Polymorphic Genes of Major Effect: Consequences for Variation,Selection and Evolution in Arabidopsis thaliana

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus can have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure—in particular the genetic correlations between branch number and flowering time traits—and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we find that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices.THE structure of the genetic variation that underlies phenotypic traits has important consequences for understanding the evolution of quantitative traits (Fisher 1930; Lande 1979; Bulmer 1980; Kimura 1983; Orr 1998; Agrawal et al. 2001). Despite the infinitesimal model''s allure and theoretical tractability (see Orr and Coyne 1992; Orr 1998, 2005a,b for reviews of its influence), evidence has accumulated from several sources (artificial selection experiments, experimental evolution, and QTL mapping) to suggest that genes of major effect often contribute to quantitative traits. Thus, the frequency and role of genes of major effect in evolutionary quantitative genetics have been a subject of intense debate and investigation for close to 80 years (Fisher 1930; Kimura 1983; Orr 1998, 2005a,b). Beyond the conceptual implications, the prevalence of major-effect loci also affects our ability to determine the genetic basis of adaptations and species differences (e.g., Bradshaw et al. 1995, 1998).Although the existence of genes of major effect is no longer in doubt, we still lack basic empirical data on how segregating variation at such genes affects key components of evolutionary process (but see Carrière and Roff 1995). In other words, How does polymorphism at genes of major effect alter patterns of genetic variation and covariation, natural selection, and the likely response to selection? The lack of data stems, in part, from the methods used to detect genes of major effect: experimental evolution (e.g., Bull et al. 1997; Zeyl 2005) and QTL analysis (see Erickson et al. 2004 for a review) often detect such genes retrospectively after they have become fixed in experimental populations or the species pairs used to generate the mapping population. The consequences of polymorphism at these genes on patterns of variation, covariation, selection, and the response to selection—which can be transient (Agrawal et al. 2001)—are thus often unobserved.A partial exception to the absence of data on the effects of major genes comes from artificial selection experiments, in which a substantial evolutionary response to selection in the phenotype after a plateau is often interpreted as evidence for the fixation of a major-effect locus (Frankham et al. 1968; Yoo 1980a,b; Frankham 1980; Shrimpton and Robertson 1988a,b; Caballero et al. 1991; Keightley 1998; see Mackay 1990 and Hill and Caballero 1992 for reviews). However, many of these experiments report only data on the selected phenotype (e.g., bristle number) or, alternatively, the selected phenotype and some measure of fitness (e.g., Frankham et al. 1968, Yoo 1980b; Caballero et al. 1991; Mackay et al. 1994; Fry et al. 1995; Nuzhdin et al. 1995; Zur Lage et al. 1997), making it difficult to infer how a mutation will affect variation, covariation, selection, and evolutionary responses for a suite of traits that might affect fitness themselves. One approach is to document how variation at individual genes of major effect affects the genetic variance–covariance matrix (“G matrix”; Lande 1979), which represents the additive genetic variance and covariance between traits.Although direct evidence for variation at major-effect genes altering patterns of genetic variation, covariation, and selection is rare, there is abundant evidence for the genetic mechanisms that could produce these dynamics. A gene of major effect could have these consequences due to any of at least three genetic mechanisms: (1) pleiotropy, where a gene of major effect influences several traits, including potentially fitness, simultaneously, (2) physical linkage or linkage disequilibrium (LD), in which a gene of major effect is either physically linked or in LD with other genes that influence other traits under selection, and (3) epistasis, in which the allele present at a major-effect gene alters the phenotypic effect of other loci and potentially phenotypes under selection. Evidence for these three evolutionary genetic mechanisms leading to changes in suites of traits comes from a variety of sources, including mutation accumulation experiments (Clark et al. 1995; Fernandez and Lopez-Fanjul 1996), mutation induction experiments (Keightley and Ohnishi 1998), artificial selection experiments (Long et al. 1995), and transposable element insertions (Rollmann et al. 2006). For pleiotropy in particular, major-effect genes that have consequences on several phenotypic traits are well known from the domestication and livestock breeding literature [e.g., myostatin mutations in Belgian blue cattle and whippets (Arthur 1995; Grobet et al. 1997; Mosher et al. 2007), halothane genes in pigs (Christian and Rothschild 1991; Fujii et al. 1991), and Booroola and Inverdale genes in sheep (Amer et al. 1999; Visscher et al. 2000)]. While these data suggest that variation at major-effect genes could—and probably does—influence variation, covariation, and selection on quantitative traits, data on the magnitude of these consequences remain lacking.Recombinant inbred line (RIL) populations are a promising tool for investigating the influence of major-effect loci. During advancement of the lines from F₂''s to RILs, alternate alleles at major-effect genes (and most of the rest of the genome) will be made homozygous, simplifying comparisons among genotypic classes. Because of the high homozygosity, individuals within RILs are nearly genetically identical, facilitating phenotyping of many genotypes under a range of environments. In addition, because of recombination, alternative alleles are randomized across genetic backgrounds—facilitating robust comparisons between sets of lines differing at a major-effect locus.Here we investigate how polymorphism at an artificially induced mutation, the erecta locus in Arabidopsis thaliana, affects the magnitude of these important evolutionary genetic parameters under ecologically realistic field conditions. We use the Landsberg erecta (Ler) × Columbia (Col) RIL population of A. thaliana to examine how variation at a gene of major effect influences genetic variation, covariation, and selection on quantitative traits in a field setting. The Ler × Col RIL population is particularly suitable, because it segregates for an artificially induced mutation at the erecta locus, which has been shown to influence a wide variety of plant traits. The Ler × Col population thus allows a powerful test of the effects of segregating variation at a gene—chosen a priori—with numerous pleiotropic effects. The ERECTA gene is a leucine-rich receptor-like kinase (LRR-RLK) (Torii et al. 1996) and has been shown to affect plant growth rates (El-Lithy et al. 2004), stomatal patterning and transpiration efficiency (Masle et al. 2005; Shpak et al. 2005), bacterial pathogen resistance (Godiard et al. 2003), inflorescence and floral organ size and shape (Douglas et al. 2002; Shpak et al. 2003, 2004), and leaf polarity (Xu et al. 2003; Qi et al. 2004).Specifically, we sought to answer the following questions: (1) Is variation at erecta significantly associated with changes to the G matrix? (2) Is variation at erecta associated with changes in natural selection on genetically variable traits? And (3) is variation at erecta associated with significantly different projected evolutionary responses to selection?     

