Retrogenes Reveal the Direction of Sex-Chromosome Evolution in Mosquitoes

The mosquito Anopheles gambiae has heteromorphic sex chromosomes, while the mosquito Aedes aegypti has homomorphic sex chromosomes. We use retrotransposed gene duplicates to show an excess of movement off the An. gambiae X chromosome only after the split with Ae. aegypti, suggesting that their ancestor had homomorphic sex chromosomes.HETEROMORPHIC sex chromosomes, both XX/XY and ZZ/ZW systems, have evolved independently multiple times in both animals and plants (Bull 1983; Charlesworth 1996; Rice 1996). Sex chromosomes are thought to evolve from a pair of autosomes that acquire a new sex-determining locus. Theory suggests that natural selection will favor tight linkage between the newly arisen sex-determining locus and sexually antagonistic alleles (i.e., genes that are beneficial in one sex, but detrimental in the other), which favors the suppression of recombination near the sex-determining locus (Charlesworth et al. 2005). In some species, this nonrecombining region includes only a small portion of the sex chromosome (hereafter referred to as homomorphic sex chromosomes), whereas in other species, this region encompasses most of the sex chromosomes (heteromorphic sex chromosomes). In many species the nonrecombining region progressively expands from only the portion near the sex-determining locus to nearly the full extent of the sex chromosomes (Lahn and Page 1999; Lawson Handley et al. 2004; Nicolas et al. 2005). However, the broad phylogenetic distribution of homomorphic sex chromosomes suggests that this progression does not happen in every species (e.g., Matsubara et al. 2006; Tsuda et al. 2007), although why it should occur in some lineages and not in others is unknown. As noted by Gilchrist and Haldane (1947, p. 187): “It is a striking fact that this [the suppression of recombination across the sex chromosome] has not happened in many large and successful groups.”Within the order Diptera, there are a wide variety of sex chromosomes and sex-determination mechanisms, including XY, ZW, multiple-X, and homomorphic systems, often varying within the same family (Marin and Baker 1998; Schutt and Nothiger 2000; Sanchez 2008). The mosquito Anopheles gambiae (a species in the subfamily Anophelinae) has fully differentiated heteromorphic X and Y chromosomes that show no evidence of recombination (Krzywinski et al. 2004). The mosquito Aedes aegypti (subfamily Culicinae) has a nonrecombining sex-determining region that spans only a few megabases on chromosome 1; this chromosome is homologous to chromosomes X and 2R of An. gambiae (Nene et al. 2007). An. gambiae and Ae. aegypti diverged ∼150 million years ago (Krzywinski et al. 2006).Because of the rapid turnover of sex-chromosome systems among the Diptera, it is not clear if the common ancestor of Ae. aegypti and An. gambiae had only a sex-determining region (i.e., homomorphic sex chromosomes) or fully differentiated heteromorphic sex chromosomes (Rai and Black 1999). The generally accepted model of sex-chromosome evolution, in which homomorphic sex chromosomes progressively suppress recombination and become heteromorphic, predicts that the common ancestor of Ae. aegypti and An. gambiae had homomorphic sex chromosomes (Figure 1A). This implies that evolution of heteromorphic sex chromosomes in An. gambiae occurred in a short period of time after the split between these two lineages and before the radiation of the Anophelines and that the homomorphic sex chromosomes of Ae. aegypti have been nearly static over evolutionary time. Alternatively, the common ancestor may have had nearly or fully differentiated sex chromosomes, and Ae. aegypti evolved from heteromorphic sex chromosomes to having only a small sex-determining region (Figure 1B; Rao and Rai 1987). We imagine this transition may have occurred by one of two mechanisms: either the sex-determining locus was transposed from the ancestral sex chromosome to an autosome or, in an XO sex-determination system, one of the “numerator” genes located on the X chromosome sustained an inactivating mutation, effectively making a karyotypic XX individual into a genetically male XO individual. (The precise mechanism of sex determination in Ae. aegypti is not known.)Open in a separate windowFigure 1.—Hypotheses for sex-chromosome evolution in Anopheles gambiae and Aedes aegypti. (A) The ancestor of An. gambiae and Ae. aegypti had homomorphic sex chromosomes and heteromorphism evolved along the Anopheline lineage. (B) The ancestor of An. gambiae and Ae. aegypti had heteromorphic chromosomes and homomorphism evolved along the Culicine lineage.To determine the state of the mosquito common ancestor, we examined genes duplicated by retrotransposition in the An. gambiae genome. Several organisms with heteromorphic sex chromosomes, including mammals and Drosophila, have an excess of retrotransposed genes moving from the X chromosome to autosomes compared to genes moving between autosomes or from the autosomes to the X (Betran et al. 2002; Emerson et al. 2004; Vinckenbosch et al. 2006; Meisel et al. 2009). This pattern is further found to be strongly associated with the origin of new X chromosomes in both mammals and Drosophila (Potrzebowski et al. 2008; Meisel et al. 2009), although it continues long after X chromosomes arise. While there are many hypotheses for the evolutionary forces that drive gene movement off X chromosomes—including sexual antagonism and meiotic sex-chromosome inactivation (e.g., Hense et al. 2007)—it is likely that all of these forces also act in mosquitoes, implying excess movement off the heteromorphic X in this clade as well. We reasoned that if the common ancestor of Ae. aegypti and An. gambiae had homomorphic sex chromosomes (Figure 1A), there should be an excess of retrogene movement off the X chromosome in An. gambiae only after the divergence of the two lineages (i.e., since An. gambiae evolved a differentiated X chromosome). In contrast, if the common ancestor had fully heteromorphic chromosomes (Figure 1B), then our prediction is that there will be an excess of gene movement off the An. gambiae X on both the shared ancestral branch and the Anopheles-specific branch after the split with Aedes. (Note that the Ae. aegypti genome is largely not assembled onto chromosomes, precluding a similar analysis in this species.)We collected data on all functional, intact duplicates in the An. gambiae genome and all orthologs between An. gambiae and Ae. aegypti from Ensembl version 54. When genes are retrotransposed there will be introns in the parental copy, but no introns in the daughter copy, allowing us to polarize gene movement. Although introns may be lost—and more rarely gained—over time, the rate of such changes is quite low (Coulombe-Huntington and Majewski 2007). Nevertheless, unless a parental gene loses all of its introns and the daughter gene gains introns, such changes will merely cause us to miss events rather than to assign them to an incorrect chromosome. Using gene-tree/species-tree reconciliation (Goodman et al. 1979), we identified retrotransposition events in the An. gambiae genome that have occurred since the split with Drosophila melanogaster and assigned them to a branch on the basis of the timing of the inferred duplication event in the gene tree. Calculating the expected number of movements on the basis of the equations presented in Betran et al. (2002), we find that an excess of movement off the X chromosome has in fact occurred since the split with D. melanogaster (χ² = 23.83, d.f. = 2, P = 6.7 × 10⁻⁶). We then divided the retrotransposition events into those that occurred before the divergence of An. gambiae and Ae. aegypti and those that occurred only in An. gambiae since the split. We determined that there is a 400% excess of retrotransposition events off the X chromosome since the An. gambiae and Ae. aegypti split (Figure 2: χ² = 51.97, d.f. = 2, P = 5.2 × 10⁻¹²). However, there is no excess of retrotransposition off the X chromosome prior to the split between An. gambiae and Ae. aegypti (Figure 2: χ² = 1.51, d.f. = 2, P = 0.47). This strongly suggests a recent origin of fully differentiated heteromorphic sex chromosomes in An. gambiae.Open in a separate windowFigure 2.—Retroposition events off the X chromosome. There is an excess of genes moving off the X chromosome on the An. gambiae-specific lineage, but not on the branch leading to the common ancestor of An. gambiae and Ae. aegypti.The deepest split between species within the subfamily Anophelinae—all of which have fully differentiated sex chromosomes—occurs soon after the split with the Culicinae (Krzywinski et al. 2006). This implies that the evolution of heteromorphic sex chromosomes must have occurred very soon after the split with Ae. aegypti. To determine whether there was a burst of retrotransposition off the X following this split, we examined the amino acid sequence identity between X-to-autosome retrotransposed proteins and their parental paralogs. A comparison of these distributions indicates that there is no difference in the percentage of identity of genes retrotransposed off the An. gambiae X chromosome and one-to-one orthologs between An. gambiae and Ae. aegypti (71.1% vs. 70.7%, t-test, P = 0.92; JTT amino acid distances, 0.508 vs. 0.436, t-test, P = 0.57). Given the fact that functional retrotransposed genes have been found to evolve more rapidly than single-copy genes (Betran et al. 2002), these results support the idea that these duplication events occurred soon after the split between An. gambiae and Ae. aegypti.Our results have important implications for two further areas of research. First, a recent article (Moyle et al. 2010) proposed that X-to-autosome duplication events could be partly responsible for the large X-effect—the disproportionate effect of the X chromosome on reproductive isolation (Coyne and Orr 2004). This is because gene movement between chromosomes can itself cause reproductive isolation (e.g., Masly et al. 2006), and any excess movement involving the X will lead to an excess of reproductive isolation loci mapping to this chromosome. One prediction of this model is that species showing the large X-effect should also show an excess of X-to-autosome gene movement. As An. gambiae does in fact exhibit patterns consistent with the large X-effect (Slotman et al. 2005), our demonstration of an excess of movement off the X supports this model.Second, it has been proposed that the excess movement off the X in Drosophila is the cause of the deficit of male-biased genes on the X in the same species (e.g., Vibranovski et al. 2009), although the number of retrotransposed genes is much smaller than the number of missing male-biased genes (Betran et al. 2002; Parisi et al. 2003). We have previously shown that there is no deficit of male-biased genes on the An. gambiae X chromosome, at any significance level (Hahn and Lanzaro 2005). Given the observed excess of gene movement off the X presented here, we therefore find little support for a causal link between movement and genome-wide patterns of male-biased gene expression.Our results suggest that retrogene movement is a general feature of sex-chromosome evolution and support the hypothesis that the common ancestor of An. gambiae and Ae. aegypti had homomorphic sex chromosomes. It appears that the nonrecombining region around the sex-determining locus in An. gambiae expanded rapidly after the divergence with Ae. aegypti. Further investigation into the causes of the rapid expansion in the An. gambiae lineage and the long-term stasis in the Ae. aegypti lineage is clearly warranted.     

