................... 16
References ................................................................................. 19
Potato (Solanum spp L) ................................................................ 22
References ................................................................................. 24
Sorghum (Sorghum bicolor L) ....................................................... 26
References ................................................................................. 27
SPECIALTY CROPS .......................................................................... 29
Coneflower (Echinacea angustifolia DC) ......................................... 29
References ................................................................................. 30
Pepermint (Mentha piperita L) ...................................................... 31
References ................................................................................. 32
Pot marigold (Calendula officinalis L) ............................................. 33
2
WP2
DELIVERABLE 2.2
Genomic Resources for the selected non-food
crops
OIL CROPS
Oilseed rape (Brassica napus)
Oilseed rape (B. napus) is an allotetraploid species consisting of two genomes,
derived from B. rapa (A genome) and B. oleracea (C genome). It is the closest major
crop relative of A. thaliana and the world's second most important oilseed crop. The
oil derived from crushing harvested seed is a major provider of calorific value to the
human food chain, with variations in fatty acid profile including combinations of
erucic acid, oleic and linolenic acid that are of industrial value as oils, lubricants,
surfactants and high-value plastics.
The Multinational Brassica rapa Genome Sequencing Project (BrGSP) has
developed valuable genomic resources, including BAC libraries, BAC-end sequences,
genetic and physical maps, and seed BAC sequences for Brassica rapa
(http://www.brassica.info/). An integrated genetic linkage map for the A genome of
Brassica napus using SSR markers derived from sequenced BACs in B. rapa has been
constructed, facilitating the rapid transfer of valuable genomic resources from B.
rapa to B. napus (Xu et al., 2010). A total of 604 SNPs that can be used for genetic
analysis, were identified in oilseed rape (Durstewitz et al., 2010). Sun et al. (2007)
constructed an ultradense genetic map containing 13,351 SRAP markers in B. napus,
which is the most saturated map in Brassica species that has ever been constructed.
Moreover, SRAP is an effective method for map-based gene cloning and molecular
marker assisted selection (MAS) and very effective in studying genetic diversity since
it does not need genome sequence information. Additionally, SRAP was adequate to
perform QTL analysis, which was demonstrated in B. napus (Fu et al., 2007). The B.
3
napus genetic map is being used to align other five genetic maps that are used to
perform QTL mapping of Sclerotinia tolerance in B. napus (Li et al., 2011).
Quantitative trait locus (QTL) mapping has been employed to gain better
understanding of the genetic factors controlling silique-traits and gain insights into
the gene networks affecting erucic acid and oil content in seeds, plant height and
flowering time, resistance to biotic and abiotic stresses (Cao et al., 2010; Kaur et al.,
2009; Mei et al., 2009; Zhang et al., 2010; Zhao et al., 2010).
The total number of ESTs from Brassica species deposited in public databases
has risen dramatically to more than 800,000 entries with about 280,000 from seed
developmental stages. Microarrays have become a widely-used tool for transcriptome
analysis in plants. Oligonucleotide microarrays constructed for A. thaliana have been
used in the past for expression profiling in B. napus, but have not provided optimal
signal intensity and reproducibility (Hudson et al., 2007; Li et al., 2005). 67,000 ESTs
from seed developmental stages have been used to develop a B. napus cDNA
microarray for analysis of seed gene expression patterns (Xiang et al., 2008). An
endosperm EST collection of over 30,000 entries and a microarray dataset have
provided a basic genomic resource for dissecting metabolic and developmental
events important for oilseed improvement (Huang et al., 2009). A Brassica
community microarray resource has been successfully developed and validated (Trick
et al., 2009).
An alternative for accurate, quantitative global expression profiling is serial
analysis of gene expression (SAGE). The LongSAGE approach was used for analysis
of global gene expression in B. napus by matching B. napus tags via Brassica ESTs to
annotated A. thaliana gene loci, including detection of tags matching in sense and
antisense orientation (Obermeier et al., 2009). The cDNA amplified fragment length
polymorphism (cDNA-AFLP) approach was employed for association of gene
expression profiles with intersubgenomic heterosis in Brassica napus (Chen et al.,
2008).
TILLING permits the rapid discovery of induced point mutations in
populations of chemically mutagenized individuals. The application of TILLING to the
generation and identification of a novel low erucic acid (LEA) genetic resource for
rapeseed improvement has already been reported (Wang et al., 2008). EcoTILLING,
a powerful genotyping method, was employed to assess FAE1 (fatty acid elongase1)
4
polymorphisms in three Brassica species
and their association with differences in
seed erucic acid contents (Wang et al., 2010).
References

Cao Z, Tian F, Wang N, Jiang C, Lin B, Xia W, Shi J, Long Y, Zhang C
and Meng J (2010) Analysis of QTLs for erucic acid and oil content in
seeds on A8 chromosome and the linkage drag between the alleles for
the two traits in Brassica napus. J Genet Genomics 37: 231-40.

Chen X, Li M, Shi J, Fu D, Qian W, Zou J, Zhang C and Meng J (2008)
Gene expression profiles associated with intersubgenomic heterosis in
Brassica napus. Theor Appl Genet 117: 1031-40.

Durstewitz G, Polley A, Plieske J, Luerssen H, Graner EM, Wieseke R
and Ganal MW (2010) SNP discovery by amplicon sequencing and
multiplex SNP genotyping in the allopolyploid species Brassica napus.
Genome 53: 948-56.

Fu FY, Liu LZ, Chai YR, Chen L, Yang T, Jin MY, Ma AF, Yan XY, Zhang
ZS and Li
JN (2007) Localization of QTLs for seed color using
recombinant inbred lines of Brassica napus in different environments.
Genome 50: 840-854.

Huang Y , Chen L , Wang L , Vijayan K , Phan S , Liu Z , Wan L et al.
(2009) Probing the endosperm gene expression landscape in Brassica
napus. BMC Genomics 10: 256.

Hudson ME, Brugginkd T, Changa SH, Yuc W, Hana B, Wanga X,
Toornd P and Zhua T (2007) Analysis of Gene Expression during
Brassica Seed Germination Using a Cross-Species Microarray Platform.
Crop Sci 47: 96-112.

Kaur S, Cogan NO, Ye G, Baillie RC, Hand ML, Ling AE, McGearey AK et
al. (2009) Genetic map construction and QTL mapping of resistance to
blackleg (Leptosphaeria maculans) disease in Australian canola
(Brassica napus L.) cultivars. Theor Appl Genet 120: 71-83.
5

Li F, Wu X, Tsang E and Cutler AJ (2005): Transcriptional profiling of
imbibed Brassica napus seed. Genomics 86: 718-730.

Li W, Zhang J, Mou Y, Geng J, McVetty PB, Hu S and Li G (2011)
Integration of Solexa sequences on an ultradense genetic map in
Brassica rapa L. BMC Genomics 12: 249.

Mei DS, WangHZ, Hu Q, Li YD, Xu YS and Li YC (2009) QTL analysis
on plant height and flowering time in Brassica napus. Plant Breed 128:
458–465.

Obermeier C , Hosseini B, Friedt W and Rod Snowdon (2009) Gene
expression profiling via LongSAGE in a non-model plant species: a case
study in seeds of Brassica napus. BMC Genomics 10: 295.

Sun Z, Wang Z, Tu J, Zhang J, Yu F, McVetty PB and Li G (2007) An
ultradense genetic recombination map for Brassica napus, consisting of
13551 SRAP markers. Theor Appl Genet 114: 1305-17.

Trick M , Cheung F , Drou1 N , Fraser F , Lobenhofer EK, Hurban P et
al. (2009) A newly-developed community microarray resource for
transcriptome profiling in Brassica species enables the confirmation of
Brassica-specific expressed sequences. BMC Plant Biology 29:50.

