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1.
Aneuploid cells are characterized by incomplete chromosome sets. The resulting imbalance in gene dosage has phenotypic consequences that are specific to each karyotype. Even in the case of Down syndrome, the most viable and studied form of human aneuploidy, the mechanisms underlying the connected phenotypes remain mostly unclear. Because of their tolerance to aneuploidy, plants provide a powerful system for a genome-wide investigation of aneuploid syndromes, an approach that is not feasible in animal systems. Indeed, in many plant species, populations of aneuploid individuals can be easily obtained from triploid individuals. We phenotyped a population of Arabidopsis thaliana aneuploid individuals containing 25 different karyotypes. Even in this highly heterogeneous population, we demonstrate that certain traits are strongly associated with the dosage of specific chromosome types and that chromosomal effects can be additive. Further, we identified subtle developmental phenotypes expressed in the diploid progeny of aneuploid parent(s) but not in euploid controls from diploid lineages. These results indicate long-term phenotypic consequences of aneuploidy that can persist after chromosomal balance has been restored. We verified the diploid nature of these individuals by whole-genome sequencing and discuss the possibility that trans-generational phenotypic effects stem from epigenetic modifications passed from aneuploid parents to their diploid progeny.THE genome of aneuploid individuals contains incomplete chromosome sets. The balance between chromosome types, and the genes they encode, is compromised, resulting in altered expression of many genes, including genes with dosage-sensitive effects on phenotypes. In humans, only a few types of aneuploid karyotypes are viable ( Hassold and Hunt 2001), highlighting the deleterious effect of chromosome imbalance. The most commonly known viable form of aneuploidy in humans is Down syndrome, which results from a trisomy of chromosome 21 in an otherwise diploid background. Down syndrome patients exhibit many specific phenotypes, sometimes visible only in a subset of patients ( Antonarakis et al. 2004). For phenotypes found in all Down syndrome patients, the penetrance of each phenotype varies between patients ( Antonarakis et al. 2004). Despite the increasing amount of information available about the human genome and the availability of a mouse model for Down syndrome ( O''Doherty et al. 2005), the genes responsible for most of the phenotypes associated with Down syndrome are still unknown ( Patterson 2007; Korbel et al. 2009; Patterson 2009). Recently, detailed phenotypic analyses of as many as 30 aneuploid patients have allowed the identification of susceptibility regions for several specific phenotypes ( Patterson 2007, 2009; Korbel et al. 2009; Lyle et al. 2009), but the specific genes remain to be identified. Understanding the physiology of aneuploidy is not only relevant to those individuals with aneuploid genomes but also to understanding cancer since most cancerous cells are aneuploid ( Matzke et al. 2003; Pihan and Doxsey 2003; Storchova and Pellman 2004; Holland and Cleveland 2009; Williams and Amon 2009) or the consequences of copy number variation and dosage sensitivity ( Dear 2009; Henrichsen et al. 2009).Plants are more tolerant of aneuploidy than animals ( Matzke et al. 2003) for reasons that remain unclear. Since the discovery of the Datura trisomic “chromosome mutants” by Blakeslee (1921, 1922), viable trisomics of each chromosome type have been described in numerous species. Trisomics exhibit phenotypes specific to the identity of the triplicated chromosome ( Blakeslee 1922; Khush 1973; Koornneef and Van der Veen 1983; Singh 2003). More complex aneuploids, i.e., individuals