Introgression of Drosophila simulans Nuclear Pore Protein 160 in Drosophila melanogaster Alone Does Not Cause Inviability but Does Cause Female Sterility

We have been analyzing genes for reproductive isolation by replacing Drosophila melanogaster genes with homologs from Drosophila simulans by interspecific backcrossing. Among the introgressions established, we found that a segment of the left arm of chromosome 2, Int(2L)S, carried recessive genes for hybrid sterility and inviability. That nuclear pore protein 160 (Nup160) in the introgression region is involved in hybrid inviability, as suggested by others, was confirmed by the present analysis. Male hybrids carrying an X chromosome of D. melanogaster were not rescued by the Lethal hybrid rescue (Lhr) mutation when the D. simulans Nup160 allele was made homozygous or hemizygous. Furthermore, we uniquely found that Nup160 is also responsible for hybrid sterility. Females were sterile when D. simulans Nup160 was made homozygous or hemizygous in the D. melanogaster genetic background. Genetic analyses indicated that the D. simulans Nup160 introgression into D. melanogaster was sufficient to cause female sterility but that other autosomal genes of D. simulans were also necessary to cause lethality. The involvement of Nup160 in hybrid inviability and female sterility was confirmed by transgene experiment.INVESTIGATING the genetic bases of reproductive isolation is important for understanding speciation (Sawamura and Tomaru 2002; Coyne and Orr 2004; Wu and Ting 2004; Noor and Feder 2006; Presgraves 2010). In fact, continued interest in this issue has led to the isolation of several genes that are responsible for hybrid sterility and inviability in Drosophila (Ting et al. 1998; Barbash et al. 2003; Presgraves et al. 2003; Brideau et al. 2006; Masly et al. 2006; Phadnis and Orr 2009; Prigent et al. 2009; Tang and Presgraves 2009). Drosophila melanogaster and Drosophila simulans are the best pair for such genetic analyses (Sturtevant 1920). Hybrid male lethality in the cross between D. melanogaster females and D. simulans males is caused by incompatibility involving chromatin-binding proteins (Barbash et al. 2003; Brideau et al. 2006), and hybrid female lethality in the reciprocal cross is caused by incompatibility between a maternally supplied factor and a repetitive satellite DNA (Sawamura et al. 1993a; Sawamura and Yamamoto 1997; Ferree and Barbash 2009). Furthermore, individuals with the genotype equivalent to the backcrossed generation exhibit different incompatibilities (Pontecorvo 1943; Presgraves 2003), two components of which have been identified (Presgraves et al. 2003; Tang and Presgraves 2009). Because of the discovery of rescuing mutations that prevent hybrid inviability and sterility (Watanabe 1979; Hutter and Ashburner 1987; Sawamura et al. 1993a,b; Davis et al. 1996; Barbash and Ashburner 2003), chromosome segments from D. simulans can be introgressed into the D. melanogaster genome (Sawamura et al. 2000; Masly et al. 2006). For example, introgression of the D. simulans chromosome 4 or Y into D. melanogaster results in male sterility (Muller and Pontecorvo 1940; Orr 1992), and the recessive sterility by the chromosome 4 introgression is attributed to an interspecific gene transposition between chromosomes (Masly et al. 2006).The other successful introgressions of this type are the tip and the middle regions of the left arm of chromosome 2, Int(2L)D and Int(2L)S, respectively (Sawamura et al. 2000). Both female and male Int(2L)S homozygotes are sterile (Figure 1A), and the recessive sterility genes have been mapped with recombination and complementation assays against deficiencies. The male sterility genes are polygenic and interact epistatically with each other (Sawamura and Yamamoto 2004; Sawamura et al. 2004b), but the female sterility gene has been mapped to a 170-kb region containing only 20 open reading frames (ORFs) (Sawamura et al. 2004a). Interestingly, Int(2L)S also carries a recessive lethal gene whose effect is detected only in a specific genotype (Figure 1B). Lethality in hybrid males from the cross between D. melanogaster females and D. simulans males is rescued by the Lethal hybrid rescue (Lhr) mutation in D. simulans (Watanabe 1979), but the hybrid males cannot be rescued if they carry the introgression, presumably because of incompatibility between an X-linked gene(s) of D. melanogaster and a homozygous D. simulans gene in the Int(2L)S region (Sawamura 2000). Because the female sterility gene and the lethal gene were not separated by recombination, Sawamura et al. (2004a) suggested that female sterility and lethality may be a consequence of the pleiotropic effects of a single gene.Open in a separate windowFigure 1.—Viability and fertility of flies with various genotypes. (A) Females and males that are heterozygous or homozygous for the D. simulans introgression Int(2L)S (Int) in the D. melanogaster genetic background. (B) Four genotypic classes from the cross between introgression heterozygote [Int(2L)S/CyO] females and D. simulans Lethal hybrid rescue (Lhr) males. (C) Four genotypic classes from the cross between D. melanogaster females with a deficiency (Df) [Df(2L)/CyO] and D. simulans Lhr males. Open chromosome regions are from D. melanogaster, and shaded ones are from D. simulans.Because the hybrid lethal gene on Int(2L)S is recessive, the gene can be mapped by deficiencies instead of using introgression (Figure 1C) (Sawamura 2000; Sawamura et al. 2004a). In fact, hybrid males carrying a deficiency encompassing this region (hemizygous for the D. simulans genes) and the D. melanogaster X chromosome are lethal even if they carry the hybrid rescue mutation (see also Presgraves 2003). Tang and Presgraves (2009) subsequently narrowed down this region with multiple deficiencies and identified the hybrid lethal gene with a complementation test and transformation. We confirmed their conclusion and report our data here. In the transformation experiment, we used the natural promoter of the gene, instead of overexpressing the gene (Tang and Presgraves 2009), and we directly indicated, for the first time, that the hybrid lethal gene is also responsible for the female sterility of introgression homozygotes. The D. simulans allele of the gene seems to be nonfunctional on the genetic background of D. melanogaster. Moreover, our results indicated that this gene and chromosome X of D. melanogaster are not sufficient to explain the inviability and that another autosomal gene(s) in D. simulans is required.     

Sex-Linked Inheritance in Macaque Monkeys: Implications for Effective Population Size and Dispersal to Sulawesi