Wang N, Shi L, Tian F, Ning H, Wu X, Long Y and Meng J (2010)
Assessment of FAE1 polymorphisms in three Brassica species using
EcoTILLING and their association with differences in seed erucic acid
contents. BMC Plant Biol 10: 137.

Wang N, Wang Y, Tian F, King GJ, Zhang C, Long Y, Shi L and Meng J
(2008)
A
functional
genomics
resource
for
Brassica
napus:
development of an EMS mutagenized population and discovery of FAE1
point mutations by TILLING. New Phytol. 180: 751-765.

Xiang D, Datla R, Li F, Cutler A, Malik MR, Krochko JE, Sharma N,
Fobert P et al. (2008) Development of a Brassica seed cDNA microarray.
Genome 51: 236-42.
6

Xu J, Qian X, Wang X, Li R, Cheng X, Yang Y, Fu J, Zhang S, King GJ,
Wu J and Liu K (2010) Construction of an integrated genetic linkage
map for the A genome of Brassica napus using SSR markers derived
from sequenced BACs in B. rapa. BMC Genomics 11: 594.

Zhang L, Yang G, Liu P, Hong D, Li S and He Q (2010) Genetic and
correlation analysis of silique-traits in Brassica napus L. by quantitative
trait locus mapping. Theor Appl Genet 122: 21-31.

Zhao H, Liu J, Shi L, Xu F and Wang Y (2010) Development of boronefficient near isogenic lines of Brassica napus and their response to low
boron stress at seedling stage. Genetika 46: 66-72.
Sunflower (Helianthus annus)
The genus Helianthus is a member of the Asteraceae family. This
cosmopolitan family comprises of 1600–1700 genera, 24000–30000 species, and
several agronomically, horticulturally, and medically important species (Jansen et al.
1991; Funk et al. 2005).
Sunflower is one of the most important oilseed crops cultivated in the world.
It is the preferred source of oil for domestic consumption and cooking in much of
central and eastern Europe. Sunflower oil contains less than 11% total saturated fat
and does not contain any trans fat. The inexpensive production of biofuel from plant
vegetable oils such as sunflower oil has been achieved. Furthermore, sunflower can
produce latex naturally. It is an ideal plant for producing high quality rubber in its
leaves and stems and some of the taller perennial species have potential for high
latex yields (Wood, 2002).
The multiple usages of sunflower products in food, feed, and industry are
stimulating the discovery of new sources of biodiversity for sunflower molecular
breeding programs in combination with the application of high throughput
approaches and genetic manipulation.
A rich and diverse germplasm collection is the backbone of every successful
crop improvement program. Assessing genetic diversity within a genetic pool of novel
breeding germplasm could make crop improvement more efficient by the directed
accumulation of desired alleles (Darvishzadeh et al., 2010).
7
The sunflower, Helianthus annuus, genome is diploid with a base
chromosome number of n=17 and an estimated genome size of about 3.500 Mb.
Dominant markers such as random amplified polymorphic DNA (RAPDs), amplified
fragment length polymorphism (AFLPs), restriction fragment length polymorphisms
(RFLPs), simple sequence repeats (SSRs) have been employed for sunflower genome
mapping and genetic variability studies. Conservatively, 18 genetic maps of varying
density and completeness have been constructed in wild and cultivated sunflowers.
The constructed sunflower genetic maps consisted of 17 to 23 stable linkage groups
and differed in their genetic length from 1,423 cM in the SSR map to 2,916 cM AFLP
map. The target region amplification polymorphism (TRAP) marker approach was
used to define Helianthus annuus linkage group ends and to expand the published
sunflower simple sequence repeat (SSR) linkage map (Hu et al., 2007).
Several BAC libraries have been constructed for sunflower (Gentzbittel et al.,
2002; Özdemir et al., 2004; Feng et al., 2006). The libraries are equivalent to
approximately 8 haploid genomes of sunflower and provide a greater than 99%
probability of obtaining a clone of interest and they have been employed for isolating
and physical mapping of loci such as the FAD2-1 locus (Tang et al. 2007), or the
fertility restorer Rf1 locus (Hamrit et al., 2008). In situ hybridization techniques
involving GISH, FISH and BAC-FISH are being optimized for diversity and
evolutionary studies between species of the genus Helianthus and development of a
physical sunflower map allowing a cross reference to the genetic map (Paniego et al.
2006).
Various EST sequencing programs have been carried out in sunflower,
including the
Compositae Genome Project, and other programs reported by Fernandez et al.
(2003), Tamborindeguy et al. (2004), and Ben et al. (2005). More than 261,699
sunflower ESTs have been developed for sunflower primarily by the Compositae
Genome Program (CGP; http://cgpdb.ucdavis.edu/) and 306090 sunflower ESTs have
been deposited in GenBank (Heesacker et al., 2008). For sunflower, 93428 ESTs
were derived from H. annuus L representing approximately 31605 unigenes
(http://compgenomics.ucdavis.edu/compositae_in
Dex.ph). Interesting associations have been detected between ESTs and QTLs for
salt tolerance and for domestication traits (Lai et al., 2005).
8
Sequencing the genome of cultivated sunflower will dramatically enhance
Compositae genomic resources. As of January 2010, a $10.5 million research project
titled “Genomics of Sunflower” use next-generation genotyping and sequencing
technologies to sequence, assemble and annotate the sunflower genome and to
locate the genes that are responsible for agriculturally important traits such as seedoil content, flowering, seed-dormancy, and wood producing-capacity. A total of 304.2
Gbp of Illumina sequence and a total of 8.5X coverage with 454 sequence have been
obtained. However, even the most complete assemblies of the sunflower genome
generated to date include several hundred thousand scaffolds and cover
approximately 60% of the genome (http://www.sunflowergenome.org).
These genomic resources are valuable tools that can be used to obtain direct
access to some genes of interest by map-based cloning or candidate gene
approaches for physical mapping or for the development of markers.
References
 Darvishzadeh R, Azizi M, Hatami-Maleki H, Bernousi I, Mandoulakani
BA, Jafari M and Sarrafi A (2010) Molecular characterization and
similarity relationships among sunflower (Helianthus annuus L.) inbred
lines using some mapped simple sequence repeats. African J Biotech 9:
7280-7288.

Feng J, Vick BA, Lee M-K, Zhang H-B and Jan CC (2006) Hong-Bin
Construction of BAC and BIBAC libraries from sunflower and
identification of linkage group-specific clones by overgo hybridization.
Theor Appl Genet 1:23-32.

Funk VA, Bayer RJ, Keeley S, Chan R, Watson L, Gemeinholzer B,
Schilling E, Panero JL, Baldwin BG, Garcia-Jagas N, Susanna A, Jansen
RK (2005) Everywhere but Antarctica: using a supertree to understand
the diversity and distribution of the Compositae. Biol Skr 55:343–374.

Gentzbittel L, Abbott A, Galaud JP, Georgi L, Fabre F, Liboz T, and
Alibert G (2002) A bacterial artificial chromosome (BAC) library for
sunflower, and identification of clones containing genes for putative
transmembrane receptors. Mol Genet Gen 266: 979-987.
9

Hamrit S, Kusterer B, Friedt W, Horn R (2008) Verification of positive
BAC clones near the Rf1 gene restoring pollen fertility in the presence
of the PET1 cytoplasm in sunflower (Helianthus annuus L.) and direct
isolation of BAC ends. In: Proc 17th Int Sunflower Conf. vol.2, Córdoba,
Spain, International Sunflower Association, Paris, pp 623-628.