carrying more than one additional chromosome, can be viable as well and have been observed in many plants species, especially among the progeny of triploid individuals ( McClintock 1929; Levan 1942; Johnsson 1945; Khush 1973). Some species appear to be more tolerant of complex aneuploidies than others, suggesting a genetic basis for aneuploidy tolerance ( Satina and Blakeslee 1938; Khush 1973; Ramsey and Schemske 2002; Henry et al. 2009). Aneuploid individuals frequently appear spontaneously within polyploid plant populations, presumably due to a failure to equally partition the multiple chromosome sets at meiosis ( Randolph 1935; Doyle 1986). These aneuploids exhibit few or subtle phenotypic abnormalities and can often compete with their euploid progenitors ( Ramsey and Schemske 1998). Plants therefore provide an excellent opportunity for a genome-wide investigation of aneuploid syndromes: sample size is not limited, phenotypes can be described and assessed in detail, and plant aneuploid populations provide a complex mixture of viable karyotypes.In this article, we report our investigation of the relationship between phenotype and karyotype in populations of aneuploid Arabidopsis thaliana plants. All simple trisomics of A. thaliana have been previously isolated and phenotypically characterized ( Steinitz-Sears 1962; Lee-Chen and Steinitz-Sears 1967; Steinitz-Sears and Lee-Chen 1970; Koornneef and Van der Veen 1983), demonstrating that they are tolerated in A. thaliana. We previously reported that aneuploid swarms—populations of aneuploid individuals of varying aneuploid karyotypes—could be obtained from the progeny of triploid A. thaliana individuals ( Henry et al. 2005, 2009). Using a combination of a quantitative PCR-based method and flow cytometry, we were able to derive the full aneuploid karyotype of each of these individuals ( Henry et al. 2006). We further crossed triploid A. thaliana to diploid or tetraploid individuals and demonstrated that at least 44 of the 60 possible aneuploid karyotypes that could result from these crosses (aneuploid individuals carrying between 11 and 19 chromosomes) were viable and successfully produced adult plants. Taken together, these populations and methods make it possible to explore the basis of aneuploid syndromes in A. thaliana. In this study, we were able to phenotypically characterize at least one individual from 25 different aneuploid karyotypes falling between diploidy and tetraploidy. We demonstrated that specific phenotypes are affected by the dosage of specific chromosome types. The effect of the dosage of specific chromosome types on traits was additive and could be used to predict the observed phenotype. The availability of multiple generations of aneuploid and euploid individuals allowed us to investigate potential long-term effects of aneuploidy as well as parent-of-origin effects on aneuploid phenotypes. 相似文献
2.
Fitness is a parameter that quantitatively measures adaptation of a virus to a given environment. We have previously reported exponential fitness gains of large populations of vesicular stomatitis virus replicating in a constant environment (I. S. Novella et al., Proc. Natl. Acad. Sci. USA 92:5841–5844, 1995). In this paper, we report that during long-term passage of such large viral populations, fitness values reached a high-fitness plateau during which stochastic fitness variations were observed. This effect appears likely to be due to bottleneck effects on very high fitness populations. 相似文献
3.
acdS基因编码产生ACC脱氨酶,该酶属于脱巯基家族,可以降低逆境乙烯的合成量。利用农杆菌介导的叶盘法将CaMV35S-2启动子和rolD启动子驱动下的acdS基因转入烟草NC89叶片中。在含有卡那霉素的MS培养基上筛选得到Kanr转化烟草。通过PCR、Southern blotting对得到的Kanr转基因烟草进行分析,结果表明,acdS基因已经整合到了烟草的基因组中。对转基因烟草的RT-PCR及cDNA进行测序分析表明,acdS基因能够正确转录。对转基因烟草进行耐盐性测定,结果显示,与对照相比,两种启动子驱动下的转基因烟草耐盐性均有增强。但是rolD启动子驱动下的转基因植株的耐盐性最强。 相似文献
4.