Sex-specific differences in dispersal, survival, reproductive success, and natural selection differentially affect the effective population size (N_e) of genomic regions with different modes of inheritance such as sex chromosomes and mitochondrial DNA. In papionin monkeys (macaques, baboons, geladas, mandrills, drills, and mangabeys), for example, these factors are expected to reduce N_e of paternally inherited portions of the genome compared to maternally inherited portions. To explore this further, we quantified relative N_e of autosomal DNA, X and Y chromosomes, and mitochondrial DNA using molecular polymorphism and divergence information from pigtail macaque monkeys (Macaca nemestrina). Consistent with demographic expectations, we found that N_e of the Y is lower than expected from a Wright–Fisher idealized population with an equal proportion of males and females, whereas N_e of mitochondrial DNA is higher. However, N_e of 11 loci on the X chromosome was lower than expected, a finding that could be explained by pervasive hitchhiking effects on this chromosome. We evaluated the fit of these data to various models involving natural selection or sex-biased demography. Significant support was recovered for natural selection acting on the Y chromosome. A demographic model with a skewed sex ratio was more likely than one with sex-biased migration and explained the data about as well as an ideal model without sex-biased demography. We then incorporated these results into an evaluation of macaque divergence and migration on Borneo and Sulawesi islands. One X-linked locus was not monophyletic on Sulawesi, but multilocus data analyzed in a coalescent framework failed to reject a model without migration between these islands after both were colonized.THE effective size of a population (N_e) determines the relative impact of genetic drift and natural selection on mutations with mild effects on fitness (Charlesworth 2009). Differences in N_e are hypothesized to affect virtually every aspect of genome evolution, including rates of molecular evolution, abundance of introns and transposable elements, and persistence of duplicate genes, and this has important implications for the evolution of complexity via both adaptive and degenerative processes (Lynch 2007). Of relevance are not only the number of different individuals in a population, but also the number of copies of a gene within each individual. In diploid species with separate sexes, sex chromosomes and mitochondrial DNA (mtDNA) differ in copy number from autosomal DNA (aDNA): both sexes have two alleles at autosomal loci whereas in species with male heterogamy, males have one X and one Y chromosome, females have two Xs, and a female/male pair has effectively only one copy of mtDNA due to maternal inheritance. Sex-specific differences in demographic parameters such as migration, adult sex ratio, and variance in reproductive success also affect relative copy number and associated levels of neutral polymorphism at mtDNA, aDNA, the X chromosome (xDNA), and the Y chromosome (yDNA) (Hedrick 2007).The effective population size is the number of individuals in a Wright–Fisher idealized population (Fisher 1930; Wright 1931) that have the same magnitude of genetic drift as an observed population, where ideal individuals are diploid, and have discrete (nonoverlapping) generations, constant population size, and random mating. N_e can be quantified in terms of variance in allele frequency over generations (variance N_e) or variance in inbreeding over time (inbreeding N_e). If population size is constant with random mating, these approaches for quantifying N_e produce identical results (Kimura and Crow 1963; Whitlock and Barton 1997). At mutation–drift equilibrium with an equal number of males and females and a Poisson distributed number of offspring with a mean of two offspring per individual, N_e-aDNA and N_e-xDNA are expected to be four and three times as large, respectively, as N_e-yDNA and N_e-mtDNA; we refer to this as the “ideal expectation with an equal proportion of males and females.”Demography can alter relationships between N_e of different parts of the genome. For example, extreme skew in adult sex ratio can cause N_e of uniparentally inherited portions of the genome to exceed N_e of biparentally inherited portions (Figure 1A; Nunney 1993; Caballero 1994; Hoelzer 1997; Hedrick 2007). With a skewed sex ratio, the more common sex has a higher variance in reproductive success than the rare one, and this causes the overall variance in reproductive success to increase as the sex-ratio bias increases (Nunney 1993). Sex-biased dispersal such as female philopatry also alters relationships between N_e-aDNA, N_e-xDNA, N_e-yDNA, and N_e-mtDNA (Figure 1B), causing N_e of portions of the genome that disperse less to increase (Nei and Takahata 1993; Hoelzer 1997; Wang and Caballero 1999).Open in a separate windowFigure 1.—N_e of aDNA, xDNA, mtDNA, and yDNA as a function of (A) sex ratio skew and (B) the probability of female dispersal. In B, a finite island model of subdivided populations of constant size is assumed with a population size of 10,000 individuals, 10 subpopulations, and a male probability of migration equal to 0.1.At least five factors related to natural selection also can cause the relative N_e of aDNA, xDNA, yDNA, and mtDNA to depart from expectations: (1) very low or absent recombination in mtDNA and a portion of yDNA, (2) haploidy of mtDNA and yDNA, (3) hemizygosity of xDNA in males, (4) sexual selection and differences in gene content, and (5) differences in the rate and variance of mutation. “Selective sweeps” in which an advantageous mutation is fixed by natural selection, reduces N_e of linked sites (Maynard Smith and Haigh 1974) and this can affect the entire mitochondrial genome and nonrecombining portion of the Y chromosome. Nonrecombining portions of yDNA and mtDNA are also affected by stochastic loss of alleles containing the fewest deleterious mutations (“Muller''s ratchet”; Muller 1964; Felsenstein 1974), which results in a gradual decline of fitness of these chromosomes over time. N_e of nonrecombining DNA is further reduced by elimination of variation linked to substantially deleterious mutations (“background selection”; Charlesworth et al. 1993), by interference between linked polymorphisms that impedes fixation of advantageous alleles and extinction of deleterious ones (the “Hill–Robertson effect”; Hill and Robertson 1966; McVean and Charlesworth 2000), and by increased frequency of deleterious mutations linked to advantageous ones during a selective sweep (“genetic hitchhiking”; Rice 1987). Hemizygous X-linked and haploid Y-linked loci in males and mtDNA loci in both sexes are more vulnerable to recessive deleterious mutations because they are not masked by a second allele (Otto and Goldstein 1992). Hemizygosity on the X chromosome can also increase the rate of selective sweeps when advantageous mutations are recessive (Charlesworth et al. 1987). Similarly, these loci are also susceptible to recessive species incompatibilities—a factor that at least partially accounts for Haldane''s rule for hybrid sterility (Haldane 1922; Orr 1997). Sexual selection differentially influences the probability of fixation of mutations depending on mode of inheritance (Wade and Shuster 2004), especially mutations with antagonistic fitness effects between the sexes (Gibson et al. 2002). Additionally, the rate of evolution of animal mtDNA is much higher than aDNA, xDNA, and yDNA (Haag-Liautard et al. 2008) and this presumably contributes to variation in the frequency of nonneutral mutations in different parts of the genome.Differences among N_e of mtDNA, yDNA, xDNA, and aDNA are thought to be particularly pronounced in papionin monkeys (macaques, baboons, geladas, mandrills, drills, and mangabeys). These monkeys have a highly sex-biased adult demography; females form stable philopatric groups of close relatives, whereas males generally change social groups and disperse more widely (Dittus 1975). Often adult sex ratio of papionins is female biased (Dittus 1975; Melnick and Pearl 1987; O''Brien and Kinnard 1997; Okamoto and Matsumura 2001), and males have higher variance in reproductive success than females (Dittus 1975; de Ruiter et al. 1992; Keane et al. 1997; Van Noordwijk and Van Schaik 2002; Widdig et al. 2004). These sex differences predict strong population subdivision of mtDNA with little or no subdivision of aDNA, deep mtDNA coalescence times, and frequent mtDNA paraphyly among species, and discordant genealogical relationships between mtDNA and yDNA—and this has been observed in multiple studies (Melnick and Pearl 1987; Melnick 1988; Melnick and Hoelzer 1992; Melnick et al. 1993; Hoelzer et al. 1994; Evans et al. 1999, 2001, 2003; Tosi et al. 2000, 2002, 2003; Newman et al. 2004). Female philopatry and obligate male migration is a common social system in mammals (Greenwood 1980; Dobson 1982; Johnson 1986), though less so in humans (Seielstad et al. 1998), and molecular variation provides an effective tool for exploring the impact of natural selection and demography on aDNA, the sex chromosomes, and mtDNA (Nachman 1997; Bachtrog and Charlesworth 2002; Stone et al. 2002; Berlin and Ellegren 2004; Hellborg and Ellegren 2004; Wilder et al. 2004; Hammer et al. 2008).We explored the genetic effects of demography and linked selection in structuring sequence polymorphism of a papionin monkey—the macaques—at two levels. We first tested whether levels of polymorphism in aDNA, xDNA, yDNA, and mtDNA in a Bornean population of the pigtail macaque, Macaca nemestrina, match expectations under scenarios involving natural selection and also whether the data might be explained by simple demographic models with sex-specific dispersal or a biased sex ratio. We then explored demography on a larger, inter-island scale by estimating the time of divergence between macaques on Borneo and Sulawesi islands and by testing for evidence of ongoing migration between these islands.     

Different Aneuploidies Arise From the Same Bridge-Induced Chromosomal Translocation Event in Saccharomyces cerevisiae

Chromosome translocations are gross chromosomal rearrangements that have often been associated with cancer development in mammalian cells. The feasibility of drastically reshaping the genome with a single translocation event also gives this molecular event a powerful capacity to drive evolution. Despite these implications and their role in genome instability, very little is known about the molecular mechanisms that promote and accompany these events. Here, at the molecular level, we describe 10 morphologically and physiologically different translocants ensuing from the induction of the same bridge-induced translocation (BIT) event in the budding yeast Saccharomyces cerevisiae. We have demonstrated that, despite their common origin from the integration of the same linear DNA construct, all 10 translocation mutant strains have different phenotypes and the ability to sporulate and regulate gene expression and morphology. We also provide insights into how heterogeneous phenotypic variations originate from the same initial genomic event. Here we show eight different ways in which yeast cells have dealt with a single initial event inducing translocation. Our results are in agreement with the formation of complex rearrangements and abnormal karyotypes described in many leukemia patients, thus confirming the modellistic value of the yeast BIT system for mammalian cells.TRANSLOCATIONS between nonhomologous chromosomes are some of the most severe genomic aberrations, which in higher eukaryotes often lead to malignant transformation. In humans, they have been associated with hematological cancers such as myelogenous leukemia but also with lymphomas, solid tumors, and recently, with mesenchymal and epithelial cancers (Dalla-Favera et al. 1982; Nowell 1988; Rowley 2001, 2008; Taki and Taniwaki 2006; Gasparini et al. 2007; Nickoloff 2008). In the past few decades, molecular and cytological studies have demonstrated that different chromosomal abnormalities such as aneuploidy can be associated with a translocation event in cancer cells. For example, acute transformation of leukemia patients is often associated with the duplication of the Philadelphia chromosome, trisomy 8 and isochromosome 17q, or other complex chromosomal rearrangements (Muehleck et al. 1984; Bernstein 1988; De Braekeleer 2007). Furthermore, unbalanced translocations can be detected in older leukemia patients. Karyotypic analyses have revealed that unbalanced translocations can advance into further rearrangements of the chromosomes involved in the translocations. This can give rise to complex and abnormal karyotypes characterized by monosomy, disomy, and trisomy for different segments of the translocation participant chromosomes (Pedersen et al. 2000).Many molecular studies have been performed to better understand the formation of such severe chromosomal aberrations (Kanaar et al. 1998; Delneri et al. 2003; Aylon and Kupiec 2004; Egli et al. 2004; Motegi et al. 2006; Weinstock et al. 2006; Motegi and Myung 2007). Nevertheless, very little is known about the mechanisms by which chromosome translocations and secondary rearrangements arising from these events occur. The occurrence of two or more double-strand breaks (DSB) and an inappropriate use of the recombination machinery of the cells are supposed to be some of the main ways in which a DNA translocation is promoted (Rowley 2001, 2008; Agarwal et al. 2006). In humans, defects in many DSB repair mechanisms such as non-allelic homologous recombination, non-homologous end-joining, and fork stalling and template switching (FoSTeS) have been reported to be involved in translocation formation (Gu et al. 2008). In yeast cells, single-strand annealing and break-induced replication (BIR) pathways have been shown to be involved in the formation of chromosome rearrangements and translocations (Bosco and Haber 1998; Haber 2006).Recently, our group described a method that allows the generation and subsequent selection of chromosomal translocations between any two chromosomal loci in diploid yeast strains. This was done by transformation of whole yeast cells with a linear DNA construct, carrying the KAN^R selectable marker flanked by DNA sequences homologous to two different chromosomal loci (Tosato et al. 2005, 2009; Nikitin et al. 2008).We used this method to investigate the multiple molecular mechanisms and pathways that might be involved during a translocation event in Saccharomyces cerevisiae. To this end, we generated a collection of 10 different mutant strains harboring a translocation between chromosomes XVI and IX (Figure 1), followed by their comparative molecular and physiological analyses. Results obtained from our experiments suggest that the induction of the same chromosomal translocation can lead to a wide variety of secondary chromosomal rearrangements, generating aneuploidy derived from partial or complete chromosome duplication or loss of genetic material.Open in a separate windowFigure 1.—Schematic of BIT between chromosomes XVI and IX. The 777,694-bp aberrant chromosome together with the two hypothetic fragments originating after a non-reciprocal BIT translocation are shown. Genes analyzed by Southern hybridization are reported along the chromosomes together with the primers used to test the integration of the BIT construct at the level of the two targeted loci. SSU1Fw and SUc2RevNEW primers were used to perform the bridge-PCR to validate the presence of the translocant chromosome (see results). TBP, translocation breakpoint.Physiological analyses also revealed that the 10 translocants obtained with the bridge-induced translocation (BIT) system in this work have different mutant phenotypes, which are analogous to the high variation of phenotypes characteristic of cancer cells. We demonstrated that these mutant strains exhibit altered behavior and fitness in different carbon sources for growth, different sporulation efficiencies, and ability to flocculate. Expression analyses also show that they exhibit different expression profiles of various genes located along the translocated chromosome or involved in cellular processes such as apoptosis, cell cycle regulation, and oxidative stress response. Overall, our work shows that the single integration of a linear DNA cassette with homology on two different chromosomes not only can generate a translocation, but also might be responsible for other complex chromosomal rearrangements. Such complex genomic rearrangements seen in yeast may play a role as key evolutionary forces, reshaping and remodeling genomes, followed by selection and adaptation into specialized cells or even neoplastic transformation, as observed in mammalian cancer cells.     