Heesacker A, Kishore VK, Gao W, Tang S, Kolkman JM,
Gingle A,
Matvienko M, Kozik A, MichelmoreRM, Lai Z, RiesebergLH, Knapp SJ
(2008) SSRs and INDELs mined from the sunflower EST database:
abundance, polymorphisms, and cross-taxa utility. Theor Appl Genet
117:1021–1029.

Hu J, Yue B, Vick BA (2007) Integration of TRAP markers onto a
sunflower SSR marker linkage map constructed from 92 recombinant
inbred lines. Helia 30: 25-36.

Jansen RK, Michaels HJ, Palmer JD (1991) Phylogeny and character
evolution in the Asteraceae based on chloroplast DNA restriction site
mapping. Syst Bot 16: 98–115.

Jan CC, Vick BA, Miller JF, Kahler AL, Butler ET (1998) Construction of
an RFLP linkage map for cultivated sunflower. Theor Appl Genet
96:15–22.

Lai Z, Livingstone K, Zou Y, Church SA, Knapp SJ, Andrews J,
Rieseberg LH (2005) Identification and mapping of SNPs from ESTs in
sunflower. Theor Appl Genet 111: 1532-1544.

Özdemir N, Horn R and
Friedt W (2004) Construction and
characterization of a BAC library for sunflower (Helianthus annuus L.).
Euphytica 138: 177-183.

Wood, M. (2002) Sunflower rubber? Agriculture Research Magazine 50:
22.

Ishitani M, Rao I, Wenzl P, Beebe S, Tohme J (2004) Integration of
genomics approach with traditional breeding towards improving abiotic
10
stress adaptation: drought and aluminum toxicity as case studies. Field
Crops Research 90:35–45
FIBRE CROPS
Flax (Linum usitatissimum L)
Flax (Linum usitatissimum L.) is a globally important agricultural crop
producing edible and industrial oils as well as fibres. It belongs to the Linaceae family
and is one of about 200 species in the genus Linum. Despite renewed interest in flax
as a source of phloem (bast) fibres, relatively few genomic resources have been
established for this crop.
Total Utilization Flax GENomics (TUFGEN) project was initiated, aiming to
develop genomic tools needed for molecular breeding. The first genome-wide
physical map of flax and the generation and analysis of BAC-end sequences (BES)
from 43,776 clones, providing initial insights into the genome was recently reported.
As a genomic resource, this map will be useful for fine mapping of target genomic
regions and map-based cloning of genes/QTLs. Flax genome size was estimated to
range from 370 Mb to 675 Mb (Ragupathy et al., 2011).
Improvement of flax varieties through breeding for various traits can be
assisted by development of molecular markers and by understanding the genetic and
biochemical bases of these characteristics. A comprehensive EST resource was
developed for flax representing developmental stages of specific seed tissues, some
vegetative and reproductive tissues (Venglat et al., 2011). A queryable flax unigene
database is also publicly available (www.bioinfo.pbi.nrc.ca/portal/flax). The recently
published flax-specific microarray based on EST sequences obtained from a fiber
focused study, provides a complimentary genomic tool for flax gene expression
analysis. Initial studies have enabled the identification of specifically-expressed cell
wall- and defence-related genes in 2 different flax varieties showing contrasting fibre
quality and resistance towards a fungal pathogen (Fenart et al., 2011).
Different research groups have developed reverse genetics and genomic
approaches to decipher fibre and seed formation in this economically-important
11
species. EMS mutagenized populations of CDC Bethune, an elite linseed cultivar,
have already been produced (10,000 individual M2 families). A proteomic approach
has been employed to increase our understanding of the proteins that contribute to
the unique properties of flax bast fibres (Hotte and Deyholos, 2008). The RNA
interference approach was employed to study the impact of reduced expression of bgalactosidase during fibre development in flax (Roach et al., 2011).
Molecular markers are highly useful to identify potentially novel genotypes
among the many flax accessions, and to assess genetic diversity for both germplasm
management and core collection assembly. A variety of marker systems, including
random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR),
amplified fragment length polymorphism (AFLP), and simple sequence repeat (SSR),
been used to analyze flax germplasm (Cloutier et al., 2009; Diederichsen and Fu,
2006; Everaert et al., 2001; Wiesnerova and Wiesner, 2004). Inter-retrotransposon
amplified polymorphism (IRAP) markers were recently developed for cultivated flax
and the genetic diversity among 708 accessions of cultivated flax comprising 143
landraces, 387 varieties, and 178 breeding lines was evaluated (Smýkal et al., 2011).
References

Cloutier S, Niu Z, Datla R and Duguid S (2009) Development and
analysis of EST-SSRs for flax (Linum usitatissimum L.). Theor Appl
Genet 119: 53–63.

Diederichsen A and Fu YB (2006) Phenotypic and molecular (RAPD)
differentiation of four infraspecific groups of cultivated flax (Linum
usitatissimum L. subsp. usitatissimum). Genet Resour Crop Evol 53:
77–90.

Everaert I, de Riek J, de Loose M, van Waes J and van Bockstaele E
(2001) Most similar variety grouping for distinctness evaluation of flax
and linseed (Linum usitatissimum L.) varieties by means of AFLP and
morphological data. Plant Var Seeds 14: 69–87.

Fenart S, Ndong Y-P, Duarte J, Riviere N, Wilmer J, van Wuytswinkel O,
Lucau A, Cariou E, Neutelings G and Gutierrez L et al: Development
12
and validation of a flax (Linum usitatissimum L.) gene expression oligo
microarray. BMC Genomics 11: 592.

Hotte NS and Deyholos MK (2008) A flax fibre proteome: identification
of proteins enriched in bast fibres. BMC Plant Biol 8:52.

Ragupathy R, Rathinavelu R and Cloutier S (2011) Physical mapping
and BAC-end sequence analysis provide initial insights into the flax
(Linum usitatissimum L.) genome. BMC Genomics 12: 217

Roach MJ, Mokshina NY, Snegireva AV, Badhan A, Hobson N, Deyholos
MK and Gorshkova TA (2011) Development of cellulosic secondary
walls in flax fibers requires {beta}-galactosidase. Plant Physiol. May 19.
[Epub ahead of print].

Smýkal P, Bačová-Kerteszová N, Kalendar R, Corander J, Schulman AH
and Pavelek M (2011) Genetic diversity of cultivated flax (Linum
usitatissimum L.) germplasm assessed by retrotransposon-based
markers. Theor Appl Genet 122: 1385-97.

Venglat P, Xiang D, Qiu S, Stone SL, Tibiche C, Cram D, Alting-Mees M
et al. (2011) Gene Expression Analysis of Flax Seed Development. BMC
Plant Biol 11: 74.

Wiesnerova D and Wiesner I (2004) ISSR-based clustering of cultivated
flax germplasm is statistically correlated to thousand seed mass. Mol
Biotechnol 26: 207–214.
Hemp (Cannabis sativa)
Cannabis sativa (hemp) has a long history of medicinal and non-medicinal
applications that date back 4,000 years. In recent years, Cannabis sativa plants are
of interest for both understanding the formation of secondary cell walls and for the
enhancement of fibre utility as industrial fibres and textiles. Furthermore, hemp as a
biomass source for biofuel production is under-exploited.
Recently, different marker systems such as RAPDs, AFLPs and microsatellites
have been developed for hemp’s germplasm fingerprinting (Mandolino and Carboni,
13
2004). The identification of molecular markers for specific traits, gathered in a
saturated linkage map, could have a remarkable impact on hemp breeding. Specific
markers for codominant alleles thought to code for the two synthases responsible for
cannabinoid biosynthesis have been sequenced, specifically with a view to their use
in breeding pharmaceutically useful lines (Mandolino et al., 2003).
The renewed interest in C. sativa as a multi-purpose crop has been the main
driving force for the application of advanced technologies on this species. It has been
announced that transcriptome resources for hemp will be made available by the
Medicinal Plant Consortium (an NIH supported project - GM092521) no later than
August 2011. Microarray analysis of bast fibre producing tissues of Cannabis sativa
has identified transcripts associated with conserved and specialised processes of
secondary wall development (De Pauw et al., 2007; Roach et al., 2008). Furthermore,
comparative proteomics of leaves, flowers, and glands of Cannabis sativa have been
performed to identify specific tissue-expressed proteins. These tissues have
significantly different levels of cannabinoids (Raharjo et al., 2004).
References

De Pauw MA, Vidmar JJ, Collins JA , Bennett RA and Deyholos MD. A
C Microarray analysis of bast fibre producing tissues of Cannabis sativa
has identified transcripts associated with conserved and specialised
processes of secondary wall development. Funct Plant Biol 34:737–749.

