Download From QTLs for enzyme activity to candidate genes in maize

Journal of Experimental Botany, Vol. 50, No. 337, pp. 1281–1288, August 1999
From QTLs for enzyme activity to candidate genes
in maize
Jean-Louis Prioul1,4, Sandrine Pelleschi1, Mamoune Séne1, Claudine Thévenot1,
Mathilde Causse3, Dominique de Vienne2 and Agnès Leonardi2
1 Institut de Biotechnologie des Plantes, Bâtiment 630 (CNRS UMR8618), Structure et Métabolisme des
Plantes, Université de Paris-Sud, F-91405, Orsay Cedex, France
2 Station de Génétique Végétale (UPS/INRA/INAPG/CNRS), Ferme du Moulon,
F-91190 Gif-sur-Yvette, France
3 Station d’Amélioration des Plantes Maraı̂chères, INRA-Domaine Saint Maurice, BP 94,
F-84143 Montfavet Cedex, France
Received 27 July 1998; Accepted 11 March 1999
Abstract
In order to facilitate the search for genes underlying
QTLs (Quantitative Trait Loci), the activities of key
enzymes of the carbohydrate metabolism in maize, and
the concentration of their substrates or products were
used as quantitative traits. For each of the chosen
enzyme, i.e. ADPglucose pyrophosphorylase, sucrosephosphate-synthase and invertases, the corresponding cDNA was available. Since biochemical traits are
more closely related to gene expression than agronomic traits, co-locations could be expected between
an enzyme structural gene and a QTL for its enzyme
activity, and/or the corresponding product or substrate
content. This approach was applied using recombinant
inbred lines on leaves at 3- or 4-leaf stage, under
control and water stress conditions and on grain, at
maturity. Several QTLs were detected for each trait,
particularly for two enzyme activities measured in
mature leaves. Apparent co-locations between QTL for
activity and structural locus were observed for sucrose-phosphate-synthase (chromosome 8) and acidsoluble invertase (chromosomes 2 and 5). Leaf acidsoluble (vacuolar) invertase provided an interesting
case since a QTL, on chromosome 5, explaining 17%
of variability was apparently co-located with the Ivr2
gene encoding a vacuolar invertase protein which was
strongly water-stress inducible. Similarly, in grain,
an amylose QTL co-located with the Sh2 gene of
ADPglucose pyrophosphorylase. The reliability of this
candidate was further tested through the examination
of Sh2 DNA polymorphism in 46 genetically unrelated
lines. A correlation was obtained between this polymorphism and kernel starch content, which further
validated Sh2 as a candidate. Some improvements or
alternatives to this strategy are briefly discussed.
Key words: Candidate genes, QTL, enzyme activity, carbon
metabolism, molecular markers.
Introduction
Quantitative traits differ from qualitative ones in that the
individuals of a progeny cannot be classified into discrete
phenotypic classes. They exhibit a continuous variation,
making it impossible to use directly Mendelian
approaches. This difficulty has been overcome with the
use of molecular markers. Being very numerous and
phenotypically neutral, molecular markers have allowed
a number of saturated genetic maps to be constructed in
cultivated and model plant species. These maps may be
used for locating newly cloned genes, but also loci controlling quantitative traits (QTLs, for Quantitative Trait
Loci). The principle for QTL detection is to look for
correlation between the value of the quantitative trait
under study and the genotype at every marker. A significant test value means that at least one QTL is located
near the marker. In addition, the effect of the QTL
(defined as a, additive effect, or R2, proportion of trait
variation explained ) and its mode of action (additive,
dominant, in the case of heterozygous populations) can
4 To whom correspondence should be addressed. Fax: +33 1 69 33 64 24. E-mail: [email protected]
© Oxford University Press 1999
1282 Prioul et al.
be assessed. Compilation from the literature shows that,
in most instances, for physiological as well as for more
integrated traits, a limited number of QTLs with quite
large effects explains the major part of the overall genetic
variation, even though a possibly high number of QTLs
with effects below the threshold of detection may exist.
Thus it has been calculated that as many as 173 genes
were effective in the inheritance of protein and starch
content in maize kernels (Dudley and Lambert, 1992),
whereas QTL analysis on the same traits (Goldman et
al., 1993) showed that 6 and 7 QTLs explained 65% and
66% of total variance, respectively. Such a conclusion
converges with the physiologist’s view about the existence
of few limiting factors acting on integrated functions,
which has been thoroughly discussed earlier (Prioul et
al., 1997).
