![Schematic depiction of the phenotypic traits used in this study
For each multivariate trait, the whole range of corolla shape variation is represented by the landmarks (red dots) and semi-landmarks (black dots) of the corolla profile; the minimum and maximum shapes correspond to the most extreme values observed in the corresponding Principal Component Analysis (PCA) (see [63]). Under the univariate traits is an example of how measurements were performed from landmarks positions on a specific floral shape from landmarks (red dots).](https://www.researchgate.net/publication/362122870/figure/fig2/AS:11431281275440995@1725410439149/Schematic-depiction-of-the-phenotypic-traits-used-in-this-study-For-each-multivariate_Q320.jpg)
![Linkage map with 12 QTLs
The linkage groups (LG) were given the same names as in Alexandre et al. [63] and QTLs are represented by coloured bars to the right of their designated linkage groups, with letters on top referring to Table 2. The patterns in QTLs bars represent different significance thresholds indicated in the legend, and the horizontal bar represents the peak location (see Table 2). The 24 candidate genes are in red, including CYC and RAD from Alexandre et al. [63]. Positions of markers are in cM. Minor QTLs are those that were detected after including the primary QTL as a covariate in the model.](https://www.researchgate.net/publication/362122870/figure/fig5/AS:11431281275440996@1725410440128/Linkage-map-with-12-QTLs-The-linkage-groups-LG-were-given-the-same-names-as-in_Q320.jpg)
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RESEARCH ARTICLE
Testing candidate genes linked to corolla
shape variation of a pollinator shift in
Rhytidophyllum (Gesneriaceae)
Vale
´rie Poulin
1
, Delase Amesefe
1¤
, Emmanuel Gonzalez
1,2,3
, Hermine Alexandre
1
,
Simon JolyID
1,4
*
1De
´partement de Sciences Biologiques, Institut de Recherche en Biologie Ve
´ge
´tale, Universite
´de Montre
´al,
Montre
´al, Canada, 2Department of Human Genetics, Canadian Centre for Computational Genomics (C3G),
McGill University, Montre
´al, QC, Canada, 3Microbiome Research Platform, McGill Interdisciplinary Initiative
in Infection and Immunity (MI4), Genome Centre, McGill University, Montre
´al, QC, Canada, 4Montreal
Botanical Garden, Montre
´al, Canada
¤Current address: ImmunRise Technologies, Paris, France
*simon.joly@montreal.ca
Abstract
Floral adaptations to specific pollinators like corolla shape variation often result in reproduc-
tive isolation and thus speciation. But despite their ecological importance, the genetic bases
of corolla shape transitions are still poorly understood, especially outside model species.
Hence, our goal was to identify candidate genes potentially involved in corolla shape varia-
tion between two closely related species of the Rhytidophyllum genus (Gesneriaceae fam-
ily) from the Antilles with contrasting pollination strategies. Rhytidophyllum rupincola has a
tubular corolla and is strictly pollinated by hummingbirds, whereas R.auriculatum has more
open flowers and is pollinated by hummingbirds, bats, and insects. We surveyed the litera-
ture and used a comparative transcriptome sequence analysis of synonymous and non-syn-
onymous nucleotide substitutions to obtain a list of genes that could explain floral variation
between R.auriculatum and R.rupincola. We then tested their association with corolla
shape variation using QTL mapping in a F
2
hybrid population. Out of 28 genes tested, three
were found to be good candidates because of a strong association with corolla shape:
RADIALIS,GLOBOSA, and JAGGED. Although the role of these genes in Rhytidophyllum
corolla shape variation remains to be confirmed, these findings are a first step towards iden-
tifying the genes that have been under selection by pollinators and thus involved in repro-
ductive isolation and speciation in this genus.
Introduction
The rapid diversification of flowering plants is commonly thought to be associated with their
adaptation to a wide range of pollinators for reproduction [1]. Indeed, for animal-pollinated
species, floral adaptations to functional groups of pollinators often result in character speciali-
zation that could lead to reproductive isolation and speciation [2–4]. Many floral traits can be
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OPEN ACCESS
Citation: Poulin V, Amesefe D, Gonzalez E,
Alexandre H, Joly S (2022) Testing candidate
genes linked to corolla shape variation of a
pollinator shift in Rhytidophyllum (Gesneriaceae).
PLoS ONE 17(7): e0267540. https://doi.org/
10.1371/journal.pone.0267540
Editor: Serena Aceto, University of Naples Federico
II, ITALY
Received: January 24, 2022
Accepted: April 12, 2022
Published: July 19, 2022
Copyright: ©2022 Poulin et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All the data generated
in this study were deposited in Figshare and are
accessible via these private links: https://doi.org/
10.6084/m9.figshare.9750566.v1,https://doi.org/
10.6084/m9.figshare.13286942.v1. In addition, the
raw sequence data for transcriptome assembly
was deposited in a Sequence Read Archive of the
National Center for Biotechnology Information
under project PRJNA732582 (https://www.ncbi.
nlm.nih.gov/sra/PRJNA732582).
involved in these adaptations to pollinators, such as nectar composition, color, scent, and mor-
phology [5] and many studies have shown that such traits are under strong selection pressure
by pollinators [6,7]. However, the specific genes on which selection acts are generally
unknown [but see 8–10].
Recently, numerous studies have shed light on the genetic and molecular basis of floral
traits, contributing to our understanding of how plants adapt and speciate [11–13]. However,
the inception and subsequent variation of these traits are often controlled by a complex net-
work of genes that has yet to be described for many groups [14,15]. A useful approach to
investigate the genetic bases of floral traits is to study the quantitative trait loci (QTL) responsi-
ble for a phenotype of interest as it can estimate the number of genomic regions involved in
determining the trait of interest and quantify their effects on the phenotypic variance and
identify cases of pleiotropy and epistasis [16,17], and identify candidate genes of interest [18–
20]. QTL mapping of floral traits has been performed in Ipomopsis [21], Iris [22], Petunia [23,
24], Mimulus [25–29], among others. Most studies on the evolution of floral traits stop after
mapping the QTLs and they rarely investigate the genes underlying the QTLs [16]. Exceptions,
however, contribute to a better understanding of the genetics of floral traits, such as the identi-
fication of an anthocyanin concentration gene in Mimulus [20] and genes involved in floral
scent in Petunia [23] and Nicotiana [30,31]. Although functional analyses and heterologous
transformation experiments are required to confirm the role of a gene and selection experi-
ments are needed to demonstrate its role in adaptation [32], association studies are important
to narrow down on the genes underlying significant morphological variation and provide tar-
gets for future research.
Corolla shape is critical in ensuring reproductive success, but the genetics of corolla shape
differentiation among species is still poorly understood. This structure is important for polli-
nator attraction and mechanical fit with the pollinator for pollen deposition but is also
involved in the deterrence of unwanted visitors [1,19]. To achieve these functions, corolla
shape varies in size (length and width), number of petals and disposition, fusion, curvature
and symmetry. The genetic basis of these corolla features was investigated in QTL studies [21,
22,29,33–35], which largely found that corolla shape is a complex trait with a polygenic archi-
tecture (i.e. several loci of small to moderate effects on the phenotype). However, very few
studies identified genes responsible for corolla shape variation outside model species. One
exception is a paper by Ding et al. [36] that discovered that a mutation in an actin gene was
responsible for a mutant phenotype causing a reduction in the corolla tube width of Mimulus
lewisii.
