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Journal of Heredity 2014:105(3):381–396
doi:10.1093/jhered/esu003
Advance Access publication January 31, 2014
© The American Genetic Association 2014. All rights reserved.
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Genome-Wide Dissection of Hybrid
Sterility in Drosophila Confirms a
Polygenic Threshold Architecture
Tomás Morán and Antonio Fontdevila
From the Grup de Biologia Evolutiva, Departament de Genètica i de Microbiologia, Universitat Autònoma de Barcelona,
Bellaterra, Barcelona, Spain (Morán and Fontdevila).
Address correspondence to Dr. Antonio Fontdevila, Departament de Genètica i Microbiologia, Edifici C, Universitat
Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, or e-mail: [email protected].
Abstract
To date, different studies about the genetic basis of hybrid male sterility (HMS), a postzygotic reproductive barrier thoroughly
investigated using Drosophila species, have demonstrated that no single major gene can produce hybrid sterility without the
cooperation of several genetic factors. Early work using hybrids between Drosophila koepferae (Dk) and Drosophila buzzatii (Db)
was consistent with the idea that HMS requires the cooperation of several genetic factors, supporting a polygenic threshold
(PT) model. Here we present a genome-wide mapping strategy to test the PT model, analyzing serially backcrossed fertile and
sterile males in which the Dk genome was introgressed into the Db background. We identified 32 Dk-specific markers significantly associated with hybrid sterility. Our results demonstrate 1) a strong correlation between the number of segregated
sterility markers and males’ degree of sterility, 2) the exchangeability among markers, 3) their tendency to cluster into lowrecombining chromosomal regions, and 4) the requirement for a minimum number (threshold) of markers to elicit sterility.
Although our findings do not contradict a role for occasional major hybrid-sterility genes, they conform more to the view
that HMS primarily evolves by the cumulative action of many interacting genes of minor effect in a complex PT architecture.
Key words: AFLP markers; Polygenes; Reproductive isolation; Speciation genes.
The biological species concept, which defines species as
groups of actually or potentially interbreeding natural populations that are reproductively isolated from other similar
groups (Dobzhansky 1935; Mayr 1942), remains one of the
most widely used criteria for species definition. Among isolation barriers, postzygotic isolation mechanisms (predominantly hybrid inviability and sterility) have long captured the
attention of evolutionists (Dobzhansky 1936). Despite decades of research, however, current knowledge of the genetic
architecture of those isolation mechanisms, albeit significantly advanced, remains contentious.
The first plausible, and most publicized, genetic model
of postzygotic hybrid incompatibility (HI) was posited independently by Bateson, Dobzhansky, and Muller (Bateson
1909; Dobzhansky 1937; Muller 1942) (the BDM model). It
predicts that HIs are the product of epistatic interactions in
the hybrid between alleles of complementary loci that have
independently evolved in populations that never coexisted
previously. This idea initiated a series of research projects,
predominantly using Drosophila species, to find “speciation
genes.”
Until the 1990s the prevailing view, at least implicitly,
was that major genes (discrete factors whose effect on the
phenotype is always evident or complete) were responsible
for HIs (Charlesworth et al. 1987; Zouros 1988; Coyne and
Charlesworth 1989; Orr 1989), but after more than 20 years
of intensive Drosophila work, it is amazing that the number of
them that has been characterized is low, totaling in Drosophila
3 for hybrid male sterility (HMS) and 5 for inviability (HMI),
and a similar number in yeast, mice, and plants (Presgraves
2010a; Maheshwari and Barbash 2011). The paradigm example concerns the study of the Odysseus (Ods) gene, which contributes to sterility in hybrids between Drosophila simulans and
Drosophila mauritiana (Coyne and Charlesworth 1989; Perez
et al. 1993). Later studies, however, concluded that Ods may
contribute to hybrid sterility but not in isolation, requiring
the cooperation of other genes (Perez and Wu 1995; Sun
et al. 2004). The same conclusion has been reached whenever the individual effect of any putative major “speciation”
gene has been tested using gene manipulations (Brideau et al.
2006; Phadnis and Orr 2009; Tang and Presgraves 2009).
This model was formerly named the “weak allele-strong
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interaction” (Wu and Hollocher 1998). Altogether, these
studies demonstrated that in general more than one pair of
interacting genes is required to produce an HI.
On the other hand, backcross hybrids between Drosophila
buzzatii and Drosophila koepferae demonstrated that, in general,
HMS was not due, at least not exclusively, to few individual
genes of large effect (Naveira and Fontdevila 1986b). The
fact that no autosomal region singly introgressed or combined with other regions could produce HMS unless the total
added region exceeded a minimum size (about 30% of the
autosomal chromosome length) prompted these investigators
to postulate a large number of minor HI factors dispersed in
the genome, whose incompatibility is only manifested when
a minimum of them are present. This polygenic threshold
(PT) model, corroborated by further research (Naveira and
Fontdevila 1991b, 1991a; Naveira 1992), retrieves the old concept of developmental threshold highly championed by early
geneticists (Waddington 1942; Lerner 1954) who favored its
role to explain discontinuity in phenotypes. Threshold characters are common and can explain the relationship between
discontinuous phenotypes and their underlying continuous
genetic information (Roff 1996). Fertility could be likened
to a threshold character whose developmental reaction norm
is disrupted by the genomic stress elicited in hybridization.
Evidence accumulated during several decades of research
(Cabot et al. 1994; Moyle and Nakazato 2009; Chang et al.
2010; Nosil and Schluter 2011) has reached a point of minimum consensus: The number of genes that constitute the
architecture of hybrid sterility is large, and often, their individual effects are small, reviewed in: (Coyne and Orr 2004;
Presgraves 2010a; Maheshwari and Barbash 2011). The intimate way in which these genes interact to elicit HI, however,
remains unresolved. Specifically, the minimum number of
factors that must interact to produce a significant HI is still
viewed differently by diverse authors. It is commonly agreed,
however, that discriminating among these views requires
high-resolution genome-wide analysis of the architecture
and function of male hybrid sterility (Chang and Noor 2007;
Reed et al. 2008; Chang et al. 2010; Maheshwari and Barbash
2011). Yet, there are still few genome-wide dissection studies of HMS factors (True et al. 1996; Presgraves et al. 2003;
Tao and Hartl 2003; Tao et al. 2003a, 2003b; Masly and
Presgraves 2007). Last but not least, the assessment of the
genetic architecture of the initial evolutionary steps of the
reproductive isolation is of paramount importance in genetic
speciation studies. Although some authors feel that genes
responsible for the first steps in postzygotic reproductive
isolation basically are identical in nature and performance
to the genes incorporated further in species divergence (Wu
and Hollocher 1998), the final proof relies on studies of HI
between recently diverged species (Ting et al. 1998; Chang
and Noor 2010). In Drosophila (Reed et al. 2007), in mice
(White et al. 2012), and in Tribolium castaneum (Demuth and
Wade 2007a, 2007b) a high variability for HMS was found
to be already present within and between populations of
incipient species with high interspecies crossability. In fact,
there is an ample evidence for mammals, fish, arthropods,
nematodes, and plants that intraspecific genetic variability
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commonly contributes to variation in interspecific HI (Cutter
2012).