There is no direct and simple strategy for characterizing
the QTLs. Marker-based cloning ( Tanksley et al., 1995)
can only be applied to small genome species, such as
Arabidopsis, rice or tomato; to current knowledge, no
QTL has been so far isolated with this method.
Alternatively, the use of candidate genes is becoming
a widespread method. As discussed in detail elsewhere
(de Vienne et al., 1999), the selection and validation
of candidate genes may rely on two non-exclusive
approaches. The first one, the ‘functional’ candidate gene
approach, is based on the a priori choice of gene(s) which
may be functionally related to the trait. A correlation
between the trait under study and allelic polymorphism
of the candidate, regardless of the genetic background, is
a strong argument in favour of the candidate gene. This
approach is largely used in human genetics; in plants for
example, it has been successful to show the role of the
polymorphism of p1, a gene encoding a transcriptional
factor, in the maysin content of maize silks (Byrne et al.,
1996). The second approach, the ‘positional’ candidate
gene approach, relies on QTL mapping and on examination of known-function genes or mutations which map in
the same region, the effect of which may be related to the
trait. As above, the first step for validation requires the
search for correlation between trait value and allelic
polymorphism. Fine mapping (Paterson et al., 1990) or
construction of near-isogenic lines ( Touzet et al., 1995)
may be a useful prerequisite to reduce the size of the
chromosomal region of interest.
In this paper, positional and functional approaches
were combined to detect and to begin validation of
candidate genes in maize. Although QTLs for physiologically related traits (fruit pH and carbohydrate content)
were investigated as long as 1988, in tomato (Paterson et
al., 1988), most QTL studies have focused on agronomic
traits. The authors chose to study biochemical traits
(enzyme activity and metabolite contents) which are more
likely related directly to gene expression than more integrated traits. Enzyme activity depends on two main vari-
ables, specific activity of the protein and enzyme amount.
The amount is controlled by expression of the corresponding gene whereas the specific activity depends on the
intrinsic property of the protein and on the various
regulators acting on it. Gene expression itself may be
modulated by various factors regulating transcription,
translation and post-translation. Thus, an enzyme structural gene is unlikely to control all the variability in
enzyme activity. In order to get some insight into this
question the activity of key enzymes of carbohydrate
metabolism and their substrate and/or product contents,
were measured in a segregating population. Several DNA
probes for each of the enzymes studied were available,
allowing their mapping and the search for co-locations
with the QTLs for enzyme activity and related traits.
Materials and methods
Genetic material
The mapping population consisted of recombinant inbred lines
(RILs—6 generations of self-pollination) derived from a cross
between two parental lines, F (French origin) and Io (North
2
America origin). In a preliminary study the carbohydrate
metabolism of the two parents was compared under normal
irrigation and water stress conditions. They were different for
nearly every physiological variable and they behaved differently
in response to water stress (Pelleschi et al., 1997). Moreover,
as predicted by genetics of complex traits, even modest
differences between parents produced a large variability in the
descendants. A total of 145 RILs was used for mapping 152
RFLP probes, including a majority of expressed sequences
(Causse et al., 1996). For QTL detection, 65 RILs were used in
the first experiment, 120 in the second and 100 in the third. In
the validation step of some candidate genes, 46 unrelated lines
covering the variability in Europe and America, were used
(experiment 4).
Plants were grown in a glasshouse until the adult fourth leaf
stage for experiments on the leaves. Grains from three cobs
were harvested at maturity in the field. Two composite samples
consisting of six grains from each cob were analysed separately.
Experiments
Measurements on the leaves: In experiment 1 (Causse et al.,
1995), measurements were done on young (sink) and mature
(source) leaves from 65 RILs grown under normal conditions.
In experiment 2 (Pelleschi, 1997) 120 RILs were used, with
four replicates; two conditions, irrigated or non-irrigated, were
compared. The measured traits concerned carbon metabolism
(presented here), and also photosynthetic gas exchange, growth
parameters, and ABA content in leaf and xylem (not shown).
The biochemical methods have been described elsewhere
(Pelleschi et al., 1997).