Although we know little of the genes involved in floral shape adaptation between species,
the development of flowers has been well studied, if only in a few model organisms. The
genetic processes implicated in petal morphogenesis mostly concerns its initiation and identity
in the floral meristem [15]. For example, the well-established ‘ABC model’ describes a group of
B-function transcription factors responsible for petal and stamen identity in Arabidopsis thali-
ana (APETALA 3 (AP3) and PISTILLATA (PI)) and Antirrhinum majus (DEFICIENS (DEF)
and GLOBOSA (GLO)) [37,38]. These flower organ identity genes were later found to have
the same DNA-binding protein domain (MADS box) and to be able to dimerize and form
complexes [39]. This led to the ‘Quartet Model’ that proposes a combinatory action for the
organ identity genes [40,41]. Substantial advances in flower development came from A.majus
and led to the bilateral symmetry model [42–44]. In this molecular network, CYCLOIDEA
(CYC) and DICHOTOMA (DICH) are TEOSINTE BRANCHED1-CYCLOIDEA-PROLI-
FERATING CELL FACTOR (TCP) genes that give dorsal identity to the petals and activate
the RADIALIS (RAD) gene. RAD is a MYB transcription factor that negatively regulates
DIVARICATA (DIV) to restrain its expression in the ventral domain [45]. All these models
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Funding: This research was supported by
Discovery grants to SJ from the Natural Sciences
and Engineering Research Council of Canada
(402363-2011 and 05027-2018). The funders had
no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: The authors have declared
that no competing interests exist.
were accompanied and followed by numerous molecular studies, each one adding a piece to
the regulation puzzle of petal morphogenesis. Furthermore, much progress has been made by
looking at cellular division in the flower [46–48]. Indeed, the regulation in time and space of
cellular division, elongation and differentiation is what gives rise to growth directionality, pat-
terning and final corolla morphology [49].
While the recent genetic studies are important to acquire molecular data, refine genetic
models, and understand the fine-scale genetic control of petal characteristics, they also give a
restricted view of corolla shape regulation. One reason for this restricted view is that our infor-
mation comes from a limited number of model species [50] and application of these models
across the angiosperms is still a matter of discussion (‘shifting boundary’ [51] and the ‘fading
borders’ [52,53] ‘(A)BC model’ [54]). Another reason is that these developmental studies do
not help to identify which genes have been particularly important for the diversification of spe-
cies. Therefore, to link the genetics of floral shape variation with its impact on plant evolution,
it seems essential to study floral variation in groups where we know it had an impact on
speciation.
The Gesneriaceae family could be one such group as corolla shape and pollination strategies
show extensive variation [55–58]. The subtribe Gesneriinae is of particular interest because it
radiated into more than 80 species in the West Indies from a common ancestor [59,60]
approximatively 10 Ma [56]. These species show various pollination strategies (hummingbird,
bats, moth, generalists) and there have been frequent transitions between strategies during the
evolution of the group [61,62] suggesting that pollination transitions have been an important
driver of evolution for the Gesneriinae.
Here, we investigate the genetic basis of the corolla shape differences between two represen-
tative species of the Rhytidophyllum genus from this subtribe: Rhytidophyllum auriculatum
(Puerto Rico and Hispaniola) and R.rupincola (Cuba) (Fig 1). The first is a generalist, polli-
nated by hummingbirds, bats and insects, with yellow flowers that have a subcampanulate
shape (bell shape with a basal constriction). The second, R.rupincola, is a hummingbird spe-
cialist with orange tubular flowers. We already know that the variation in corolla shape
between R.auriculatum and R.rupincola is explained by a few QTLs of moderate to small
effect [63]. However, we still have no idea which genes underly these QTLs and are thus
responsible for the changes in flower shape between the two typical corolla morphotypes.
Hence, the goal of this study was to identify candidate genes responsible for corolla shape vari-
ation between R.auriculatum and R.rupincola. This will help to better understand how flowers
adapt to their pollinators, how transitions of pollination modes occur, and how floral traits
Fig 1. The two parental species studied. The small maps at the bottom-right corner of the images indicate their
geographic origin. Photo credits: S. Joly.
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evolve. We surveyed the literature and used a comparative transcriptome sequence analysis of
synonymous and non-synonymous nucleotide substitutions to identify genes that could
explain floral variation between R.auriculatum and R.rupincola. We then used transcriptome
sequences to identify single nucleotide polymorphisms in these genes, genotyped them in a F
2
hybrid population described in [63], and tested if these genes explain a significant amount of
corolla shape variation between the species. Of the twenty-two genes investigated in this study,
we identified 3 candidate genes that may underlie variation in corolla shape associated with
pollination syndrome transition. We discuss these genes and their potential roles in determin-
ing corolla shape in Rhytidophyllum.
Material and methods
Transcriptome sequence comparison
Our objective was to compare the transcriptome sequences of Rhytidophyllum rupincola and
R.auriculatum to identify genes associated with corolla shape determination that show evi-
dence of directional selection. We assembled de novo the floral transcriptome of three Rhyti-
dophyllum species with contrasting pollination strategies to identify candidate genes
potentially involved with floral shape determination. In addition to R.auriculatum and R.
rupincola described above, also included was R.vernicosum, an endemic species of the north-
ern Dominican Republic that is bat pollinated with a corolla shape similar to R.auriculatum.
Rhytidophyllum auriculatum and R.vernicosum are likely to form a monophyletic group [61,
62] and the addition of R.vernicosum in the transcriptome analysis increases the chance of
identifying genes associated with the corolla shape because genes showing evidence of direc-
tional selection only with one of R.auriculatum or R.vernicosum could be discarded. All acces-
sions used in this study come from the collections of the Montreal Botanical Garden:
Rhytidophyllum rupincola (accession 113–1991), R.auriculatum (937–1971), and R.vernico-
sum (1267–1966).
Our initial objective was to obtain floral transcriptomes as complete as possible. Therefore,
buds from all developmental stages as well as fully developed flowers were pooled prior to
RNA extraction. Each sample thus contained multiple floral tissues such as sepals, petals,
anthers, and the gynoecium. Buds were flash-frozen in liquid nitrogen immediately after har-
vest and were kept at -80 C until the RNA extraction.
Total RNA was extracted using a CTAB protocol [64] and RNA quantity and quality were
assessed with a BioAnalyser (Agilent; Mississauga, Canada). mRNAs were extracted using
poly-Toligo attached magnetic beads and cDNA synthesis was conducted using hexamer pair-
ing. Illumina TrueSeq 100 bp paired-ends libraries were constructed (Illumina1TruSeq1
RNA Sample Preparation Kit v1; San Diego, USA) and sequenced in a single lane of an Illu-
mina HiSeq 2000 sequencing system at the Genome Quebec Innovation Centre (Montreal,
Canada).