In revisiting the D. buzzatii and D. koepferae species pair we
assessed the underlying architecture of HMS with respect to
the roles of 1) polygenes acting in a cumulative threshold way,
2) co- and inter-specific epistatic interactions among these
polygenic loci, and 3) the physical distribution, molecular
identity, and functional nature of the relevant loci. Drosophila
buzzatii and D. koepferae are sibling species that belong to the
mulleri subgroup of the repleta group (Wasserman 1982,
1992). They originated and inhabit diverse arid regions of
South America (Northwest Argentina and Bolivia) where
they are associated with cacti. Their time of divergence is
about 4.6 million years (range 4.0–5.0) (Laayouni et al. 2003;
Oliveira et al. 2012), but they can hybridize and produce fertile
females and sterile males. Their crossability and backcrossing ability in the lab and their ample ecological and genetic
knowledge (Fontdevila 1995) makes them a very appropriate
material for speciation studies.
Drosophila mojavensis also belongs to the mulleri subgroup
of the repleta group and is associated with different cacti
(Ruiz and Heed 1988; Heed 1989; Reed et al. 2007). We were
able to take advantage of the whole genome sequence available for D. mojavensis (Clark et al 2007), whose divergence time
from D. buzzatii is estimated to be about 11.3 million years
(Oliveira et al. 2012) to investigate the nature of the speciation
genes through comparative genomics. By combining molecular, cytogenetic and bioinformatics studies with these species,
we aimed to detect, map, and characterize a representative set
of hybrid sterility-associated amplified fragment length polymorphism (AFLP) markers in the genome fraction of D. koepferae introgressed into D. buzzatii by serial backcrossing.
Materials and Methods
Drosophila Stocks and Cross Design
Two stocks were used: D. koepferae, Ko2 (collected in Sierra
de San Luis, Argentina, 1979) and D. buzzatii, Bu28 (collected
in Los Negros, Bolivia, 1985). The Bu28 stock has the standard species chromosome constitution (Xabc 2abmnz7 3b 4
5g 6). The Ko2 stock has 2 fixed chromosomal inversions
(2l9m9), that are normally polymorphic in natural populations. Their fertility/viability and chromosome karyotype
were tested before the study began (2006) and were equal to
those normally present in freshly collected flies despite the
stocks being kept in the lab for several decades (Bu28: 96%
of viable matings with a mean offspring of 41 individuals per
cross; Ko2: 88% of viable matings with a mean offspring of
38 individuals per cross) (data not shown).
The mating scheme used is depicted in Figure 1. We
began with several crosses between 15 Ko2 females and 15
Bu28 males (the reciprocal cross never yields progeny). As
the hybrid F1 males are always sterile, hybrid females were
individually backcrossed to males of Bu28 (1 female × 1
male). Backcrossed (BC) female progeny were used to continue backcrossing for several generations (1 hybrid female ×
1 Bu28 male) until the backcrossed lineage (family) yielded
Morán and Fontdevila • Genetics of Hybrid Male Sterility
Figure 1. Mating scheme used to obtain the hybrid families. Db: Drosophila buzzatii; Dk: D. koepferae; F1: first hybrid generation;
BC: backcross generation; Db/Dk-Db: genomic introgression into the Db genetic background.
at least 1 fertile male. As the number and ratio of fertile to
sterile males varied significantly among families, 4 families
were selected that demonstrated an unbiased proportion of
approximately 50% fertile versus sterile males and had a minimum number (12) of males to allow comparisons. All hybrid
males came from the same hybrid line established in the BC1.
Each of the 53 selected hybrid males (27 fertile and 26 sterile) were processed in vivo. The fertility of all reared hybrid
males at each generation was tested and scored by crossing
them individually with Bu28 females; and only the third backcross (BC3) yielded fertile males in 80% of the families. Four
of these families were utilized in subsequent analyses. We
did not work further to BC3 families, because the following backcrosses yielded only fertile males. Males were scored
as sterile when no offspring were produced. Earlier studies
with the same species have shown that introgressed hybrid
males that did not produce any offspring when backcrossed
to a parental line always present immotile sperm (Naveira and
Fontdevila 1986b). Other studies (Tao et al. 2003) showed
that no progeny is always a reliable indication of total male
sterility in Drosophila. Moreover, because the motility of
sperm is not a guarantee of male fertility (Campbell and
Noor 2001; Reed and Markow 2004; Chang and Noor 2010)
we decided to rely on the presence/absence of offspring for
assessing fertility/sterility of males.
DNA Isolation and AFLP Fingerprinting
Since the AFLP technique was described (Vos et al. 1995), several studies have successfully used it as a rapid blind method for
isolating molecular markers to saturate genetic maps. Perhaps
the most important advantage of AFLPs markers is that they
combine accuracy and reproducibility with their capacity to
simultaneously detect genome-wide dispersed differences with
no prior knowledge, which makes these markers more powerful,
and cheaper, than other markers like single nucleotide polymorphisms, random amplified polymorphic DNA, or multigenic
tag sequencing markers. Other interesting facts about AFLPs
are that 1) they normally follow a normal Mendelian inheritance, 2) they could be used in samples of different genomic
complexities, 3) they require small sample amounts of DNA,
and 4) they show high resolution for detection of intra and
interspecific variations and also to detect genome introgression or hybridization (Mueller and Wolfenbarger 1999; Bensch
and Akesson 2005). Although AFLPs have some disadvantages, namely that they are dominant markers and can demonstrate moderate levels of homoplasy, these issues were unlikely
to have affected our research, as we only scored heterospecific dominant markers. Furthermore, given that size homoplasy among AFLP markers has been reported in empirical
and in silico studies (Vekemans et al. 2002), at least 10 clones
for each marker, isolated from various individuals among the
different families, were sequenced and no different sequences
with identical sizes for any of the sterility-associated markers
were detected. In addition, although some studies reported a
distribution of AFLP markers biased towards an increase in
repetitive DNA (for instance heterochromatic DNA) (Mueller
and Wolfenbarger 1999; Reamon-Büttner et al. 1999), we only
detected 2 markers likely to be of heterochromatic origin
(TGGAT5 & GAGCG19), and no other marker with clear
repetitive DNA characteristics. Therefore, AFLPs are suitable
markers for characterizing introgression in Drosophila hybrids.
For the fingerprints (standard AFLP band segregation patterns) of each species (pool of individuals) and each individual hybrid male, genomic DNA was isolated using modified
standard techniques as described in Laayouni and collaborators (2000) (Laayouni et al. 2000). The AFLP markers were
obtained and analyzed using the original procedure (Vos et al.
1995), with few modifications. EcoRI and MseI restriction
enzymes were used and 50 selective primer combinations
were tested, but only 47 combinations showed clear and recognizable banding patterns. Two selective nucleotides in the
primers were used against the EcoRI adapter and 3 nucleotides against the MseI adapter (Supplementary Table S1).
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AFLP fingerprints were separated on 8% nondenaturing
polyacrylamide gels, and the most conspicuous bands, ranging from 50 bp to 2 Kb in size, were scored. All bands were
classified as Db-specific markers, Dk-specific markers, Db–
Dk shared markers and hybrid-specific markers (exemplified
in Supplementary Figure S1). Finally, Dk-specific markers
that segregate in hybrids were manually scored and encoded
in a binary matrix (presence/absence) for statistical analysis.
This process was performed twice to reduce observation
errors. A specific name was assigned to each band (marker),
consisting of 5 letters and a number. The 2 first letters identified the selective nucleotides of the EcoRI side and the next
3 letters those of the MseI side (primer combination), and
the number identified each band; the greater the number of
marker, the larger the size.