Measurements on the grains: In experiment 3, mature grains
were harvested on 100 RILs from the same population grown
in the field at Gif-sur-Yvette (France, 30 km SW Paris), in
1994. Starch content and composition (amylose and amylopectin), vitrosity and some technological traits were measured. In
experiment 4, starch content was measured on mature grains
harvested on the 46 unrelated lines. They were field grown,
except a few late maturing lines which were grown in a
From QTLs for enzyme activity to candidate genes in maize 1283
glasshouse. DNA was extracted from the leaves of these lines,
restricted with various enzymes and hybridized with an Sh2
cDNA probe, after gel separation and Southern blotting. This
probe encodes the ADPglucose pyrophosphorylase large subunit
(Sh2) which is specifically expressed in endosperm. Sh2
polymorphism was evaluated by scoring the presence/absence
of specific restriction sites among the 46 lines.
Choice of the carbohydrate metabolism enzymes
Carbohydrate metabolism in plants is central in that it bridges
energetic metabolism and growth. In maize the main products
of this metabolism are sucrose and starch. Sucrose which is the
mobile form is produced in leaf cytosol by a chain of reactions
in which sucrose-phosphate-synthase (SPS ) is a key enzyme.
Sucrose is then exported through the phloem to sinks where it
is cleaved by one of the two enzymes: sucrose-synthase (SuSy)
and invertase (INv). The products are further used for
metabolism and for synthesis of storage products. Starch is
both synthesized in leaves and sinks but always in plastids; the
regulating enzyme on that pathway is ADPglucose pyrophosphorylase (AGP). This study focused on these four enzymes
(SPS, AGP, INv, SuSy) to represent the carbohydrate metabolism in sources and/or sinks. An additional advantage of this
choice is that DNA probes were available for all of them.
Further gene analyses showed that there are several sequences
coding for each enzyme with a frequent tissue specialization. In
general, multiple locations were observed by gene mapping (see
below). Briefly, AGP consists of two subunits coded by Bt2
and Sh2 in grain, and by Agp1 and Agp2 in the embryo; in the
leaf a subunit is encoded by L2, which has homology with Bt2
(Prioul, 1996). One Sps gene has been described which maps
at three loci. Sucrose-synthase is encoded by two genes, one is
more expressed in grain (Sh1) and the other one in non mature
leaf (Sus1) (Nguyen-Quoc et al., 1990); only one locus is
detected for each gene, on chromosome 9; invertase activity is
either located in cell wall, vacuole or cytosol. Two main genes
are reported for cell wall invertase (Incw1, Shanker et al., 1995;
Incw2, Cheng et al., 1996). Two other ones (Incw3, Incw4) have
recently been isolated ( Kim, 1998). Two genes coding for
vacuolar invertase (Ivr1, Ivr2) have also been isolated ( Xu et
al., 1996), but none for the cytosolic isoform.
QTL detection
The QTL detection methods have been extensively presented
elsewhere and were discussed in relation to physiological QTLs
in a previous paper (Prioul et al., 1997). ANOVA was used in
experiment 1 and MQTL (Tinker and Mather, 1995) in
experiments 2 and 3, allowing a greater power than ANOVA
or MAPMAKER-QTL (Lander and Botstein, 1989) through
composite interval mapping (CIM ) (Zeng, 1994). In experiment
4 rank correlation was used ( Wilcoxon test).The principle of
composite interval mapping is to test if a QTL is present at a
specific location while simultaneously accounting for the effect
of other QTLs. The variation of these other QTLs is accounted
for by ‘background markers’ located in the vicinity. These
‘background markers’ were carefully chosen by stepwise regression and their number was limited to fewer than 10, in order
to reduce the detection of false positive, as shown by simulation
studies (A. Leonardi, unpublished results). The threshold level
for test statistics was determined by a permutation test
(Churchill and Doerge, 1994). Normality and inequality in
intra-class variances were checked for each variable and, when
needed, transformations (square-root, 1/Y or Log) were used.
These transformations improved QTL detection. The individual
QTL effects (R2) were estimated as partial R2 when all the
other QTLs found for the same trait were included in the model.