Raw reads were filtered for quality and for poor quality nucleotides at the beginning and
end of each read using Trimmomatic vers. 0.32 [65] with the following parameters: “LEAD-
ING:15 TRAILING:15 SLIDINGWINDOW:5:15 MINLEN:40”. The Trinity software [66] was
used to reconstruct de novo transcriptomes using default settings, discarding transcript <201
bp. Sequences qualified as a “gene” were the union of transcripts similar enough to be consid-
ered by Trinity as putative isoforms of the same gene. These sequences were used for all further
analyses. The quality of the transcriptomes was assessed with BUSCO v3 [67] against a set of
single-copy plant orthologs (embryophyte_odb9).
Open reading frames (ORF) were obtained for each “gene” using Transdecoder [68], select-
ing the best ORF by transcript using results from a protein blast of the putative ORFs peptides
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on the uniref90 protein database [69]. Transcripts were annotated with Trinotate using protein
and nucleotide blasts on the uniref90 protein database and Pfam annotation. Gene ontology
(GO) annotations were extracted with Trinotate and functional enrichment tests were per-
formed using the Bioconductor package GOseq [70].
Putative orthologous ORFs between species were obtained using OrthoMCL using default
parameters [71]. Orthologous transcripts with a single sequence in all three species were
aligned with MAFFT version 7.164 [72]. Synonymous and non-synonymous substitution rates
were obtained between species for each ORF using the maximum likelihood method of Yang
and Nielsen (2000) in PAML version 4.7 [73]. Alignments were trimmed to remove terminal
stop codons.
Identifying putative candidate genes involved in floral variation
We used two different approaches to a priori select genes that could be associated with corolla
shape determination in Rhytidophyllum. First, among the orthologous genes with ORFs con-
taining at least 100 synonymous sites (S), we extracted those with the greatest evidence of selec-
tion (dN/dS >1) between the tubular-shaped corolla species R.rupincola and the two bell-
shaped corolla species (R.auriculatum and R.vernicosum) and retained candidate genes asso-
ciated with floral development GO-terms (S1 Table in S1 File).
For the second approach, we searched the literature and Gene Ontology [74] to find candi-
date genes potentially involved in floral shape variation. We thus searched for all the genes
known to affect floral development in model species, or that were known to affect corolla or
leaf shape. The main functions we were looking for were petal identity, bilateral symmetry, tis-
sue polarity and petal growth regulation.
For each candidate gene, we obtained their nucleotide or amino acid sequences in data-
bases such as Phytozome, UniProt or GenBank (see S2 Table for accession numbers in S1
File). We then searched for homologues in our transcriptomes using BLASTn in Geneious
(version 8.1.9; Biomatters, Auckland, New Zealand) with an e-value cut-off of 1×10
−20
.
Reciprocal BLASTn were done for each candidate transcript using GenBank to confirm the
homology with their original candidate gene, which was deemed valid if the gene of origin
was among the top 10 results (See S2 Table for details in S1 File). When multiple copies of
the genes appeared to be present in the genomes of Rhytidophyllum, we included these in the
survey. In such cases, a phylogenetic tree of the Rhytidophyllum gene copies and the original
genes sequences was performed. Coding sequences were aligned using MAFFT vers. 7.450
[72] and a phylogenetic tree was reconstructed with RAxML vers. 8 [75] with a GTRGAM-
MAI nucleotide model (S1 Fig). For candidate genes to be considered further, transcripts for
R.auriculatum (A) and R.rupincola (R) had to align with a minimum percent identity of
70%.
SNP identification and validation
In order to genotype the candidate genes identified, we searched for single nucleotide poly-
morphisms (SNP) that differentiate the transcripts of the two parental species, R.auriculatum
and R.rupincola. Transcripts were aligned in MAFFT (version 7.164 [72]) and putative SNPs
were validated by PCR with primers located ca. 200 base pairs (bp) downstream and upstream
of the SNP locations. For all the PCR reactions, the same master mix was used: 0.3 μl of DNA
(ca. 1 to 10 ng of DNA) was added to 0.75 U of Dream Taq (Thermoscientific, Waltham, Mas-
sachusetts, USA), 1.5 μl of 10X Dream Taq Buffer, 0.6 μl of each 10 μM primers and 0.3 μl of
10 mM dNTPs in a total reaction volume of 15 μl. The amplification conditions were the same
for all candidate genes, except for the annealing temperature (see S3 Table for details in S1
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File): 2 min at 94˚C, followed by 40 cycles of denaturation at 94˚C for 15s, annealing at 48–
54˚C for 15s, elongation at 72˚C for 30s, and a final extension step at 72˚C for 1min. We PCR
amplified 23 putative candidate genes from the two parental species and sequenced them
using the Sanger platform at the Genome Quebec Innovation Centre (Montreal, Canada). The
putative candidate gene sequences obtained were deposited in Genbank (see S4 Table for
accession numbers in S1 File).
Genotyping of the F
2
hybrid population
The material genotyped consisted of extracted DNA from Rhytidophyllum auriculatum,R.
rupincola, a first-generation (F
1
) hybrid from these parents and 173 individuals constituting a
second-generation (F
2
) hybrid population obtained by selfing the F1 hybrid (see Alexandre
et al. (2015) for more information). All candidate genes were genotyped using the Sequenom
iPLEX Gold technology [76], which uses base-specific cleavage and mass spectrometry to
genotype individuals in multiplex, by Genome Quebec. For six genes, two SNPs were geno-
typed resulting in 6 duplicates (differentiated by the suffix “.A” or “.B” after the gene name)
(Table 1). Two putative candidate genes (NAC29.1 and HUA1) were also genotyped using
PCR and enzymatic digestion as further validation. For this, DNA samples were amplified as
described earlier (see S3 Table for primers in S1 File). For NAC29.1, 4 μL of the PCR ampli-
cons were digested with NsiI (New England Biolabs, Whitby, Ontario, Canada), a restriction
enzyme that overlap a SNP between parent species, in a total reaction volume of 15 μl accord-
ing to the company’s protocol and digestion products were visualized on a 1% agarose gel.
Genotyping of HUA1 was performed by migrating the PCR amplicons on agarose gel (1%) as
the two parental alleles were of different lengths.
Linkage map construction
A linkage map was built with CarthaGene [77] using 557 Genotyping By Sequencing (GBS)
markers from Alexandre et al. (2015), two putative candidate genes from the same study
(CYCLOIDEA (CYC) and RADIALIS (RAD)), eight markers from the anthocyanin biosyn-
thetic pathway (S. Joly, L. Fronteau, P.-A. Bourdon and H. Alexandre, unpublished data), and
our 22 putative candidate genes (see results), for a total of 589 markers. Markers were divided
into linkage groups with a maximum two points distance of 25 centimorgans (cM) between
markers and a minimum LOD threshold of 3 using the group command. The mrkdouble and
mrkmerge commands were used to eliminate possible double markers. After the groups were
delimited, we used the lkhd function to order the markers in each linkage group using the
2-points distance optimization based on the Link-Kernighan heuristic algorithm. Then, for
each linkage group, the best map was selected and the positions of markers in cM were
obtained with the cumulative Haldane function.
Table 1. Summary statistics of the three floral transcriptomes.