Mandolino G and Carboni A (2004) Potential of marker-assisted
selection in hemp genetic improvement. Euphytica 140: 107–120.

Mandolino, G. et al. (2003) The control of the chemical phenotype in
Cannabis sativa L.: genetic analysis and molecular markers. Proc.XLVII
Italian Soc. Agric. Genet. – SIGA Ann Cong, 24–27 Sept 2003, Verona,
Italy.

Raharjo TJ, Widjaja I, Roytrakul S and Verpoorte R (2004) Comparative
Proteomics of Cannabis sativa Plant Tissues. J Biomol Tech 15: 97–106.

Roach MJ and Deyholos MK (2008) Microarray Analysis of Developing
Flax Hypocotyls Identifies Novel Transcripts Correlated with Specific
Stages of Phloem Fibre Differentiation. Ann Bot 102: 317-330.
14
Kenaf (Hibiscus cannabinus L.)
Kenaf is an important fiber crop classified in the genus Hibiscus (Malvaceae)
with a diversity of uses including board or packaging materials, textiles, animal
bedding and bio-composite materials mainly for the automotive and construction
industries (Li, 2002).
Varietal identification of kenaf is always difficult and knowledge on genetic
diversity of kenaf varieties is also limited, which significantly stalled the effective
utilization and conservation of the valuable kenaf germplasm. RAPD analysis has
used to study the genetic diversity in 14 kenaf varieties and effectively trace their
genetic relationships (Cheng et al., 2002).
A screen for cellulose and lignin biosynthesis genes uncovered CesA, cellulose
synthase A, a gene involved in cellulose microfibrils synthesis and four genes that are
involved in lignin biosynthesis, 4-coumarate:CoA ligase, cinnamate 4-hydroxylase,
cinnamyl alcohol dehydrogenase and cinnamoyl-CoA oxidoreductase (Chiaiese et al.,
2010). The screen of a cDNA library of kenaf by using a yeast two-hybrid system
identified interacting proteins with Hibiscus chlorotic ringspot virus coat protein
(Zhang and Wong, 2009).
Transgenic approaches will undoubtedly benefit the advance of fiber
producing crops. An Agrobacterium- mediated genetic transformation system has
been established for kenaf (Kojima et al., 2004). It has been reported that silencing
of gibberellin 2-oxidase gene via RNA interference in kenaf produces faster growing
and taller plants which have higher phloem and/or xylem fiber content (USPTO
Patent Application 20110004958).
References

Cheng Z, Lu BR, Baldwin BS, Sameshima K and Chen JK (2002)
Comparative studies of genetic diversity in kenaf (Hibiscus cannabinus
L.) varieties based on analysis of agronomic and RAPD data. Hereditas
136: 231-239.
15

Chiaiesea P, Ruotoloa G, Di Matteob A, De Santo Virzoc A, De Marcoc
A and Filipponea E (2010). Cloning and expression analysis of kenaf
(Hibiscus cannabinus L.) major lignin and cellulose biosynthesis gene
sequences and polymer quantification during plant development.
doi:10.1016/j.physletb.2003.10.071

Kojima M, Shioiri H, Nogawa M, Nozue M, Matsumoto D, Wada A, Saiki
Y and Kiguchi K (2004) In planta transformation of kenaf plants
(Hibiscus
cannabinus
var.
aokawa
No.
3)
by
Agrobacterium
tumefaciens. J Biosci Bioeng 98: 136-139.

Li D (2002) Kenaf production, research and development in China.
International Kenaf Symposium T.N: (USA).

Zhang X and Wong S-M (2009) Hibiscus chlorotic ringspot virus
upregulates plant sulfite oxidase transcripts and increases sulfate levels
in kenaf (Hibiscus cannabinus L.). J Gen Virol 90: 3042-3050.
CARBOHYDRATE CROPS
Maize (Zea mays L)
The genome of an inbred line of maize called B73, an important commercial
crop variety has been decoded. The 2.3-billion-base sequence — the largest genetic
blueprint yet worked out for any plant species — includes more than 32,000 proteincoding genes spread across maize's 10 chromosomes (Schnable et al., 2009). It has
been reported that the Palomero genome, a corn variety diverged from B73 about
9,000 years ago, is around 400 million nucleotides smaller and contains about 20%
less repetitive DNA than B732 (Vielle-Calzada et al., 2009). To map maize haplotypes
a part of the gene-rich region of 27 maize varieties was sequenced. 'HapMap'
revealed thousands of genes around the centres of the chromosomes, where they
were unlikely to be shuffled around during recombination (Gore et al., 2009).
Recently, it has been demonstrated that the maize subgenomes are differentiated by
genome dominance and both ancient and ongoing gene loss (Schnable et al., 2011).
16
Genetic markers designed to cover a genome extensively allow not only
identification of individual genes associated with complex traits by quantitative trait
loci (QTL) analysis but also the exploration of genetic diversity with regard to natural
variations. Most of the economically important traits considered in maize breeding
are inherited quantitatively. Multiple genes or QTLs affecting flowering traits, root
characteristics, cell wall traits, and tolerance to biotic/abiotic stresses panicle
morphology and grain development have been cloned, and gene expression research
has provided new information about the nature of complex genetic networks involved
in the expression of these traits (Buckler et al., 2009; Chung et al., 2011; Messmer et
al., 2009; Poland et al., 2011; Salas-Fernandez et al., 2009; Trachsel et al., 2009). A
meta-analysis of quantitative trait loci (QTL) associated with plant digestibility and
cell wall composition in maize was carried out, identifying key chromosomal regions
involved in silage quality and potentially associated genes for most of these regions
(Truntzler et al., 2011).
Association mapping is a method to associate specific DNA polymorphisms
with traits of interest based on linkage disequilibrium. McMullen et al. (2009)
described the maize NAM population generated by crossing 25 diverse inbred lines to
a common line, inbred B73. Sequenom-based SNP-typing assay was used to identify
1,359 SNPs in maize transcriptome and 75% of these SNPs were confirmed and
applied in association analysis (Liu et al., 2010).
Currently, there are over 2 million maize ESTs in GenBank (Benson et al.,
2009); the majority of these ESTs are drawn from 10 inbred lines. However, the
assembly of these ESTs into gene models presents practical problems. Therefore, a
full
length
cDNA
library
has
been
recently
constructed
for
Zea
mays
(http:www.maizecdna.org/) (Soderlund et al., 2009). A normalized cDNA library,
covering most of the developmental stages of maize seeds, was also constructed and
57 putative transcription factors were identified (Wang et al., 2010). The cDNA
libraries can serve as primary resources for designing microarray probes and as clone
resources for genetic engineering to improve crop efficiency.
MaizeGDB
(http://www.maizegdb.org/)
is
a
database
that
provides
documentation and data for the microarrays produced by the Maize Gene Discovery
Project. An extensive expression atlas covering a wide array of tissues and
developmental stages of maize using a NimbleGen microarray encompassing 80 301
17
probe sets was recently constructed (Sekhon et al., 2011). Microarray studies have
also been performed to study cell wall metabolism in maize, with the aim of
identifying tissue-specific or developmentally regulated gene expression of members
of multigene families or to obtain a better understanding of regulatory networks that
are exposed when cell wall-related genes are mutated (Guillaumie et al. 2007a;
Guillaumie et al. 2007b). The MAIZEWALL sequence database and expression
profiling resource has been developed (www.polebio.scsv.ups-tlse.fr/MAIZEWALL).
Rajhi and co-workers performed transcriptome analysis in maize root cortical cells
during lysigenous aerenchyma formation and discovered a number of genes whose
expression changed in response to ethylene under waterlogged conditions (Rajhi et
al., 2011).
In maize, small RNAs in the wild type and in the isogenic mop1-1 lossof- function mutant have been analysed by deep sequencing using Illumina’s
sequencing-by-synthesis (SBS) technology to analyze the size distribution of maize
small RNAs (Nobuta et al., 2008). Small RNAs are playing roles as major components
of epigenetic processes and gene networks involved in development and homeostasis.
It has been recently demonstrated that a change in expression of a key component
of the RNA silencing pathway is associated with both vegetative phase change and
shifts in epigenetic regulation of a maize transposon (Li et al., 2010).
RNA interference (RNAi) is a popular method for RNA-mediated gene
silencing by sequence-specific degradation of homologous mRNA triggered by
double-stranded RNA (dsRNA). The RNAi system was used to improve resistance to
maize dwarf mosaic virus on transgenic maize (Zhang et al., 2011). Maize lines
expressing RNAi to chromatin remodeling factors were shown to be similarly
hypersensitive to UV-B radiation but exhibit distinct transcriptome responses (Casati
and Walbot, 2008).
By using near infrared reflectance spectroscopy (NIRS), a set of 39 maize mutants
with altered spectral phenotypes (‘spectrotypes’) have been identified (Vermerris et
al. 2007). A number of these mutants were shown to have altered lignin-tocarbohydrate ratios (Penning et al. 2009).
Sequence- specific DNA binding Transcription Factors (TFs) are key molecular
switches that control or influence many biological processes, such as development or
response to environmental changes. The Maize Transcription Factor Database
18
provides a comprehensive collection of transcription factors from maize. Links to
information on mutants available, map positions or putative functions for these
transcription
factors
are
provided
(MaizeTFDB)
(http://grassius.org/browsefamily.html?species=Maize).
Information resources related to metabolomics can play major role not only in
matabolomics research but also in synergistic integration with other omics data.
MaizeCYc is a biochemical pathway database that provides manually curated or
reviewed information about metabolic pathways in maize.
References

Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J and Sayers EW
(2009) GenBank. Nucleic Acids Res 37: D26–31.

Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ (2009) The
Genetic Architecture of Maize Flowering Time. Science 325: 714-718.

Casati P and Walbot V (2008) Maize lines expressing RNAi to chromatin
remodeling factors are similarly hypersensitive to UV-B radiation but
exhibit distinct transcriptome responses. Epigenetics 3:216-29.

Chung CL, Poland J, Kump K, Benson J, Longfellow J, Walsh E, BalintKurti P and Nelson R (2011) Targeted discovery of quantitative trait loci
for resistance to northern leaf blight and other diseases of maize.
Theor Appl Genet. 2011 [Epub ahead of print].

Gore MA, Chia J-M, Elshire RJ, Sun Q et al. (2009) A First-Generation
Haplotype Map of Maize. Science 20: 1115-1117.

Guillaumie S, Pichon M, Martinant JP, Bosio M, Goffner D and Barriere
Y (2007a) Differential expression of phenylpropanoid and related genes
in brown-midrib bm1, bm2, bm3, and bm4 young nearisogenic maize
plants. Planta 226: 235–250.

Guillaumie S, San-Clemente H, Deswarte C, Martinez Y, Lapierre C,
Murgneux A, Barriere Y, PichonM and GoffnerD (2007b) MAIZEWALL.
Database and developmental gene expression profiling of cell wall
biosynthesis and assembly in maize. Plant Physiol. 143: 339–363.
19

Li H, Freeling M and Lisch D (2010) Epigenetic reprogramming during
vegetative phase change in maize. Proc Natl Acad Sci U S A 107:
22184-9.

Liu S, Chen HD, Makarevitch I, Shirmer R, Emrich SJ, Dietrich CR,
Barbazuk WB,
Springer NM, Schnable PS (2010) High-throughput
genetic mapping of mutants via
quantitative single nucleotide
polymorphism typing. Genetics 184: 19-26.

McMullen MD, Kresovich S, Villeda HS, Bradbury P, Li H, Sun Q et al.
(2009) Genetic properties of the maize nested association mapping
population. Science 325: 737–740.

Messmer R, Fracheboud Y, Bänziger M, Vargas M, Stamp P and Ribaut
JM (2009) Drought stress and tropical maize: QTL-by-environment
interactions and stability of QTLs across environments for yield
components and secondary traits. Theor Appl Genet 119: 913-930.

Nobuta K, Lu C, Shrivastava R, Pillay M et al. (2008). Distinct size
distribution of endogenous siRNAs in maize: Evidence from deep
sequencing in the mop1-1 mutant. Proc. Natl. Acad. Sci. USA 105:
14958–14963.

Polanda JA, Bradbury PJ, Bucklera ES and Nelson RJ (2011) Genomewide nested association mapping of quantitative resistance to northern
leaf blight in maize. Proc Natl Acad Sci USA 108: 6893-6898.

Rajhi I, Yamauchi T, Takahashi H, Nishiuchi S, Shiono K, Watanabe R,
Mliki A, Nagamura Y, Tsutsumi N, Nishizawa NK, Nakazono M (2011)
Identification of genes expressed in maize root cortical cells during
lysigenous aerenchyma formation using laser microdissection and
microarray analyses. New Phytol 190: 351-368.

Salas Fernandez MG, Becraft PW, Yin Y and Lubberstedt T (2009) From
dwarves to giants? Plant height manipulation for biomass yield. Trends
Plant Sci 14: 454-461.
20

Schnable J, Springer N and Freeling M (2011) Differentiation of the
maize subgenomes by genome dominance and both ancient and
ongoing
gene
loss.
Proc
Natl
Acad
Sci,
USA
doi:
10.1073/pnas.1101368108.

Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S (2009)
The B73 Maize Genome: Complexity, Diversity, and Dynamics. Science
326:1112-1115.

Soderlund C, Descour A, Kudrna D, Bomhoff M, Boyd L et al. (2009)
Sequencing, Mapping, and Analysis of 27,455 Maize Full-Length cDNAs.
Plos Genet 5:e1000740

Trachsel S, Messmer R, Stamp P and Hund A (2009) Mapping of QTLs
for lateral and axile root growth of tropical maize. Theor Appl Genet.
doi:10.1007/s00122-009-1144-9

Truntzler M, Barrière Y, Sawkins MC, Lespinasse D, Betran J,
Charcosset A and Moreau L (2010) Meta-analysis of QTL involved in
silage quality of maize and comparison with the position of candidate
genes. Theor Appl Genet 121: 1465-82.

Vermerris W, Saballos A, Ejeta G, Mosier NS, Ladisch MR and Carpita
NC (2007) Molecular breeding to enhance ethanol production from corn
and sorghum stover. Crop Sci. 47, S145–S153.

Vielle-Calzada J-P, Martínez de la Vega O, Hernández-Guzmán G,
Ibarra-Laclette E et al. (2009) The Palomero Genome Suggests Metal
Effects on Domestication. Science 326, 1078.