The Effect of Recent Admixture on Inference of Ancient Human Population History

Despite the widespread study of genetic variation in admixed human populations, such as African-Americans, there has not been an evaluation of the effects of recent admixture on patterns of polymorphism or inferences about population demography. These issues are particularly relevant because estimates of the timing and magnitude of population growth in Africa have differed among previous studies, some of which examined African-American individuals. Here we use simulations and single-nucleotide polymorphism (SNP) data collected through direct resequencing and genotyping to investigate these issues. We find that when estimating the current population size and magnitude of recent growth in an ancestral population using the site frequency spectrum (SFS), it is possible to obtain reasonably accurate estimates of the parameters when using samples drawn from the admixed population under certain conditions. We also show that methods for demographic inference that use haplotype patterns are more sensitive to recent admixture than are methods based on the SFS. The analysis of human genetic variation data from the Yoruba people of Ibadan, Nigeria and African-Americans supports the predictions from the simulations. Our results have important implications for the evaluation of previous population genetic studies that have considered African-American individuals as a proxy for individuals from West Africa as well as for future population genetic studies of additional admixed populations.STUDIES of archeological and genetic data show that anatomically modern humans originated in Africa and more recently left Africa to populate the rest of the world (Tishkoff and Williams 2002; Barbujani and Goldstein 2004; Garrigan and Hammer 2006; Reed and Tishkoff 2006; Campbell and Tishkoff 2008; Jakobsson et al. 2008; Li et al. 2008). Given the central role Africa has played in the origin of diverse human populations, understanding patterns of genetic variation and the demographic history of populations within Africa is important for understanding the demographic history of global human populations. The availability of large-scale single-nucleotide polymorphism (SNP) data sets coupled with recent advances in statistical methodology for inferring parameters in population genetic models provides a powerful means of accomplishing these goals (Keinan et al. 2007; Boyko et al. 2008; Lohmueller et al. 2009; Nielsen et al. 2009).It is important to realize that studies of African demographic history using genetic data have come to qualitatively different conclusions regarding important parameters. Some recent studies have found evidence for ancient (>100,000 years ago) two- to fourfold growth in African populations (Adams and Hudson 2004; Marth et al. 2004; Keinan et al. 2007; Boyko et al. 2008). Other studies have found evidence of very recent growth (Pluzhnikov et al. 2002; Akey et al. 2004; Voight et al. 2005; Cox et al. 2009; Wall et al. 2009) or could not reject a model with a constant population size (Pluzhnikov et al. 2002; Voight et al. 2005). It is unclear why studies found such different parameter estimates. However, these studies all differ from each other in the amount of data considered, the types of data used (e.g., SNP genotypes vs. full resequencing), the genomic regions studied (e.g., noncoding vs. coding SNPs), and the types of demographic models considered (e.g., including migration vs. not including migration postseparation of African and non-African populations).Another important way in which studies of African demographic history differ from each other is in the populations sampled. Some studies have focused on genetic data from individuals sampled from within Africa (Pluzhnikov et al. 2002; Adams and Hudson 2004; Voight et al. 2005; Keinan et al. 2007; Cox et al. 2009; Wall et al. 2009), while other studies included American individuals with African ancestry (Adams and Hudson 2004; Akey et al. 2004; Marth et al. 2004; Boyko et al. 2008). While there is no clear correspondence between those studies which sampled native African individuals (as opposed to African-Americans) and particular growth scenarios, it is clear from previous studies that African-American populations do differ from African populations in their recent demographic history. In particular, genetic studies suggest that there is wide variation in the degree of European admixture in most African-American individuals in the United States and that they have, on average, ∼80% African ancestry and 20% European ancestry (Parra et al. 1998; Pfaff et al. 2001; Falush et al. 2003; Patterson et al. 2004; Tian et al. 2006; Lind et al. 2007; Reiner et al. 2007; Price et al. 2009; Bryc et al. 2010). Furthermore, both historical records and genetic evidence suggest that the admixture process began quite recently, within the last 20 generations (Pfaff et al. 2001; Patterson et al. 2004; Seldin et al. 2004; Tian et al. 2006). Recent population admixture can alter patterns of genetic variation in a discernible and predictable way. For example, recently admixed populations will exhibit correlation in allele frequencies (i.e., linkage disequilibrium) among markers that differ in frequency between the parental populations. This so-called admixture linkage disequilibrium (LD) (Chakraborty and Weiss 1988) can extend over long physical distances (Lautenberger et al. 2000) and decays exponentially with time the since the admixture process began (i.e., recently admixed populations typically exhibit LD over a longer physical distance than anciently admixed populations).While it is clear that African-American populations have a different recent demographic history than do African populations from within Africa and that admixture tracts can be identified in admixed individuals (Falush et al. 2003; Patterson et al. 2004; Tang et al. 2006; Sankararaman et al. 2008a,b; Price et al. 2009; Bryc et al. 2010), the effect that admixture has on other patterns of genetic variation remains unclear. For example, Xu et al. (2007) found similar LD decay patterns when comparing African-American and African populations. It is also unclear whether the recent admixture affects our ability to reconstruct ancient demographic events (such as expansions that predate the spread of humans out of Africa) from whole-genome SNP data. Most studies of demographic history have summarized the genome-wide SNP data by allele frequency or haplotype summary statistics. If these summary statistics are not sensitive to the recent European admixture, then the African-American samples may yield estimates of demographic parameters that are close to the true demographic parameters for the ancestral, unsampled, African populations. This would suggest that the differences in growth parameter estimates obtained from African populations cannot be explained by certain studies sampling African-American individuals and others sampling African individuals from within Africa. However, if these statistics are sensitive to recent admixture, then they may give biased estimates of growth parameters.Here, we examine the effect of recent admixture on the estimation of population demography. In particular, we estimate growth parameters from simulated data sets using SNP frequencies as well as a recently developed haplotype summary statistic (Lohmueller et al. 2009). We compare the demographic parameter estimates made from the admixed and nonadmixed populations and find that some parameter estimates are qualitatively similar between the two populations when inferred using allele frequencies. Inferences of growth using haplotype-based approaches appear to be more sensitive to recent admixture than inferences based on SNP frequencies. We discuss implications that our results have for interpreting studies of demography in admixed populations.     

Bayesian Inference of Genetic Parameters Based on Conditional Decompositions of Multivariate Normal Distributions