Results and discussion
Candidate genes associated with carbon metabolism in
source and sink leaves
In a first experiment the feasibility of the project was
tested using 65 RILs. The enzyme activities (SPS and
AGP) and the carbohydrate contents (sucrose, glucose
and fructose) were measured in the source (expanded
third leaf ) and sink (base of growing fourth leaf ), in
parallel with plant growth. In source leaves (Fig. 1), three
QTLs were obtained for SPS, three for AGP, four for
hexoses (G+F ), and four for sucrose (Suc). These QTLs
were frequently located in the same regions as the QTLs
for growth and carbohydrate metabolism in sink leaves
(vertical bars in Fig. 1; and in Causse et al., 1995), thus
forming clusters. Regarding source leaves (Fig. 1),
co-locations were observed between enzyme activities and
product contents: one AGP and one SPS QTL were
associated either with a hexose (G+F ) or a sucrose (Suc)
QTL. Concerning the candidate genes, none of the numerous loci for AGP coincided with any of the three QTLs,
whereas for SPS an apparent co-location showed up on
chromosome 8L between the QTL and a Sps locus. This
first attempt encouraged the investigation of a larger
sample of lines and under varying conditions.
In the second experiment 120 RILs of the same cross
were grown under control or drought-stress conditions,
in four successive replicates. Carbohydrate metabolism
traits in source leaves were complemented by parameters
from photosynthetic gas exchange, water status, ABA,
and growth. As previously noted, the QTLs from different
levels of organization (metabolism, leaf ) tended to be
clustered (Pelleschi, 1997). Moreover, the clusters of
QTLs found under control and drought-stress conditions
mapped frequently at different locations, which supports
the existence of stress-specific gene regulation.
Examination of possible co-locations between structural genes and QTLs for enzyme activity showed coincidences on chromosomes 2, 5 and 8 (Fig. 2). A SPS QTL
was close to the Sps locus on chromosome 8, confirming
the result of the first experiment. A second coincidence
corresponded, on chromosome 5, to a vacuolar invertase
gene (Ivr2) and QTLs for acid-soluble invertase activity
(i.e. vacuolar) in control and water-stress conditions. It
is noteworthy that the QTL for invertase activity at this
locus presented the highest score (R2=17%) for explaining
the activity variability in control conditions. The lower
R2 in stressed plants (4.4%) is explained by larger contributions of other regions of the genome, especially on
chromosome 2 (11%) (results in Fig. 3). In this chromosome 2 region a third co-location occurred between an
1284 Prioul et al.
Fig. 1. QTLs for enzyme activity of sucrose-phosphate-synthase (SPS ) and ADPglucose pyrophosphorylase (AGP), and for carbohydrate content
(sucrose, Suc, and glucose+fructose, G+F ) in adult leaves ( leaf 3) from 65 recombinant inbred lines (experiment 1). The QTLs are placed on the
right of the chromosome arms, the arrow tip indicating the locus position. The vertical bars present the clusters of QTLs formed with growthrelated traits not displayed on this figure (Causse et al., 1995).The genes coding for functions related to QTLs are placed on the left. L2 codes for
one AGP subunit, Ivr1 and Ivr2 for vacuolar invertases, Incw1 for a cell wall invertase and Sps for sucrose-phosphate-synthase. The large grey
arrow indicates the co-location between a SPS QTL and the Sps gene locus. (All the data were extracted from Fig. 1 in Causse et al., 1995.)
invertase activity QTLs and Ivr1 and Incw2 loci, encoding
for a vacuolar and a cell wall invertase, respectively. As
previously noted, more QTLs were revealed in stress than
in control conditions, and only a few of them were at
common locations (Pelleschi et al., 1999).
Candidate genes associated with carbon metabolism in
mature grains
A similar candidate gene approach was attempted in
mature grains, and is in progress in developing grains. In
mature grains, numerous traits were measured and especially the content in starch and in its two components,
amylose and amylopectin (Séne, 1996; Séne et al., 1997).
QTLs were obtained for these traits (Fig. 3, grey boxes).