Wang G, Wang H, Zhu J, Zhang J, Zhang X, Wang F, Tang Y, Mei B, Xu
Z and Song R (2010) An expression analysis of 57 transcription factors
derived from ESTs of developing seeds in Maize (Zea mays). Plant Cell
Rep 29: 545-559.

Zhang ZY, Yang L, Zhou SF, Wang HG, Li WC and Fu FL (2011)
Improvement of resistance to maize dwarf mosaic virus mediated by
transgenic RNA interference. J Biotechnol 153: 181-7.
21
Potato (Solanum spp L)
Cultivated potato is the world’s third most important human food crop. It is
also used as raw material for starch and alcohol production. The basic chromosome
number for potato species is 12. It seems that in Europe the development and
marketing of new potato varieties
is generally the responsibility of private breeding companies. Public programs have
been mostly discontinued and currently public institutions are involved in variety
release only in a few countries (mainly in Eastern Europe).
Despite its importance as a food crop throughout the world, the genetics of
many potato traits is poorly understood. For studying the inheritance of important
agronomic traits in potato, several genetic maps have been created. Molecular
markers such CAPS, SCAR, RFLP and AFLP have been developed and applied to study
patterns of genetic diversity in potato germplasm/cultivars collections. An ultrahighdensity (UHD) genetic map composed of approximately 10,000 amplified fragment
length polymorphism (AFLP) markers was developed, which is most likely the
densest map for a plant species ever constructed (van Os et al., 2006). Recently, the
relationship between the genetic and chromosome map in potato was displayed and
a cytogenetic map has been developed for diploid potato (Solanum tuberosum). A
molecular-linkage map based on functional gene markers involved in carbohydrate
metabolism and transport has been generated. The availability of this molecularfunction map allowed a candidate-gene approach to be used for studying starch and
other sugar-related agronomic traits in potato (Chen et al., 2001). Using RFLP and
AFLP markers, a QTL and linkage map of two segregating diploid populations
previously evaluated for sugar content after cold storage, was generated. Ten potato
genes with unknown function in carbon metabolism or transport were mapped, and
tested for their effects on sugar content. Results displayed linkage between glucose,
fructose and sucrose QTLs and all of eight candidate gene loci (AGPaseS, AGPaseB,
SbeI, GapC, Invap, Ppa1, Sut1, Sut2) (Menendez et al., 2002). Several QTLs
affecting the ability to form tubers under long photoperiods (earliness) have been
identified (Simko et al., 1999). A functional map for pathogen resistance, enriched
with RGA (resistance gene analog) and DRL (defencerelated locus) sequences, SNPs
22
and InDels tightly linked or located within NBS-LRR-like genes, has been developed
on the basis of two potato populations (BC9162 and F1840) (Trognitz et al., 2002;
Rickert et al., 2003). Only recently, twenty-one QTL and eight reference published
potato maps were merged together and the first consensus map was built. Individual
QTLs for resistance to Phytophthora infestans and maturity traits were projected
onto the consensus map and the first meta-analysis performed deals with both
development trait and resistance to a biotic stress in potato (Danan et al., 2011).
As a major follow-up, the genome of potato (850 Mb) was sequenced by the
Potato Genome Sequencing Consortium (PGSC) an international consortium, which
was comprised by 13 countries [http://www.potatogenome.net/]. The new genome
sequence data provides a major boost to gaining a better understanding of potato
trait biology and will underpin future breeding efforts. Comparative sequence
analysis of Solanum and Arabidopsis in a hot spot for pathogen resistance on potato
chromosome V has also been performed and revealed a patchwork of conserved and
rapidly evolving genome segments (Ballvora et al., 2007).
Several efforts to generate EST resources for potato have been perfomed
(Flinn et al., 2005). The potato gene index (http://compbio.dfci.harvard.edu/tgi/cgibin/tgi/gimain.pl?) contains almost 220000 ESTs, assembled into more than 30
000“contigs” with over 26 000 singletons (Bryan et al., 2008). Considerable advances
have also been made with genome-wide gene expression analysis, through
microarray studies and serial analysis of gene expression. Potato cDNA microarray
analysis was performed to assess the potential of transcriptomics to detect
differences in gene expression due to genetic differences or environmental conditions
(van Dijk et al., 2009). A cDNA-amplified fragment length polymorphism (AFLP)
approach and bulked segregant analysis (BSA) was used to identify genes cosegregating with earliness of tuberization in a diploid potato population. 81 candidate
TDFs showing polymorphism between the early and late bulks were selected for
further analysis (Fernández-del-Carmen et al., 2007).
Genetic engineering could enhance desirable characteristics of crops by
modifying key regulatory steps for entire metabolic or developmental pathways. The
optimal conditions for genetic transformation of Solanum spp mediated by
Agrobacterium tumefaciens have been established (Chakravarty et al., 2007). It has
been demonstrated that thransgenic katahdin plants containing the RB gene showed
23
resistance to all tested Pythophtora isolates, including a super race that can
overcome all eleven known R genes in potato. An RNA interference (RNAi)-based
potato gene silencing approach using agroinfiltration, has been recently established
(Bhaskar et al., 2009).
Metabolic profile data sets can contribute to the understanding of the cellular
system in response to changes in intracellular and extracellular environments.
Several databases for Solanaceae are already available. The Armec Repository
Project has provided metabolome data on the potato and serves as a data repository
for
metabolite
peaks
detected
by
ESI-MS
(http://www.armec.org/MetaboliteLibrary/index.jsp).
Potato
is
a
model
crop
for
tissue
culture
approaches.
Indeed,
micropropagation, protoplasts isolation and fusion and anther culture are usually
performed in a wide range of potato genotypes (Jones et al., 1989).
References

Ballvora A, Jöcker A, Viehöver P, Ishihara H, Paal J, Meksem K,
Bruggmann R, Schoof H, Weisshaar B and Gebhardt C (2007)
Comparative sequence analysis of Solanum and Arabidopsis in a hot
spot for pathogen resistance on potato chromosome V reveals a
patchwork of conserved and rapidly evolving genome segments. BMC
Genomics 8:112.

Bhaskar P, Venkateshwaran M, Wu L, Ane J-M and Jiang J (2009)
Agrobacterium-Mediated Transient Gene Expression and Silencing:A
Rapid Tool for Functional Gene Assay in Potato. PLoS One 4(6): e5812.

Bryan GJ and Hein I (2005) Genomic Resources and Tools for Gene
Function
Analysis
in
Potato.
Inter
J
Plant
Genom,
doi:10.1155/2008/216513.

Chakravarty B, Wang-Prusld G, Flinn B, Gustafson V and Regan S
(2007) Genetic Transformation in Potato: Approaches and Strategies.
Amer J of Potato Res 84:301-311.
24

Danan S, Veyrieras J-B and Lefebvre V (2011) Construction of a potato
consensus map and QTL meta-analysis offer new insights into the
genetic architecture of late blight resistance and plant maturity traits.
BMC Plant Biol 11:16.

Fernández-del-Carmen A, Celis-Gamboa C, Visser RGF and Bachem
CWF (2007) Targeted transcript mapping for agronomic traits in potato.
J Exp Bot 58: 2761-2774.

Flinn B,
Rothwell C, Griffiths R et al. (2005) Potato expressed
sequence tag generation and analysis using standard and
unique
cDNA libraries. Plant Mol Biol 59: 407–433.

Jones H, Karp A and Jones MGK (1989) Isolation, culture, and
regeneration of plants from potato protoplasts. Plant Cell Reports
8:307-311.

Powell W and Uhrig H (1987) Anther culture of Solanum genotypes
Plant Cell Tiss Org Culture 1:13-24.