R. auriculatum R. rupincola R. vernicosum
Number of genes (Trinity clusters) 66,955 67,396 70,662
Number of transcripts 164,127 166,900 165,516
Median gene length (bp) 923.5 910.2 899.7
Mean gene length (bp) 462 455 456
Total nb. of ORFs 95,149 96,916 91,424
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Phenotypic data
In order to obtain corolla shape quantitative trait loci (QTLs), we used the phenotypic data of
Alexandre et al. [63] for six floral traits (Fig 2). These phenotypic data were available for 130
individuals among the 141 F
2
hybrids that produced flowers. These six traits consist of a com-
bination of geometric morphometric analyses and linear measurements, obtained using three
approaches. First, the morphometric variation present among the F
2
hybrids summarized by a
principal component analysis (PCA) and the first three principal components (PC) that
explain 71.9% of the variance (PC1: 35%, PC2: 22.7%, PC3: 14.2%,) were used for our QTL
analysis (traits 1, 2 and 3 respectively). Broadly described, traits 1 and 3 explain variation in
corolla opening (tube shape vs. campanulate shape) and petal lobe reflection, whereas trait 2
explains variation in corolla curvature (Fig 2). The second approach used landmarks data from
the geometric morphometrics approach to obtain two univariate traits: corolla curvature (trait
4) and the corolla tube opening (trait 5). Finally, the third approach consisted of a PCA of the
F
2
individuals that included the two parents. We used the PC1 of this analysis that indicates
the level of resemblance of hybrid individuals to each parent (Trait 6). Trait 6 summarizes
information in corolla curvature, tube opening and petal reflection. There is redundancy in
the shape variation between the different traits (Fig 3), but these alternative approaches
increase the likelihood of detecting genes affecting the corolla shape variation.
Quantitative trait loci detection
QTL mapping was performed to test an association between our putative candidate genes and
corolla shape variation. QTL mapping was done in R Studio version 3.4.3 with the R/qtl
Univariate traitsMultivariate traits
Min. Max.
T
rait 1
T
rait 2
T
rait 3
T
rait 6
θ
Trait 4
Trait 5
Fig 2. Schematic depiction of the phenotypic traits used in this study. For each multivariate trait, the whole range of
corolla shape variation is represented by the landmarks (red dots) and semi-landmarks (black dots) of the corolla
profile; the minimum and maximum shapes correspond to the most extreme values observed in the corresponding
Principal Component Analysis (PCA) (see [63]). Under the univariate traits is an example of how measurements were
performed from landmarks positions on a specific floral shape from landmarks (red dots).
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package version 1.42–8 [78]. Phenotypic and genotypic data used for this study are accessible
in the supplemental information. The recombination fractions between linkage groups were
verified using plotRF function (see S2 Fig). We also searched for potential genotyping errors
with the command calc.errorlod and marked the problematic genotypes (error LOD score >4)
as ‘missing’ in the data before executing the QTL mapping. We finally looked for distorted
trait1 trait2 trait3 trait4 trait5 trait6
trait1 trait2 trait3 trait4 trait5 trait6
−0.1 0.0 0.1 −0.10−0.05 0.00 0.05 −0.05 0.00 0.05 0.10 20 40 60 80 0.7 0.9 1.1 −0.1 0.0 0.1
0
4
8
12
−0.10
−0.05
0.00
0.05
−0.05
0.00
0.05
0.10
20
40
60
80
0.7
0.9
1.1
−0.1
0.0
0.1
Fig 3. Morphological variation (diagonal) for the traits in the F
2
hybrid population and pairwise scatterplots showing their correlation (lower diagonal).
The yellow and red vertical lines on the histograms show the position of the parents, R.auriculatum and R.rupincola, respectively. Regression slopes are shown
for traits comparisons with a significant Pearson correlation test (p<0.05).
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segregation patterns of the markers using the function geno.table, that is genotypes with fre-
quencies that departed significantly from the expected 1:2:1 ratio.
QTL mapping was done one trait at a time. Genotype probabilities were calculated every 1
cM with the function calc.genoprob. The probability of an association between the trait and the
genotypes was calculated using the scanone function. The logarithm of odds (LOD) threshold
above which an association was considered significant was determined with 10,000 permuta-
tions under the normal model and the Haley-Knott method. These thresholds values were cal-
culated for alpha error thresholds of 5% and 10%. Only the sections of the linkage groups that
scored higher than the LOD thresholds estimated by permutations were reported; these
regions correspond to the position of the QTL. To obtain the confidence region of the QTL,
the lodint command was used on the linkage group with the QTL using the default interval of
1.5 (below the LOD peak). To detect minor QTLs, we did the procedure again but with the pri-
mary QTL as a covariate in the model. After the all QTLs were detected for a phenotype, we
used the fitqtl command to obtain the percentage of the variance explained (PVE) by the QTLs
and their associated P-values. Interaction between QTLs was not included in the model and
the variance was obtained by dropping one QTL at a time from the model. We tested for epis-
tasis for each trait using the scantwo function of the rqtl package using Haley-Knott regression
from multipoint genotype probabilities calculated at every 2.5 cM. A significance threshold of
0.05% was determined from 1000 simulations. We also calculated the LOD for all putative can-
didate genes to explain the morphological traits as well as probabilities of association using
10,000 permutations using the scanone function.
Protein domain prediction and polymorphism localization
For genes that were associated with at least one shape QTL, we searched for SNPs or indels
that could produce a protein-coding change between the sequences of R.auriculatum and R.
rupincola. We looked for stop codons in the transcript sequences and for non-synonymous
mutations in important protein domains. The domains were predicted from the transcriptome
sequences by searching the CDD database using the Conserved Domains Search Service from
NCBI [79]. Nucleotide insertions or deletion that caused a shift in the open reading frame of a
gene for a species were validated by Sanger sequencing of gene amplicons obtained from geno-
mic DNA or from floral bud RNA that was reversed-transcripted to DNA (SuperScript™III
Reverse Transcriptase, Thermo Fisher Scientific).
Results
Transcriptome sequence comparison
We obtained 60.1 ×10
6
, 58.5 ×10
6
, and 74.6 ×10
6
raw reads for R.auriculatum,R.rupincola,
and R.vernicosum, respectively. These were deposited in NCBI SRA under project
PRJNA732582. The characteristics of the floral transcriptome of the three species are presented
in Table 1. The BUSCO analyses on the Trinity gene transcripts suggest that the floral tran-
scriptome of R.rupincola,R.auriculatum and R.vernicosum contained 80.3% (61.7%), 80.3%
(60.2%) and 81.9% (61.7% complete) of a set of 1440 single-copy plant orthologous genes.
Selection for the best ORF for each “gene” resulted in 95,149 ORFs for R.auriculatum,
96,916 ORFs for R.rupincola, and 91,424 ORFs for R.vernicosum. The transcriptomes and
their annotation are made available in a public repository [80].