Epithelial Polarity: Interactions Between Junctions and Apical–Basal Machinery

Epithelial polarity is established and maintained by competition between determinants that define the apical and basolateral domains. Cell–cell adhesion complexes, or adherens junctions, form at the interface of these regions. Mutations in adhesion components as well as apical determinants normally lead to an expansion of the basolateral domain. Here we investigate the genetic relationship between the polarity determinants and adhesion and show that the levels of the adhesion protein Armadillo affect competition. We find that in arm mutants, even a modest reduction in the basolateral component lgl leads to a full apical domain expansion or lgl phenotype. By using an allelic series of Armadillo mutations, we show that there is a threshold at which basolateral expansion can be reversed. Further, in embryos lacking the Wingless signaling component zw3, the same full apical expansion occurs again with only a reduction in lgl. We propose a model where zw3 regulates protein levels of apical and adhesion components and suggest that a reciprocal interaction between junctions and polarity modules functions to maintain stable apical and basolateral domains.A major reason that epithelial cells require apical–basal polarity is to differentiate between the interior of the organism and the external environment. To accomplish this, epithelial cells generate molecularly distinct domains along their plasma membranes: an apical domain that is exposed to the outside, a basolateral domain that contacts the interior, and, in between, an adhesion complex that holds the cell sheet together. In Drosophila embryos, at least three polarity complexes are used to establish and maintain this subcellular organization commonly known as apical–basal cell polarity. On the apical side, the Crumbs (Crb) and Stardust (Std, Pals) proteins form one complex (Jurgens et al. 1984; Tepass et al. 1990; Tepass and Knust 1993; Wodarz et al. 1995; Muller and Wieschaus 1996). The second one is composed of Bazooka (Baz, Par-3), Par-6, and atypical protein kinase C (aPKC) (Wieschaus et al. 1984; Muller and Wieschaus 1996; Wodarz et al. 2000; Hutterer et al. 2004). On the opposite or basolateral side of the cell, Lethal giant larvae (Lgl), Discs large (Dlg), and Scribble (Scrib) determine the basolateral domain of the plasma membrane (Gateff and Schneiderman 1974; Mechler et al. 1985; Woods and Bryant 1989; Bilder and Perrimon 2000). In between the complexes lie the adherens junctions (AJ) composed of E-cadherin, Armadillo (Arm, β-catenin), and α-catenin (Oda et al. 1993, 1994; Peifer et al. 1993; Tepass et al. 1996).In Drosophila embryos, mutations that affect apical components often lead to the crumbs phenotype, where ectodermal cells lose integrity and many die through apoptosis. The surviving cells secrete cuticle in a discontinuous fashion, leaving pieces apparently floating within the eggshell (Tepass et al. 1990; Tanentzapf and Tepass 2003). This phenotype is also seen in embryos deficient for AJ proteins (Oda et al. 1993; Cox et al. 1996; Magie et al. 2002). On the other hand, mutations that affect the basolateral genes display a very different phenotype. Zygotic only (Z) mutants for scrib, lgl, and dlg have a significant maternal mRNA contribution that allows normal embryonic development to proceed. Phenotypes are observed only in larvae, which die with significantly overgrown imaginal discs (Gateff 1978; Bilder and Perrimon 2000). Removal of the maternal mRNA complement, as well as the zygotic contribution (M/Z) through the induction of germline clones, leads to a poorly differentiated and convoluted cuticle with a bubbly appearance (Figure 1) (Bilder et al. 2003; Tanentzapf and Tepass 2003).Open in a separate windowFigure 1.—Schema and cuticles representing wild-type vs. the opposing phenotypes of apical and basolateral expansion. (A) A wild-type cuticle shows rows of denticles separated by naked regions in a highly organized or patterned fashion. The apical determinants localize to the apical surface of cells, establishing the apical domain (green), the basolateral determinants localize to the basolateral surface of cells, establishing the basolateral domain (blue), and the adherens junctions (red) form at the interface between these two opposing regions. (B) The crumbs phenotype is observed when an apical determinant is mutated, causing an expansion of the basolateral domain. (C) The lgl phenotype, or bubble phenotype, is observed when a basolateral determinant is mutated, causing an expansion of the apical domain.These studies led to a comprehensive competition model where apical and basal components opposed each other (Figure 1, schema); however, a strangely neglected topic was the interaction of junctions and the apical and basal determinants. Therefore, we used a genetic approach to investigate the interaction of apical–basal polarity proteins and adherens junctions.     

Targeted Genome Modification in Mice Using Zinc-Finger Nucleases

Homologous recombination-based gene targeting using Mus musculus embryonic stem cells has greatly impacted biomedical research. This study presents a powerful new technology for more efficient and less time-consuming gene targeting in mice using embryonic injection of zinc-finger nucleases (ZFNs), which generate site-specific double strand breaks, leading to insertions or deletions via DNA repair by the nonhomologous end joining pathway. Three individual genes, multidrug resistant 1a (Mdr1a), jagged 1 (Jag1), and notch homolog 3 (Notch3), were targeted in FVB/N and C57BL/6 mice. Injection of ZFNs resulted in a range of specific gene deletions, from several nucleotides to >1000 bp in length, among 20–75% of live births. Modified alleles were efficiently transmitted through the germline, and animals homozygous for targeted modifications were obtained in as little as 4 months. In addition, the technology can be adapted to any genetic background, eliminating the need for generations of backcrossing to achieve congenic animals. We also validated the functional disruption of Mdr1a and demonstrated that the ZFN-mediated modifications lead to true knockouts. We conclude that ZFN technology is an efficient and convenient alternative to conventional gene targeting and will greatly facilitate the rapid creation of mouse models and functional genomics research.CONVENTIONAL gene targeting technology in mice relies on homologous recombination in embryonic stem (ES) cells to target specific gene sequences, most commonly to disrupt gene function (Doetschman et al. 1987; Kuehn et al. 1987; Thomas and Capecchi 1987). Advantages of gene targeting in ES cells are selective target sequence modification, the ability to insert or delete genetic information, and the stability of the targeted mutations through subsequent generations. There are also potential limitations, including limited rates of germline transmission and strain limitations due to lack of conventional ES cell lines (Ledermann 2000; Mishina and Sakimura 2007). Moving the targeted allele from one strain to another requires 10 generations of backcrosses that take 2–3 years. A minimum of 1 year is necessary for backcrossing if speed congenics is applied (Markel et al. 1997).Zinc-finger nucleases (ZFNs) are fusions of specific DNA-binding zinc finger proteins (ZFPs) and a nuclease domain, such as the DNA cleavage domain of a type II endonuclease, FokI (Kim et al. 1996; Smith et al. 1999; Bibikova et al. 2001). A pair of ZFPs provide target specificity, and their nuclease domains dimerize to cleave the DNA, generating double strand breaks (DSBs) (Mani et al. 2005), which are detrimental to the cell if left unrepaired (Rich et al. 2000). The cell uses two main pathways to repair DSBs: high-fidelity homologous recombination and error-prone nonhomologous end joining (NHEJ) (Lieber 1999; Pardo et al. 2009; Huertas 2010). ZFN-mediated gene disruption results from deletions or insertions frequently introduced by NHEJ. Figure 1 illustrates the cellular events following the injection of a pair of ZFNs targeting the mouse Mdr1a (also known as Abcb1a) gene.Open in a separate windowFigure 1.—The ZFN targeting mechanism. ZFN pairs bind to the target site, and FokI endonuclease domain dimerizes and makes a double strand break between the binding sites. If a DSB is repaired so that the wild-type sequence is restored, ZFNs can bind and cleave again. Otherwise, nonhomologous end joining (NHEJ) introduces deletions or insertions, which change the spacing between the binding sites so that ZFNs might still bind but dimerization or cleavage cannot occur. Insertions or deletions potentially disrupt the gene function.ZFNs have been successfully applied to generate genome modifications in plants (Shukla et al. 2009; Townsend et al. 2009), fruit flies (Bibikova et al. 2002), Caenorhabditis elegans (Morton et al. 2006), cultured mammalian cells (Porteus and Baltimore 2003; Santiago et al. 2008), zebrafish (Doyon et al. 2008; Meng et al. 2008), and most recently in rats (Geurts et al. 2009; Mashimo et al. 2010). The technology is especially valuable for rats because rat ES cell lines have only become available recently (Buehr et al. 2008; Li et al. 2008), and successful homologous recombination-mediated genome modification has not been reported. Previously, ENU mutagenesis (Zan et al. 2003) or transposons (Kitada et al. 2007) were the two main methods for generating gene knockout rats, both of which are random approaches and require labor-intensive and time-consuming screens to obtain the desired gene disruptions.Although ES cell-based knockout technology is widely used in mice, ZFN technology offers three advantages: (i) high efficiency; (ii) drastically reduced timeline, similar to that of creating a transgene (Gordon et al. 1980); and (iii) the freedom to apply the technology in various genetic backgrounds. In addition, no exogenous sequences need to be introduced because selection is not necessary.Here, we created the first genome-engineered mice using ZFN technology. Three genes were disrupted in two different backgrounds: Mdr1a, Jag1, and Notch3 in the FVB/N strain and Jag1 also in the C57BL/6 strain. All founders tested transmitted the genetic modifications through the germline.     

