fpls-07-01781 November 25, 2016 Time: 14:9 # 1
MINI REVIEW
published: 28 November 2016
doi: 10.3389/fpls.2016.01781
Edited by:
Manoj Prasad,
National Institute of Plant Genome
Research, India
Reviewed by:
Kevin Murphy,
Washington State University, USA
Chandra Bhan Yadav,
University of Milan, Italy
*Correspondence:
Pu Huang
phuang@danforthcenter.org
Specialty section:
This article was submitted to
Plant Genetics and Genomics,
a section of the journal
Frontiers in Plant Science
Received: 22 September 2016
Accepted: 11 November 2016
Published: 28 November 2016
Citation:
Huang P, Shyu C, Coelho CP, Cao Y
and Brutnell TP (2016) Setaria viridis
as a Model System to Advance Millet
Genetics and Genomics.
Front. Plant Sci. 7:1781.
doi: 10.3389/fpls.2016.01781
Setaria viridis as a Model System to
Advance Millet Genetics and
Genomics
Pu Huang
*
, Christine Shyu, Carla P. Coelho, Yingying Cao and Thomas P. Brutnell
Donald Danforth Plant Science Center, St Louis, MO, USA
Millet is a common name for a group of polyphyletic, small-seeded cereal crops that
include pearl, finger and foxtail millet. Millet species are an important source of calories
for many societies, often in developing countries. Compared to major cereal crops such
as rice and maize, millets are generally better adapted to dry and hot environments.
Despite their food security value, the genetic architecture of agronomically important
traits in millets, including both morphological traits and climate resilience remains poorly
studied. These complex traits have been challenging to dissect in large part because
of the lack of sufficient genetic tools and resources. In this article, we review the
phylogenetic relationship among various millet species and discuss the value of a
genetic model system for millet research. We propose that a broader adoption of green
foxtail (Setaria viridis) as a model system for millets could greatly accelerate the pace
of gene discovery in the millets, and summarize available and emerging resources in
S. viridis and its domesticated relative S. italica. These resources have value in forward
genetics, reverse genetics and high throughput phenotyping. We describe methods and
strategies to best utilize these resources to facilitate the genetic dissection of complex
traits. We envision that coupling cutting-edge technologies and the use of S. viridis for
gene discovery will accelerate genetic research in millets in general. This will enable
strategies and provide opportunities to increase productivity, especially in the semi-arid
tropics of Asia and Africa where millets are staple food crops.
Keywords: Setaria viridis, foxtail millet, bulked segregant analysis, stress tolerance, high-throughput
phenotyping, model grass, C4 photosynthesis
INTRODUCTION
Although less prominent than major crops such as rice, maize, and wheat, the polyphyletic millets
are important food sources worldwide. Generally, millets are some of the most well-adapted crops
to drought, heat, and low nutrient input conditions (Dwivedi et al., 2011; Goron and Raizada,
2015; Saha et al., 2016). Given the increasing global population and decreasing arable lands, the
stress tolerant millets are ideal candidates for crop production in climates that are not suitable
for major crops. This is especially important for millet-growing developing countries in Asia and
Africa. However, common features of millets, including complex polyploid genomes, large plant
stature, and long generation times (Table 1) hinder both breeding and genetic research (Goron
and Raizada, 2015; Saha et al., 2016).
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Huang et al. Millet Genetic Model Setaria viridis
TABLE 1 | Comparison of millet species and model grass Setaria viridis.
Taxon Common name Plant stature
a
Chromosome
no.