Sterility-Marker Association Analyses
Treating sterility as a binary character, 3 independent statistical
analyses were performed to detect associations between the
sterile phenotype and the introgression marker. First, detected
associations were treated as independent quantitative trait loci
(QTLs) using analyses of variance (ANOVAs) (Broman 2001;
Bewick et al. 2004b). Some authors (Lunney 1970; D’Agostino
1971) reported that 1-way ANOVAs perform well even with
dichotomous dependent variables (binary: sterile/fertile), and
can be used as an acceptable explorative method. Second, we
used chi-square 2 × 2 contingency tables to detect associations,
as these independence tests are generally used to infer markerphenotype associations (Bewick et al. 2004a) and to measure
risk statistics (Bewick et al. 2004c). Finally, as we had a reduced
sample size, we used the stricter methodology of Fisher exact
tests to detect associations (Fisher 1935; Agresti 1992; Bewick
et al. 2004a). In all analyses, 4 phenotypic categories were used
to classify and make statistical comparisons on marker segregation: 1) number of fertile males not segregating the Dk
specific marker, 2) number of fertile males segregating the Dk
specific marker, 3) number of sterile males not segregating the
Dk-specific marker, and 4) number of sterile males segregating
the Dk specific marker. All analyses were corrected for multiple tests by means of the false discovery rate (FDR) (Benjamini
and Hochberg 1995). For principal component analysis (PCA),
only sterility-associated markers were included and the evaluated factors were: marker origin (endogenous or exogenous)
and male hybrid fertility (fertile or sterile phenotype). We performed all statistical tests using SPSS v14.0 (SPSS Inc. 2009),
Microsoft® Office Excel® 2007, MapDisto v1.7 (Lorieux, M.,
http://mapdisto.free.fr/), and QVALUE (Copyright© 2002–
2008 by John D. Storey., http://genomics.princeton.edu/storeylab/qvalue/) (Storey et al. 2004).
Marker Isolation and Characterization
Polymerase chain reaction (PCR) products of sterility markers
were run on 1% agarose gels, and the bands of interest were
excised. These were purified using QIAquick Gel Extraction
Kit (QIAGEN), and cloned into the pGEM-T Easy Vector
(Promega) using Escherichia coli DH5α competent cells,
following the manufacturer’s methods. Positive clones were
384
rapidly identified using colony-PCR, and at least 10 clones of
each marker were isolated. This plasmid DNA was used for
sequencing and for fluorescence in situ hybridization (FISH)
using the QIAprep Spin Miniprep Kit (QIAGEN). We obtained
the sequences with the ABI Prism BigDye Terminator Cycle
Sequencing Ready Reaction Kit (Applied Biosystems), using
the vector’s T7, M13/pUC and SP6 universal primer sites. For
sequence editing, BioEdit®, v7.0.9. (Hall, T., http://www.
mbio.ncsu.edu/BioEdit/bioedit.html) was used.
The physical map of all sterility markers was constructed
using FISH on the polytene chromosomes of Dk. To identify all chromosome bands, the chromosome maps established for the D. repleta species group were used (Wasserman
1992). For in situ hybridizations standard protocols were followed (Schmidt 1992), employing the plasmid DNA of each
clone as probes labeled with the Alexa Fluor® 488 Signal
Amplification kit for Fluorescein and Oregon Green® Dye
Conjugated Probes (Roche Farma S.A., Spain). FISH preparations were visualized using an AXIO Imager A1 microscope (AxioVision digital image processing software v4.0;
Carl Zeiss, Germany). Owing to technical ambiguities, the
physical location of a few markers was confirmed or inferred
using the computer application “Chromosome Browser”
(Schaeffer et al. 2008) (http://flybase.org/).
Intergenomic Comparisons
To detect homologies and/or similarities between marker
sequences and other genomic data, we carried out BLAST
searches using different algorithms (blastn, megablast, discontiguous megablast and blastx) and relaxed parameters.
The searches were performed against all data deposited in
GenBank and FlyBase (Drysdale 2008) (http://flybase.org),
that include the sequences of the 12 genomes of Drosophila
species that are available (Clark et al. 2007). To identify genes
in orthologous genomic regions, those markers that demonstrated high scores of homology with any Drosophila genomic
data were used. D. koepferae and D. mojavensis are related species, and considering 1) that D. mojavensis genome fortunately is
one of the published genomes and 2) that predicted orthology
and paralogy was shown to be consistent among the different
Drosophila species (Heger and Ponting 2007), we positioned the
markers’ sequences on the D. mojavensis genome scaffolds to
obtain their coordinates. The surrounding areas of the markers’ putative locations (landmarks) were investigated using
GBrowse implemented in FlyBase and in the UCSC Drosophila
mojavensis Genome Browser Gateway (http://genome.ucsc.
edu). We identified all D. mojavensis genes near the landmarks
that have orthologous genes in the D. melanogaster genome,
because good functional annotations only exist for this species.
As we were interested in the effect that the distance between
markers and genes could have on association detection, 3 different subsets or collections of these genes were constructed
in genomic windows of 100, 200, and 400 Kb, which included
the landmark in the middle.
The 100, 200, and 400-Kb data sets were analyzed by
means of functional enrichment analyses (gene ontology
“GO” terms; see [Thomas et al. 2007; Hill et al. 2010]) using
Morán and Fontdevila • Genetics of Hybrid Male Sterility
the bioinformatics online applications implemented in the
websites of BABELOMICS v3.1 (Al-Shahrour et al. 2007),
FlyMine v19.0 (Lyne et al. 2007), GOstat (Beissbarth and
Speed 2004), and DAVID Bioinformatics Resources (Huang
da et al. 2009). First, we compared all our input data sets
against the whole genome as background gene list. Second,
in order to test whether the observed enriched GO terms
for our gene subsets represented a true ontological enrichment of the mapped chromosomal areas, or just reflected
the normal path for any other sampled genomic region,
we contrasted our results using a list of randomly sampled
genes. We named it as Random data set, which was similar
in size to the 400-Kb gene collection and was constructed
randomly sampling 15 genomic areas in windows of 400 Kb
outside the chromosomal regions mapped by our sterilityassociated markers (see Figure 2). Initially, we compared
directly our 400-Kb data set against the Random data set,
using the GOstat bioinformatics platform (Beissbarth and
Speed 2004), with the purpose of uncover those GO terms
that are over/underrepresented in the 400-Kb data set in
relation to the random sample. Later, as a complementary
analysis to check the robustness of the preceding results, we
independently reanalyzed the 400-Kb and the Random data
sets comparing them against the genome and also against
a new background gene list (named Random background
gene list, containing all genes present in the 400 Kb and the
Random data sets). These analyses were done using DAVID
Bioinformatics Resources (Huang da et al. 2009).
We took advantage of results derived from all the functional enrichment analyses to construct the list of candidate
genes, ranking them by the number of times that each gene
appeared among the different GO terms significantly overrepresented for the gene subsets that surround the markers.
Finally, because several genes included in these subsets have
no functional annotations, but their expression levels have
been previously assayed (Chintapalli et al. 2007), we searched
for the relationships between their expression and tissue
specificity. This was carried out via searches in the FlyAtlas
database (http://www.flyatlas.org).