It is widely recognized that the mixed linear model is an important tool for parameter estimation in the analysis of complex pedigrees, which includes both pedigree and genomic information, and where mutually dependent genetic factors are often assumed to follow multivariate normal distributions of high dimension. We have developed a Bayesian statistical method based on the decomposition of the multivariate normal prior distribution into products of conditional univariate distributions. This procedure permits computationally demanding genetic evaluations of complex pedigrees, within the user-friendly computer package WinBUGS. To demonstrate and evaluate the flexibility of the method, we analyzed two example pedigrees: a large noninbred pedigree of Scots pine (Pinus sylvestris L.) that includes additive and dominance polygenic relationships and a simulated pedigree where genomic relationships have been calculated on the basis of a dense marker map. The analysis showed that our method was fast and provided accurate estimates and that it should therefore be a helpful tool for estimating genetic parameters of complex pedigrees quickly and reliably.MUCH effort in genetics has been devoted to revealing the underlying genetic architecture of quantitative or complex traits. Traditionally, the polygenic model has been used extensively to estimate genetic variances and breeding values of natural and breeding populations, where an infinite number of genes is assumed to code for the trait of interest (Bulmer 1971; Falconer and Mackay 1996). The genetic variance of a quantitative trait can be decomposed into an additive part that corresponds to the effects of individual alleles and a part that is nonadditive because of interactions between alleles. Attention has generally been focused on the estimation of additive genetic variance (and heritability), since additive variation is directly proportional to the response of selection via the breeder''s equation (Falconer and Mackay 1996, Chap. 11). However, to estimate additive genetic variation and heritability accurately, it can be important to identify potential nonadditive sources in genetic evaluations (Misztal 1997; Ovaskainen et al. 2008; Waldmann et al. 2008), especially if the pedigree being analyzed contains a large proportion of full-sibs and clones, as these in particular give rise to nonadditive genetic relationships (Lynch and Walsh 1998, pp. 145). The polygenic model using pedigree and phenotypic information, i.e., the animal model (Henderson 1984), has been the model of choice for estimating genetic parameters in breeding and natural populations (Abney et al. 2000; Sorensen and Gianola 2002; O′Hara et al. 2008).Recent breakthroughs in molecular techniques have made it possible to create genome-wide, single nucleotide polymorphism (SNP) maps. These maps have helped to uncover a vast amount of new loci responsible for trait expression and have provided general insights into the genetic architecture of quantitative traits (e.g., Valdar et al. 2006; Visscher 2008; Flint and Mackay 2009). These insights can help when calculating disease risks in humans, when attempting to increase the yield from breeding programs, and when estimating relatedness in conservation programs. High-density SNPs of many species of importance to science and agriculture can now be scored quickly and relatively cheaply, for example, in mice (Valdar et al. 2006), chickens (Muir et al. 2008), and dairy cattle (VanRaden et al. 2009).In the analysis of populations of breeding stock, the inclusion of dense marker data has improved the predictive ability (i.e., reliability) of genetic evaluations compared to the traditional phenotype model, both in simulations (Meuwissen et al. 2001; Calus et al. 2008; Hayes et al. 2009) and when using real data (Legarra et al. 2008; VanRaden et al. 2009; González-Recio et al. 2009). Meuwissen et al. (2001) suggested that the effect of all markers should first be estimated, and then summed, to obtain genomic estimated breeding values (GEBVs). An alternative procedure, where all markers are used to compute the genomic relationship matrix (in place of the additive polygenic relationship matrix) has also been suggested (e.g., Villanueva et al. 2005; VanRaden 2008; Hayes et al. 2009); this matrix is then incorporated into the statistical analysis to estimate GEBVs. A comparison of both procedures (VanRaden 2008) yielded similar estimates of GEBVs in cases where the effect of an individual allele was small. In addition, if not all pedigree members have marker information, a combined relationship matrix derived from both genotyped and ungenotyped individuals could be computed; this has been shown to increase the accuracy of GEBVs (Legarra et al. 2009; Misztal et al. 2009). Another plausible option to incorporate marker information is to use low-density SNP panels within families and to trace the effect of SNPs from high-density genotyped ancestors, as suggested by Habier et al. (2009) and Weigel et al. (2009). However, fast and powerful computer algorithms, which can use the marker information as efficiently as possible in the analysis of quantitative traits, are needed to obtain accurate GEBVs from genome-wide marker data.This study describes the development of an efficient Bayesian method for incorporating general relationships into the genetic evaluation procedure. The method is based on expressing the multivariate normal prior distribution as a product of one-dimensional normal distributions, each conditioned on the descending variables. When evaluating the genetic parameters of natural and breeding populations, high-dimensional distributions are often used as prior distributions of various genetic effects, such as the additive polygenic effect (Wang et al. 1993), multivariate additive polygenic effects (Van Tassell and Van Vleck 1996), and quantitative trait loci (QTL) effects via the identical-by-decent matrix (Yi and Xu 2000). A Bayesian framework is adopted to obtain posterior distributions of all unknown parameters, estimated by using Markov chain Monte Carlo (MCMC) sampling algorithms in the software package WinBUGS (Lunn et al. 2000, 2009). By performing prior calculations in the form of the factorized product of simple univariate conditional distributions, the computational time of the MCMC estimation procedure is reduced considerably. This feature permits rapid inference for both the polygenic model and the genomic relationship model. Moreover, the decomposition allows for inbreeding of varying degree, since the correct genetic covariance structure can be inferred into the analysis. In this article, we test the method on two previously published pedigree data sets: phenotype data from a large pedigree of Scots pine, incorporation of information on both additive and dominance genetic relationships (Waldmann et al. 2008); and genomic information obtained from a genome-wide scan of a simulated animal population (Lund et al. 2009).     

Parallel Genetic and Proteomic Screens Identify Msps as a CLASP–Abl Pathway Interactor in Drosophila

Regulation of cytoskeletal structure and dynamics is essential for multiple aspects of cellular behavior, yet there is much to learn about the molecular machinery underlying the coordination between the cytoskeleton and its effector systems. One group of proteins that regulate microtubule behavior and its interaction with other cellular components, such as actin-regulatory proteins and transport machinery, is the plus-end tracking proteins (MT+TIPs). In particular, evidence suggests that the MT+TIP, CLASP, may play a pivotal role in the coordination of microtubules with other cellular structures in multiple contexts, although the molecular mechanism by which it functions is still largely unknown. To gain deeper insight into the functional partners of CLASP, we conducted parallel genetic and proteome-wide screens for CLASP interactors in Drosophila melanogaster. We identified 36 genetic modifiers and 179 candidate physical interactors, including 13 that were identified in both data sets. Grouping interactors according to functional classifications revealed several categories, including cytoskeletal components, signaling proteins, and translation/RNA regulators. We focused our initial investigation on the MT+TIP Minispindles (Msps), identified among the cytoskeletal effectors in both genetic and proteomic screens. Here, we report that Msps is a strong modifier of CLASP and Abl in the retina. Moreover, we show that Msps functions during axon guidance and antagonizes both CLASP and Abl activity. Our data suggest a model in which CLASP and Msps converge in an antagonistic balance in the Abl signaling pathway.COORDINATION of cytoskeletal dynamics is an essential process for the regulation of virtually all aspects of cellular behavior including cell shape changes, cell division, and cell motility (e.g., Rodriguez et al. 2003; Kodama et al. 2004). Not only is the cytoskeletal system coordinated with numerous cellular pathways to control cell behavior, it also functions as a central organizing scaffold for multiple effector protein complexes downstream of signaling pathways and cellular processes such as intracellular transport. Yet, little is known regarding the molecular machinery that governs the integrated coordination of various cytoskeletal components. Evidence suggests that the microtubule (MT) plus-end tracking protein CLASP [cytoplasmic linker protein (CLIP)-associated protein], which has been implicated in mitotic spindle formation (Inoue et al. 2000, 2004) and in linking MT ends to other cell structures such as the cell cortex and kinetochore (Akhmanova et al. 2001; Maiato et al. 2003; Mimori-Kiyosue et al. 2005; Reis et al. 2009), may play a pivotal role in the overall coordination of cytoskeletal networks. Not only does it affect MT dynamics, but CLASP may function as an actin-MT crosslinker as well, as it possesses actin-binding activity (Tsvetkov et al. 2007) and CLASP-bound microtubules appear to track along F-actin bundles in growth cones (Lee et al. 2004).We previously showed that CLASP functions downstream of Abelson (Abl) nonreceptor tyrosine kinase (Lee et al. 2004), which is a key signaling molecule that modulates the cytoskeleton downstream of numerous cell surface receptor inputs and plays essential roles in various contexts including cell motility and human disease (Van Etten 1999; Moresco and Koleske 2003; Bradley and Koleske 2009). While most cytoskeletal-related studies of Abl have focused on its regulation of actin dynamics (Lanier and Gertler 2000; Wills et al. 2002; Hernandez et al. 2004; Bradley and Koleske 2009), few studies have examined its MT effectors such as CLASP and how they may be involved in the coordination of both cytoskeletal networks. Additionally, we previously reported that Drosophila CLASP is necessary for accurate embryonic axon guidance at the central nervous system (CNS) midline where conserved guidance factors (Netrins and Slits) control growth cone navigation (Lee et al. 2004). In embryonic axons, we found that CLASP is required for Abl function (Lee et al. 2004); however, additional partner proteins required for CLASP activity during axon guidance are largely unknown.Recent studies of CLASP function focusing on cell culture and imaging have identified several CLASP-binding proteins, such as additional MT+TIPs (CLIP-170 and EB1) (Akhmanova et al. 2001; Mimori-Kiyosue et al. 2005) and cell cortex-associated proteins (LL5beta and ELKS) (Lansbergen et al. 2006). While investigation of the detailed interactions and functional significance of these types of molecules and their relation to CLASP has been important to understanding the CLASP molecular mechanism, elucidating how CLASP functions in a broader context will require expanding our awareness of the entire CLASP network, or “interactome.” As of yet, there has not been a comprehensive unbiased survey of CLASP functional interactors.A long history of molecular pathway dissection in multiple model systems and biological contexts has shown that genetic and proteomic interactome screens are powerful tools for defining the network of functional partners for any given gene of interest (Xu et al. 1990; Simon et al. 1991; Carthew et al. 1994; Karim et al. 1996; Rebay et al. 2000; St Johnston 2002). Determining the CLASP interaction network can not only define MT+TIP-associated proteins and regulators of MT biology, but it can also reveal new classes of molecules that interact with CLASP. For example, a recent study suggested that CLASPs function as actin-MT crosslinkers because they possess actin-binding activity (Tsvetkov et al. 2007), but few specific actin-binding CLASP interactors have been identified, and the functional relevance of CLASP–actin interaction is still unclear. A systematic approach to define the CLASP interactome has the potential for significantly increasing our ability to understand the CLASP mechanism.Therefore, to expand our knowledge of the CLASP functional mechanism, we have performed a multilevel genetic and proteomic screen for CLASP interactors in Drosophila. The single Drosophila CLASP ortholog has been given multiple names [orbit/multiple asters (MAST)/chromosome bows (chb)] (Fedorova et al. 1997; Inoue et al. 2000; Lemos et al. 2000), but here, we refer to it as CLASP. Our screen has identified novel and specific partners in several functional categories including cytoskeletal components, signaling proteins, and the unanticipated class of translation/RNA regulators. To validate the findings of this screen, we focused our initial investigation on the conserved MT+TIP identified among the cytoskeletal effectors in both the genetic and proteomic screens, Minispindles (Msps, ortholog of the human CKAP5 [cytoskeleton associated protein 5)/TOG (tumor overexpressed gene)/Xenopus Xmap215)]. Msps function and regulation of MT stability has been studied previously in the context of the mitotic spindle (Cullen et al. 1999; Lee et al. 2001; Barros et al. 2005) and in centrosomes (Popov et al. 2002; Cassimeris and Morabito 2004), although it has not been shown to functionally interact with CLASP nor play any role in the nervous system.Here, we report that Msps is an in vivo antagonist of CLASP and interacts strongly with Abl. Furthermore, we show that Msps functions during axon guidance. Our data suggest a model in which CLASP and Msps act antagonistically to provide the growth cone with a rapidly adaptable output for Abl-dependent responses to attractive and repulsive guidance cues.     