Exact Tests for Hardy–Weinberg Proportions

Exact conditional tests are often required to evaluate statistically whether a sample of diploids comes from a population with Hardy–Weinberg proportions or to confirm the accuracy of genotype assignments. This requirement is especially common when the sample includes multiple alleles and sparse data, thus rendering asymptotic methods, such as the common χ²-test, unreliable. Such an exact test can be performed using the likelihood ratio as its test statistic rather than the more commonly used probability test. Conceptual advantages in using the likelihood ratio are discussed. A substantially improved algorithm is described to permit the performance of a full-enumeration exact test on sample sizes that are too large for previous methods. An improved Monte Carlo algorithm is also proposed for samples that preclude full enumeration. These algorithms are about two orders of magnitude faster than those currently in use. Finally, methods are derived to compute the number of possible samples with a given set of allele counts, a useful quantity for evaluating the feasibility of the full enumeration procedure. Software implementing these methods, ExactoHW, is provided.WHEN studying the genetics of a population, one of the first questions to be asked is whether the genotype frequencies fit Hardy–Weinberg (HW) expectations. They will fit HW if the population is behaving like a single randomly mating unit without intense viability selection acting on the sampled loci. In addition, testing for HW proportions is often used for quality control in genotyping, as the test is sensitive to misclassifications or undetected null alleles. Traditionally, geneticists have relied on test statistics with asymptotic χ²-distributions to test for goodness-of-fit with respect to HW proportions. However, as pointed out by several authors (Elston and Forthofer 1977; Emigh 1980; Louis and Dempster 1987; Hernandez and Weir 1989; Guo and Thompson 1992; Chakraborty and Zhong 1994; Rousset and Raymond 1995; Aoki 2003; Maiste and Weir 2004; Wigginton et al. 2005; Kang 2008; Rohlfs and Weir 2008), these asymptotic tests quickly become unreliable when samples are small or when rare alleles are involved. The latter situation is increasingly common as techniques for detecting large numbers of alleles become widely used. Moreover, loci with large numbers of alleles are intentionally selected for use in DNA identification techniques (e.g., Weir 1992). The result is often sparse-matrix data for which the asymptotic methods cannot be trusted.A solution to this problem is to use an exact test (Levene 1949; Haldane 1954) analogous to Fisher''s exact test for independence in a 2 × 2 contingency table and its generalization to rectangular tables (Freeman and Halton 1951). In this approach, one considers only potential outcomes that have the same allele frequencies as observed, thus greatly reducing the number of outcomes that must be analyzed. One then identifies all such outcomes that deviate from the HW null hypothesis by at least as much the observed sample. The total probability of this subset of outcomes, conditioned on HW and the observed allele frequencies, is then the P-value of the test. When it is not possible to enumerate all outcomes, it is still feasible to approximate the P-value by generating a large random sample of tables.The exact HW test has been used extensively and eliminates the uncertainty inherent in the asymptotic methods (Emigh 1980; Hernandez and Weir 1989; Guo and Thompson 1992; Rousset and Raymond 1995). However, there are two difficulties with the application of this method and its interpretation, both of which are addressed in this report.The first issue is the question of how one decides which of the potential outcomes are assigned to the subset that deviates from HW proportions by as much as or more than the observed sample. If the alternative hypothesis is specifically an excess or a dearth of homozygotes, then the tables can be ordered by Rousset and Raymond''s (1995) U-score or, equivalently, by Robertson and Hill''s (1984) minimum-variance estimator of the inbreeding coefficient. However, when no specific direction of deviation from HW is suspected, then there are several possible test statistics that can be used (Emigh 1980). These include the χ²-statistic, the likelihood ratio (LR), and the conditional probability itself. The last option is by far the most widely used (Elston and Forthofer 1977; Louis and Dempster 1987; Chakraborty and Zhong 1994; Weir 1996; Wigginton et al. 2005) and implemented in the GENEPOP software package (Rousset 2008). The idea of using the null-hypothesis probability as the test statistic was originally suggested in the context of rectangular contingency tables (Freeman and Halton 1951), but this idea has been criticized for its lack of discrimination between the null hypothesis and alternatives (Gibbons and Pratt 1975; Radlow and Alf 1975; Cressie and Read 1989). For example, suppose a particular sample was found to have a very low probability under the null hypothesis of HW. Such a result would usually tend to argue against the population being in HW equilibrium. However, if this particular outcome also has a very low probability under even the best-fitting alternative hypothesis, then it merely implies that a rare event has occurred regardless of whether the population is in random-mating proportions. The first part of this report compares the use of probability vs. the likelihood ratio as the test statistic in HW exact tests. Reasons for preferring the likelihood ratio are presented.The second difficulty in performing HW exact tests is the extensive computation needed for large samples when multiple alleles are involved. In this report I present a new algorithm for carrying out these calculations. This method adapts some of the techniques originally developed for rectangular contingency tables in which each possible outcome is represented as a path through a lattice-like network (Mehta and Patel 1983). Unlike the loop-based method currently in use (Louis and Dempster 1987), the new algorithm uses recursion and can be applied to any number of alleles without modification. In addition, it improves the efficiency by about two orders of magnitude, thus allowing the full enumeration procedure to be applied to larger samples and with greater numbers of alleles.The recursion algorithm has been tested successfully on samples with as many as 20 alleles when most of those alleles are rare. However, there are still some samples for which a complete enumeration is not practical. For example, the data from the human Rh locus in Figure 1D would require examining 2 × 10⁵⁶ tables (see below). For such cases a Monte Carlo approach must be used (Guo and Thompson 1992). Several improvements to the method of independent random tables are suggested here to make that approach practical for even the largest of realistic samples, thus eliminating the need for the less-accurate Markov chain approach.Open in a separate windowFigure 1.—Sample data sets: examples that have been used in previous discussions of exact tests for HW proportions. For each data set, a triangular matrix of genotype counts is shown next to the vector of allele counts. (A) From Table 2, bottom row, of Louis and Dempster (1987). (B) From Figure 2 of Guo and Thompson (1992). (C) From the documentation included with the GENEPOP software package (Rousset 2008). (D) From Figure 5 of Guo and Thompson (1992).Finally, I address the problem of determining the number of tables of genotype counts corresponding to a given set of allele counts. This number is needed for determining whether the exact test can be performed by full enumeration. Previously, this number could not be obtained except by actually carrying out the complete enumeration.The methods described are implemented in a software package, ExactoHW, for MacOS X10.5 or later. It is available in compiled form (supporting information, File S1) or as source code for academic use on request from the author.     

The Power of the Methods for Detecting Interlocus Gene Conversion

Interlocus gene conversion can homogenize DNA sequences of duplicated regions with high homology. Such nonvertical events sometimes cause a misleading evolutionary interpretation of data when the effect of gene conversion is ignored. To avoid this problem, it is crucial to test the data for the presence of gene conversion. Here, we performed extensive simulations to compare four major methods to detect gene conversion. One might expect that the power increases with increase of the gene conversion rate. However, we found this is true for only two methods. For the other two, limited power is expected when gene conversion is too frequent. We suggest using multiple methods to minimize the chance of missing the footprint of gene conversion.INTERLOCUS (ectopic or nonallelic) gene conversion occurs between paralogous regions such that their DNA sequences are shuffled and homogenized (Petes and Hill 1988; Harris et al. 1993; Goldman and Lichten 1996). As a consequence, the DNA sequences of paralogous genes become similar (i.e., concerted evolution, Ohta 1980; Dover 1982; Arnheim 1983). This homogenizing effect of gene conversion sometimes causes problems in the inference of the evolutionary history of duplicated genes or multigene family. Common misleading inferences include an underestimation of the age of duplicated genes (Gao and Innan 2004; Teshima and Innan 2004). This is largely because the concept of the molecular clock is automatically incorporated in most software of phylogenetic analyses, and those software are frequently applied to multigene families without careful consideration of the potential effect of gene conversion.To understand the evolutionary roles of gene duplication, it is crucial to date each duplication event. To do this, we first need to know precisely the action of gene conversion among the gene family of interest. There have been a number of methods for detecting gene conversion, but their power has not been fully explored. Here, we systematically compare their performance by simulations to provide a guideline on which method works best under what condition. Our simulations show that some methods have a serious problem that causes a misleading interpretation: they do not detect any evidence for gene conversion when the gene conversion rate is too high. Thus, as is always true, lack of evidence is no evidence for absence, and we must be very careful about this effect when analyzing data with those tests, as is demonstrated below.There seem to be four major ideas behind the methods for detecting gene conversion, which are summarized below. A number of methods have been developed to detect interlocus gene conversion, and they belong to one of these four broad categories.