b
Genome size (Mb,1C) Reference genome Recent
transcriptomic
studies
Transformation
Eleusine coracana Finger millet 0.5–1.2 m 4x = 36 1589 (Bennett and Smith, 1976) In process (ICRISAT) An et al., 2014; Kumar
et al., 2014; Rahman
et al., 2014; Singh
et al., 2014
Tissue culture (Ceasar and
Ignacimuthu, 2011;
Ignacimuthu and Ceasar, 2012)
Panicum miliaceum Proso millet 0.2–1.5 m 4x = 36 1017 (Kubešová et al., 2010) Yue et al., 2016
Cenchrus/Pennisetum
glaucum
Pearl millet up to 3 m 2x = 14 2616 (Bennett and Smith, 1976) In process (ICRISAT) Sahu et al., 2012;
Choudhary et al., 2015;
Kulkarni et al., 2016
Tissue culture (Ramadevi et al.,
2014)
Setaria italica Foxtail millet up to 1.5 m 2x = 18 513 (Bennetzen et al., 2012;
Zhang et al., 2012)
Bennetzen et al., 2012;
Zhang et al., 2012
Puranik et al., 2013; Yi
et al., 2013; Jo et al.,
2016
Tissue culture (Wang, 2011)
Eragrostis tef Teff 4x = 40 660 (Bennett and Smith, 1976) Cannarozzi et al., 2014 Jost et al., 2014
Echinochloa
esculenta
Japanese
barnyard millet
1–1.5m 6x = 54
Echinochloa
frumentacea
Indian barnyard
millet
1–1.5m 6x = 54 1296 (Bennett and Smith, 1976)
Panicum sumatrense Little millet 0.2–1.5 m 4x = 36
Setaria viridis Green millet 0.1–0.15 m 2x = 18 515 (Li and Brutnell, 2011) Pre-publication release
(phytozome)
Xu et al., 2013; John
et al., 2014; Martin
et al., 2016
Tissue culture (Brutnell et al.,
2010; Van Eck and Swartwood,
2015) and floral-dip (Martins
et al., 2015; Saha and
Blumwald, 2016)
a
Height range from Flora of China (http://www.efloras.org/flora_page.aspx?flora_id=2).
b
Chromosome count show median value from Chromosome count database (http://ccdb.tau.ac.il/) (Rice et al., 2014).
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Huang et al. Millet Genetic Model Setaria viridis
In this review, we discuss the recent development of several
genetic and genomic resources in the model grass Setaria viridis
(green foxtail) and its domesticated relative S. italica (foxtail
millet). We provide several use cases that demonstrate the
value of these resources and their potential to provide new
opportunities for breeding and research in millets. S. viridis was
originally developed as a genetic model for bioenergy feedstocks
and panicoid food crops like switchgrass, sorghum, and maize
(Doust et al., 2009; Li and Brutnell, 2011; Diao et al., 2014;
Brutnell, 2015; Brutnell et al., 2015; Muthamilarasan and Prasad,
2015), and as a model for C
4
photosynthesis (Brutnell et al., 2010,
2015; Huang and Brutnell, 2016). S. viridis, like all millet species,
is a member of the PACMAD clade of grasses (Figure 1). Previous
work in genome organization (Benabdelmouna et al., 2001) and
diversity (Huang et al., 2014) shows S. viridis is most closely
related to and interfertile with foxtail millet. Genetic resources
are largely shared between foxtail millet and S. viridis, but we
emphasize on S. viridis in this review because of its nature as
an ideal lab organism. Similar to the dicot model Arabidopsis
thaliana, S. viridis has a short life span (6∼8 weeks under
greenhouse conditions), small plant stature (less than 30 cm at
maturity) and small diploid genome (∼500 Mb).
PHYLOGENY AND PHOTOSYNTHETIC
SUBTYPES OF MILLETS
Despite the common small grain nature, millets include grasses
from a broad range of phylogenetic clades. We compared
the phylogenetic relationship among eight small-seed cereal
crops along with other major crops and model species in the
Poaceae family based on a previous study (Grass Phylogeny
Working Group II, 2011). In this phylogeny, “millet” refers
to species from at least four distinct tribes of PACMAD
grasses: Paniceae, Paspaleae, Cynodonteae, and Eragrostideae
(Figure 1A). This polyphyletic nature is also reflected by
independent domestications of various millets in different areas
of the world (Dwivedi et al., 2011; Goron and Raizada, 2015).
Five out of eight species belong to tribe Paniceae, including
three major species: pearl millet (Cenchrus/Pennisetum glaucum),
foxtail millet and proso millet (Panicum milliacum), along with
the model grass S. viridis (Figure 1A). Close phylogenetic
relatedness generally implies shared genetic mechanism behind
complex traits. That is, the more closely related two species are
the easier it is to translate genetic discoveries between them.
Therefore, compared to other grass models and major crops
(Figure 1A), S. viridis is the most suitable model for most millets
from a phylogenetic perspective.
A key feature shared by all millets is C
4
photosynthesis,
regardless of their separate domestication history. Most C
4
plants, including all the C
4
grasses utilize specialized bundle
sheath and mesophyll cells (Kranz anatomy) to concentrate CO
2
in the vicinity of ribulose bisphosphate carboxylase/oxygenase.