On chromosome 3, at one telomeric end, a QTL for
amylose was located close to the Sh2 locus corresponding
to the gene encoding the AGP large subunit specifically
expressed in endosperm. This apparent co-location is
interesting since, in a completely different genetic material,
i.e. lines which have been divergently selected for starch
content, a QTL for starch content was obtained at the
same locus (Goldman et al., 1993). In addition, a purely
genetic approach, based on the comparison of mutants
and revertants in the 3∞ coding region of the Sh2 gene,
obtained an increase in total starch per grain (Giroux et
al., 1996). On chromosome 10, QTLs for an amylose/
amylopectin ratio mapped at the same position as a gene
coding for a vacuolar invertase (marker gsy348a Ivr1). It
is noteworthy that the other locus of Ivr1 gene, located
on chromosome 2 (gsy348c Ivr1), is also close to amylopectin QTLs ( Fig 3).
Synthetic map of QTLs for carbohydrates of carbon
metabolism in leaves and grains
All the QTLs presented in Figs 1 and 2 for leaves have
been pooled with those of starch components in mature
grains (Fig. 3). The bar length is proportional to the
percentage of explanation of total trait variability (R2).
However, these values should be taken cautiously since
the QTL detection methods were not all the same. The
older published results ( Fig. 1) were based on ANOVA
which tends to overestimate R2 especially when the
number of RILs is low, whereas in further results, based
on larger RIL number and a better data analysis due to
estimation of missing data, the R2 value was lower in
general. Taking into account these restrictions, it is striking to note that the QTLs from the different experiments
and for different traits frequently form clusters in specific
regions of the genome: on chromosome 1 at the S-telomere
(umc11 to gsy143b csu59) and in the lower centromere
region (gsy296 to gsy18 KN1), on chromosome 2 near
the S-telomere (umc6 to gsy108 GUTR), on chromosome
5 in the centromere region (umc43, Ivr2) and near the
L-telomere (umc104a), on chromosome 8 (gsy126 csu131
to gsy224b SPS), on chromosome 9 close to the
S-telomere (umc113 to umc 114) in the vicinity of Sh1,
From QTLs for enzyme activity to candidate genes in maize 1285
Fig. 2. QTLs for enzyme activity of sucrose-phosphate-synthase (SPS ), vacuolar invertase (INv) and ADPglucose pyrophosphorylase (AGP), in
adult leaves ( leaf 4) from 120 recombinant inbred lines normally irrigated (-c) or water stressed for 9 d (*) (experiment 2). The QTLs are placed on
the right of the chromosome arms and the genes coding for functions related to QTLs on the left, Ivr1, Ivr2 and Incw1, Incw2 code for vacuolar
and cell wall invertases, respectively. The appendix */c represents the relative stress (*)/control (c) effect. The large grey arrows indicate locations
of possible candidate genes for INv and SPS QTLs. The bar length is proportional to the R2 value calculated from MQTL (composite interval
mapping) output (see text), black or open bars indicates F or Io allele effects, respectively.
2
Wx and Sus1 loci, and on chromosome 10 (around
gsy348a Ivr1). In general, the carbohydrate content QTLs
frequently did not map at the same position as the
corresponding enzyme (Fig. 1). A similar observation
was made in experiment 2 (not shown). Among these
eight clusters displayed in Fig. 3, five of them are located
close to loci of the structural gene of an enzyme closely
related to carbohydrate metabolism and, as mentioned
earlier, in two instances there is a close conjunction
between the enzyme activity and the structural gene (INv
and SPS on chromosomes 5 and 8, respectively).
Towards validation of the candidate genes
The observation of an apparent co-location between a
QTL and a candidate gene functionally related to the
trait does not provide definitive evidence for the role of
the gene in trait variation. Fine mapping and analysis of
gene polymorphism in coding and regulatory regions are
required. Finally transformation, when possible, could be
attempted. For fine mapping, several backcrosses are
performed in order to obtain recombinants in the region
of the QTL, and to reach homozygosity for the recurrent
in the rest of the genome. The near-isogenic lines are
compared in order to locate the QTL more accurately,
and to check if the candidate gene is still in the confidence
interval of the QTL position. For example, it has been
shown that plant height variability in maize was better
explained by Dwarf3, a gibberellin biosynthetic gene, than
by PhyB a phytochrome gene located 30 cM apart ( Touzet
et al., 1995). The problem with this method is that one
cannot exclude the participation of an unknown gene
tightly linked to the candidate. For further validation,
correlation can be sought between the polymorphism of
the gene, or its flanking regions, and the value of the trait
in a genetic material presenting a minimum linkage disequilibrium. This method has been successfully used in
humans to validate the angiotensinogen gene as a candidate for blood pressure in a French and an American