1. Incompatibility between an estimated gene tree and the true duplication history: Figure 1A illustrates a simple situation of a pair of duplicated genes, X and Y, that arose before the speciation event of species A and B. The upper tree of Figure 1A shows a tree representing the true history. When a gene tree is estimated from their DNA sequences, it should be consistent with the true tree when genes X and Y have accumulated mutations independently. Gene conversion potentially violates this relationship. When genes X and Y are subject to frequent gene conversion, the two paralogous genes in each species should be more closely related, resulting in a gene tree illustrated in the bottom tree in Figure 1A. Thus, incongruence between the real tree and an inferred gene tree can provide strong evidence for gene conversion (unless there is no lineage sorting or misinference of the gene tree).Open in a separate windowFigure 1.—Summary of the simulations in the two-species two-locus model. (A) Illustration of the model. (B–E) The power of the four approaches. The average gene conversion tract length (1/q) is assumed to be 100 bp. See Figure S1 for the results with 1/q = 1000 bp.It should be noted that a single gene conversion event usually transfers a short fragment. Consequently, it occasionally happens that incongruence is detected only in a part of the duplicated region. Thus, searching local regions of incongruence has been a well-recognized method for detecting nonvertical evolutionary events such as recombination, gene conversion, and horizontal gene transfer (Farris 1971; Brown et al. 1972), and some computational methods based on this idea have been developed (Balding et al. 1992).
2. Incompatibility of gene trees in different subregions: The idea of (i) can work even without knowing the real history. As mentioned above, incompatibility in the tree shape between different subregions can be evidence for local gene conversion because those subregions should have different histories of gene conversion (Sneath et al. 1975; Stephens 1985). A number of statistical algorithms incorporate this idea (e.g., Jakobsen et al. 1997; McGuire et al. 1997; Weiller 1998).
3. GENECONV: A local gene conversion also leaves its trace in the alignment of sequences. GENECONV is a software developed by Sawyer (1989) to detect such signatures (http://www.math.wustl.edu/∼sawyer/geneconv/). GENECONV looks at an alignment of multiple sequences in a pairwise manner and searches unusually long regions of high identity between the focal pair conditional on the pattern of variable sites in the other sequences, which are candidates of recent gene conversion (a similar idea is also seen in Sneath et al. 1975). The statistical significance is determined by random shuffling of variable sites in the alignment.
4. Shared polymorphism: Suppose polymorphism data are available in both of the duplicated genes. Then, with gene conversion, there could be polymorphisms shared by the two genes, which can be evidence for gene conversion (Innan 2003a). It should be noted that parallel mutations can create shared polymorphism even without gene conversion, but the chance should be very low when the point mutation rate is usually very low. Polymorphism data usually have tremendous amounts of information on very recent events and can be a powerful means to detect gene conversion (e.g., Stephens 1985; Betrán et al. 1997; Innan 2002).

In this study, we investigate and compare the performance of the methods based on these four ideas with simple settings. It should be noted that because our primary focus is on interlocus gene conversion, we ignore methods that can be used for detecting only allelic gene conversion, such as Fearnhead and Donnelly (2001), Hudson (2001), and Gay et al. (2007).     

Genetic Control of Photoperiod Sensitivity in Maize Revealed by Joint Multiple Population Analysis

Variation in maize for response to photoperiod is related to geographical adaptation in the species. Maize possesses homologs of many genes identified as regulators of flowering time in other species, but their relation to the natural variation for photoperiod response in maize is unknown. Candidate gene sequences were mapped in four populations created by crossing two temperate inbred lines to two photoperiod-sensitive tropical inbreds. Whole-genome scans were conducted by high-density genotyping of the populations, which were phenotyped over 3 years in both short- and long-day environments. Joint multiple population analysis identified genomic regions controlling photoperiod responses in flowering time, plant height, and total leaf number. Four key genome regions controlling photoperiod response across populations were identified, referred to as ZmPR1–4. Functional allelic differences within these regions among phenotypically similar founders suggest distinct evolutionary trajectories for photoperiod adaptation in maize. These regions encompass candidate genes CCA/LHY, CONZ1, CRY2, ELF4, GHD7, VGT1, HY1/SE5, TOC1/PRR7/PPD-1, PIF3, ZCN8, and ZCN19.MAIZE (Zea mays L. subsp. mays) was domesticated in southern Mexico and its center of diversity is in tropical Latin America (Goodman 1999; Matsuoka et al. 2002), where precipitation rates and day lengths cycle annually. The presumed ancestor of maize, teosinte (Zea mays L. subsp. parviglumis), likely evolved photoperiod sensitivity to synchronize its reproductive phases to the wetter, short-day growing season (Ribaut et al. 1996; Campos et al. 2006). A critical event in the postdomestication evolution of maize was its spread from tropical to temperate regions of the Americas (Goodman 1988), requiring adaptation to longer day lengths. The result of this adaptation process is manifested today as a major genetic differentiation between temperate and tropical maize (Liu et al. 2003) and substantially reduced photoperiod sensitivity of temperate maize (Gouesnard et al. 2002). Tropical maize exhibits delayed flowering time, increased plant height, and a greater total leaf number when grown in temperate latitudes with daily dark periods <11 hr (Allison and Daynard 1979; Warrington and Kanemasu 1983a,b). Identifying the genes underlying maize photoperiod sensitivity will provide insight into the postdomestication evolution of maize and may reduce barriers to the use of diverse tropical germplasm resources for improving temperate maize production (Holland and Goodman 1995; Liu et al. 2003; Ducrocq et al. 2009).Natural variation at key genes in flowering time pathways is related to adaptation and evolution of diverse plant species (Caicedo et al. 2004; Shindo et al. 2005; Turner et al. 2005; Cockram et al. 2007; Izawa 2007; Slotte et al. 2007). Identification of some of the genes controlling adaptation in numerous plant species relied on regulatory pathways elucidated in Arabidopsis (Simpson and Dean 2002). Many key genes in the Arabidopsis flowering time regulatory pathways are conserved across diverse plant species (Kojima et al. 2002; Hecht et al. 2007; Kwak et al. 2008), but their functions have diverged, resulting in unique regulatory pathways in some phylogenetic groups (Colasanti and Coneva 2009). For example, FRI and FLC control most natural variation for vernalization response in Arabidopsis (Caicedo et al. 2004; Shindo et al. 2005), but wheat and barley appear to lack homologs of these genes and regulate vernalization response with different genes (Yan et al. 2004).Maize exhibits tremendous natural variation for flowering time (Gouesnard et al. 2002; Camus-Kulandaivelu et al. 2006), for which numerous QTL have been identified (Chardon et al. 2004). In contrast, only a few flowering time mutants are known and only a handful of flowering time genes, including DWARF8 (D8), DELAYED FLOWERING1 (DLF1), VEGETATIVE TO GENERATIVE TRANSITION1 (VGT1), and INDETERMINATE GROWTH1 (ID1), have been cloned in maize (Thornsberry et al. 2001; Colasanti et al. 2006; Muszynski et al. 2006; Salvi et al. 2007; Colasanti and Coneva 2009). Variation at or near D8 and VGT1 is related to latitudinal adaptation, but these genes do not appear to regulate photoperiod responses and account for only a limited proportion of the standing flowering time variation in maize (Camus-Kulandaivelu et al. 2006, 2008; Ducrocq et al. 2008; Buckler et al. 2009).Quantitative trait loci (QTL) mapping was a key first step to identifying the genes underlying natural variation for flowering time in Arabidopsis (Koornneef et al. 2004). Photoperiodic QTL have been mapped previously in individual biparental maize mapping populations (Koester et al. 1993; Moutiq et al. 2002; Wang et al. 2008; Ducrocq et al. 2009). Such studies are informative with respect to the parents from which the populations were derived, but often do not reflect the genetic heterogeneity of broader genetic reference populations (Holland 2007).Association mapping (Thornsberry et al. 2001; Ersoz et al. 2007) and combined analysis of multiple biparental crosses (Rebaï et al. 1997; Rebaï and Goffinet 2000; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008) represent alternative approaches to understanding the variation in genetic control for complex traits among diverse germplasm. Association mapping has limited power to identify genes that affect traits closely associated with population structure, such as flowering time in maize (Camus-Kulandaivelu et al. 2006; Ersoz et al. 2007). In contrast, joint QTL analysis of multiple populations is not hindered by the associations between causal genes and population structure. Combined QTL analysis of multiple mapping populations provides improved power to detect QTL, more precise estimation of their effects and positions, and better understanding of their functional allelic variation and distribution across more diverse germplasm compared to single-population mapping (Rebaï et al. 1997; Wu and Jannink 2004; Jourjon et al. 2005; Blanc et al. 2006; Verhoeven et al. 2006; Yu et al. 2008; Buckler et al. 2009). Joint analysis also provides a direct test of the importance of higher-order epistatic interactions between founder alleles at individual loci with genetic backgrounds (Jannink and Jansen 2001; Blanc et al. 2006). In this study, joint analysis of multiple populations was used to test directly the hypothesis that diverse tropical maize lines carry functionally similar alleles at key photoperiod loci, which would imply genetic homogeneity for a common set of mutations and a shared evolutionary pathway for photoperiod insensitivity.The objective of this study was to integrate candidate gene analyses with photoperiod QTL mapping across multiple maize populations. We tested candidate floral regulators known from other species for associations with natural variation for photoperiod response in maize. We analyzed flowering time in four interrelated recombinant inbred line (RIL) populations, each derived from crosses between temperate and tropical maize parents (Figure 1), in both long- and short-day environments to characterize their responses to distinct photoperiods. Joint population analysis provided high resolution of many QTL positions, permitting robust testing of underlying candidate genes. We directly and indirectly mapped homologs of flowering time candidates genes from Arabidopsis, rice, and barley on a dense consensus genetic map of these four populations, permitting identification of homologs that colocalize with genome regions associated with variation for photoperiod response. These mapping families are being integrated into the maize nested association mapping (NAM) population (Buckler et al. 2009; McMullen et al. 2009) because they were genotyped with the maize NAM map SNP markers, they involve the common parent B73, and their seed and genotypic information (File S1 cont.) are publicly available. Their availability further expands the genetic diversity represented by the maize NAM population and enhances this valuable public community resource.Open in a separate windowFigure 1.—Factorial mating of two temperate (B73 and B97) and two tropical (CML254 and Ki14) inbred maize lines to create four related recombinant inbred line mapping populations.     