The OrthoMCL analysis resulted in 16,066 clusters of putative homologous sequences
between the two species. Of these, there were 9,435 clusters with one and only one homologous
sequence in each genome. These were considered for the analyses of selection. After align-
ments and cleaning, 8,448 clusters could be properly analyzed for dN/dS ratios. A total of
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Genetics of corolla shape
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1,419 genes had a dN/dS >1 among all pairwise species comparisons (Fig 4). Of these, we
focused on the 194 genes found to have a dN/dS >1 for both the R.rupincola /R.auriculatum
and the R.rupincola /R.vernicosum comparison as these two comparisons involved tubular
and bell shape corollas and thus were more likely to be involved in floral shape determination
(Fig 4B). GO term enrichment analysis of these 194 genes compared to the genes represented
in the homologous set suggested that the biological processes of “regulation of photoperiod-
ism, flowering” (p= 0.0428) and “vegetative to reproductive phase transition of meristem”
(p= 0.0192) were significantly over-represented.
A list of twenty-eight genes potentially involved in floral variation
The comparison of transcriptome sequences identified seven putative candidate genes among
the 194 selected above as having homology with a gene annotated to be involved in flower
rupincola vs auriculatum vernicosum vs auriculatum vernicosum vs rupincola
0.01 0.10 1.00 0.01 0.10 1.00 0.01 0.10 1.00
0.001
0.010
0.100
dS
dN
r
u
p
i
n
c
o
l
a
v
s
a
u
r
i
c
u
l
a
t
u
m
r
u
p
i
n
c
o
l
a
v
s
v
e
r
n
i
c
o
s
u
m
v
e
r
n
i
c
o
s
u
m
v
s
a
u
r
i
c
u
l
a
t
u
m
A)
B)
Fig 4. Comparative transcriptome sequence analysis of synonymous and non-synonymous nucleotide
substitutions of the three Rhytidophyllum species. A) Plot of dN/dS ratio for each pairwise species comparisons. The
line indicates the 1:1 slope and blue points represent open reading frames (ORFs) that have a dN/dS >1. B) Venn
diagram showing the ORFs that were found significant in the different pairwise comparisons in A.
https://doi.org/10.1371/journal.pone.0267540.g004
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Genetics of corolla shape
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development. Most of these were transcription factors characterized in Arabidopsis thaliana or
Antirrhinum majus known to be involved in floral development (S1 Table in S1 File).
The literature search resulted in a total of 28 genes, from which 21 were represented by
pairs of homologous transcripts in R.rupincola and R.auriculatum (S2 Table in S1 File). Some
genes had good homology with more than one pair of putative homologous transcripts, in
which case we considered all pairs as different “putative candidate genes” and annotated them
by numbers following the gene name (ex. GRXC7.1, and GRXC7.2). We thus obtained a list of
28 genes to test, 7 from the bioinformatic approach and 21 from the literature survey.
Twenty-two genes were successfully genotyped
PCR amplification and Sanger sequencing were done on 23 of the 28 putative candidate genes.
Of these, 20 genes had chromatograms of sufficient quality to validate the SNPs (S5 Table in S1
File). The remaining 8 genes were not validated but were still kept for genotyping; the
sequences and SNP positions were based on the transcriptomes.
Thirty-seven markers were sent for genotyping by Sequenom iPLEX Gold, which included
makers for 28 candidate genes, duplicate SNPs for six of these candidate genes (marked by suf-
fix “.B”), RAD from Alexandre et al. (2015), U73C6, a putative anthocyanin pathway gene
from an unpublished study, and NAC29.2, which was included by mistakenly in the genotyp-
ing. We obtained good genotype data for 30 of the 35 SNPs (S5 Table in S1 File). The markers
that failed were CUC1, CUC2, CIN2A, DIV.2 and NAC29.2. Four more markers (two candi-
dates and their duplicates) were eliminated because of problematic genotypic data: CYC1C.A,
CYC1C.B, DIV.A and DIV.B. In the case of CYC1C.A and CYC1C.B, their SNPs were identi-
fied solely from the transcriptomic data and all F
2
hybrids scored as homozygous for the R.
auriculatum parent (AA). The two other markers DIV.A and DIV.B were discarded because R.
auriculatum scored as homozygous for the R.rupincola parent allele (RR) and because the call
reliability (or quality) was rated “fair” (rates of 88.57% and 88.00%). The SNPs used for these
two markers were located in introns and as such were based only on Sanger sequences. Geno-
typic data for all other markers had call rates ranging from 97.7% to 100%, which is rated
“excellent”, except for ER (candidate gene ERECT) that was rated “good” (see S1 File).
The gel genotyping for HUA1(PCR) and NAC29.1 (enzymatic digestion) confirmed the
Sequenom results (100%) and these results were used for the linkage map instead of the results
from Sequenom because they contained fewer missing data. For genes with two SNPs geno-
typed, the results were always identical and the SNP with fewer missing genotypes was kept for
the linkage map construction: CYC.2.A, NAC54.B, PER53.A and PHAN.B (Table 1,S1 File).
In summary, we obtained good genotype data for 22 genes, bringing the total to 24 when
including the genes RAD and CYC genotyped by Alexandre et al. (2015). An additional
marker (U73C6) genotyped in the Sequenom approach was also used to build the linkage map.
Five of the 24 genes had segregation patterns that deviated significantly from the expected
1:2:1 ratio (S6 Table in S1 File). We decided to keep these markers in our analyses as we believe
the deviation was not caused by an error in genotyping and could add valuable information to
the linkage map and QTL mapping.
Linkage map and QTL mapping
The analysis of the 589 markers with CarthaGene resulted in 16 linkage groups that were con-
cordant with the study of Alexandre et al. (2015). This is also very similar to the number of
chromosomes of these species of n = 14. Our linkage map has a total length of 1772.5 cM with
an average distance between markers of 3.75 cM (S7 Table in S1 File).
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Genetics of corolla shape
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QTL detection resulted in 9 primary and 3 minor QTLs, for a total of 12 (Table 2,Fig 5).
Because some QTL from different traits colocalized, corolla shape was found to be associated
with 8 distinct genomic regions on 8 linkage groups (Fig 5). All traits resulted in at least one
QTL except for trait 5. In terms of percentage of variance explained (PVE), the individual
QTLs ranged between 4.3% to 17% (mean: 10.5%) and the total variance explained per trait
(excluding trait 5) varied between 15.2% to 43.0% (mean 27%) of variance explained (Table 2).
No epistasis was detected for any trait (p<0.05).
We found that six candidate genes colocalized with at least one QTL (Fig 5). However,
because the QTLs are large, we also estimated the probability that each of these genes is signifi-
cantly associated with floral traits. Three genes had a logarithm of odds (LOD) greater than 3
(a 1000 to 1 ratio) to be associated with at least one corolla shape trait (Table 3). The only sta-
tistically significant result involves RAD.2 that is significantly associated with the PC1 of the
hybrid population PCA (LOD = 4.58; p= 0.0093). Yet, JAG is marginally significantly associ-
ated with the PC1 of parental differences (LOD = 3.69; p= 0.061) and GLOBOSA is marginally
significantly associated with the PC2 of the hybrid population PCA (LOD = 3.55; p= 0.087;
Table 3).
Protein domain predictions
To identify candidate SNPs that could explain the variation in corolla shape in the seven genes
that colocalized with the shape QTLs, we examined the ORFs for mutations that could affect
the proteins they code for. No non-synonymous SNPs were detected in the conserved domains
in any of the seven genes, nor any insertion or deletion that caused a shift in the open reading
frames of the genes (S8 Table in S1 File).