population (Jeunemaı̂tre et al., 1992). The authors tried
to apply such an approach to starch content and the Sh2
1286 Prioul et al.
Fig. 3. Pooling of QTLs for carbohydrate metabolism in mature leaves from the experiments presented in Figs 1 and 2 with those from starch
components: amylose (AM ), amylopectin (AP), and amylose to amylopectin ratio (AM/AP) in mature kernels from 100 recombinant inbred lines
(1994 experiment=experiment 3). All the QTLs are placed on the right of chromosome arms and the markers on the left. For the sake of
simplification the allele effects are not shown. QTLs from the first experiment are boxed and R2 values were calculated by ANOVA on 62 RILs.
QTLs and R2 values from the second experiment were calculated on up to 120 RILs and the QTL names possess an appendix (‘*’, ‘-c’ ‘-*/c’) as in
Fig. 2. The grain QTLs are in a shaded box (experiment 3). Differing from an earlier presentation (Séne, 1996) they were recalculated by MQTL
from the 100 RILs. All the markers originate from the map fully describes by Causse (Causse et al., 1996). Anchor markers (umc or bnl ) are given
in addition to the known function genes. For these non-anonymous genes, the second member of the name which is in italics represents an
abbreviated name of the function. The functions which are more or less related to carbohydrate metabolism are in bold. Exhaustive decoding of
the abbreviations is given in Causse et al. (1996). Chromosomes 4 and 7 have been deleted because of the virtual absence of QTLs.
candidate gene. Forty-six lines representative of the
diversity of the European and American material, including tropical ones, were chosen. Using a set of restriction
enzymes, the lines were classified for the presence or
absence of restriction sites and kernel starch content. In
the example given in Fig. 4, it is clear that presence of a
particular Sac1 site is, on average, significantly associated
with a higher starch content (P <0.01, Wilcoxon test).
The exceptions may be explained by the action of other
factors on the genome, since it is unlikely that the site
itself alters the property of the gene; the alteration is
more likely due to a closely located site which may be
subjected to some recombination.
A main criticism to this restriction analysis is that the
total length of the gene fragments under analysis is variable
from one line to another, depending on the position of the
distal sites. This may hinder the role of sites possibly placed
upstream or downstream of the distal site. A way to overcome this problem is to PCR-amplify the gene in several
overlapping fragments which may be analysed by restriction
mapping or to use different covering probes (Templeton et
al., 1987). Finally, the most powerful alternative is sequencing of all alleles from the lines. Such an approach has been
performed for Opaque-2 gene encoding for a transcription
factor modifying zein composition in kernels (Henry and
Damerval, 1997).
Finally, other indirect but valuable methods may be
envisaged to help in QTL validation, namely comparative
mapping and bulk segregant analysis. In the first case
one can take advantage of possible co-location of the
From QTLs for enzyme activity to candidate genes in maize 1287
Fig. 4. Relationship between the polymorphism for the presence of a particular restriction site (SacI ) of the Sh2 gene and the kernel starch content
in 46 unrelated inbred lines. Lines are placed in increasing order for starch content on the abscissa and the starch content values are on the
ordinate. Closed symbols: presence of the restriction site; open symbols: absence of the site.
same QTL/candidate gene couples in different species of
the same family. In the second, (Quarrie, 1999) the
association, on a large number of genotypes, of an
improved (or non-favourable) physiological property with
a change in the polymorphism of a specific gene provides
a reliable indication. However, an ultimate piece of evidence would be provided by specific replacement of an
allele by an other, using homologous recombination; this
technique is unfortunately unavailable in plants. An
alternative approach would be mutagenesis if partial
impairment (or possibly improvement) can be obtained,
as in the case of Sh2 (Giroux et al., 1996).
Conclusion
The candidate gene approach is applicable to traits related
to the carbohydrate metabolism (enzyme activities and
substrate levels). Several co-locations were obtained with
structural genes for SPS and invertase. The validation of
such candidates is in progress from the analysis of DNA
polymorphism. However, these QTLs account only for a
part of the variability, particularly for the stress response
traits, which means that other factors are located apart
from the structural genes. The possible role of regulatory
factors in quantitative traits (illustrated by Byrne et al.,
1996) may provide a clue for explaining such QTLs.
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