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.     

Patterns and Processes of Genome-Wide Divergence Between North American and African Drosophila melanogaster

Genomic tools and analyses are now being widely used to understand genome-wide patterns and processes associated with speciation and adaptation. In this article, we apply a genomics approach to the model organism Drosophila melanogaster. This species originated in Africa and subsequently spread and adapted to temperate environments of Eurasia and the New World, leading some populations to evolve reproductive isolation, especially between cosmopolitan and Zimbabwean populations. We used tiling arrays to identify highly differentiated regions within and between North America (the United States and Caribbean) and Africa (Cameroon and Zimbabwe) across 63% of the D. melanogaster genome and then sequenced representative fragments to study their genetic divergence. Consistent with previous findings, our results showed that most differentiation was between populations living in Africa vs. outside of Africa (i.e., “out-of-Africa” divergence), with all other geographic differences being less substantial (e.g., between cosmopolitan and Zimbabwean races). The X chromosome was much more strongly differentiated than the autosomes between North American and African populations (i.e., greater X divergence). Overall differentiation was positively associated with recombination rates across chromosomes, with a sharp reduction in regions near centromeres. Fragments surrounding these high F_ST sites showed reduced haplotype diversity and increased frequency of rare and derived alleles in North American populations compared to African populations. Nevertheless, despite sharp deviation from neutrality in North American strains, a small set of bottleneck/expansion demographic models was consistent with patterns of variation at the majority of our high F_ST fragments. Although North American populations were more genetically variable compared to Europe, our simulation results were generally consistent with those previously based on European samples. These findings support the hypothesis that most differentiation between North America and Africa was likely driven by the sorting of African standing genetic variation into the New World via Europe. Finally, a few exceptional loci were identified, highlighting the need to use an appropriate demographic null model to identify possible cases of selective sweeps in species with complex demographic histories.THE study of genetic differentiation between populations and species has recently been empowered by the use of genomic techniques and analysis (e.g., Noor and Feder 2006; Stinchcombe and Hoekstra 2008). In the past decade, genetic studies of adaptation and speciation have taken advantage of emerging molecular techniques to scan the genomes of diverging populations for highly differentiated genetic regions (e.g., Wilding et al. 2001; Emelianov et al. 2003; Beaumont and Balding 2004; Campbell and Bernatchez 2004; Scotti-Saintagne et al. 2004; Achere et al. 2005; Turner et al. 2005; Vasemagi et al. 2005; Bonin et al. 2006, 2007; Murray and Hare 2006; Savolainen et al. 2006; Yatabe et al. 2007; Nosil et al. 2008, 2009; Turner et al. 2008a,b; Kulathinal et al. 2009). As a result, genome scans can identify candidate regions that may be associated with adaptive evolution between diverging populations and, more broadly, are able to describe genome-wide patterns and processes of population differentiation (Begun et al. 2007; Stinchcombe and Hoekstra 2008).Genome scans in well-studied genetic model species such as Drosophila melanogaster gain particular power because differentiated loci are mapped to a well-annotated genome. Moreover, the evolutionary history of D. melanogaster is rich with adaptive and demographic events with many parallels to human evolution. Most notable is the historical out-of-Africa migration and subsequent adaptation to temperate ecological environments of Europe, Asia, North America, and Australia. This has resulted in widespread genetic and phenotypic divergence between African and non-African populations (e.g., David and Capy 1988; Begun and Aquadro 1993; Capy et al. 1994; Colegrave et al. 2000; Rouault et al. 2001; Takahashi et al. 2001; Caracristi and Schlötterer 2003; Baudry et al. 2004; Pool and Aquadro 2006; Schmidt et al. 2008; Yukilevich and True 2008a,b). Further, certain populations in Africa and in the Caribbean vary in their degree of reproductive isolation from populations in more temperate regions (Wu et al. 1995; Hollocher et al. 1997; Yukilevich and True 2008a,b). In particular, the Zimbabwe and nearby populations of southern Africa are strongly sexually isolated from all other populations, designating them as a distinct behavioral race (Wu et al. 1995).D. melanogaster has received a great deal of attention from the population geneticists in studying patterns of sequence variation across African and non-African populations. Many snapshots have been taken of random microsatellite and SNP variants spread across X and autosomes, and these have generated several important conclusions. Polymorphism patterns in European populations are characterized by reduced levels of nucleotide and haplotype diversity, an excess of high frequency-derived polymorphisms, and elevated levels of linkage disequilibrium relative to African populations (e.g., Begun and Aquadro 1993; Andolfatto 2001; Glinka et al. 2003; Haddrill et al. 2005; Ometto et al. 2005; Thornton and Andolfatto 2006; Hutter et al. 2007; Singh et al. 2007). These results have been generally interpreted as compatible with population size reduction/bottlenecks followed by recent population expansions. On the other hand, African populations are generally assumed either to have been relatively constant in size over time or to have experienced population size expansions. They generally show higher levels of nucleotide and haplotype diversity, an excess of rare variants, and a deficit of high frequency-derived alleles (Glinka et al. 2003; Ometto et al. 2005; Pool and Aquadro 2006; Hutter et al. 2007; but see Haddrill et al. 2005 for evidence of bottlenecks in Africa).Previous work also shows that the ratio of X-linked to autosomal polymorphism deviates from neutral expectations in opposite directions in African and European populations with more variation on the X than expected in Africa and less variation on the X than expected in Europe (Andolfatto 2001; Kauer et al. 2002; Hutter et al. 2007; Singh et al. 2007). The deviation from neutrality in the ratio of X-autosome polymorphism may be explained by positive selection being more prevalent on the X in Europe and/or by a combination of bottlenecks and male-biased sex ratios in Europe and female-biased sex ratios in Africa (Charlesworth 2001; Hutter et al. 2007; Singh et al. 2007). The selective explanation stems from the argument that, under the hitchhiking selection model, X-linked loci are likely to be more affected by selective sweeps than autosomal loci (Maynard Smith and Haigh 1974; Charlesworth et al. 1987; Vicoso and Charlesworth 2006, 2009).The relative contribution of selective and demographic processes in shaping patterns of genomic variation and differentiation is highly debated (Wall et al. 2002; Glinka et al. 2003; Haddrill et al. 2005; Ometto et al. 2005; Schöfl and Schlötterer 2004; Thornton and Andolfatto 2006; Hutter et al. 2007; Singh et al. 2007; Shapiro et al. 2007; Stephan and Li 2007; Hahn 2008; Macpherson et al. 2008; Noor and Bennett 2009; Sella et al. 2009). This is especially the case in D. melanogaster because derived non-African populations have likely experienced a complex set of demographic events during their migration out of Africa (e.g., Thornton and Andolfatto 2006; Singh et al. 2007; Stephan and Li 2007), making population genetics signatures of demography and selection difficult to tease apart (e.g., Macpherson et al. 2008). Thus it is still unclear what role selection has played in shaping overall patterns of genomic variation and differentiation relative to demographic processes in this species.While there is a long tradition in studying arbitrarily or opportunistically chosen sequences in D. melanogaster, genomic scans that focus particularly on highly differentiated sites across the genome have received much less attention. Such sites are arguably the best candidates to resolve the debate on which processes have shaped genomic differentiation within species (e.g., Przeworski 2002). Recently, a genome-wide scan of cosmopolitan populations in the United States and in Australia was performed to investigate clinal genomic differentiation on the two continents (Turner et al. 2008a). Many single feature polymorphisms differentiating Northern and Southern Hemisphere populations were identified. Among the most differentiated loci in common between continents, 80% were differentiated in the same orientation relative to the Equator, implicating selection as the likely explanation (Turner et al. 2008a). Larger regions of genomic differentiation within and between African and non-African populations have also been discovered, some of them possibly being driven by divergent selection (e.g., Dopman and Hartl 2007; Emerson et al. 2008; Turner et al. 2008a, Aguade 2009). Despite this recent progress, we still know relatively little about large-scale patterns of genomic differentiation in this species, especially between African and non-African populations, and whether most of this differentiation is consistent with demographic processes alone or if it requires selective explanations.In this work, we explicitly focus on identifying differentiated sites across the genome between U.S., Caribbean, West African, and Zimbabwean populations. This allows us to address several fundamental questions related to genomic evolution in D. melanogaster, such as the following: (1) Do genome-wide patterns of differentiation reflect patterns of reproductive isolation? (2) Is genomic differentiation random across and within chromosomes or are some regions overrepresented? (3) What are the population genetics properties of differentiated sites and their surrounding sequences? (4) Can demographic historical processes alone explain most of the observed differentiation on a genome-wide level or is it necessary to involve selection in their explanation?In general, our findings revealed that most genomic differentiation within D. melanogaster shows an out-of-Africa genetic signature. These results are inconsistent with the notion that most genomic differentiation occurs between cosmopolitan and Zimbabwean reproductively isolated races. Further, we found that the X is more differentiated between North American and African populations and more strongly deviates from pure neutrality in North American populations relative to autosomes. Nevertheless, our article shows that much of this deviation from neutrality is broadly consistent with several demographic null models, with a few notable exceptions. Athough this does not exclude selection as a possible alternative mechanism for the observed patterns, it supports the idea that most differentiation in D. melanogaster was likely driven by the sorting of African standing genetic variation into the New World.     