This machinery reduces photorespiration and increases water use
efficiency in C
4
plants (Rawson et al., 1977), especially under
drought and heat stress. C
4
plants also have a better nitrogen
use efficiency, namely they require less nitrogen input to achieve
similar photosynthetic rates as C
3
plants (Sage et al., 1987; Sage
and Pearcy, 1987a,b). These features of C
4
correspond nicely
with, and likely contribute to the climatic resilience and low
soil nutrient demands of millets. Thus, dissecting the genetic
basis of C
4
is an important route to understand the mechanism
underlying climatic resilience in millets.
Setaria viridis promises to greatly accelerate the pace of
discovery in dissecting C
4
photosynthesis in grasses (Brutnell
et al., 2010; Huang and Brutnell, 2016). While genetic screens
for C
4
related mutants in S. viridis are currently ongoing,
comparative genomics has already provided new insights. For
example, Huang et al. (2016) searched for signals of adaptive
evolution in two independently evolved C
4
lineages, Setaria
and the maize-sorghum clade to identify a candidate gene
list for C
4
. The results also indicated a potential for “cross
species engineering” of C
4
transporters. John et al. (2014)
showed an 87% correlation between the bundle sheath/mesophyll
expression specificity between S. viridis and maize, indicating
phylogenetically conserved genetic modules controlling C
4
development. These findings can be generalized to understand
C
4
in other millets. Downstream of candidate gene identification,
S. viridis as a transformable C
4
model system also plays a key role
in functional characterizations (Martins et al., 2015; Van Eck and
Swartwood, 2015; Huang and Brutnell, 2016; Saha and Blumwald,
2016).
ADVANCES OF FOR WARD GENETICS IN
Setaria AND OTHER MILLETS
Classical forward genetic approaches such as linkage and
association mapping have been widely applied in most millet
species (Table 1). However, the lack of high density marker maps
is a major limiting factor for the resolution of these applications.
Although many quantitative trait loci (QTLs) have been identified
for various agronomic traits such as plant height, flowering time,
lodging, and drought tolerance (Mauro-Herrera et al., 2013;
Parvathaneni et al., 2013; Sato et al., 2013; Babu et al., 2014; Qie
et al., 2014; Mauro-Herrera and Doust, 2016; Rajput et al., 2016),
the QTL intervals are often large (>1 Mb) and difficult to fine
map. A partial solution is to generate high density linkage maps
using technologies like genotyping by sequencing (Moumouni
et al., 2015; Fang et al., 2016; Rajput et al., 2016), but the ultimate
solution is to build high-quality reference genomes. To date,
foxtail millet remains the only millet that has a chromosomal
scale genome assembly (Bennetzen et al., 2012; Zhang et al.,
2012), while Eragrostis tef has a draft genome (Cannarozzi et al.,
2014), and the genome sequencing of finger millet and pearl
millets are still ongoing (Table 1). Complete genome sequencing
not only enables high density maps (Fang et al., 2016), but also
large scale genome wide association studies (GWAS; Jia et al.,
2013). Recently, a pre-publication release of an S. viridis genome
de novo assembly became available through phytozome
1
. A panel
of accessions in S. viridis with a greater genetic diversity than
1
http://phytozome.jgi.doe.gov/
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Huang et al. Millet Genetic Model Setaria viridis
FIGURE 1 | (A) Cladogram showing phylogenetic relationships and photosynthetic subtypes of millets and other Poaceae species. Black, gray and red taxa names
represent millets, major crops and model grasses, respectively. Millet lineages are also highlighted in yellow. Green, red, purple, and black branch colors represent
three subtypes of C
4
(NADP-malic enzyme, NAD-malic enzyme and phosphoenolpyruvate phosphatase) and C
3
photosynthesis respectively. Dashed colors
represents mixed subtypes of C
4
. Tree topology is extracted from Grass Phylogeny Working Group II (2011). (B) Summary chart of available genetic resources and
technologies for Setaria viridis. RIL, recombinant inbred line; GWAS, genome wide association study; VIS, visual; NIR, near infra-red.
foxtail millet was also assembled for ongoing GWAS (Huang
et al., 2014).
Molecular markers are often shared across multiple grass
species, further enabling the use of a model species to accelerate
gene discovery. For example, Rajput et al. (2014) showed 62%
of a total of 339 microsatellite markers are shared between
switchgrass and proso millet. One important application of
reference genomes is to assist marker development and inform
the selection of candidate genes (Parvathaneni et al., 2013).