GC-biased gene conversion (gBGC) is a recombination-associated process mimicking selection in favor of G and C alleles. It is increasingly recognized as a widespread force in shaping the genomic nucleotide landscape. In recombination hotspots, gBGC can lead to bursts of fixation of GC nucleotides and to accelerated nucleotide substitution rates. It was recently shown that these episodes of strong gBGC could give spurious signatures of adaptation and/or relaxed selection. There is also evidence that gBGC could drive the fixation of deleterious amino acid mutations in some primate genes. This raises the question of the potential fitness effects of gBGC. While gBGC has been metaphorically termed the “Achilles'' heel” of our genome, we do not know whether interference between gBGC and selection merely has practical consequences for the analysis of sequence data or whether it has broader fundamental implications for individuals and populations. I developed a population genetics model to predict the consequences of gBGC on the mutation load and inbreeding depression. I also used estimates available for humans to quantitatively evaluate the fitness impact of gBGC. Surprising features emerged from this model: (i) Contrary to classical mutation load models, gBGC generates a fixation load independent of population size and could contribute to a significant part of the load; (ii) gBGC can maintain recessive deleterious mutations for a long time at intermediate frequency, in a similar way to overdominance, and these mutations generate high inbreeding depression, even if they are slightly deleterious; (iii) since mating systems affect both the selection efficacy and gBGC intensity, gBGC challenges classical predictions concerning the interaction between mating systems and deleterious mutations, and gBGC could constitute an additional cost of outcrossing; and (iv) if mutations are biased toward A and T alleles, very low gBGC levels can reduce the load. A robust prediction is that the gBGC level minimizing the load depends only on the mutational bias and population size. These surprising results suggest that gBGC may have nonnegligible fitness consequences and could play a significant role in the evolution of genetic systems. They also shed light on the evolution of gBGC itself.GC-BIASED gene conversion (gBGC) is increasingly recognized as a widespread force in shaping genome evolution. In different species, gene conversion occurring during double-strand break recombination repair is thought to be biased toward G and C alleles. In heterozygotes, GC alleles undergo a kind of molecular meiotic drive that mimics selection (reviewed in Marais 2003). This process can rapidly increase the GC content, especially around recombination hotspots ( Spencer et al. 2006), and, more broadly, can affect genome-wide nucleotide landscapes ( Duret and Galtier 2009a). For instance, it is thought to play a role in shaping isochore structure evolution in mammals ( Galtier et al. 2001; Meunier and Duret 2004; Duret et al. 2006) and birds ( Webster et al. 2006). Direct experimental evidence of gBGC mainly comes from studies in yeast ( Birdsell 2002; Mancera et al. 2008; but see Marsolier-Kergoat and Yeramian 2009) and humans ( Brown and Jiricny 1987). However, associations between recombination and the nucleotide landscape and frequency spectra biased toward GC alleles provide indirect evidence in very diverse organisms ( |
Open in a separate windowThe impact of gBGC on noncoding sequences and synonymous sites has been studied in depth, especially because of confounding effects with selection on codon usage (
Marais et al. 2001). More recently,
Galtier and Duret (2007) pointed out that gBGC may also interfere with selection when affecting functional sequences. They argued that gBGC could leave spurious signatures of adaptive selection and proposed to extend the null hypothesis of molecular evolution. Indeed, gBGC can lead to a ratio of nonsynonymous (
dN) over synonymous (
dS) substitutions above one (
Berglund et al. 2009;
Galtier et al. 2009),
i.e., a typical signature of positive selection (
Nielsen 2005). This hypothesis has been widely debated for human-accelerated regions (HARs). These regions are extremely conserved across mammals but show evidence of accelerated evolution along the human lineage, which has been interpreted as evidence of positive selection (
Pollard et al. 2006a,
b;
Prabhakar et al. 2006,
2008). On the contrary, other authors argued that patterns observed in HARs, such as the AT → GC substitution bias, the absence of a selective sweep signature, or the propensity to occur within or close to recombination hotspots, are more likely explained by gBGC rather than positive selection (
Galtier and Duret 2007;
Berglund et al. 2009;
Duret and Galtier 2009b; but see also
Pollard et al. 2006a who also suggested that gBGC might play a role in HARs evolution). It is thus crucial to take gBGC into account when interpreting genomic data.Moreover,
Galtier and Duret (2007) initially suggested that gBGC hotspots could contribute to the fixation of slightly deleterious AT → GC mutations and could represent the Achilles'' heel of our genome. This hypothesis was reinforced later in primates, with evidence of gBGC-driven fixation of deleterious mutations in proteins (
Galtier et al. 2009). A similar result was also found in some grass species, whose genomes are also supposed to be affected by gBGC ().