Unraveling the Complex Trait of Crop Yield With Quantitative Trait Loci Mapping in Brassica napus

Yield is the most important and complex trait for the genetic improvement of crops. Although much research into the genetic basis of yield and yield-associated traits has been reported, in each such experiment the genetic architecture and determinants of yield have remained ambiguous. One of the most intractable problems is the interaction between genes and the environment. We identified 85 quantitative trait loci (QTL) for seed yield along with 785 QTL for eight yield-associated traits, from 10 natural environments and two related populations of rapeseed. A trait-by-trait meta-analysis revealed 401 consensus QTL, of which 82.5% were clustered and integrated into 111 pleiotropic unique QTL by meta-analysis, 47 of which were relevant for seed yield. The complexity of the genetic architecture of yield was demonstrated, illustrating the pleiotropy, synthesis, variability, and plasticity of yield QTL. The idea of estimating indicator QTL for yield QTL and identifying potential candidate genes for yield provides an advance in methodology for complex traits.YIELD is the most important and complex trait in crops. It reflects the interaction of the environment with all growth and development processes that occur throughout the life cycle (Quarrie et al. 2006). Crop yield is directly and multiply determined by yield-component traits (such as seed weight and seed number). Yield-related traits (such as biomass, harvest index, plant architecture, adaptation, resistance to biotic and abiotic constraints) may also indirectly affect yield by affecting the yield-component traits or by other, unknown mechanisms. Increasing evidence suggests that “fine-mapped” quantitative trait loci (QTL) or genes identified as affecting crop yield involve diverse pathways, such as seed number (Ashikari et al. 2005; Tian et al. 2006b; Burstin et al. 2007; Xie et al. 2008; Xing et al. 2008; Xue et al. 2008), seed weight (Ishimaru 2003; Song et al. 2005; Shomura et al. 2008; Wang et al. 2008; Xie et al. 2006, 2008; Xing et al. 2008; Xue et al. 2008), flowering time (Cockram et al. 2007; Song et al. 2007; Xie et al. 2008; Xue et al. 2008), plant height (Salamini 2003; Ashikari et al. 2005; Xie et al. 2008; Xue et al. 2008), branching (Clark et al. 2006; Burstin et al. 2007; Xing et al. 2008), biomass yield (Quarrie et al. 2006; Burstin et al. 2007), resistance and tolerance to biotic and abiotic stresses (Khush 2001; Brown 2002; Yuan et al. 2002; Waller et al. 2005; Zhang 2007; Warrington et al. 2008), and root architecture (Hochholdinger et al. 2008).Many experiments have explored the genetic basis of yield and yield-associated traits (yield components and yield-related traits) in crops. Summaries of identified QTL have been published for wheat (MacCaferri et al. 2008), barley (Von Korff et al. 2008), rice, and maize (http://www.gramene.org/). The results show several common patterns. First, QTL for yield and yield-associated traits tend to be clustered in the genome, which suggests that the QTL of the yield-associated traits have pleiotropic effects on yield. Second, this kind of pleiotropy has not been well analyzed genetically. The QTL for yield (complicated factor), therefore, have not been associated with any yield-associated traits (relatively simple factors, such as plant height). Therefore, they are unlikely to predict accurately potential candidate genes for yield. Third, only a few loci (rarely >10) have been found for each of these traits. Thus, the genetic architecture of yield has remained ambiguous. Fourth, trials were carried out in a few environments and how the mode of expression of QTL for these complex traits might respond in different environments is unclear.In this study, the genetic architecture of crop yield was analyzed through the QTL mapping of seed yield and eight yield-associated traits in two related populations of rapeseed (Brassica napus) that were grown in 10 natural environments. The complexity of the genetic architecture of seed yield was demonstrated by QTL meta-analysis. The idea of estimating indicator QTL (QTL of yield-associated traits, which are defined as the potential genetic determinants of the colocalized QTL for yield) for yield QTL in conjunction with the identification of candidate genes is described.     