Directionality of Epistasis in a Murine Intercross Population

Directional epistasis describes a situation in which epistasis consistently increases or decreases the effect of allele substitutions, thereby affecting the amount of additive genetic variance available for selection in a given direction. This study applies a recent parameterization of directionality of epistasis to empirical data. Data stems from a QTL mapping study on an intercross between inbred mouse (Mus musculus) strains LG/J and SM/J, originally selected for large and small body mass, respectively. Results show a negative average directionality of epistasis for body-composition traits, predicting a reduction in additive allelic effects and in the response to selection for increased size. Focusing on average modification of additive effect of single loci, we find a more complex picture, whereby the effects of some loci are enhanced consistently across backgrounds, while effects of other loci are decreased, potentially contributing to either enhancement or reduction of allelic effects when selection acts at single loci. We demonstrate and discuss how the interpretation of the overall measurement of directionality depends on the complexity of the genotype–phenotype map. The measure of directionality changes with the power of scale in a predictable way; however, its expected effect with respect to the modification of additive genetic effects remains constant.EPISTASIS is present when the effect of a genetic substitution depends on the genotypes at other loci. At the population level, this means that average allelic effects change as allele frequencies at other loci change, and thus that gene effects can evolve. The evolutionary significance of epistasis has been recognized mainly in relation to the allele-frequency changes that are caused by genetic drift (e.g., Goodnight 1987, 1988, 1995; Cheverud and Routman 1996; Barton and Turelli 2004; De Brito et al. 2005; Turelli and Barton 2006), whereas the epistatic effects under directional selection have been treated only recently (e.g., Carter et al. 2005; Weinreich et al. 2005; Carlborg et al. 2006; Hansen et al. 2006; Yukilevich et al. 2008). Technically, the effects of epistatic interaction can be considered as having two aspects: the architecture itself (i.e., the existence of a nonadditive component, the so-called functional aspect, see below), and the effect of allele frequency on genetic variance (i.e., the statistical aspect). An additional consideration is crucial for the response to selection of a given trait, namely that the response is generated by the joint action of many epistatic interactions. Each of the interactions can either enhance or diminish the additive genetic effect in any specific phenotypic dimension. Their composite effect depends on the pattern, i.e., whether the effects accumulate or cancel each other out (Hansen and Wagner 2001a,b; Carter et al. 2005; Hansen et al. 2006). In the following we provide a brief general account of epistasis and then focus on the effect of its composite pattern, the empirical assessment of which is the goal of this study.The traditional population-genetic approach to selection response initially emphasized additive genetic variance and treated any variance unexplained by the additive effects, including variance due to interactions within or between loci, as residual variance (Fisher 1918). Later this model was extended to account for epistasis (Cockerham 1954, Kempthorne 1954). The interaction component of this residual variance is dependent on population allele frequencies at the interacting loci. Starting with nonadditive effects within a single locus (dominance), Falconer (1960) described the effect of allele frequencies on the statistical measure of average allelic effect. Cheverud and Routman (1995) explored the analogous effect at the two-locus level, leading to distinction between allele-frequency-dependent statistical epistasis (contributing to epistatic variance) on population level, and allele-frequency-independent physiological (or functional) epistasis on the individual level, which contributes to all the genetic variance components, i.e.,, additive, dominance, and epistatic. Thus while physiological epistasis describes the genetic architecture of a given phenotype defining the potential for epistatic effects, statistical epistasis describes the realization of individual-level epistatic effects in terms of allele-frequency-dependent genetic variance components. Several authors have worked out tools to estimate gene interaction effects at the individual level independently of the allele frequencies to distinguish between the physiological and statistical epistasis (Cheverud and Routman 1995; Wagner et al. 1998; Hansen and Wagner 2001a; Barton and Turelli 2004; Yang 2004; Zeng et al. 2005; Wang and Zeng 2006). These methods have been recently generalized in a common framework for measuring epistasis and translating between the population and individual levels (NOIA: Alvarez-Castro and Carlborg 2007; Alvarez-Castro et al. 2008; see Le Rouzic 2008 for R software package). An understanding of the pattern of epistatic architecture enables prediction of its effects at any given set of allele frequencies.The original physiological epistasis has often referred to isolated pairwise interactions. The genetic basis of a complex trait is affected by more than two interacting loci; thus the trait''s genetic variance is affected by the combination of these interaction effects. Effects at the individual loci can add up, or cancel out. For example, when the alleles at background loci become fixed (e.g., due to a bottleneck), previously background-dependent genetic effects at loci A and B can become additive effects of the same or of the opposite sign. Depending on the sign and size of their effects, the two allele substitutions at loci A and B therefore add up to increase, decrease, or have no overall effect on additive genetic variance. Hansen and Wagner (2001a) introduced the notion of directionality of epistasis to emphasize the importance of the pattern of epistatic architecture for the system''s evolvability. Directionality measures the consistency of epistatic effects on additive variance for a specific locus and trait across the genome, given a defined reference genotype. It describes whether epistasis tends to enhance or diminish the additive effects of interacting loci on a trait in a specified phenotypic direction. The population-dynamic studies analyzing effects of the epistatic pattern show that the directionality of epistasis can be a major determinant of evolution on time scales beyond a few generations (Hermisson et al. 2003; Carter et al. 2005; Hansen et al. 2006; Yukilevich et al. 2008; Alvarez-Castro et al. 2009; Fierst and Hansen 2010). Averaged across interactions, positive directional epistasis increases additive variance and the response to positive selection relative to that predicted by the additive genetic effects alone, while negative directional epistasis tends to decrease the response to selection in that same phenotypic direction. An absence of epistatic directionality occurs when positive and negative directional epistatic effects cancel out on average or, trivially, from the absence of epistasis. Thus directionality describes the local, population-specific curvature of the genotype–phenotype map (Figure 1).Open in a separate windowFigure 1.—Directionality of epistasis describes the local curvature of the genotype–phenotype map. The genotypic values of the trait of interest in the two parental inbred populations and in an intercross are plotted on y-axis. Given a defined direction on a trait axis and a reference point for measurement of genetic effects, the directionality () describes whether the epistasis increases or decreases the effect of allelic substitutions relative to the value predicted by additive effects alone. The extrapolation of the curvature beyond the local effects requires the knowledge of higher-order epistatic effects; however, the local effects can be calculated for different reference points and different phenotypic directions (see Hansen and Wagner 2001a,b).Aspects of epistatic directionality have been addressed previously with respect to the effect on fitness (reviewed in Phillips et al. 2000), with focus on the synergistic epistasis for deleterious effect enhancement (e.g., Kondrashov 1988; Charlesworth 1990; Hansen and Wagner 2001b). However, the empirical study of overall directionality of epistasis, and its measurement in morphological or physiological traits, is still lacking (Hansen 2006). Implicit indications from empirical studies are ambiguous (see examples in Carter et al. 2005). Here, we present an assessment of directionality of two-way epistasis between QTL in an intercross population of laboratory mice, paying special attention to scale effects.     

Excision of an Active CACTA-Like Transposable Element From DFR2 Causes Variegated Flowers in Soybean [Glycine max (L.) Merr.]