Haudry et al. (2008) compared two outcrossing and two selfing grass species and showed that GC-biased genes exhibit higher
dN/
dS ratio in outcrossing than in selfing lineages. The reverse pattern would be expected under pure selective models because of the reduced selection efficacy in selfers (
Charlesworth 1992;
Glémin 2007). This pattern is in agreement with a genomic Achilles'' heel associated with outcrossing, while gBGC is inefficient in selfing species because they are mainly homozygous.Twenty years ago,
Bengtsson (1990) already pointed out that biased conversion can generally affect the mutation load. The mutation load is the reduction in the mean fitness of a population due to mutation accumulation, which could lead to population extinction if it is too high (
Lynch et al. 1995). At this time, Bengtsson concluded that “it is impossible to know if biased conversion plays a major role in determining the magnitude of the mutation load in organisms such as ourselves, but the possibility must be considered and further investigated (
Bengtsson 1990, p. 186).” Now, one can propose gBGC could be such a widespread biased conversion process. It thus appears timely to thoroughly investigate the fitness consequences of gBGC through its potential effects on the dynamics of deleterious mutations. The fitness consequences of gBGC were also pointed out as a major future issue to be addressed by
Duret and Galtier (2009a). In addition to the load, deleterious mutations have many other evolutionary consequences (for review see
Charlesworth and Charlesworth 1998). They are thought to be the main determinant of inbreeding depression,
i.e., the reduction in fitness of inbred individuals compared to outbred ones. They also play a key role in the evolution of genetic systems (sexual reproduction and recombination, inbreeding avoidance mechanisms, ploidy cycles), of senescence, or in the degeneration of nonrecombining regions, such as Y chromosomes. So far, we know little, if anything, about how gBGC might affect these processes.In his seminal work,
Bengtsson (1990) did not address several important points. First, he did not include genetic drift in his model. Nearly neutral mutations, for which drift and selection are of similar intensities, are the most damaging ones because they can drift to fixation, unlike strongly deleterious mutations that are maintained at low frequency (
Crow 1993;
Lande 1994,
1998). While gBGC intensities are rather weak (
Birdsell 2002;
Spencer et al. 2006), they could markedly affect the fate of nearly neutral mutations (see also
Galtier et al. 2009). Second, Bengtsson did not study the effect of gene conversion on inbreeding depression, while he showed that recessive mutations, mostly involved in inbreeding depression, are the most affected by gene conversion. Third, he did not envisage systematic GC bias with its opposite effects on A/T and G/C deleterious alleles. Fourth, while he noted that selfing affects both the efficacy of selection and that of conversion, he did not fully investigate the effect of mating systems. On one hand, selfing is efficient in purging strongly deleterious mutations causing inbreeding depression. However, since selfing is expected to increase drift, weakly deleterious mutations can fix in selfing species, contributing to the so-called “drift load” (
Charlesworth 1992;
Glémin 2007). Self-fertilizing populations are thus expected to exhibit low inbreeding depression and high drift load. On the other hand, gBGC, and thus its cost, vanishes as the selfing rate and homozygosity increase (
Marais et al. 2004). gBGC could thus challenge classical views on mating systems and it was even speculated that gBGC could affect their evolution (
Haudry et al. 2008).Here I present a population genetics model that includes mutation, selection, drift, and gBGC, which extends previous studies (
Gutz and Leslie 1976;
Lamb and Helmi 1982;
Nagylaki 1983a,
b;
Bengtsson 1990). I specifically examine how gBGC can affect inbreeding depression and the mutation load. I also focus on the effect of mating system, which is especially interesting with regard to the interaction between biased conversion and selection. Finally, I discuss how these results could give insight into how gBGC evolved.