Reproductive Isolation in Hybrid Mice Due to Spermatogenesis Defects at Three Meiotic Stages

Early in the process of speciation, reproductive failures occur in hybrid animals between genetically diverged populations. The sterile hybrid animals are often males in mammals and they exhibit spermatogenic disruptions, resulting in decreased number and/or malformation of mature sperms. Despite the generality of this phenomenon, comparative study of phenotypes in hybrid males from various crosses has not been done, and therefore the comprehensive genetic basis of the disruption is still elusive. In this study, we characterized the spermatogenic phenotype especially during meiosis in four different cases of reproductive isolation: B6-ChrX^MSM, PGN-ChrX^MSM, (B6 × Mus musculus musculus-NJL/Ms) F₁, and (B6 × Mus spretus) F₁. The first two are consomic strains, both bearing the X chromosome of M. m. molossinus; in B6-ChrX^MSM, the genetic background is the laboratory strain C57BL/6J (predominantly M. m. domesticus), while in PGN-ChrX^MSM the background is the PGN2/Ms strain purely derived from wild M. m. domesticus. The last two cases are F₁ hybrids between mouse subspecies or species. Each of the hybrid males exhibited cell-cycle arrest and/or apoptosis at either one or two of three distinct meiotic stages: premeiotic stage, zygotene-to-pachytene stage of prophase I, and metaphase I. This study shows that the sterility in hybrid males is caused by spermatogenic disruptions at multiple stages, suggesting that the responsible genes function in different cellular processes. Furthermore, the stages with disruptions are not correlated with the genetic distance between the respective parental strains.WHEN animals from genetically diverged populations hybridize, complete or partial sterility is often observed in the F₁ hybrids or in their descendants. This phenomenon belonging to postzygotic reproductive isolation accelerates irreversible genetic divergence by preventing free gene flow across the two diverging populations, and thereby plays a pivotal role in speciation. Sexual dimorphism is a general feature of reproductive isolation (Wu and Davis 1993; Laurie 1997; Orr 1997; Kulathinal and Singh 2008). In mammals, impairment is much more severe in males than in females, and in general the heterogametic sex is more sensitive to interspecific and intersubspecific genetic incompatibility. This phenomenon is well known as Haldane''s rule (Haldane 1922; Laurie 1997; Orr 1997).In many animals, the reproductive isolation is caused by spermatogenic disruptions characterized by reduced number of germ cells and small testis size. These animals include Drosophila (Joly et al. 1997), stickleback fish Pungitius (Takahashi et al. 2005), caviomorph rodent Thrichomys (Borodin et al. 2006), house musk shrew Suncus (Borodin et al. 1998), wallaby Petrogale (Close et al. 1996), and genus Mus (Forejt and Iványi 1974; Matsuda et al. 1992; Hale et al. 1993; Yoshiki et al. 1993; Kaku et al. 1995; Gregorová and Forejt 2000; Elliott et al. 2001, 2004; Good et al. 2008). Although reproductive isolation by spermatogenic impairment is a well-known phenomenon, its underlying genetic mechanism and molecular basis have remained elusive. The Dobzhansky–Muller model, which infers that hybrid sterility or inviability is caused by deleterious epistatic interactions between nuclear genes derived from their respective parent species or subspecies (Dobzhansky 1936; Muller 1942), is widely accepted in animals and plants and is also applicable to the sterility of hybrid animals in F₂ or backcross generations, so-called hybrid breakdown, in which the genes causing postzygotic reproductive isolation are partially recessive (Orr 2005).The genetic incompatibility between house mouse subspecies is an ideal animal model for studying the early stage of speciation. Two subspecies of mouse, Mus musculus domesticus and M. m. musculus, diverged from their common ancestor 0.3–1.0 MYA (Yonekawa et al. 1980; Moriwaki 1994; Bonhomme and Guénet 1996; Boursot et al. 1996; Din et al. 1996). M. m. domesticus ranges across western Europe and the Middle East, whereas M. m. musculus ranges throughout eastern Europe and northern Asia (Bonhomme and Guénet 1996). The two subspecies meet in a narrow hybrid zone, which is most likely maintained by a balance between dispersal and selection against hybrids (Hunt and Selander 1973; Bonhomme and Guénet 1996; Payseur et al. 2004). M. m. domesticus also displays reproductive isolation from the Japanese wild mouse, M. m. molossinus, which originated from hybridization of M. m. castaneus and M. m. musculus and its nuclear genome is predominantly derived from M. m. musculus (Yonekawa et al. 1980, 1988; Moriwaki 1994; Sakai et al. 2005). To investigate the reproductive isolation between M. m. domesticus and M. m. molossinus, we previously constructed a consomic strain B6-ChrX^MSM (Oka et al. 2004). This strain has the X chromosome from the MSM/Ms strain, which is derived from M. m. molossinus, in the genetic background of the laboratory strain C57BL/6J (B6), which is predominantly derived from M. m. domesticus (Moriwaki 1994). F₁ hybrid animals between B6 and MSM/Ms strains are fully fertile. On the contrary, B6-ChrX^MSM shows male-specific sterility characterized by a reduced sperm number and dysfunction of the sperm, including abnormal morphology and low motility, indicating that B6-ChrX^MSM is a model of hybrid breakdown in animals (Oka et al. 2004, 2007). Our previous study indicated that the abnormal morphology of the sperm head results from the genetic incompatibility between MSM/Ms-derived X-linked genes and B6 genes on autosomes including chromosomes 1 and 11 (Oka et al. 2007).In this study, to understand the genetic mechanism of reproductive isolation in mice, we first undertook in-depth characterization of phenotype for each B6-ChrX^MSM male especially during meiosis. Meiosis is a special cell division that produces four haploid cells after one round of chromosome replication and two rounds of chromosome segregation. During meiosis, homologous chromosomes pair, synapse, undergo crossing over, and achieve bipolar attachment to the spindle to segregate one set of chromosomes to each daughter cell. Homologous recombination is initiated during the leptotene stage of meiotic prophase I with the formation of DNA double-strand breaks (DSBs), which are repaired immediately during the zygotene stage or after crossing over of homologous chromosomes during the pachytene stage (Roeder 1997; Tarsounas and Moens 2001).During the first wave of spermatogenesis, most mitotic spermatogonia in the B6-ChrX^MSM testes fail to initiate meiotic DNA replication. Some proportion of those spermatogonia that enter into meiosis are again arrested and eliminated by apoptosis at the pachytene stage, resulting in the production of a small number of sperms. We extended the same analysis to three other cases of reproductive isolation. Another consomic strain PGN-ChrX^MSM has an MSM/Ms-derived X chromosome in the genetic background of the PGN2/Ms strain derived from wild mice (M. m. domesticus). PGN-ChrX^MSM males produce a small number of dysfunctional sperms as was the case with B6-ChrX^MSM males, but the former males show apoptosis mainly at metaphase of meiosis I. Furthermore, we examined F₁ hybrid males from intersubspecific cross of (B6 × M. m. musculus-NJL/Ms) and interspecific cross of (B6 × M. spretus). These F₁ hybrid males exhibited apoptosis at metaphase I and at the zygotene-to-pachytene stage of prophase I. As a whole, the postzygotic reproductive isolation in mice is caused by disruptions at a minimum of three different spermatogenic stages.     