Rickert AM, Kim JH, Meyer S, Nagel A, Ballvora A, Oefner PJ and
Gebhardt C (2003) First-generation SNP/InDel markers tagging loci for
pathogen resistance in the potato genome. Plant Biotechnol J 1:399410.

Simko I, Vreugdenhil D, Jung CS, May GD (1999) Similarity of QTLs
detected for in vitro and greenhouse development of potato plants.
Molec Breed 5:417-428.

Trognitz F, Manosalva P, Gysin R, Nino-Liu D, Simon R, Herrera MD,
Trognitz B, Ghislain M, Nelson R (2002) Plant defense genes associated
with quantitative resistance to potato late blight in Solanum phureja x
dihaploid S. tuberosum hybrids. Molecular Plant-Microbe Interactions
15:587-597.

van Dijk JP, Cankar K, Scheffer SJ, Beenen HG, Shepherd LV, Stewart
D, Davies HV, Wilkockson SJ, Leifert C, Gruden K and Kok E (2009)
25
Transcriptome analysis of potato tubers--effects of different agricultural
practices. J Agric Food Chem Feb 57:1612-23.

van Os H, Andrzejewski S, Bakker E et al (2006) Construction of a 10,
000-marker
ultra-dense
genetic
recombination
map
of
potato:
providing a framework for accelerated gene isolation and a genome
wide physical map. Genetics 173:1075–1087.
Sorghum (Sorghum bicolor L)
Grasses are the most economically important of all plant families. Three out
of twelve grass subfamilies include many of the species with the potential to address
human needs for energy, food, feed, fiber and other industrial applications.
Sorghum bicolor L is a C4 grass, and a member of the family Andropogonae.
It’s the 5th most important cereal crop in the world and a significant source of either
fermentable sugars or lignocellulosic. Much of the plant breeding focus is on
maintaining production under the typical conditions of high temperature and low
water availability at some stage of the summer production cycle. However, many
breeding programs aim to “bring” into adapted breeding germplasm many traits for
pest and disease resistance, as well as resistance to abiotic stresses, such as drought
and soil toxicity.
Centuries of domestication has brought a number of crops to high level of
ploidy. This makes genome sequencing and genetic analyses particularly important
for species with large genomes. The whole genome shotgun sequencing produced a
high coverage draft for sorghum genome (Paterson et al., 2009) and a genotype
associated mapping panel was established (Casa et al., 2008). It has been shown
that the number and the sizes of sorghum gene families are similar to these of
Arabidopsis, rice and poplar. Almost 53% of the gene families (ca. 9500) were
shared among all four species. Nearly, 94% (ca. 25875) of sorghum genes have
orthologs in Arabidopsis, rice and poplar. Only 1153 genes appear to be unique in
sorghum. Comparative analysis of sorghum, rice and other genomes clarifies the
grass gene set. Identification of conserved DNA sequences may help us understand
essential genes tha define grasses. The sorghum genome is now being used as a
26
template for resequencing native varieties of Chinese sweet sorghum (Jing HC, 2009).
Specifically, comparison of sweet sorghum and field sorghum genomes is expected to
identify genes or regulatory elements responsible for increased sugar production
(Ishitani et al., 2004).
Molecular markers such as RAPDs, RFLP, AFLP, Dart and SSR have been
developed and applied in studying patterns of genetic diversity in sorghum
germplasm collections (Ritter et al., 2008; Mace et al., 2009). SSR markers have
been employed to determine genetic diversity in 68 US sweet sorghum cultivars and
lines (Pei et al., 2010). The information gained can be used to select parents for
hybrid development to maximize sugar content and total biomass.
The challenge for researchers is to decipher the function of sorghum genes.
Reverse genetics approaches have already been employed. Chemical mutagen EMS
was used to generate the mutagenized sorghum population (Xin et al., 2008). Five
mutations were identified by TILLING using 4 target genes. Two independent mutant
lines were identified in the target gene encoding caffeic acid-O-methyl transferase
(COMT) in two independent mutant lines. Alterations in COMT gene have already
been associated with brown rib mutations in maize and sorghum, respectively. The
bmrib mutant is characterized by a brown mid rib, reduced lignin content and
increased digestibility (Basu et al., 2010). Brown midrib mutants have been exploited
in Sorghum breeding programs, and have great utility in developing Sorghum plants
for bioenergy. The SWEETFUEL project also aims to breed sweet sorghum varieties
better adapted to temperate region and semi arid tropics for bio-ethanol production.
Successful genetic engineering relies on tissue culture to regenerate
transformed plants. Regeneration systems have been established for sweet sorghum.
Particle bombardment and Agrobacterium-mediated transformation systems have
been developed for sorghum (Pandey et al., 2010). However, at the moment, there
aren’t any transgenic sorghum lines in the market.
References

Basu A, Maiti MK, Kar S, Sen SK and Pandey B, Transgenic sweet
sorghum with altered lignin composition and process of preparation
thereof. United States Patent Application 20100058496 (2010).
27

Casa AM, Pressoir G, Brown PJ, Mitchell SE, Rooney WL, Tuinstra MR et
al. (2008) Community Resources and Strategies for Association
Mapping in Sorghum. Crop Sci 48:30-40.

Ishitani M, Rao I, Wenzl P, Beebe S, Tohme J (2004) Integration of
genomics approach with traditional breeding towards improving abiotic
stress adaptation: drought and aluminum toxicity as case studies. Field
Crops Research 90:35–45

Jing HC, Bayon C, Kanyuka K, Berry S, Wenzl P, Kilian A, HammondKosack K (2009) DArT markers: diversity analyses, mapping and
integration with SSR markers in Triticum monococcum. BMC Genomics
10: 458

Mace ES, Rami JF, Bouchet S, Klein PE, Klein RR and Kilian A (2009) A
consensus genetic map of sorghum that integrates multiple component
maps and high-throughput Diversity Array Technology (DArT) markers.
BMC Plant Bio 9: http://www.biomedcentral.com/1471-2164/9/26.

Pandey AK, BhatBV, Balakrishna D and Seetharama N(2010) Genetic
Transformation of Sorghum (Sorghum bicolor (L.) Moench.). Int J
Biotech Biochem 6:45–53.

Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J,
Gundlach H et al. (2009) The Sorghum bicolor genome and the
diversification of the grasses. Nature 457:551-556.

Pei Z, Gao J, Chen Q, Wei J, Li Z, Luo F, Shi L, Ding B and Sun S
(2010) Genetic diversity of elite sweet sorghum genotypes assessed by
SSR markers. Biol Plant 54:653-658.

Ritter KB, Jordan DR, Chapman SC, Godwin ID, Mace ES, et al. (2007)
An assessment of the genetic relationship between sweet and grain
sorghums, within Sorghum bicolor ssp. bicolor (L.) Moench, using AFLP
markers. Euphytica 157:161-176.