Impacts of gBGC on inbreeding depression:
Inbreeding depression is defined as the reduction in fitness of selfed (and more generally inbred) individuals compared to outcrossed individuals,(15)where and are the mean fitness of outcrosses and selfcrosses, respectively (
Charlesworth and Charlesworth 1987;
Charlesworth and Willis 2009). The approximation is very good in most conditions, because under weak (
s ≪ 1) and strong selection (
x ≪ 1) (see
Glémin et al. 2003). Similar to the load, considering both sites for which either
S or
W alleles are deleterious, in proportion
q and 1 –
q, respectively, we get(16)
gBGC and the genetic basis of inbreeding depression in panmictic populations:
In infinite panmictic populations without gBGC, inbreeding depression depends only on mutation rates and dominance levels. Partially recessive mutations () contribute only to inbreeding depression, and the more recessive they are, the higher the inbreeding depression (
Charlesworth and Charlesworth 1987). In finite populations, deterministic results hold for strongly deleterious mutations (
s ≫ 1/
Ne), which contribute mostly to inbreeding depression. Contrary to the load, weakly deleterious mutations (∼
s ≤ 1/
Ne) contribute little to inbreeding depression (, and see
Bataillon and Kirkpatrick 2000).
Open in a separate windowInbreeding depression (×10
6) as a function of
s without (a and c) or with (b and d) gBGC (
b = 0.0002). (a and b)
h = 0.2: thick lines,
N = 5000; thin lines,
N = 10,000; dashed lines,
N = 50,000; dotted lines,
N = 100,000. (c and d)
N = 10,000: thick lines,
h = 0.4; thin lines,
h = 0.2; dashed lines,
h = 0.1; dotted lines,
h = 0.05.
u = 10
−6, λ = 2.Like the load, gBGC affects both the magnitude and the structure of inbreeding depression. In infinite populations, and more generally for strongly deleterious alleles (
Nes ≫ 1), replacing
x by
xeq given by Equations 4 in
Equations 15 and
16 leads to(17a)(17b)(17c)The effect of gBGC on inbreeding depression is not monotonic. Like the load, gBGC increases inbreeding depression if
b >
hs(1 − 2
q/(
q + λ −
qλ)). However, contrary to the load, a strong gBGC decreases inbreeding depression, which tends to 0 as
b increases, while the load tends to
qs (
Equation 10c). An analysis of
Equation 17b shows that mutations that maximize inbreeding depression are those that also maximize the load,
i.e.,
S deleterious mutations with
s ≈ 2
b.In finite populations, inbreeding depression must be integrated over the Φ distribution, which leads to(18)(see also
Glémin et al. 2003). While it is not possible to get an analytical expression of (18), numerical computations (see
appendix b) show that
S deleterious mutations with
s ≈ 2
b also maximize inbreeding depression in finite populations (). More broadly, inbreeding depression is maximal under the overdominant-like selection regime (gray area in ). Once again, even low to moderate gBGC markedly affects the genetic structure of inbreeding depression. First, mutations of intermediate effects contribute the most to inbreeding depression,
i.e., up to one order of magnitude higher than strongly deleterious mutations (compare with 4b). Second, even nearly additive mutations can have a substantial effect (compare ).Since little is known about the distribution of dominance coefficients, especially the dominance of mildly deleterious mutations (of the order of
b), it is difficult to quantitatively predict the full impact of gBGC on inbreeding depression. We can conclude that, on average, gBGC should increase inbreeding depression. However, further insight into mutational parameters is crucial to assess the quantitative impact of gBGC.
Joint effect of gBGC and mating system on the load and inbreeding depression:
Selfing, or more generally inbreeding, slightly reduces the segregating load through the purging of recessive mutations (
Ohta and Cockerham 1974), but can substantially increase the fixation load because of the effective population size reduction under inbreeding: (see above and
Pollak 1987;
Nordborg 1997;
Glémin 2007). In numerical examples, I assumed that α decreases with
F according to the background selection model (
Charlesworth et al. 1993;
Nordborg et al. 1996), as in
Glémin (2007). With gBGC, selfing thus has two opposite effects on the fixation load. Selfing increases the drift load
sensu stricto but decreases the fixation load due to gBGC. A surprising consequence is that the load can be higher in outcrossing than in selfing populations (). Quantitatively this is also expected, even with a gBGC hotspot affecting just 3% of the genome ( and
Open in a separate windowEffective population size (a and b) and the load (×10
6) (c–f) as a function of
F for different gBGC intensities (thick lines,
b = 0; thin lines,
b = 0.0001; dashed lines,
b = 0.0002; dotted lines,
b = 0.0005). The effective population size depends on
F under the background selection (BS) model (
Charlesworth et al. 1993), using
Equations 16 and 17 in
Glémin (2007): , where
U is the genomic deleterious mutation rate,
R is the genomic recombination rate,
sd is the mean selection coefficient against strongly deleterious mutations, and
hd is their dominance coefficient.