Estimating the Parameters of Selection on Nonsynonymous Mutations in Drosophila pseudoobscura and D. miranda

We present the results of surveys of diversity in sets of >40 X-linked and autosomal loci in samples from natural populations of Drosophila miranda and D. pseudoobscura, together with their sequence divergence from D. affinis. Mean silent site diversity in D. miranda is approximately one-quarter of that in D. pseudoobscura; mean X-linked silent diversity is about three-quarters of that for the autosomes in both species. Estimates of the distribution of selection coefficients against heterozygous, deleterious nonsynonymous mutations from two different methods suggest a wide distribution, with coefficients of variation greater than one, and with the average segregating amino acid mutation being subject to only very weak selection. Only a small fraction of new amino acid mutations behave as effectively neutral, however. A large fraction of amino acid differences between D. pseudoobscura and D. affinis appear to have been fixed by positive natural selection, using three different methods of estimation; estimates between D. miranda and D. affinis are more equivocal. Sources of bias in the estimates, especially those arising from selection on synonymous mutations and from the choice of genes, are discussed and corrections for these applied. Overall, the results show that both purifying selection and positive selection on nonsynonymous mutations are pervasive.SURVEYS of DNA sequence diversity and divergence are shedding light on a number of questions in evolutionary genetics (for recent reviews, see Akey 2009; Sella et al. 2009). Two of the most important questions of this kind concern the distribution of selection coefficients against deleterious mutations affecting protein sequences and the proportion of amino acid sequence differences between related species that have been fixed by positive selection. Several different methods have been proposed for studying each of these questions, using different features of data on polymorphism and divergence at nonsynonymous and silent sites.For example, the parameters of the distribution of selection coefficients against deleterious amino acid mutations have been estimated by contrasting the numbers of nonsynonymous and silent within-species polymorphisms and fixed differences between species (Sawyer and Hartl 1992; Bustamante et al. 2002; Piganeau and Eyre-Walker 2003; Sawyer et al. 2007); by fitting the frequency spectra of nonsynonymous and silent variants to models of selection, mutation, and drift (Akashi 1999; Eyre-Walker et al. 2006; Keightley and Eyre-Walker 2007; Kryukov et al. 2007; Boyko et al. 2008; Eyre-Walker and Keightley 2009); or by comparing levels of nonsynonymous and silent diversities between species with different population sizes (Loewe and Charlesworth 2006; Loewe et al. 2006). The results of these different approaches generally agree in suggesting that there is a wide distribution of selection coefficients against nonsynonymous mutations and that the mean selection coefficient against heterozygous carriers of such mutations is very small. The results imply that a typical individual from a human population carries several hundred weakly deleterious mutations (Eyre-Walker et al. 2006; Kryukov et al. 2007; Boyko et al. 2008); for a typical Drosophila population, with its much higher level of variability, the number is probably an order of magnitude greater (Loewe et al. 2006; Keightley and Eyre-Walker 2007).The presence of this large load of slightly deleterious mutations in human and natural populations, most of which are held at low frequencies by natural selection, has many implications. From the point of view of understanding human genetic disease, it means that we have to face the likelihood that susceptibility to a disease can be influenced by variants at many loci, each with small effects (Kryukov et al. 2007). The pervasive presence of deleterious mutations throughout the genome contributes to inbreeding depression (Charlesworth and Willis 2009) and may mean that the effective population size is reduced by background selection effects, even in regions of the genome with normal levels of genetic recombination (Loewe and Charlesworth 2007). Their presence may contribute so strongly to Hill–Robertson effects (Hill and Robertson 1966; Felsenstein 1974) that they cause severely reduced levels of diversity and adaptation in low-recombination regions of the genome (Charlesworth et al. 2010) and create a selective advantage to maintaining nonzero levels of recombination (Keightley and Otto 2006; Charlesworth et al. 2010). In addition, having an estimate of the distribution of selection coefficients against deleterious nonsynonymous mutations allows their contribution to between-species divergence to be predicted, providing a way of estimating the fraction of fixed nonsynonymous differences caused by positive selection (Loewe et al. 2006; Boyko et al. 2008; Eyre-Walker and Keightley 2009).It is thus important to collect data that shed light on the properties of selection against nonsynonymous mutations in a wide range of systems and also to compare the results from different methods of estimation, since they are subject to different sources of difficulty and biases. In a previous study, we proposed the use of a comparison between two related species with different effective population sizes for this purpose (Loewe and Charlesworth 2006; Loewe et al. 2006), using Drosophila miranda and D. pseudoobscura as material. These are well suited for this type of study, as they are closely related, live together in similar habitats, and yet have very different levels of silent nucleotide diversity, indicating different effective population sizes (N_e). This study was hampered by our inability to compare the same set of loci across the two species and by the small number of loci that could be used. We here present the results of a much larger study of DNA variation at X-linked and autosomal loci for these two species, using D. affinis as a basis for estimating divergence. We compare the results, applying the method of Loewe et al. (2006) with that of Eyre-Walker and Keightley (2009) for estimating the distribution of deleterious selection coefficients and with McDonald–Kreitman test-based methods for estimating the proportion of nonsynonymous differences fixed by positive selection. While broadly confirming the conclusions from earlier studies, we note some possible sources of bias and describe methods for minimizing their effects.     

Sexual Isolation in Acinetobacter baylyi Is Locus-Specific and Varies 10,000-Fold Over the Genome

Naturally transformable bacteria acquire chromosomal DNA from related species at lower frequencies than from cognate DNA sources. To determine how genome location affects heterogamic transformation in bacteria, we inserted an nptI marker into random chromosome locations in 19 different strains of the Acinetobacter genus (>24% divergent at the mutS/trpE loci). DNA from a total of 95 nptI-tagged isolates was used to transform the recipient Acinetobacter baylyi strain ADP1. A total of >1300 transformation assays revealed that at least one nptI-tagged isolate for each of the strains/species tested resulted in detectable integration of the nptI marker into the ADP1 genome. Transformation frequencies varied up to ∼10,000-fold among independent nptI insertions within a strain. The location and local sequence divergence of the nptI flanking regions were determined in the transformants. Heterogamic transformation depended on RecA and was hampered by DNA mismatch repair. Our studies suggest that single-locus-based studies, and inference of transfer frequencies from general estimates of genomic sequence divergence, is insufficient to predict the recombination potential of chromosomal DNA fragments between more divergent genomes. Interspecies differences in overall gene content, and conflicts in local gene organization and synteny are likely important determinants of the genomewide variation in recombination rates between bacterial species.HORIZONTAL gene transfer (HGT) contributes to bacterial evolution by providing access to DNA evolved and retained in separate species or strains (Cohan 1994a,b; Bergstrom et al. 2000; Ochman et al. 2000; Feil et al. 2001; Koonin 2003; Lawrence and Hendrickson 2003; Fraser et al. 2007). Multilocus sequence typing (MLST) has provided strong evidence for frequent transfer and recombination of chromosomal DNA between related bacterial strains within the same species (Maiden et al. 1998; Enright et al. 2002). HGT occurring by natural transformation allows bacteria to exploit the presence of nucleic acids in their environment for the purposes of nutrition, DNA repair, reacquisition of lost genes, and/or acquisition of novel genetic diversity (Redfield 1993; Mehr and Seifert 1998; Dubnau 1999; Claverys et al. 2000; Szöllösi et al. 2006; Johnsen et al. 2009). It can be inferred from observations of the presence of extracellular DNA in most environments that bacteria are constantly exposed to DNA from a variety of sources, without such exposure necessarily producing observable changes in the genetic compositions of bacterial populations over evolutionary time (Thomas and Nielsen 2005; Nielsen et al. 2007a,b).The absence of sequence similarity between the donor DNA and the DNA of the recipient bacterium is the strongest barrier to the horizontal acquisition of chromosomal genes in bacteria (Matic et al. 1996; Vulic et al. 1997; Majewski 2001; Townsend et al. 2003) as illegitimate recombination occurs only at extremely low frequencies in bacteria (Hülter and Wackernagel 2008a). Single-locus transfer models have been extensively applied and have demonstrated a log-linear decrease in recombination frequencies with increasing sequence divergence for Bacillus subtilis (Roberts and Cohan, 1993; Zawadzki et al. 1995), Acinetobacter baylyi (Young and Ornston 2001), Escherichia coli (Shen and Huang 1986; Vulic et al. 1997), and Streptococcus pneumoniae (Majewski et al. 2000). For instance, heterogamic transformation between nonmutator isolates at the rpoB locus of B. mojavensis is undetectable at sequence divergences >16.7% (Zawadzki et al. 1995) and between S. pneumoniae isolates with sequence divergences >18% (Majewski et al. 2000). In A. baylyi, the nonmutator sequence divergence limit for detectable transformation at the pcaH locus of strain ADP1 was found to be 20% (Young and Ornston 2001), and up to 24% overall divergence yielded transformants at 16S rRNA loci in strain DSM587 (Strätz et al. 1996).Several recent studies also show that short stretches (<200 bp) of DNA sequence identity can facilitate additive or substitutive integration of longer stretches (>1000 bp) of heterologous DNA in bacteria (Prudhomme et al. 1991, 2002; de Vries and Wackernagel 2002; Hülter and Wackernagel 2008a). Thus, the uptake of DNA in bacteria can facilitate larger substitutions within gene sequences and the integration of additional DNA material on the basis of recombination initiated in flanking DNA stretches (either at one or both ends) with high sequence similarity (Nielsen et al. 2000). On the other hand, segments of heterologous DNA interrupting the synteny of homologous DNA have also been shown to be a barrier in intraspecies transformation in S. pneumoniae (Pasta and Sicard 1996, 1999).The various studies of the interspecies transfer potential of single genes demonstrate that the immediate local sequence divergence of the transferred locus is of high importance in determining recombination frequencies in hosts up to 20% divergent (at the housekeeping gene level). However, it can be hypothesized that the broader structural, organizational, and biochemical properties of the genome region surrounding a particular locus will determine its transfer potential to more divergent host species (Cohan 2001; Lawrence 2002). The interspecies transfer potential of various genome regions/loci between more diverged species (>20% at the housekeeping gene level) may therefore differ substantially from a log-linear model (determined experimentally for more closely related species) as local gene organization becomes less conserved with evolutionary time. The barriers to gene exchange between divergent bacterial species is likely a combination of inefficient recombination due to both mismatched base pairs (the main determinator in the log-linear model) and conflicting gene order and organization across the local recombining DNA regions. In addition, selective barriers due to negative effects on host fitness of the transferred DNA regions may become increasingly important for the removal of recombination events from the bacterial population. Recent bioinformatics-based genome analysis of E. coli and Salmonella genomes suggests various parts of the bacterial genome may have different suceptibilities to undergo evolutionarily successful recombination leading to temporal fragmentation of speciation (Lawrence 2002; Retchless and Lawrence 2007). Nevertheless, few studies have experimentally tested the effect of variable species and chromosome locations of genes on their transfer potential between bacteria (Ravin and Chen 1967; Ravin and Chakrabarti 1975; Siddiqui and Goldberg 1975; Cohan et al. 1991; Huang et al. 1991; Fall et al. 2007).Here, we determine to what extent genome location contributes to sexual isolation between the recipient A. baylyi strain ADP1 and 19 sequence divergent (24–27% divergent at the mutS/trpE loci) donor Acinetobacter strains and species (carrying a selectable nptI gene in a total of 95 random genome locations).     