Results
Segregating AFLP Markers
We compared the segregation pattern of approximately
1000 D. koepferae (Dk) AFLP bands with the D. buzzatii (Db)
Figure 2. Chromosomal distribution of HMS-associated markers. Markers: 1. CGGCG9; 2. TGTAT15; 3. GCGGG17;
4. CGGGG10; 5. CGGGG14; 6. TGGGG11; 7. TGGAT19; 8. TGCAT13; 9. CAGCA24; 10. GAGCA9; 11. CAGCC21; 12.
TGTCG10; 13. TGGGG8; 14. GGGCG17; 15. GAGAT19; 16. GAGGG5; 17. CAGCG8; 18. TGCCC6; 19. CAGGG3; 20.
CAGCC10. The polytene map corresponds to the standard configuration for D. koepferae stock Ko2: Xabc 2abmnz7j9l9m9 3b 4
5. The physical locations of markers 1, 17, 18, 19, and 20 (represented as open rectangles, because their location is approximate)
were inferred using the computer application “Chromosome Browser” (http://flybase.org/). The light and dark gray shaded
areas represent chromosome regions previously proposed to carry sterility factors (Marín 1996) (intensity of gray represents
independent studies using different Dk and Db stocks). Open dotted circles represent the chromosome areas where interspecific
fixed inversion breakpoints are located (D. koepferae: 2j9l9m9, D. buzzatii: 5g). Asterisks (*) show the approximate chromosomal
regions randomly sampled to construct the Random data set used during the functional enrichment analyses. T: telomere. C:
centromere.
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fingerprint pattern to determine which were specific to Dk.
This genotyping effort allowed 340 Dk-specific markers
(bands) that, under a uniform distribution, yield a high saturation genetic map of 3.4 markers per 1% of the genome.
The mean number of Dk-specific bands segregating (introgressed) in each hybrid family ranged from 119 to 129, totaling 163 if all families are considered together. Segregation
summaries of the introgression markers for each hybrid family and for the whole set of families are presented in Table 1
and Supplementary Table S1, respectively.
In our mating scheme (Figure 1), approximately 36%
of the haploid Dk genome introgressed per family, more
than the expected 25% for a third hybrid generation of
introgression (12.5% per individual and about 25% for the
whole family). A chi-square goodness-of-fit test for the 4
families confirmed this difference as statistically significant
(χ2 = 77.976; 3 df.; P < 0.01). Of these 163 Dk-specific markers, 89 were common to all families (Table 1). Only these
common markers were considered in the analyses, reducing
the maximum studied introgression to approximately 26%
of all Dk-specific markers. Deviations from the expected 1:1
segregation pattern for the dominant AFLP markers were calculated using χ2 tests. At least 28% of markers deviated from
expectation (data partially presented in Table 2 for sterilityassociated markers and in additional Supplementary Figure
S2 for all markers). Almost all segregations were biased in
favor of heterospecific introgressed individuals, suggesting
that some markers were favored.
Characterization of Sterility-Associated AFLP Markers
Each of the 3 statistical methods used to detect association
between the 89 shared markers and hybrid sterility produced
comparable results (Supplementary Table S2); namely, 1-way
ANOVAs and χ2 demonstrated significant associations in
37 markers (FDR α = 0.05). Fisher exact tests detected 32
significant associations, confirming the majority of the previous associations. As this test is more restrictive, we continued to characterize these 32 markers. Table 2 and Figure 2
summarize the molecular and chromosomal characterization of these markers, respectively. For sequence homology
data (nt/aa) the best hits from various searches and comparisons (Blastn, Blastx & tBlastx) are presented in Table 2.
The sequences reported in this paper were deposited in the
GenBank database (accession nos. HR616932–HR616963).
Markers are short to middle sized (range: 42–1201 bp)
and only 22 of them could be directly or indirectly mapped.
However, each of these 22 markers demonstrated good
sequence homology with Drosophila or eukaryote genomic
sequences. These markers included 4 (CAGCG8, TGCCC6,
GAGGG3, and CAGCC10) that were indirectly localized
using their sequence, as clear signals could not be obtained
using in situ hybridizations, and 2 (TGGAT5 and GAGCG19)
that demonstrated heterochromatic properties, preventing a
defined physical position being assigned to them. Markers
without a confirmed location on D. koepferae chromosomes
were assigned based on nucleotide homology and the correspondence of chromosomal elements between D. melanogaster and D. repleta. Each marker with positive FISH signals
demonstrated good correspondence with the predicted
positions inferred from their sequence homology with the
genome assemblies of the closest relative species D. mojavensis and D. virilis, indirectly validating the use of the Drosophila
“Chromosome Browser” bioinformatics tool (Schaeffer et al.
2008) to infer physical locations.
Interestingly, the remaining 10 markers that could not be
localized using FISH demonstrated clear sequence homology
with bacterial DNA, suggesting a prokaryotic origin. Db/Dk
hybrid sterility has never been associated with any prokaryote
and considering that more than 70 different prokaryote taxa
are normally associated with natural fly populations (CorbyHarris et al. 2007), it is significant that the detected exogenous AFLP markers belong to a few genera of commensal
bacteria (predominantly Gluconacetobacter, Gluconobacter,
and Pseudomonas). Although it might reflect just sample
contamination, there is also a possible role for prokaryotes
to produce the Db/Dk hybrid dysfunctions. To test this
hypothesis, we attempted to raise Dk under axenic conditions
to discern whether bacteria may play a role in hybrid sterility,
but we found that the cultures did not survive when treated
with antibiotics. Therefore, although we could not go further
to conclude whether bacteria are causal or not in the establishment of reproductive barriers, it is possible that the Dk
normal microbiota might be important, and we hope future
studies will help to solve this enigma.
Table 2 presents results from the association analysis
including determination coefficients and risk reduction
values, which are useful for inferring the relative phenotypic contribution of each marker. The observed variation
explained by individual markers (R2) ranges from 8% to 28%,
Table 1 Summary of segregation of the specific D. koepferae markers in families
Sample size (♂ fertile/♂ sterile)
Total specific Dk markers
% About total specific Dk markers
% About all the Dk bands
Family 1
Family 2
Family 3
Family 4
All families
Shared markers
among families
15 (8/7)
125
36.76
12.77
12 (6/6)
129
32.66
13.18
12 (5/7)
129
37.94
13.18
14 (8/6)
119
35.00
12.16
53 (27/26)
163
41.27
16.65
—
89
26.18
9.09
The % about total specific Dk markers was estimated considering only the 340 Dk-specific AFLP markers that are not present in Db standard AFLP fingerprints. The % about all the Dk bands was estimated considering all 979 Dk AFLP markers that segregated in the D. koepferae reference pool of individuals
(standard AFLP fingerprints). Data also shown in Supplementary Table S1.
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Morán and Fontdevila • Genetics of Hybrid Male Sterility
Table 2 Synopsis of the HMS-associated markers
Size bp
188
132
586
341
577
156
430
253
413
109
135
423
215
137
52
97
134
459
347
76
45
433
1201
912
778
729
625
616
498
406
237
236
Marker
CGGCG9
TGTAT15
GCGGG17
CGGGG10
CGGGG14
TGGGG11
TGGAT19
TGCAT13
CAGCA24
CAGCG8
GAGCA9
CAGCC21
TGTCG10
TGCCC6
CAGGG3
TGGGG8
CAGCC10
GGGCG17
GAGAT19
GAGGG5
TGGAT5
GAGCG19
TGCCC18
TGGAT24
CAGGG23
TGCCC14
TGCGG22
CGGCG18
CAGGG20
CAGGG17
CGGAT15
TGCGG13
Chr
Cyt
X
2
2
2
2
2
2
2
2
3R = 2
3
3
3
2L = 3
2L = 3
4
3L = 4
5
5
6
Het
Het
A3hinf
C2e
G1g
C4a
C3f
G2h
G3c
G3d
G4g
G4a-cinf
A3e
G3a
G5a
C5a-cinf
C5c-dinf
F4g/G5d
B4c-einf
G1a
G2b
H
R2
nt/aa
Dros/?