With a closer phylogenetic relationship, more shared synteny
and no complicated duplication history, S. viridis is generally
a better reference than sorghum or maize for both purposes.
For example, Hu et al. (2015) examined a diverse panel of
pearl millet and showed that shared markers and size of
syntenic regions between Setaria and pearl millet is more
than double of those between sorghum and pearl millet. In
addition, S. viridis allelic variation can be directly introgressed
into foxtail millet through interspecific crosses. Such crosses
result in dense molecular markers and additional phenotypic
variations, thus greatly facilitating genetic mapping of traits
such as flowering time, tillering, and drought tolerance (Mauro-
Herrera et al., 2013; Qie et al., 2014; Mauro-Herrera and Doust,
2016).
The short life cycle and small genome of Setaria makes
it an ideal fit for bulked segregant analysis (BSA). BSA was
originally developed for rapid gene mapping in F2 generations
(Michelmore et al., 1991). When coupled with deep sequencing
technologies, BSA can be conducted faster and without prior
knowledge of markers (Takagi et al., 2015). Empirically, the
expense of this approach correlates with genome size, and the
time to discovery largely depends on the generation time, so
this approach is most suitable for model systems. Using this
method, Li et al. (2016) mapped a yellow–green leaf mutation
in foxtail millet to a chlorophyll biosynthesis related gene
SiYGL1. Masumoto et al. (2016) mapped a branching panicle
mutation, a yield related trait in foxtail millet, to a candidate
gene NEKODE1. In chemically induced mutants of S. viridis, BSA
can be expected to define causative mutations to a one to few
gene interval within two generations (<7 months). This approach
will greatly facilitate genetic dissection of traits such as seed size,
inflorescence architecture, flowering time, and climatic resilience
(Brutnell, 2015; Brutnell et al., 2015).
Setaria viridis AS A MODEL SYSTEM TO
DISSECT GENE FUNCTION IN MILLETS
Reverse genetics is a powerful tool that enables gene validation
and characterization from transcriptomic datasets and/or
forward genetics. In light of recent advances in plant
biotechnology, reverse genetics is becoming a faster and
cheaper routine. There are several important features for a
model species to have successful reverse genetic applications:
(1) Plant transformation is often the most limiting step for
most species and therefore it should not be recalcitrant to
Agrobacterium-mediated transformation (Gelvin, 2003; Ceasar
and Ignacimuthu, 2009; Plaza-Wüthrich and Sonia, 2012; Tadele
and Plaza-Wüthrich, 2013). (2) Controlled crosses and prolific
seed production are also essential for rapid genetic analyses
(Li and Brutnell, 2011; Brutnell, 2015). (3) Short life cycle and
plant size is highly advantageous to conduct experiments in
controlled environments, and to reduce costs (Brutnell et al.,
2010). (4) Transcriptomic and genomic information facilitates
the selection of candidate genes and inference of potential
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Huang et al. Millet Genetic Model Setaria viridis
function based on orthology and/or synteny compared to
its relatives (Huang et al., 2016; Huang and Brutnell, 2016).
Unfortunately the majority of features are not inherent to most
millet species, except in Setaria. To date, the techniques and
methods of reverse genetics in millets are still very limited, thus a
genetic model for millets is greatly needed (Goron and Raizada,
2015).
In recent years, remarkable technical advances were made
in the development of resources and techniques for conducting
reverse genetics in S. viridis. Its inbreeding nature and the
ability to perform crosses (Jiang et al., 2013) not only facilitates
the generation of homozygous offspring carrying the allele
of interest but also enables controlled outcrosses to different
populations (i.e., for complementation assays). Agrobacterium
tumefaciens-mediated gene transfer in S. viridis has been
successfully developed and first generation events can be
produced within 15 weeks (Brutnell et al., 2010; Van Eck
and Swartwood, 2015). Alternatively, floral-dip protocols are
being developed and would accelerate immensely the pace
of gene discovery by reducing the time of callus generation
(Martins et al., 2015; Saha and Blumwald, 2016). Together
with the rise of genome editing technology using CRISPR/Cas9,
model species like S. viridis hold the key to accelerate
reverse genetic discoveries in C
4
grasses. It is now possible
to generate biallelic mutations and begin downstream gene
function characterizations within 1 year, a timeframe which
is nearly impossible to match in most crop species. More
subtle gene expression manipulations are also possible using
modified versions of Cas9 (dCas9) and adding an activator
and/or repressor motif to enhance or repress gene expression
(Piatek et al., 2015; Zhang et al., 2015). These features and
technological advancements in S. viridis are especially important
for timely characterizations of candidate genes underlying
complex traits, including the development of Kranz anatomy and
stress tolerance.