N = 10,000,
U = 0.2,
hd = 0.1, and
sd = 0.05. (a, c, and e)
R = 5, “weak” BS; (b, d, and f)
R = 0.5, “strong” BS. (c and d) Load averaged over half GC and half AT deleterious alleles, with a bias in favor of AT alleles. (e and f) Load averaged over 10% of GC deleterious alleles and 90% of AT deleterious alleles with a bias in favor of AT alleles; see .
h = 0.5,
u = 10
−6, and λ = 2.Generally, the effect of selfing is simpler for inbreeding depression. Purging,
Ne reduction, and suppression of gBGC contribute to decreasing inbreeding depression in selfing populations (). However, there are special cases in which maximum inbreeding depression is reached for intermediate selfing rates (). In such cases, in outcrossing populations, gBGC is strong enough to sweep polymorphism out and reduce inbreeding depression (
b >
s, regime 1 in ). As the selfing rate increases, gBGC declines, and the selection dynamics become overdominant-like (regime 2, ), thus maximizing inbreeding depression. For high selfing rates, gBGC vanishes (regime 3 in ) and deleterious alleles are either purged or fixed if there is substantial drift. This is similar to the effect of selfing on inbreeding depression caused by asymmetrical overdominance, where inbreeding depression also peaks for intermediate selfing rates (
Ziehe and Roberds 1989;
Charlesworth and Charlesworth 1990). In the present case, the range of parameters leading to this peculiar behavior is narrow because the overdominant-like region depends on the selfing rates and can vanish either for low or for high selfing rates ().
Open in a separate windowInbreeding depression (×10
6) as a function of
F for different gBGC intensities (thick lines,
b = 0; thin lines,
b = 0.0001; dashed lines,
b = 0.0002; dotted lines,
b = 0.0005). Inbreeding depression is averaged over half GC and half AT deleterious alleles. The effective population size depends on
F as in (same parameters). (a)
s = 0.002; (b)
s = 0.0005; (c)
s = 0.0002.
h = 0.2,
u = 10
−6, and λ = 2.
Minimum load and the evolution of gBGC and recombination landscapes:
Although gBGC may have deleterious fitness consequences, it is surprising that it evolved in many taxa ().
Birdsell (2002) initially suggested that gBGC may have evolved as a response to mutational bias toward AT (λ > 1, here). Indeed, I show that a minimum load is reached for weak gBGC (
b ≈ ln(λ)/4
N,
Equation 14). This result is very general whatever the distribution of fitness effects of mutations (
appendix d). However, the range of optimal gBGC is narrow, and gBGC increases the load as far as
b > ln(λ)/2
N (
appendix c). In humans, using
N = 10,000 and λ = 2, gBGC levels that minimize the load are ∼1.17 × 10
−5,
i.e., one order of magnitude lower than the average bias observed in recombination hotspots (
Myers et al. 2005). However, selection on conversion modifiers will not necessarily minimize the load because of gametic disequilibrium generated between modifiers and fitness loci (
Bengtsson and Uyenoyama 1990). Selection for limitation of somatic AT-biased mutations could also have selected for GC-biased mismatch repair machinery (
Brown and Jiricny 1987). If the bias level that would be selected for somatic reasons is >ln(λ)/2
N, a side effect would be the generation of a substantial load at the population level. Finally, it is interesting to note that when synonymous codon positions are under selection for translation accuracy, optimal gBGC levels can be higher than gBGC levels that minimize the protein load, especially when most optimal codons end in G or C ().Conversely, gBGC could also affect the evolution of recombination landscapes, which could evolve to reduce the gBGC load. Surprisingly, for a given recombination/conversion level, the hotspot distribution does not appear to be optimal (), one can speculate that the hotspot localization outside genes could be a response to avoid the deleterious effects of gBGC.Up to now, these verbal arguments have not been assessed theoretically (but see
Bengtsson and Uyenoyama 1990 for a different kind of conversion bias). Population genetics models are necessary to test these hypotheses concerning the evolution of gBGC and recombination landscapes and to pinpoint the key parameters that might govern their evolution.