Dros/Dros
Dros/Dros
Euk/?
Dros/Dros
Dros/Dros
Dros/Dros
Euk/?
Dros/Dros
Dros/Dros
Dros/Dros
Dros/Dros
Dros/Dros
Dros/Dros
Dros/Dros
Euk/?
Dros/Dros
Dros/Dros
Dros/Dros
Euk/?
?/?
Euk/?
?/Cau
Gluac/Gluac
Gluac/Gluc
Pseu/Pseu
Syn/Syn
?/Gluac
Gluc/Gluc
Gluac/Gluac
Gluac/Gluac
Och/Mar
P1
0.08
0.16
0.20
0.17
0.19
0.10
0.16
0.28
0.10
0.28
0.10
0.09
0.10
0.16
0.10
0.12
0.13
0.09
0.14
0.19
0.19
0.09
0.20
0.19
0.10
0.18
0.15
0.15
0.17
0.12
0.17
0.20
0.65
0.69
0.59
0.78
0.72
0.65
0.62
0.63
0.57
0.63
0.57
0.60
0.56
0.69
0.68
0.59
0.67
0.61
0.64
0.64
0.61
0.61
0.59
0.64
0.54
0.65
0.60
0.57
0.62
0.59
0.60
0.67
P2
0.37
0.30
0.00
0.34
0.29
0.33
0.19
0.00
0.18
0.00
0.18
0.28
0.13
0.30
0.35
0.17
0.31
0.30
0.25
0.18
0.08
0.30
0.00
0.18
0.00
0.21
0.15
0.00
0.14
0.17
0.09
0.20
ARR
−0.29
−0.40
−0.59
−0.43
−0.43
−0.32
−0.43
−0.63
−0.39
−0.63
−0.39
−0.32
−0.43
−0.40
−0.33
−0.42
−0.36
−0.31
−0.39
−0.46
−0.53
−0.31
−0.59
−0.46
−0.54
−0.44
−0.45
−0.57
−0.47
−0.42
−0.50
−0.47
RR
1.78
2.34
∞
2.27
2.52
1.96
3.32
∞
3.14
∞
3.14
2.16
4.44
2.34
1.92
3.51
2.17
2.02
2.55
3.62
7.32
2.02
∞
3.62
∞
3.07
3.90
∞
4.31
3.51
6.55
3.33
OR
3.24
5.34
∞
6.71
6.43
3.78
7.12
∞
6.00
∞
6.00
3.90
8.75
5.34
3.90
7.06
4.50
3.59
5.25
8.26
17.19
3.59
∞
8.26
∞
6.88
8.25
∞
9.60
7.06
14.71
8.00
χ21:1 / P
Hm:Ht
3.4 × 10−1
30:23
27:26
9:44
35:18
28:25
27:26
16:37
12:41
11:42
12:41
11:42
18:35
8:45
27:26
31:22
12:41
26:27
20:33
20:33
17:36
12:41
20:33
9:44
17:36
5:48
19:34
13:40
7:46
14:39
12:41
11:42
20:33
8.9 × 10−1
1.5 × 10−6a
2.0 × 10−2
6.8 × 10−1
8.9 × 10−1
3.9 × 10−3
6.8 × 10−5a
2.1 × 10−5a
6.8 × 10−5a
2.1 × 10−5a
2.0 × 10−2
3.7 × 10−7a
8.9 × 10−1
2.2 × 10−1
6.8 × 10−5a
8.9 × 10−1
7.4 × 10−2
7.4 × 10−2
9.1 × 10−3
6.8 × 10−5a
7.4 × 10−2
1.5 × 10−6a
9.1 × 10−3
3.5 × 10−9a
3.9 × 10−2
2.1 × 10−4a
8.5 × 10−8a
5.9 × 10−4
6.8 × 10−5a
2.1 × 10−5a
7.4 × 10−2
Abbreviations: bp: base pairs. Chr: chromosome. Cyt: cytological band. nt: nucleotide sequences. aa: amino-acid sequences.?: nucleotide or amino-acid sequence
uncertainty; no clear blast homology results. R2: determination coefficient. P1: probability that a hybrid male be sterile if it segregates the particular marker. P2:
probability that a hybrid male, that does not segregate the particular marker, be sterile. ARR: absolute risk reduction (ARR = p2 –p1); it varies between −1 and 1, and
the 0 value indicates no-association. RR: risk reduction (RR = p1/p2); hybrid males carrying the marker would have a RR higher probability to be sterile than those
males without it. OR: odds ratio [OR = (p1/q1)/(p2/q2) were q1 = 1–p1 and q2 = 1–p2]; it varies between 0 and ∞, and the 1 value indicates no-association. χ21:1 /
p: chi-square test for the expected 1:1 Mendelian segregation ratio; data corrected by Bonferroni, α = 0.00056. Hmz:Htz: number of males without (homozygote:
Hm) and with (heterozygote: Ht) marker. Inf: Bioinformatics inference. Het: sequence with heterochromatic characteristics. Dros: Drosophila. Euk: Eukaryotes.
Cau: Caulobacter. Gluac: Gluconacetobacter. Gluc: Gluconobacter. Pseu: Pseudomonas. Syn: Synechococcus. Och: Ochrobactrum. Mar: Marinobacter.
aHigh statistical significance.
which in general correlates with the relative probabilities of
being sterile when a specific marker is segregating in a hybrid
male (evaluated by means of risk reduction values or odds
ratios). Interestingly, it is apparent that those markers that
are strongly associated with sterility demonstrated the highest
segregation distortion. Therefore, segregation bias of genes
near the sterility-associated markers could help to maintain
HI throughout the introgression process. Interestingly, some
authors argue that this phenomenon might be explained by
hybrid vigor and / or genomic conflict as a force in postzygotic isolation (Johnson 2010; McDermott and Noor 2010;
Presgraves 2010b).
Two-way ANOVAs were performed in pairwise comparisons in an attempt to detect 2-locus epistatic interactions
between markers. However, after multiple test corrections,
none were statistically significant (data not shown). It was
also possible that the small sample size affected the analysis
allowing only very large interaction effects to be detected,
if they did exist. To obtain a more general picture we performed a factorial PCA. This exploration confirmed that
almost all the observed variation could be reduced to a few
variables; it is possible to explain approximately 79% of variation using 8 components (eigenvalues > 1, Supplementary
Table S3), although the first component alone explains 41%,
with all markers showing positive eigenvector scores (component loadings). Figure 3 presents the dispersion graph of
sterile and fertile males in relation to principal components
1 and 3, which explain the fertility phenotype best (tested
using a multiple linear regression model among the 8 principal components). Positive principal component scores in
387
Journal of Heredity
Figure 4. Mean number of sterility-associated and
nonassociated segregated AFLP markers. **: Difference with
high statistical significance (P < 0.01).
Figure 3. PCA of the segregation of sterility-associated
markers. Y and X axes represent the principal component
scores estimated for each hybrid male from the 2 principal
components that better explain the variability associated with
the fertility phenotype.
component 1 can identify and group almost all sterile males.
This discrimination is reinforced by component 3, because
all males but one in the upper right quadrant are sterile.