Stress tolerance is probably the most explored trait in millets
(Charu Lata, 2015; Tadele, 2016). In foxtail millet, several studies
have reported on candidate genes regulating drought stress.
For example, overexpression of SiLEA14, a homolog of the
Late embryogenesis abundant (LEA) proteins showed increased
salt/drought tolerance and improved growth in foxtail millet
(Wang et al., 2014). One important component of abiotic
stress responses are Dehydration-Responsive Element Binding
(DREB) transcription factors (Li et al., 2014). An abscisic acid
(ABA)-responsive DREB-binding protein gene, cloned from
foxtail millet (SiARDP), was shown to mediate a response that
increases tolerance to drought and high salinity stress (Li et al.,
2014). Similarly, Lata et al. (2011) identified a DREB2-like
gene (SiDREB2) that is associated with dehydration tolerance
and developed an allele-specific marker for tolerant accessions.
Technical advances in Setaria can also be useful for other
millet species for the purposes of functional complementation
of orthologous genes. Two recent studies found a NAC and a
bZIP transcription factor from finger millet can enhance abiotic
tolerance in rice and tobacco, respectively (Babitha et al., 2015;
Rahman et al., 2016). As reverse genetic tools advance in S. viridis,
the pace of gene discovery will also accelerate, enabling the
identification of candidate genes that can be introduced into
other grasses to confer enhanced abiotic stress tolerance. It
will also facilitate the testing of candidate gene function as
genes isolated from related millet species can be introduced into
S. viridis and phenotypes rapidly characterized.
HIGH-THROUGHPUT PHENOTYPING AS
A CRITICAL TOOL TO ADVANCE MILLET
RESEARCH
With the rapid development of genetic tools in Setaria, it is
critical to have advanced phenotyping techniques to maximize
the value of these resources. Automated high-throughput
hardware platforms and corresponding software packages are
transforming the field of plant-based phenotyping (Yang et al.,
2013; Fahlgren et al., 2015b; Rahaman et al., 2015). Here we
highlight phenotyping platforms and software packages that have
been utilized for Setaria and millet research.
Above ground architectural traits such as plant height,
biomass and leaf area are important traits for plant breeding
(Duvick, 2005). To obtain this information in a high-throughput
manner, images are acquired from plants by scanner-based
systems or conveyer belt systems under controlled (Fahlgren
et al., 2015a; Neilson et al., 2015) or field environments
(Vadez et al., 2015). One advantage of these platforms is they
allow measurements in a time-dependent manner. For example,
Fahlgren et al. (2015a) studied drought responses in Setaria
using a conveyer belt-based platform. Through image analysis,
the authors found that S. viridis grows faster and earlier than
foxtail millet though they have similar biomass at later time
points. S. viridis was also found to respond faster to water
limitations than foxtail millet. In parallel to 2D images, 3D
images can be generated using scanner-based systems. For
example, Vadez et al. (2015) used 3D scanning to characterize
variations in leaf areas between breeding populations in pearl
millet.
Physiological traits can also be measured using specialized
imaging systems. For example, using near infra-red (NIR)
imaging, Fahlgren et al. (2015a) found strong water content
differences between Setaria treated with and without water
limitation. In addition, fluorescence imaging efficiently measures
photosynthesis rate in 2D leaves (Attaran et al., 2014; Cruz et al.,
2016), but it is still challenging to measure 3D plants due to
confounding height effects (Fahlgren et al., 2015a). Spectroscopy
imaging can also be used to examine stress responses (Fahlgren
et al., 2015b; Rahaman et al., 2015), but so far this technology has
not been utilized in millet research.
Below ground traits contribute greatly to crop performance,
but are challenging to image. Therefore, methods for obtaining
root images is critical. Rhizotrons are root visualizing systems
which hold a thin volume of soil or nutrient substrates between
two plastic sheets (Neufeld et al., 1989; Rellán-Álvarez et al., 2015;
Passot et al., 2016). This system has been utilized in pearl millets
to measure root growth rates (Passot et al., 2016). In S. viridis,
transgenic lines with a constitutively expressed luciferase reporter
provides an imaging system with a cleaner background, known
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