Xin Z, Wang ML, Barkley NA, Burow G, Franks C, Pederson G, Burke J
(2008) Applying genotyping (TILLING) and phenotyping analyses to
28
elucidate gene function in a chemically induced sorghum mutant
population. BMC Plant Biol 14:103.
SPECIALTY CROPS
Coneflower (Echinacea angustifolia DC)
Echinacea species are members of the Asteraceae family and include E.
angustifolia, E. pallida, E. simulata, E. paradoxa, E. tennesseensis, E. laevigata, E.
sanguinea, E. atrorubens, E. gloriosa, along with E. purpurea. However, only three
species of Echinacea are generally used medicinally: E. purpurea Moench (roots and
tops), E. angustifolia DC (roots) and E. pallida Nutt (roots).
Different types of DNA-based markers viz., RAPD, RFLP (Restriction Fragment
Length Polymorphism), ISSR (Inter Simple Sequence Repeat), AFLP (Amplified
Fragment Length Polymorphism), SSR (Simple Sequence Repeat) etc. are employed
for plant species discrimination coupled with methods of plant identification involving
taxonomy, physiology and embryology. AFLP molecular markers have been
developed to study the genetic diversity and phylogenetic relationships among
Echinacea taxa (Russi, 2009). The three medicinal species of the Echinacea genus
were distinguished by RAPD analysis. Genetic distance analysis has indicated a high
degree of difference among the three species with a relative lower difference
between E. angustifolia and E. pallida (Nieri et al., 2003). SCAR (Sequence
Characterized Amplified Region) markers are potential tools for authentication of
herbal drugs. Adinolfi et al. (2007) developed a SCAR marker to differentiate
Echinacea purpurea from E. angustifolia and E. pallida.
Recent advances in genomics involve gas and high performance liquid
chromatography, mass spectrometry and data mining for high-throughput metabolic
fingerprinting. A high-performance liquid chromatography method, was optimized,
and validated for the detection and quantification of the major phenolic compounds:
cichoric acid, chlorogenic acid, caftaric acid, cynarin, and echinacoside, in root and
aerialparts of dried E. angustifolia, E. pallida, and E. purpurea (Brown et al., 2010).
Comparative metabolomics approach coupled with cell- and gene-based assays was
29
employed for species classification and anti-inflammatory bioactivity validation of
Echinacea plants (Hou et al., 2010).
For production of high-quality Echinacea for medicinal plant preparations, it is
necessary to eliminate the chemical variability, eliminate abiotic and biotic
contamination, breed elite plant genotypes and optimize the growing systems.
Transformation systems based on Agrobacterium tumefaciens are well established
for Echinacea species (Wang and To, 2004). Cloning the genes controlling the
production of medicinal compounds will yield commercially useful transgenic plants
capable of producing important secondary metabolites.
References

Adinolfi B, Chicca A, Martinotti E, Breschi MC and Nieri P (2007)
Sequence characterized amplified region (SCAR) analysis on DNA from
three medicinal Echinacea species. Fitoterapia 78: 43–5.

Hou CC, Chen CH, Yang NS, Chen YP, Lo CP, Wang SY, Tien YJ, Tsai
PW and Shyur LF (2010) Comparative metabolomics approach coupled
with cell- and gene-based assays for species classification and antiinflammatory bioactivity validation of Echinacea plants. J Nutr Biochem
21: 1045-59.

Nieri P, Adinolfi B, Morelli I, Breschi MC, Simoni G and Martinotti E
(2003) Genetic characterization of the three medicinal Echinacea
species using RAPD analysis. Planta Med 69: 685-6.

Russi L, Moretti C, Raggi L, Albertini E and Falistocco E (2009)
Identyfing commercially relevant Echinacea species by AFLP molecular
markers. Genome 52: 912-918.

Wang HM and To KY (2004) Agrobacterium-mediated transformation in
the high-value medicinal plant Echinacea purpurea. Plant Sci. 66:
1087–1096
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Pepermint (Mentha piperita L)
Mentha is a genus of aromatic perennial herbs belonging to the family
Lamiaceae. It is distributed mostly in the temperate and sub-temperate regions of
the world. Several Mentha species are considered industrial crops as they are a
source of essential oils enriched in certain monoterpenes, widely used in food,
flavour, cosmetic and pharmaceutical industries. Mentha has a large number of
species that differ widely in their characteristics and polyploidy level. It is known to
comprise about forty recognizable species.
The similarity and diversity based on RAPD profiles of released cultivars of
different
mint species including Mentha piperita have been described (Khanuja et al.,2000).
Additionally, nuclear DNA (ITS), chloroplast DNA (non-coding regions trnL intron,
intergenic spacers trnL-trnF, and psbA-trnH), and AFLP and ISSR, markers were used
to reconstruct the phylogeny of mints related to M. piperita (Gobert et al., 2006).
In Mentha species, essential oil biosynthesis and storage is restricted to the
peltate glandular trichomes (oil glands). A functional genomics approach towards the
characterization of genes involved in essential oil formation in peppermint has been
employed. Sequence information from 1,316 randomly selected cDNA clones, or
expressed sequence tags (ESTs), from a peppermint (Mentha piperita) oil gland
secretory cell cDNA library has been obtained (Lange et al., 2000). Furthermore, a
systems biology approach was employed to identify the biochemical mechanisms
regulating monoterpenoid essential oil composition in peppermint (Rios-Estepa et al.,
2008).
Genetic engineering to up-regulate a flux-limiting step and down-regulate a
side route reaction has led to improvement in the composition and yield of
peppermint oil. Practical levels of field resistance to glufosinate in peppermint have
been achieved and attempts to enhance yield-limiting pathway steps have also been
productive (Li et al., 2001; Mahmoud et al., 2001). Transgenic pepermint plants
overexpressing the gene coding for (−)-limonene 3-hydroxylase (L3H) did not
accumulate increased levels of the recombinant protein, and the composition and
yield of the essential oils were the same as in wild-type controls; however, cosuppression of the L3H gene resulted in a vastly increased accumulation of the
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intermediate (−)-limonene, without notable effects on oil yield (elite transgenic line
designed L3H20) (Mahmoud et al., 2004).
References

Gobert V, Moja S, Taberlet P and Wink M (2006) Heterogeneity of
three molecular data partition phylogenies of mints related to M. x
piperita (Mentha; Lamiaceae). Plant Biol (Stuttg) 8:470-85.

Khanuja SPS, Shasany AK, Srivastava A and Kumar S (2000)
Assessment of genetic relationships in Mentha species. Euphyt 111:
121–125.

Lange BM, Wildung MR, Stauber EJ, Sanchez C, Pouchnik D, Croteau R
(2000) Probing essential oil biosynthesis and secretion by functional
evaluation of expressed sequence tags from mint glandular trichomes.
Proc Natl Acad Sci USA 97: 2934-9.

Li X, Gong Z, Koiwa H, Niu X, Espartero J, Zhu X, Veronese P, Ruggiero
B, Bressan RA, Weller SC, Hasegawa PM (2001) Bar-expressing
peppermint (Mentha x piperita L. var. Black Mitcham) plants are highly
resistant to the glufosinate herbicide Liberty. Mol Breed 8:109–118.

Mahmoud SS, Williams M and Croteau R (2004) Co-suppression of
limonene-3-hydroxylase in peppermint promotes accumulation of
limonene in the essential oil. Phytochemistry 65: 547–554

Mahmoud SS and Croteau R (2001) Metabolic engineering of essential
oil yield and composition
in
mint by
altering expression of
deoxyxylulose phosphate reductoisomerase and menthofuran synthase.
Proc Natl Acad Sci USA 98:8915–8920

Rios-Estepa R, Turner GW, Lee JM, Croteau RB and Lange BM (2008)
A systems biology approach identifies the biochemical mechanisms
regulating monoterpenoid essential oil composition in peppermint. Proc
Natl Acad Sci USA 105: 2818-23.
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Pot marigold (Calendula officinalis L)
C. officinalis is an annual flowering plant that has recently drawn scientific
attention due to health claims of the essential oil in the flowers and the industrial
potential of calendic acid in the seed oil.
.
Pot marigold seems to be an orphan crop. The term orphan is usually used to
describe crops that receive little scientific research or funding despite their
significance in agriculture or medicine. Calendula officinalis has literally been ‘orphan’
from the genomics revolution. However, the common ancestry of all flowering plants
provides opportunities for ‘translational biology’. Important new opportunities for
improving calendula now exist in the knowledge and the tools gained through
research on major crops and on model species, notably Arabidopsis.
Recent achievements in the fields of genetics and genomics provide a more
unified understanding of the biology of plants, which in turn can provide new
opportunities for applying advanced science to orphan crops.
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