The majority of sterility-associated markers thus appear to
contribute jointly to producing male hybrid sterility, rather
than such sterility being the product of few markers with
major effects. Even so, among the other components there
are also small clusters of markers with extreme positive or
negative eigenvector scores (principal component loadings in
Supplementary Table S3), denoting that some markers are
linked to genetic factors that are able to produce sterility by
independent ways, have different dominance properties or
act in specific genetic pathways. In no case, we distinguished
between the endogenous and the exogenous markers, confirming their involvement in the sterile phenotype.
Comparison of the mean number of sterility-associated
and nonassociated markers that segregate in fertile and sterile
males (Figure 4) demonstrates that the introgressed genome
of sterile males contains significantly more sterility-associated markers than that of fertile males. On the other hand
fertile and sterile male genomes do not differ significantly in
the number of nonassociated markers to sterility. Ordering
these data by the number of endogenous sterility-associated
markers that segregate in each hybrid male (Figure 5), supports the cumulative action, with a threshold, of genes surrounding these sterility markers. Moreover, the results also
suggest that the threshold number of sterility-associated
markers required to elicit sterility is approximately 14–16,
corresponding to the transition zone between the fertile and
388
sterile hybrid phenotypes. Some outlier individuals exist, as
their number of sterility-associated markers deviates from
that expected according to their degree of sterility. The effect
on sterility of genes linked to markers therefore is variable,
and in some instances, a particular mixture of a number of
low-effect (high-effect) markers over (under) the threshold
can produce a fertile (sterile) phenotype (see hybrid males
F26, F11 and F14 over, and S19 under the threshold). Despite
these exceptions, an interesting result that supports the polygenic and cumulative architecture of male sterility between
these species is the ample exchangeability of mapped markers (Figure 6). The data shows that presence of a specific
marker to produce sterility is generally not a necessity;
rather the presence of a minimum number of markers over
a threshold induces sterility with high probability. Figure 6
shows that even some ubiquitous or highly present markers
in sterile hybrids are also present in many fertile ones (e.g.,
GCGGG17, CAGCG8, TGCAT13 markers are present in
all sterile males and in many fertile ones) and that no marker
exclusively occurs in sterile males.
Chromosomal Distribution of Sterility Markers
Interestingly, the physical distribution of sterility markers
(Figure 2) demonstrates a tendency to map in chromosomal
regions known to have low recombination rates. This is particularly true for most pericentromeric regions, some heterochromatic constriction areas and regions near the breakpoints
of species-specific paracentric inversions. At least 4 markers
(TGGGG8, TGCCC6, CAGGG3, and CAGCC10; Figure 2)
were positioned on chromosomal areas that coincide with
those previously proposed to contain sterility factors (Marín
1996). Consequently, these results provide further evidence
for the formerly detected associations between these proposed genomic regions and male hybrid sterility. More generally, they demonstrate the reliability of the present blind
strategy to detect sterility factors, even in those regions that
were difficult to isolate as cytological selected chromosomal
introgressions.
Morán and Fontdevila • Genetics of Hybrid Male Sterility
Figure 5. Ordered representation of the number of endogenous sterility-associated markers segregated by each hybrid male.
Ordered representation of the fertile (F) and sterile (S) hybrid males in relation to the increasing number of sterility-associated
markers scored among hybrids. *: fertile individual with an observed reduced offspring (only 3 adult flies). The dotted lines
represent the limits of the proposed threshold that separate the fertile/sterile phenotypes.
AFLPs as Landmarks to Explore Orthologous Genomic
Regions
Based on the high genome sequence similarity and structural genome homology among Drosophila species (Heger
and Ponting 2007), and specially between D. koepferae and
D. mojavensis, we explored the genomic regions orthologous to
the AFLP sterility markers of D. koepferae. Initially, we looked
for GO terms significantly enriched by genes in physical linkage with sterility-associated markers, and we found that those
genes located near the markers predominantly correspond to
those involved in cell metabolism, reproduction and developmental processes including important genes for gamete
generation, cell cycle, and sexual differentiation and development of genital structures (see Supplementary Table S4).
Moreover, these specific functional categories seemed to be
significantly more enriched by those genes located closest to
the marker (the 100 Kb data set) (depicted in Supplementary
Figure S3; only the most general GO classification levels are
shown). Nevertheless, in order to be sure that our results
reflect the true genetic composition of the putative genomic
regions surrounding the sterility-associated markers, and
do not correspond to a general conformation of the whole
genome, we directly contrasted our 400-Kb data set against
a random sampled gene list (Random data set and Random
background gene list, see methodology). These results are
even clearer that those from our first approach, because the
most enriched GO terms are in fact those related with developmental and reproductive biological processes (Figure 7
and Supplementary Table S4). Then, our results suggest that
detected sterility-associated markers map to genomic regions
that contain a subset of genes with specific functions on the
organism fertility and fitness and do not show associations
with large linkage blocks that include all sorts of genes.
In Table 3 we propose a list of 53 genes present among
the enriched GO terms derived from the 100 Kb data set.
We consider them as good gene candidates contributing to
the sterile phenotype that shall be considered in future studies. The list was ranked by counting the number (N) of times
each gene appeared at any enriched GO term related with
development and/or reproduction, as a way to highlighting
the significant effect that pleiotropic genes and their epistatic
interactions may have on complex traits. Using the platform
FlyMine (Lyne et al. 2007), which permits the simultaneous study of all GO levels in one direct analysis, the list was
reanalyzed and filtered. The results (Supplementary Table S5)
confirmed again that all candidates are in a close relationship
with reproductive and developmental pathways.
Gene expression patterns could be important when inferring possible effects in the development of any specific
phenotype. Therefore, we used public data from FlyAtlas
(Chintapalli et al. 2007) to review the known gene expression
levels of candidate genes. Although some well-characterized
genes were upregulated in specific tissues, a few demonstrated an expression preference for reproductive tissues
(data not shown). Yet, when the whole 400-kb data set was
checked, we found 68 poorly characterized genes that are in
fact specifically expressed in reproductive organs (testis, male
glands, ovaries, and spermathecae; Supplementary Figure
S4). Our results suggest that the characterized markers might
389
Journal of Heredity
Figure 6. Segregation matrix of sterility-associated markers. Sterility-associated markers (shown in columns) were scored in a
binary matrix as 0 (white cells) when absent or 1 (gray cells) when present. To facilitate the matrix inspection all the fertile males
were grouped in the upper rows of the matrix and separated from the sterile males by a straight black line. Inside each fertility
category (F: fertile; S: sterile) males (left margin) were organized by their increasing content of associated markers (right margin).
be associated with still unknown genes that could directly
affect reproductive traits if their expression was altered in
hybrid tissues.
Discussion
Genome Resolution and Segregation Recovery of AFLP
Sterility-Associated Markers
Up to 340 Dk-specific markers were detected, implying a
mean of approximately 7 species-specific markers per combination; a good molecular and genetic resolution. When
up to one-third of the markers were excluded because of
their possible exogenous origin (31% of the isolated markers), the map resolution continued to be satisfactory, ranging from 3.4 to 2.3 specific markers per 1% of the genome.
The genome sizes of Db and Dk are not known with precision, but accepting that 150 to 170 Mb is a good genome
size approximation, based on measurements of other closely
related species (Bosco et al. 2007), 1% of the genome is likely
390
to correspond to 1500–1700 Kb. Therefore, our real map
saturation could be 1 marker for each 650–740 Kb. It might
correspond to approximately 1 marker per 6–7 polytene
chromosome bands (assuming each band contains approximately 100 Kb [Zhimulev et al. 1996]). Therefore, the results
represent a significant augmentation of resolution compared
with previous studies using these species pair (Naveira and
Fontdevila 1986b, 1991a, 1991b). These indirect inferences,
however, rely on the assumption of a random distribution of
AFLPs in the genome and, consequently, are not definitive.