The Drosophila Claudin Kune-kune Is Required for Septate Junction Organization and Tracheal Tube Size Control

The vertebrate tight junction is a critical claudin-based cell–cell junction that functions to prevent free paracellular diffusion between epithelial cells. In Drosophila, this barrier is provided by the septate junction, which, despite being ultrastructurally distinct from the vertebrate tight junction, also contains the claudin-family proteins Megatrachea and Sinuous. Here we identify a third Drosophila claudin, Kune-kune, that localizes to septate junctions and is required for junction organization and paracellular barrier function, but not for apical-basal polarity. In the tracheal system, septate junctions have a barrier-independent function that promotes lumenal secretion of Vermiform and Serpentine, extracellular matrix modifier proteins that are required to restrict tube length. As with Sinuous and Megatrachea, loss of Kune-kune prevents this secretion and results in overly elongated tubes. Embryos lacking all three characterized claudins have tracheal phenotypes similar to any single mutant, indicating that these claudins act in the same pathway controlling tracheal tube length. However, we find that there are distinct requirements for these claudins in epithelial septate junction formation. Megatrachea is predominantly required for correct localization of septate junction components, while Sinuous is predominantly required for maintaining normal levels of septate junction proteins. Kune-kune is required for both localization and levels. Double- and triple-mutant combinations of Sinuous and Megatrachea with Kune-kune resemble the Kune-kune single mutant, suggesting that Kune-kune has a more central role in septate junction formation than either Sinuous or Megatrachea.EPITHELIA are essential for separating physiologically distinct body compartments and regulating trafficking between them. For proper function, it is imperative that epithelia maintain effective barriers against free paracellular diffusion. To this end, epithelial cells contain occluding junctions, which regulate paracellular permeability. In vertebrates, this is accomplished by tight junctions (TJ), structures that are characterized by regions of close membrane apposition between adjacent cells known as “kissing points” (Tsukita and Furuse 2002). While the TJ is made up of at least 40 different components (Schneeberger and Lynch 2004), the core proteins responsible for the paracellular barrier are the claudins (Angelow et al. 2008).Claudins are four-transmembrane domain proteins that form homo- and heterophilic interactions within the same cell (Furuse et al. 1999; Blasig et al. 2006) and with claudins in adjacent cells (Furuse et al. 1999), thereby establishing the paracellular seal. There are 24 members of the claudin family in mammals, many of which display distinct, tissue-specific expression patterns (Kiuchi-Saishin et al. 2002; Angelow et al. 2008). Mutations in several claudins can cause significant paracellular permeability defects in mice. For example, mutations in claudin-14 increase TJ permeability in the organ of Corti and cause deafness (Ben-Yosef et al. 2003), while loss of claudin-1 compromises epidermal barrier function (Furuse et al. 2002).In Drosophila, primary (ectodermally derived) epithelia lack discernable TJs and instead use pleated septate junctions (SJ) for the paracellular barrier (Baumgartner et al. 1996; Lamb et al. 1998; Genova and Fehon 2003; Paul et al. 2003). However, despite sharing a common barrier function, vertebrate TJs and invertebrate SJs differ in several ways. While vertebrate TJs are positioned apical to adherens junctions (AJ) and contain conserved apical polarity proteins, SJs are basal to AJs and contain conserved basolateral polarity proteins (reviewed in Tepass 2003; Wu and Beitel 2004). In addition, SJs do not contain kissing points, but rather ladder-like septa that span the intermembrane space (Lane and Swales 1982; Tepass and Hartenstein 1994).Beyond their general epithelial barrier function, SJs are also required for several tissue-specific processes. Glial cells, for example, ensheath nerve fibers and use SJs to maintain the blood–brain barrier (Auld et al. 1995; Baumgartner et al. 1996; Schwabe et al. 2005). In the embryonic tracheal system, SJs are required for the apical secretion of the lumenal matrix modifying proteins, Vermiform (Verm) and Serpentine (Serp), which act through undefined pathways to restrict tube length (Wang et al. 2006). This secretory pathway appears to be specific for Verm and Serp, since other apical proteins are secreted normally in SJ mutants. SJ proteins have also been shown to play a role in morphogenesis of the heart tube, even though this tissue lacks typical SJ septa (Yi et al. 2008).Although SJs have clear differences from vertebrate TJs, SJs contain at least two claudins, Megatrachea (Mega) and Sinuous (Sinu), both of which are required for the paracellular barrier (Behr et al. 2003; Wu et al. 2004; Stork et al. 2008). In this article, we identify a third claudin, Kune-kune (Kune), that is an integral SJ protein. Like the other claudins, Kune is required for maintaining epithelial paracellular barrier and tracheal tube size control and is not required for apical-basal polarity. We also find that, of all three characterized claudins, Kune has a more severe SJ phenotype, suggesting that it is a more central player in SJ organization and function than previously characterized Drosophila claudins.