Discussion
The shape of corollas is critical for the efficient reproduction of many species. Yet, little is
known of the genes involved in corolla shape determination apart from a few model species.
Table 2. Information about the twelve QTLs identified.
Trait number
and name
QTL Linkage
group
Peak
position
Confidence
region
Alpha LOD
threshold
Peak
LOD
score
Additive
effect
Dominance
effect
PVE Total
PVE
Alexandre
et al. PVE
1. Variation in
the hybrids:
PC1
A LG1 12.6 0.0–16.8 0.05 3.8 4.92 0.024 ±0.005 0.008 ±0.007 16.97% 26.24% 15.13%
B LG11 38 19–46 0.1 3.5 3.72 0.021 ±0.005 0.001 ±0.007 10.13%
2. Hybrids: PC2 C LG15 56 51–76 0.05 3.81 3.84 -0.007 ±0.005 -0.022 ±0.006 10.25% 22.81% 14.06%
D LG2 60 43–80 0.1 3.48 3.76 0.016 ±0.004 -0.014 ±0.006 10.00%
3. Hybrids: PC3 E LG9 51.4 45–62 0.05 3.88 4.63 -0.017 ±0.004 0.001 ±0.005 15.23% 15.23% 14.9%
4. Corolla
curvature
F LG2 46.6 43–85 0.1 3.48 3.77 -5.117 ±1.443 6.244 ±2.012 11.67% 28.64% 12.84%
G LG1 3.2 0.0–16.8 0.1 3.53 3.7 4.347 ±1.484 2.215 ±2.014 6.72%
H LG15 56.0 37–77 0.1 3.53 3.58 1.915 ±1.878 6.012 ±2.139 5.99%
6. Parental
differences
I LG11 28 0.0–37.6 0.05 3.85 3.86 0.016 ±0.004 -0.001 ±0.005 9.69% 43.02% 29.82%
J LG5 20 7–37,0 0.05 3.85 3.97 0.018 ±0.007 0.003 ±0.008 8.83%
K LG14 107 72–119 0.05 3.85 4.03 0.015 ±0.003 -0.012 ±0.005 12.29%
L LG13 91 42–108.9 0.1 3.49 3.76 0.011 ±0.004 -0.003 ±0.005 4.32%
The letters attributed to each QTL for identification purposes are illustrated in Fig 5. Positions of confidence intervals (1.5 LOD score decrease) are given in
centimorgan (cM) from the beginning of the linkage group. Alpha indicates the significance threshold at which the QTL was significant. PVE stands for the proportion
of variance explained. Trait 5 did not result in any QTL.
https://doi.org/10.1371/journal.pone.0267540.t002
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Genetics of corolla shape
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0.0
1.0
5.3
5.6
6.0
6.4
9.2
10.8
11.4
13.0
13.8
14.8
20.0
25.2
30.6
42.2
45.7
55.4
58.6
60.0
62.2
ER 65.9
72.5
77.1
78.6
87.9
90.5
91.7
96.3
96.8
97.7
LG6
GRXC7.1 0.0
3.7
7.4
15.7
16.4
16.8
17.6
20.5
24.1
26.9
27.9
29.3
35.8
CYC 42.2
44.6
45.6
46.6
47.5
48.4
51.7
52.6
53.5
57.3
57.7
59.5
61.3
71.3
74.4
76.2
78.7
79.6
81.1
88.3
88.6
90.3
91.6
98.0
107.6
113.4
119.0
PER53 127.9
LG16
0.0
6.3
13.2
14.3
18.7
21.4
22.5
LG8
0.0
1.4
4.7
5.6
11.7
13.7
16.6
19.3
33.4
CIN1 34.4
40.2
46.5
49.9
50.6
51.0
58.4
58.8
59.6
60.3
61.2
61.8
63.5
63.9
67.7
70.8
71.8
73.1
78.3
80.5
86.5
87.4
88.6
89.4
91.7
94.2
100.0
104.0
110.4
119.2
123.2
123.8
124.8
126.6
EXS 130.4
132.8
135.4
137.4
147.4
164.1
165.4
165.7
167.0
169.7
LG7
Trait 2
Trait 3
Trait 4
Trait 6
major QTL 5%
major QTL 10%
minor QTL 10%
Trait 1
0.0
1.5
2.4
4.0
5.1
6.4
7.7
CYC1D 9.2
14.5
18.9
25.1
38.3
38.6
39.4
40.5
41.5
43.8
NAC54 45.1
47.5
56.6
65.5
70.8
92.3
98.5
100.0
101.9
LG4
J
GRXC7.2 0.0
NAC29.3 10.2
MYB16 10.2
JAG 23.9
CIN 37.7
52.3
56.6
60.5
63.1
66.9
73.4
75.4
78.6
79.6
81.4
LG5
0.0
1.5
1.8
4.2
12.4
PHAN 26.6
28.1
32.3
41.2
48.0
48.5
50.5
PTL 51.3
52.8
55.6
64.4
65.2
66.0
68.0
70.2
73.9
LG3
AG
0.0
1.2
2.5
3.2
4.6
8.4
8.7
9.5
RAD.2 11.3
12.6
16.8
LG1
DF
0.0
11.8
14.5
17.4
30.1
31.1
32.2
34.7
35.7
38.1
40.4
42.3
44.0
46.6
47.6
DEF 55.6
60.0
64.0
70.0
74.6
98.4
102.4
LG2
I
0.0
4.8
6.0
6.4
7.3
9.4
9.7
12.7
13.3
15.1
15.5
18.9
26.7
32.4
33.3
35.0
37.6
38.5
40.3
41.6
46.6
NAC29.1 53.9
55.7
58.1
61.4
66.9
73.6
74.9
75.2
76.5
86.3
88.9
92.8
95.2
95.9
97.5
DIV1 103.6
104.9
115.2
128.9
131.2
131.6
133.8
135.3
LG11
0.0
22.7
42.9
45.0
50.6
52.1
59.0
63.5
64.3
64.6
64.9
66.4
68.1
LG10
E
0.0
13.6
17.2
19.1
19.8
21.8
30.8
32.3
33.3
33.8
35.1
40.9
43.9
51.4
55.1
60.0
68.8
69.5
70.7
71.1
73.5
76.9
83.7
92.6
95.4
96.8
105.8
107.5
109.4
125.1
131.5
139.3
143.5
145.3
147.3
151.6
152.6
157.5
161.7
LG9
C
F
0.0
0.4
6.1
12.0
14.0
18.5
19.8
20.5
23.6
24.0
24.6
24.9
26.8
RAD 31.8
33.5
34.6
39.2
40.3
43.3
47.4
48.4
50.6
51.0
55.3
GLOB 57.6
62.8
63.1
63.5
64.4
69.8
76.2
77.0
79.1
80.0
83.6
84.0
84.3
87.8
IAA3 88.6
89.5
90.3
100.4
102.3
102.7
108.0
108.8
112.8
123.3
124.9
127.1
128.8
129.5
134.1
134.9
142.6
143.0
147.0
149.0
150.3
157.0
164.1
169.2
170.0
170.6
172.6
173.9
176.2
LG15
K
0.0
6.6
8.1
8.6
13.5
27.1
28.8
37.6
40.3
51.7
53.2
53.6
54.2
56.4
57.2
62.7
69.3
71.3
73.8
74.8
75.8
79.1
80.0
80.2
81.2
91.6
101.9
103.4
112.1
116.2
118.0
119.9
120.3
122.0
123.5
125.1
128.9
133.0
135.4
136.2
138.6
138.9
140.6
141.5
153.1
162.5
167.2
167.5
168.8
178.4
185.3
187.9
190.4
LG14
0.0
0.8
8.2
9.0
14.1
25.2
CYC.2 30.1
32.7
36.5
36.9
37.5
38.8
40.8
41.2
44.1
45.7
55.8
58.0
59.1
61.2
62.2
63.1
73.4
74.0
81.8
91.0
93.0
96.1
100.0
107.3
114.8
118.8
120.2
121.9
127.1
132.3
133.1
137.7
LG12
L
0.0
1.0
1.4
2.8
4.8
5.4
8.8
9.2
10.3
12.7
23.1
24.0
24.7
26.5
30.2
33.6
44.6
49.2
51.3
HUA1 53.4
55.0
57.1
62.3
63.8
72.8
74.7
76.0
81.1
90.5
92.1
104.6
106.0
108.9
LG13
Fig 5. Linkage map with 12 QTLs. The linkage groups (LG) were given the same names as in Alexandre et al. [63] and QTLs are represented
by coloured bars to the right of their designated linkage groups, with letters on top referring to Table 2. The patterns in QTLs bars represent
different significance thresholds indicated in the legend, and the horizontal bar represents the peak location (see Table 2). The 24 candidate
genes are in red, including CYC and RAD from Alexandre et al. [63]. Positions of markers are in cM. Minor QTLs are those that were detected
after including the primary QTL as a covariate in the model.