Albeit this increase in resolution is similar to that attained
in other studies that characterized few small isolated introgressions [see for instance Odysseus gene mapping and characterization (Perez et al. 1993; Perez and Wu 1995)], our
mapping approach is advantageous because it better reflects
how HI cause HMS in a genomic context. Our results thus
reveal the true overall complexity of HMS genetic architecture: 1) we simultaneously detected the influence on hybrid
fertility of several cointrogressed genetic factors in different combinations (regardless of their individual penetrance,
Morán and Fontdevila • Genetics of Hybrid Male Sterility
Figure 7. Functional enrichment analysis of genes potentially linked with sterility markers in comparison to a random sampled
gene collection. X axis: relative gene frequencies in each subset of genes that are in the genomic vicinity of markers (400-Kb data
set, black bars) or randomly distributed (Random data set, gray bars), in relation to the total number of genes present in each
specific data set. Y axis: the most enriched GO terms for all Biological processes levels, for which there is a significant difference
between the 400 Kb and Random data sets (P values adjusted by means of FDR, using fixed α = 0.05). Analysis done with GOstat
(Beissbarth and Speed 2004). GO: Gene Ontology Consortium (www.geneontology.org).
physical position or molecular nature), and 2) we examined
the possible roles of all kinds of major and minor genes and/
or other genetic factors and their interaction pathways.
Out of the total 340 Dk-specific AFLP bands scored
in this study, approximately 36% of them were detected
as introgression markers among families (a mean of 122.4
Dk-specific markers segregated per family equals to 36% of
340 total DK-specific markers). This percentage is higher
than the theoretical maximum of 25% expected for whole
families (see Figure 1 and Table 1), suggesting an accumulation of AFLPs in a nonuniform distribution. Although this
observation may just mirror the accumulation of sterilityassociated AFLP markers in zones of low recombination, it
could also reflect that species hybrids can retain more introgressive genome fragments owing to a generalized impairment of recombination in them. This idea is supported
by the high number of somatic asynapses observed in the
introgressed regions of polytene chromosomes of Db/
Dk hybrids (Naveira and Fontdevila 1986a), indicating that
the molecular divergence between species could affect the
homologous chromosomal recognition in hybrids and subsequently their recombination rate. On the other hand, the
391
Journal of Heredity
Table 3 List of candidate genes derived from the functional
enrichment analysis
Symbol
Abd-B
Sox100B
Fas2
Hmgcr
mus209
tu
foi
Rab11
abd-A
shot
fws
Cp36
syt
dia
bam
ppan
Nrx-IV
rtet
CadN
qua
sec15
CG6416
poe
Ptp99A
up
brn
Prm
N
56
29
26
22
16
16
15
15
13
13
12
11
11
9
8
8
7
7
6
6
6
5
5
5
5
4
4
Chr
D
R
2
2
X
2
5
2
4
2
2
5
3
2
3
3
2
2
4
2
3
3
2
4
3
2
2
X
4
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
Symbol
Pxd
stg
bnk
dmrt99B
FBgn0001309
FBgn0037925
knk
mei-9
spen
sqz
Bsg
CG3987
fs(2)ltoPP43
kay
ord
Aph-4
CG7466
cib
Cka
Cp16
Cp18
Cp19
Cp38
Es2
RpL14
ss
N
4
4
3
3
3
3
3
3
3
3
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
Chr
D
R
2
2
2
2
3
2
2
X
3
2
3
2
3
2
5
2
3
X
3
4
4
4
2
2
4
2
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
a
Abbreviations: N: number of times that each gene appeared among the
different GO terms significantly overrepresented for the gene subsets that
surround the markers. Chr: chromosome from where marker belongs. D:
developmental pathways. R: reproductive traits.
aknown phenotype associations.
detected Dk-band excess could be related to hybrid heterozygosity (understood as the presence of different alleles
of hybrid origin in one or more loci), as has been recently
suggested (Moehring 2011). That study reported that markers associated with sterility show a greater amount of heterozygosity than those not associated with sterility, regardless
of chromosome mapping. In our case, an excess of heterozygosity and a relationship of the degree of marker introgression with sterility was also observed (Figure 4 and Table 2),
which suggests that the degree of heterozygosity may contribute to the hybrid sterility. The cause of this segregation
bias has recently been subjected to speculation. Among the
most favored hypotheses, a kind of “heterozygote drive” by
which the gametes could sense the likeness of their fertilization partner (Fisher and Hoekstra 2010) and selectively
increase the chances of heterozygous (heterospecific) offspring has recently been advanced (Moehring 2011). The
rationale of this argument can also be related to the growing view that HMS is related to genome conflict (Presgraves
2010). Namely, speciation genes might be associated with
selfish elements that arise in natural populations where they
often are selectively suppressed; however, if this suppression
is not fully dominant it can be ineffective in species hybrids.
392
As an example, a segregation distorter has been found to
be associated with Ovd, a speciation gene that causes HMS
(Phadnis and Orr 2009). Although still a matter of debate,
there is growing evidence that other HMS genes have also
evolved by genetic conflict (Presgraves 2010b; Maheshwari
and Barbash 2011). Our observation of segregation bias in
favor of sterility-associated markers in hybrid backcross offspring is consistent with, although does not prove, the conflict hypothesis of hybrid sterility evolution.
The Genetic Architecture of HMS Follows an Epistatic
PT Model
As mentioned above, early studies concerning D. koepferae and D. buzzatii suggested that their postzygotic isolation depends on the cumulative action of several nonallelic
interacting HI factors with small effects (polygenes) under
a threshold (Naveira and Fontdevila 1986b, 1991a, 1991b).
However, at that time the lack of genome-wide high-resolution techniques impeded the precise molecular characterization of the genomic regions that contain these polygenes.
The results of the present study overcome these difficulties
and support the PT model at a molecular level of resolution.
For instance, we propose that the minimum number of sterility-associated markers required to produce sterility is a true
threshold of approximately 14–16 markers (see Figure 5),
which represents at least 6–7% of the introgressed genome
(accepting our map resolution estimate of 2.3 markers per
1% of genome). It closely resembles previous observations
and theoretical studies (Naveira and Fontdevila 1986b) where
the proposed threshold of the average 25–30% of an autosome approximately corresponds to 7–9% of the genome
and not less than 15 epistatic sterility factors.
Figure 5 also depicts a few specific hybrid males as outliers of the relationship between sterility and the number of
associated markers. There are several possible explanations
for this. First, these individuals could just reflect the interaction with other genetic factors not detected during the
analysis, as only markers shared by all families were analyzed.
Second, it is also possible that our statistical approach could
not detect other genetic factors, with very small effects present throughout all the introgressed genome of hybrid males
that could modify the fertility penetrance. For instance, the
fertile male that contains the highest number of sterilityassociated markers (identified in Figure 5 with an asterisk)
yielded only 3 adult fly offspring, demonstrating a phenotype
close to sterility. Finally, another explanation could be that
an exceptional combination over the threshold of several
low effect polygenes does not elicit sterility. These considerations show that although the exchangeability between sterility
factors is important, it is by no means complete, underscoring the individuality of sterility genes as has been demonstrated for some major speciation genes (see a summary in
[Presgraves 2010]). However, accepting the individuality of
the sterility genes, the present results show that the number of sterility factors is so large that there is a confounding
effect between the introgressed genome size and the number
of introgressed sterility polygenes that looks as if only their
Morán and Fontdevila • Genetics of Hybrid Male Sterility
addition matters, as appears in other works (True et al. 1996;
Presgraves 2003; Tao and Hartl 2003; Tao et al. 2003a, 2003b;
Masly and Presgraves 2007).