Active endogenous transposable elements, useful tools for gene isolation, have not been reported from any legume species. An active transposable element was suggested to reside in the W4 locus that governs flower color in soybean. Through biochemical and molecular analyses of several revertants of the w4-m allele, we have shown that the W4 locus encodes dihydroflavonol-4-reductase 2 (DFR2). w4-m has arisen through insertion of Tgm9, a 20,548-bp CACTA-like transposable element, into the second intron of DFR2. Tgm9 showed high nucleic acid sequence identity to Tgmt*. Its 5′ and 3′ terminal inverted repeats start with conserved CACTA sequence. The 3′ subterminal region is highly repetitive. Tgm9 carries TNP1- and TNP2-like transposase genes that are expressed in the mutable line, T322 (w4-m). The element excises at a high frequency from both somatic and germinal tissues. Following excision, reinsertions of Tgm9 into the DFR2 promoter generated novel stable alleles, w4-dp (dilute purple flowers) and w4-p (pale flowers). We hypothesize that the element is fractured during transposition, and truncated versions of the element in new insertion sites cause stable mutations. The highly active endogenous transposon, Tgm9, should facilitate genomics studies specifically that relate to legume biology.IN soybean [Glycine max (L.) Merr.], five loci W1, W3, W4, Wm, and Wp control the pigmentations in flowers and hypocotyls (Palmer et al. 2004). Soybean plants with genotype W1_ w3w3 W4_ Wm_ Wp_ produce wild-type purple flowers (Figure 1) and purple hypocotyls. Mutations at the W4 locus in the W1_ background result in altered pigment accumulation patterns in petals and reduced levels of purple pigments in flowers and hypocotyls. Four mutant alleles, w4, w4-m, w4-dp, and w4-p have been mapped to this locus. The w4 allele represents a spontaneous mutation, which produces near-white flowers (Figure 1) and green hypocotyls (Hartwig and Hinson 1962; Groose and Palmer 1991). The w4-m allele was identified from a cross between two experimental breeding lines with white and purple flowers, respectively (Palmer et al. 1989; Weigelt et al. 1990). w4-m is characterized by variegated flowers (Figure 1) and green hypocotyls with purple sectors (Groose et al. 1988).Open in a separate windowFigure 1.—Variation in flower color among soybean lines carrying different W4 alleles.w4-m has been proposed to harbor a class II transposable element (Palmer et al. 1989). Presumably, somatic excision of the putative transposable element results in the variegated (Groose et al. 1988) and germinal excision wild-type phenotypes, purple flowers and purple pigments on hypocotyls (Palmer et al. 1989; Groose et al. 1990). The mutable line carrying w4-m undergoes germinal reversion at a very high frequency, about 6% per generation (Groose et al. 1990). Approximately 1% of the progeny derived from germinal revertants contain new mutations in unlinked loci, presumably resulting from reinsertion of the element (Palmer et al. 1989). For example, female partial-sterile 1 (Fsp1), female partial-sterile 2 (Fsp2), female partial-sterile 3 (Fsp3), and female partial-sterile 4 (Fsp4) were isolated from progenies of germinal revertants with purple flowers and were mapped to molecular linkage groups (MLG) C2, A2, F, and G, respectively (Kato and Palmer 2004). Similarly, 36 male-sterile, female-sterile mutants mapped to the st8 region on MLG J (Kato and Palmer 2003; Palmer et al. 2008a), 24 necrotic root (rn) mutants mapped to the rn locus on MLG G (Palmer et al. 2008b), and three Mdh1-n y20 mutants, mapped to a chromosomal region on MLG H (Palmer et al. 1989; Xu and Palmer 2005b), were isolated among progenies of germinal revertants.In addition to germinal revertants with purple flowers, the w4 mutable line also generated intermediate stable revertants that produce flowers with variable pigment intensities ranging from purple to near-white (Figure 1). Two stable intermediate revertants, w4-dp and w4-p, are allelic to W4. Plants carrying w4-dp or w4-p alleles produce dilute purple flowers or pale flowers, respectively (Figure 1) (Palmer and Groose 1993; Xu and Palmer 2005a).Pigment formation requires two types of genes: structural genes that encode anthocyanin biosynthetic enzymes [e.g., CHS (chalcone synthase), F3H (flavanone 3-hydroxylase), DFR (dihydroflavonol-4-reductase), ANS (anthocyanidin synthase); Figure S1] and regulatory genes that control expression of structural genes (Holton and Cornish 1995). Among the five genes, W1, W3, W4, Wp, and Wm, controlling pigment biosynthesis in soybean, four have been characterized at the molecular level (Figure S1). W1 encodes a flavonoid 5′, 3′-hydroxylase (Zabala and Vodkin 2007). W3 cosegregates with a DFR gene, Wp encodes a flavonone 3-hydroxylase (F3H), and Wm encodes a flavonol synthase (FLS) (Fasoula et al. 1995; Zabala and Vodkin 2005; Takahashi et al. 2007).Nine CACTA-type class II transposable elements, Tgm1, Tgm2, Tgm3, Tgm4, Tgm5, Tgm6, Tgm7, Tgm-Express1, and Tgmt*, have been reported in soybean (Rhodes and Vodkin 1988; Zabala and Vodkin 2005, 2008). Tgm-Express1 causes mutation in Wp (Zabala and Vodkin 2005) and Tgmt* ({"type":"entrez-nucleotide","attrs":{"text":"EU190440","term_id":"158514866","term_text":"EU190440"}}EU190440) in T that encodes a flavonoid 3′ hydroxylase (F3′H) (Zabala and Vodkin 2003, 2008). The objectives of the present study were to characterize the W4 locus and then investigate whether the w4-m allele harbors an active transposable element. Our results showed that a CACTA-like transposable element located in a dihydroflavonol-4-reductase gene causes variegated flower phenotype in soybean.     

Focus Issue on Plant Cell Walls: Sequencing around 5-Hydroxyconiferyl Alcohol-Derived Units in Caffeic Acid O-Methyltransferase-Deficient Poplar Lignins

Caffeic acid O-methyltransferase (COMT) is a bifunctional enzyme that methylates the 5- and 3-hydroxyl positions on the aromatic ring of monolignol precursors, with a preference for 5-hydroxyconiferaldehyde, on the way to producing sinapyl alcohol. Lignins in COMT-deficient plants contain benzodioxane substructures due to the incorporation of 5-hydroxyconiferyl alcohol (5-OH-CA), as a monomer, into the lignin polymer. The derivatization followed by reductive cleavage method can be used to detect and determine benzodioxane structures because of their total survival under this degradation method. Moreover, partial sequencing information for 5-OH-CA incorporation into lignin can be derived from detection or isolation and structural analysis of the resulting benzodioxane products. Results from a modified derivatization followed by reductive cleavage analysis of COMT-deficient lignins provide evidence that 5-OH-CA cross couples (at its β-position) with syringyl and guaiacyl units (at their O-4-positions) in the growing lignin polymer and then either coniferyl or sinapyl alcohol, or another 5-hydroxyconiferyl monomer, adds to the resulting 5-hydroxyguaiacyl terminus, producing the benzodioxane. This new terminus may also become etherified by coupling with further monolignols, incorporating the 5-OH-CA integrally into the lignin structure.Lignins are polymeric aromatic constituents of plant cell walls, constituting about 15% to 35% of the dry mass (Freudenberg and Neish, 1968; Adler, 1977). Unlike other natural polymers such as cellulose or proteins, which have labile linkages (glycosides and peptides) between their building units, lignins’ building units are combinatorially linked with strong ether and carbon-carbon bonds (Sarkanen and Ludwig, 1971; Harkin, 1973). It is difficult to completely degrade lignins. Lignins are traditionally considered to be dehydrogenative polymers derived from three monolignols, p-coumaryl alcohol 1h (which is typically minor), coniferyl alcohol 1g, and sinapyl alcohol 1s (Fig. 1; Sarkanen, 1971). They can vary greatly in their composition in terms of their plant and tissue origins (Campbell and Sederoff, 1996). This variability is probably determined and regulated by different activities and substrate specificities of the monolignol biosynthetic enzymes from different sources, and by the carefully controlled supply of monomers to the lignifying zone (Sederoff and Chang, 1991).Open in a separate windowFigure 1.The monolignols 1, and marker compounds 2 to 4 resulting from incorporation of novel monomer 15h into lignins: thioacidolysis monomeric marker 2, dimers 3, and DFRC dimeric markers 4.Recently there has been considerable interest in genetic modification of lignins with the goal of improving the utilization of lignocellulosics in various agricultural and industrial processes (Baucher et al., 2003; Boerjan et al., 2003a, 2003b). Studies on mutant and transgenic plants with altered monolignol biosynthesis have suggested that plants have a high level of metabolic plasticity in the formation of their lignins (Sederoff et al., 1999; Ralph et al., 2004). Lignins in angiosperm plants with depressed caffeic acid O-methyltransferase (COMT) were found to derive from significant amounts of 5-hydroxyconiferyl alcohol (5-OH-CA) monomers 15h (Fig. 1) substituting for the traditional monomer, sinapyl alcohol 1s (Marita et al., 2001; Ralph et al., 2001a, 2001b; Jouanin et al., 2004; Morreel et al., 2004b). NMR analysis of a ligqnin from COMT-deficient poplar (Populus spp.) has revealed that novel benzodioxane structures are formed through β-O-4 coupling of a monolignol with 5-hydroxyguaiacyl units (resulting from coupling of 5-OH-CA), followed by internal trapping of the resultant quinone methide by the phenolic 5-hydroxyl (Ralph et al., 2001a). When the lignin was subjected to thioacidolysis, a novel 5-hydroxyguaiacyl monomer 2 (Fig. 1) was found in addition to the normal guaiacyl and syringyl thioacidolysis monomers (Jouanin et al., 2000). Also, a new compound 3g (Fig. 1) was found in the dimeric products from thioacidolysis followed by Raney nickel desulfurization (Lapierre et al., 2001; Goujon et al., 2003).Further study with the lignin using the derivatization followed by reductive cleavage (DFRC) method also confirmed the existence of benzodioxane structures, with compounds 4 (Fig. 1) being identified following synthesis of the authentic parent compounds 9 (Fig. 2). However, no 5-hydroxyguaiacyl monomer could be detected in the DFRC products. These facts imply that the DFRC method leaves the benzodioxane structures fully intact, suggesting that the method might therefore be useful as an analytical tool for determining benzodioxane structures that are linked by β-O-4 ethers. Using a modified DFRC procedure, we report here on results that provide further evidence for the existence of benzodioxane structures in lignins from COMT-deficient plants, that 5-OH-CA is behaving as a rather ideal monolignol that can be integrated into plant lignins, and demonstrate the usefulness of the DFRC method for determining these benzodioxane structures.Open in a separate windowFigure 2.Synthesis of benzodioxane DFRC products 12 (see later in Fig. 6 for their structures). i, NaH, THF. ii, Pyrrolidine. iii, 1g or 1s, benzene/acetone (4/1, v/v). iv, DIBAL-H, toluene. v, Iodomethane-K₂CO₃, acetone. vi, Ac₂O pyridine.