https://doi.org/10.1371/journal.pone.0267540.g005
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Genetics of corolla shape
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Here we used a literature search and a transcriptome sequence comparison to identify putative
candidate genes that might be involved in corolla shape transition between R.rupincola and R.
auriculatum, two species that possess contrasting flower shapes. Although a QTL study previ-
ously outlined the genetic structure of the corolla for these species [63], nothing was known of
the genes that may be involved in corolla shape modifications.
The genetic structure of corolla shape transition between pollination
strategies
The number of linkage groups found (16) and their size range (16.8 to 190.4 cM) are concor-
dant with the linkage map obtained by Alexandre et al. [63] and we used the same linkage
group numbers as in that previous study to facilitate comparisons. Despite the addition of 30
new markers (minus RAD and CYC which were included in the previous analysis), the two
linkage maps were vastly concordant. The main difference relies in the longer length of LG5
due to the addition of the new markers, which contributed to a slightly longer total map length
(1772.5 compared to 1650.6 cM). This extension of LG5 compared to Alexandre et al. is due to
the addition of markers (GRXC7.2, NAC29.3, MYB16, JAG, CIN) that show significant segre-
gation distortion (p<< 0.01; S6 Table in S1 File), whereas markers with segregated distortion
were automatically removed from the linkage map construction in Alexandre et al. It is not
clear why all these markers located in this region of LG5 would be affected by segregation
distortion.
Considering all phenotypic traits, eight genomic regions were found to be involved in floral
shape determination; one more than obtained by Alexandre et al. [63]. This is caused by the
loss of one QTL and a gain of two compared to this previous study. The LOD scores on LG16
where a QTL was lost for trait 5 are still relatively high, but not significant in this analysis (S3
Fig). The gain of two QTLs (C and J; Fig 5) appears to be due to the addition of new markers
on the map. Still, many QTLs certainly remain to be detected as the 12 QTLs found in this
study only explain a small to moderate portion of the variance of those traits (15.23% to
43.02% total variance explained for each trait). Nevertheless, the addition of new markers in
this studied allowed to explain a greater proportion of the variance in the F
2
population com-
pared to the QTL study of Alexandre et al. (see Table 2).
Our results show that some shape QTLs co-localize onto a few linkage groups (LG1, LG11,
LG15 and LG2; Fig 5,Table 2). On LG1, QTLs of traits 1 and 4 co-localize and cover the whole
linkage group, which is the smallest. Two traits, 2 and 4, share more than one QTL as both
have QTLs on LG15 and LG2. These two traits correspond to variation in corolla curvature
(Fig 2) and are strongly correlated (Fig 3) highlighting that they essentially describe the same
morphological variation. As such, it is reassuring that they identified the same genomic
regions. However, QTLs for different traits also sometimes colocalized in the genome (e.g.,
traits 1 and 4 on LG1), which might suggest phenotypic integration, i.e. correlated variation of
traits forming a functional unit [17,81].
Table 3. Candidate genes that have a logarithm of odds (LOD) greater than three (1000 to 1 odds) for an association with a floral trait, along with the statistical
probability of association as determined by permutation of the linkage map.
Gene Trait LG QTL type Logarithm of odds (LOD) p-value
RAD.2 1. Variation in the hybrids: PC1 1 Major 4.58 p = 0.0093
RAD.2 4. Corolla curvature 1 Minor 3.32 p = 0.16
JAG 6. Parental differences 5 Major 3.69 p = 0.061
GLO 2. Hybrids: PC2 15 Major 3.55 p = 0.087
GLO 4. Corolla curvature 15 Minor 3.43 p = 0.12
https://doi.org/10.1371/journal.pone.0267540.t003
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Genetics of corolla shape
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Our findings imply that there are several genes involved in flower shape variation with
minor to moderate effect on the phenotype. This polygenic genetic architecture for floral
shape was also found in Mimulus [82], Leptosiphon [33], Iris [22], Ipomopsis [21], Penstemon
[34] and Primulina [35]. Though QTL studies on flower morphology often detect many loci
dispersed in the genome, each explaining a small portion of the variance (reviewed in [16,
83]), this is not always the case. For instance, Bradshaw et al. [26] found at least one QTL of
large effect (PVE >25%) for the majority of flower shape traits measured (7/9) while studying
differences between sympatric species Mimulus lewisii and M.cardinalis. Another QTL study
by Ferris et al. [29] on sympatric populations of Mimulus guttatus and M.laciniatus found 5
QTLs of large effect responsible for flower size and they suggested that ongoing geneflow
could homogenize mutations of small effect. In our case, Rhytidophyllum rupincola (Cuba)
and R.auriculatum (Hispaniola and Puerto Rico) live in allopatry and thus cannot exchange
genes in the wild, which might have given more time for genes with small effects to contribute
to the corolla shape differences between the two species.
Many things need to be considered when interpreting QTL studies, such as the number of
markers and the size of the mapping population [16], but also whether the traits studied appro-
priately capture all the morphological variation [84]. A greater number of markers, in particu-
lar those that are likely to be associated with the trait of interest (the candidate genes), did
seem to increase QTL detection in our study as well as the percentage of phenotypic variance
explained. Yet, our small population size limits our capacity to detect QTLs of small effect and
to precisely define the QTLs [63]. Indeed, some of our QTLs are quite large (up to 66.9cM),
which increases the probability that colocalization between some candidate gene and QTLs
could be only caused by chance.