The PT model of HMS approximates the genetic model
of threshold traits in populations sponsored by earlier geneticists (Waddington 1942; Lerner 1954). In our case, however,
one must distinguish the action of genes, which is epistatic
(not additive), from their cumulative effect on fertility, which
may loosely be considered additive. Yet the innovative difference of the PT model is that 1) it applies to interspecific
hybrids, where the merging of 2 genomes is the stress that
triggers the genomic instability once the introgressed sterility factors exceeds a certain threshold, and that 2) the effect
of genes is the result of the cumulative epistatic interactions
between cospecific and/or interspecific alleles. Interestingly,
some researchers on HMS have already suggested either a
kind of threshold weak allele–strong interaction effect (Wu
and Hollocher 1998) or an explicit threshold model of several introgressed epistatic factors that interact individually
with background loci (Johnson 2000).
Sterility-Associated Markers Map to Regions of Low
Recombination Rates
The physical distribution of sterility-associated markers
demonstrates their high density in genomic regions of low
recombination (Figure 2 and Table 2), suggesting that these
regions enabled the linked transmission of blocks of interacting polygenes involved in HMS. Interestingly, this observation was already predicted from simulation analyses, where
a systematic bias in QTLs mapping was observed in favor of
strong QTLs in low recombining regions (Noor et al. 2001).
The authors argued that this phenomenon is a consequence
of the huge variation in gene density per centimorgan across
the Drosophila genome, that seems to be higher in these particular regions. Even so, genes contained in these regions
could be of great importance in maintaining or reinforcing
the reproductive isolation barriers between hybridizing species, as some theoretical recombination-related speciation
models and empirical studies indicate (Navarro and Barton
2003; Butlin 2005). An alternative explanation posits that
these genomic regions may contain genes that are in linkage disequilibrium with other genes that directly contribute
to produce reproductive isolation (Carneiro et al. 2009),
suggesting that they act coordinately in specific adaptive
pathways.
Thus, the search for new candidate speciation genes acting
in these adaptive pathways continues to be an intense field of
investigation (Maheshwari and Barbash 2011). The present
functional enrichment analysis (see Figure 7, Supplementary
Figure S3, Supplementary Tables S4and S5) clearly demonstrates that chromosomal regions flanking the AFLP markers likely contain key candidate genes for developmental and
reproductive traits (Table 3), and that many of them are in
fact often clustered in chromosomal areas of low recombination rates. Nonetheless, this does not exclude that other
selective or stochastic events act to maintain some sterility
factors outside low recombination areas that also contribute
to hybrid sterility, as we also detected for markers 1, 10, 13,
18, 19, and 20 (Figure 2).
Interestingly, in Drosophila there is good evidence that some
genes related to sexual traits are evolutionarily flexible and
adaptable, and could be differentially regulated across species, perhaps playing active roles in speciation (Sánchez and
Santamaria 1997; Kopp et al. 2000; Jeong et al. 2008; Shirangi
et al. 2009). In this regard, additionally to the proposed annotated candidate genes with reproductive related functions,
the deregulation of genes with no functional annotation but
with a known expression preference for male reproductive
tissues, as those 68 uncharacterized genes detected in the
present research (Supplementary Figure S4), exemplify the
strong influence that such genes could have on the overall
fertility of hybrids, possibly due to their intrinsically fast rate
of evolution (Johnson and Porter 2000; Landry et al. 2005).
Regulatory Incompatibilities (RI) are common in hybrids and
their intensity normally correlates with the degree of genetic
divergence between parental species. Therefore, RI could
gradually appear at the same time as other more general HIs
and accumulate during the speciation process, contributing
to the reproductive isolation barriers formation (Haerty and
Singh 2006; Artieri et al. 2007; Landry et al. 2007; OrtízBarrientos et al. 2007).
Conclusions
The present dissection of the underlying architecture of hybrid
sterility throws new light on speciation genetics. First, it supports the PT model. Although previous work also supported
this model, it was not possible to characterize individual genes
with a minor effect. The use of the AFLP technique allowed
a sufficient set of introgressed genome fragments (26% of
the genome) to be retrieved that could be associated with
hybrid sterility. When these marker genome fragments were
allocated to hybrid genotypes it became clear that a minimum
number of them (threshold) were required to induce significant male sterility. Interestingly, this minimum did not consist
of a specific polygene set; rather it was a nonspecific set in
which polygenes can be exchangeable in large degree so far
they interact sufficiently to impair fertility. Yet, this exchangeability does not mean their action is equally effective, some of
them interact more strongly than others, so the threshold for
sterility is not trespassed with the same efficiency. Second, in
this study polygenes could be mapped using FISH or other
indirect techniques. The map revealed a nonuniform distribution of polygenes, with a tendency to cluster in chromosomal
regions of low recombination. Though rather enigmatic, this
polygene distribution has been related to the effect that these
clusters of genes could have to maintain species cohesion in
critical genomic regions (Turner et al. 2005; Noor et al. 2007;
Carneiro et al. 2009). Third, compared with previous attempts
the current study represents a breakthrough into the molecular characterization of hybrid male-associated genetic factors.
Some surveys have been carried out to test specific rules of
speciation, but our genome-wide analysis was blind, that is, we
did not use previously manipulated material (e.g., P element
393
Journal of Heredity
insertion or deletion stocks), where the level of introgressed
material is difficult to assess. However, we did estimate the
level of introgressed genome during the present study, which
allowed the hybrid background to be controlled. The main
advantage of the present work is to reveal the molecular characterization of regions that flank every marker. Using bioinformatics it was demonstrated that the marker surroundings
are enriched with genes whose functions are implicated in
development and reproductive routes. We have characterized,
albeit in a preliminary way, the molecular nature of the architecture of the sterility of hybrid males between D. buzzatii and
D. koepferae. The fine-scale genome analysis performed here
agrees with other analyses in that a complex set of epistatic
interactions between genetic factors underlies the HMS. In
sum, our results support the PT nature of the hybrid sterility as a common evolutionary mechanism of postzygotic
isolation.
Supplementary Material
Supplementary material can be found at http://www.jhered.
oxfordjournals.org/.
Funding
Ministerio de Educación y Ciencia, Spain (BOS 200305904-C02-01, CGL 2006-13423-C02-01); Ministerio de
Ciencia e Innovación, Spain (CGL 2010-15395); Agencia
d’Ajuts Universitaris de Recerca (AGAUR), Generalitat de
Catalunya, Spain, (2005-SGR-00995; 2009-SGR-636). T.M.
was supported by a fellowship from AGAUR, Generalitat de
Catalunya, Spain.
Acknowledgments
We thank M. Peiró for her technical assistance, and M. Santos and
E. Rezende for their helpful suggestions during the statistical analysis. Two
anonymous reviewers contributed with their helpful comments to improve
the manuscript.
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Received June 20, 2013; First decision August 12, 2013; Accepted
December 23, 2013
Corresponding Editor: Therese Markow