gBGC and the evolution of mating systems:
Deleterious mutations also play a crucial role in the evolution of mating systems. They are the main source of inbreeding depression, which balances the automatic advantage of selfing. The drift load is also thought to contribute to the extinction of selfing species. Since they are mainly homozygous, selfing species are mostly free from gBGC and its deleterious impacts. I discuss below how this might affect the evolution of mating systems.
Inbreeding depression and the shift in mating systems:
Inbreeding depression plays a key role in the evolution of mating systems (
Charlesworth and Charlesworth 1987;
Charlesworth 2006b). Since it balances the automatic advantage of selfing, high inbreeding depression favors outcrossing, while selfing can evolve when it is low. Moreover, selfing helps to purge strongly deleterious mutations, thus decreasing inbreeding depression. This positive feedback reinforces the disruptive selection on the selfing rate and prevents the transition from selfing to outcrossing (
Lande and Schemske 1985).Theoretical results suggest that, in most conditions, gBGC would reinforce inbreeding depression in outcrossing populations (), which would prevent the evolution of selfing. In reverse, if selfing is initially selected for, recurrent selfing would reduce the load through both purging and avoidance of gBGC. Under this scenario, gBGC would reinforce disruptive selection on mating systems. However, under some conditions (see ), inbreeding depression peaks at intermediate selfing rates, as observed for asymmetrical overdominance (
Ziehe and Roberds 1989;
Charlesworth and Charlesworth 1990). In theory, this could prevent the shift toward complete selfing and maintain stable mixed mating systems (
Charlesworth and Charlesworth 1990;
Uyenoyama and Waller 1991). However, this pattern is observed under restrictive conditions and it is very unlikely on the whole-genome scale. Dominance patterns are crucial for predicting inbreeding depression, especially with gBGC. Contrary to the load, it is thus difficult to evaluate the quantitative impact of gBGC on inbreeding depression. However, increased inbreeding depression in outcrossing species subject to gBGC seems to be the most likely scenario.
gBGC and the long-term evolution of mating systems:
In the long term, the gBGC-induced load also challenges the “dead-end hypothesis,” which posits that, because of the reduction of selection efficacy, self-fertilizing species would accumulate weakly deleterious mutations in the long term, eventually leading to extinction (
Takebayashi and Morrell 2001). Because of gBGC, not drift, outcrossing species could also accumulate a load of weakly deleterious mutations (), and they could suffer from a higher load than highly self-fertilizing species ( found that in two outcrossing grass species, but not in two self-fertilizing ones, the
dN/
dS ratio is significantly higher for genes exhibiting GC enrichment. They speculated that substitutions in these genes might contribute to increasing the load in these two outcrossing grass species. Such results are still very sparse. In plants, evidence of strong gBGC is mainly restricted to grasses (but see
Wright et al. 2007). It will be necessary to conduct more in-depth studies to assess the phylogenetic distribution of gBGC in plants and other hermaphrodite organisms and to further test the genomic Achilles'' heel hypothesis in relation to mating systems. While theoretically possible, the quantitative effect of gBGC on the evolution of mating systems remains a new, open, and challenging question.
Conclusion:
I showed that the interaction between gBGC and selection might have surprising qualitative consequences on load and inbreeding depression patterns. Given the few quantitative data available on gBGC levels and selection intensities (mainly in humans), it turns out that even weak genome-wide gBGC can have significant fitness impacts. gBGC should be taken into account not only for sequence analyses (
Berglund et al. 2009;
Galtier et al. 2009), but also for its potential fitness consequences, for instance concerning genetic diseases. Interferences between gBGC and selection also give rise to new questions on the evolution of mating systems. However, most of the challenging conclusions given here have yet to be quantitatively evaluated. Quantification of gBGC and its interaction with selection in various organisms will be crucial in the future.
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