Molecular and biological functions of the three genes strongly associated
with corolla shape variation
Although seven candidate genes co-localized with shape QTLs, only three of them stood as
solid candidates by having a good probability of being associated with corolla trait variation
(LOD >3 and p-value <0.1). These three genes were all derived from the literature search
and have homology to well-known transcription factors from A.majus (GLO and RAD) or A.
thaliana (JAGGED (JAG)). As these genes have been well studied in many angiosperm species,
their functions in other organisms might provide information as to their role in the determina-
tion of floral shape differentiation in Rhytidophyllum.
GLO is a transcription factor from the MADS-box family that assume petal and stamen
identity across angiosperms, albeit with few exceptions [85]. It can form heterodimers with
DEFICIENS (DEF) and auto-regulate its expression. It has been shown that the relative expres-
sion levels of GLO and DEF differs during the developmental stages of Antirrhinum flowers
and that this could explain the progression of the petal development, notably in the flower
opening [86]. Expression patterns of GLO is asymmetric in Maize [87] and Commelina [88]
flowers, suggesting a possible role in floral zygomorphy. Moreover, GLO gene duplications are
associated with increasing perianth dimorphism in Zingiberales [89]. These evidences of asso-
ciation between GLO and corolla shape suggest that small differences in its expression level in
Rhytidophyllum could influence corolla growth and shape inthis genus. Interestingly, the Rhy-
tidophyllum putative GLO on LG15 was associated to QTLs for traits 2 and 4 that are linked
with variation in corolla curvature. This could suggest that their effect on the phenotype is sim-
ilar, which is concordant with their function described in the literature.
Another well studied gene in A.majus is RAD, a MYB transcription factor that plays a role
in bilateral symmetry by promoting the dorsal identity of flowers [90]. The key factor that
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Genetics of corolla shape
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emerges from the zygomorphy genetic network is the asymmetrical expression pattern of the
TCP and MYB transcription factors (bilateral symmetry genes) [91]. Many studies have inves-
tigated this genetic network in the order Lamiales [92–94], to which Rhytidophyllum belongs,
and more specifically in the Gesneriaceae family [95–98]. Among the bilateral symmetry genes
tested (6 including paralogs), only RAD.2 is associated with a QTL involved in floral morphol-
ogy. It nevertheless represents a promising candidate gene because it is located near the peak
of the QTL A on LG1 (Fig 5), explains the greatest proportion of variance in floral shape
between the two parental species (Table 2) and is strongly associated with corolla trait 1
(Table 3) that describes overall corolla shape from bell-shaped corollas with large opening to
tube-shaped corollas with narrow opening (Fig 2).
Finally, JAG is associated with trait 6 that combines variation in the position of the constric-
tion at the base the corolla, curvature, as well as the reflection of the tip of the petals (Fig 2).
JAG encodes a C
2
H
2
type zinc finger protein that acts as a transcription factor (repressor and
activator) to control lateral organ growth and patterning in Arabidopsis [99–102]. It is associ-
ated with cell proliferation in leaves, sepals and petals, with an overall effect on organ shape in
Arabidopsis [99,100], but also in Oryza [103], Solanum [104] and Aquilegia [105], suggestion a
relatively preserved function across flowering plants. Its mechanism of action has been unrav-
elled piece by piece over the years [106,107]. Notably, Sauret-Gu¨eto et al. [108] showed that
JAG could increase the growth rate in the distal region of petals during their development, a
function that could certainly impact corolla shape by controlling the growth and thus the
width and length of the petals. In addition, JAG’s target genes show a broad range of action in
many floral developmental process [102].
Comparison of methods for the research of candidate genes
Of the two approaches used to find genes putatively involved in corolla shape differences in
Rhytidophyllum, the literature search gave a longer list than the transcriptomics approach (21
vs 7). Our criterion for selecting genes under positive selection was very strict (dN/dS >1),
but lowering the threshold would have resulted in a large number of putative candidate genes.
Other aspects limiting the power of this approach are the relatively few genes associated with
floral development in the Gene Ontology database, that it could not consider genes that are
not expressed at all in the flower tissues in one of the species, and that it only focusses on pro-
tein variants and as such will miss genes which expression is affected by cis-regulatory regions.
Although this approach did not result in a strong candidate gene to explain corolla shape, it
might represent an interesting approach to find sequences of interest in non-model species,
especially when the genetic bases of a trait are not very well defined as with floral shape [109].
For future studies, differential gene expression, which was not possible here given the nature
of our transcriptomes, might provide more candidates. In contrast, the literature approach
resulted in 21 candidates that could be tested, of which 3 were found to have a strong associa-
tion with corolla shape. Though certainly not exhaustive, the literature approach was success-
ful in identifying very good candidates for genes involved in corolla shape determination in
non-model species.
Candidate genes for determining inter-specific floral shape differences associated with dis-
tinct pollination strategies are rarely searched for. We show here that three genes known to be
associated with corolla shape in other species are excellent candidates to be involved in floral
shape differentiation and adaptation to different pollinator guilds in Rhytidophyllum. This
study cannot confirm this relationship and these genes may also only be under linkage disequi-
librium with a gene involved in corolla shape differentiation. Further validation through finer
mapping or transformation is required.
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Genetics of corolla shape
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In conclusion, we found that the literature search and the comparison of transcriptome
sequences approaches, followed by QTL mapping on a F
2
population, represents an interesting
approach to obtain a list of candidate genes putatively involved in corolla shape associated
with a pollinator shift in non-model species. This approach can later be integrated with other
methods to fully understand the evolution of flower morphology in Rhytidophyllum and helps
to understand the great floral diversification in this genus.
Supporting information
S1 Fig. Phylogenies of the copies of the genes CIN (A), CUC (B), CYC (C), and DIV (D)
found in Rhytidophyllum and included in the study along with the reference sequences. The
species name, the gene name and the GenBank accession number are indicated for each
sequence.
(PDF)
S2 Fig. Recombination fractions between linkage groups.
(PDF)
S3 Fig. Graphs of the LOD scores for the traits along the chromosomes.
(PDF)
S1 File. Supplementary information tables 1 to 8.
(XLSX)
Acknowledgments
The authors would like to thank A. Blakney, D. Schoen, and two anonymous reviewers for
comments on previous versions of the manuscript. The authors also thank the Genome Que-
bec Innovation Centre for support with the RNA and DNA sequencing and the Montreal
Botanical Garden for granting access to their collections.
Author Contributions
Conceptualization: Vale
´rie Poulin, Hermine Alexandre, Simon Joly.
Formal analysis: Vale
´rie Poulin, Delase Amesefe, Emmanuel Gonzalez, Simon Joly.
Funding acquisition: Simon Joly.
Methodology: Emmanuel Gonzalez, Hermine Alexandre, Simon Joly.
Supervision: Emmanuel Gonzalez, Hermine Alexandre, Simon Joly.
Visualization: Vale
´rie Poulin.
Writing – original draft: Vale
´rie Poulin.
Writing – review & editing: Vale
´rie Poulin, Delase Amesefe, Emmanuel Gonzalez, Hermine
Alexandre, Simon Joly.
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