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Specific α- and β-Tubulin Isotypes Optimize the Functions of Sensory Cilia in Caenorhabditis elegans
Primary cilia have essential roles in transducing signals in eukaryotes. At their core is the ciliary axoneme, a microtubule-based structure that defines cilium morphology and provides a substrate for intraflagellar transport. However, the extent to which axonemal microtubules are specialized for sensory cilium function is unknown. In the nematode Caenorhabditis elegans, primary cilia are present at the dendritic ends of most sensory neurons, where they provide a specialized environment for the transduction of particular stimuli. Here, we find that three tubulin isotypes—the α-tubulins TBA-6 and TBA-9 and the β-tubulin TBB-4—are specifically expressed in overlapping sets of C. elegans sensory neurons and localize to the sensory cilia of these cells. Although cilia still form in mutants lacking tba-6, tba-9, and tbb-4, ciliary function is often compromised: these mutants exhibit a variety of sensory deficits as well as the mislocalization of signaling components. In at least one case, that of the CEM cephalic sensory neurons, cilium architecture is disrupted in mutants lacking specific ciliary tubulins. While there is likely to be some functional redundancy among C. elegans tubulin genes, our results indicate that specific tubulins optimize the functional properties of C. elegans sensory cilia.THE fitness of all organisms depends on an ability to appropriately sense and respond to the environment. At the cellular level, many specific architectures have evolved to optimize these sensory functions. Prominent among these is the sensory cilium, a tubulin-based cytoplasmic extension that interrogates the extracellular environment in many biological contexts (Davenport and Yoder 2005; Berbari et al. 2009). Cilia are important for the transduction of a broad range of visual, auditory, mechanical, thermal, and chemical stimuli. They also function during development to receive a variety of signals, both chemical and mechanical, that regulate proliferation and differentiation (Goetz and Anderson 2010). Indeed, the disruption of cilium assembly and function can give rise to a spectrum of human diseases collectively known as ciliopathies (Berbari et al. 2009; Lancaster and Gleeson 2009). These disorders, which include autosomal dominant polycystic kidney disease (ADPKD) and autosomal recessive polycystic kidney disease (ARPKD), Bardet–Biedl syndrome, Meckel–Gruber syndrome, and Joubert syndrome, are associated with a variety of pathogenic conditions including polycystic kidneys and neurological impairments.At the core of all cilia and flagella is the microtubule axoneme. This characteristic structural element comprises nine doublet outer microtubules that may surround a central pair, the presence of which often indicates a motile cilium/flagellum. Like all microtubule-based structures, ciliary axonemes are built of heterodimers of α- and β-tubulins, highly conserved small GTP-binding proteins. The recruitment of other cilium components, including signal transduction machinery, requires a conserved assembly and maintenance process called intraflagellar transport (IFT) (Blacque et al. 2008; Pedersen and Rosenbaum 2008). IFT employs two major complexes that transport ciliary cargo bidirectionally by traveling along the axonemal microtubules. Loss of individual IFT components can cause a broad spectrum of defects in the assembly, maintenance, and function of cilia.Important insights into cilium structure and function have come from studies of genetically tractable organisms, particularly the green alga Chlamydomonas and the nematode Caenorhabditis elegans (Bae and Barr 2008; Pedersen and Rosenbaum 2008). In C. elegans, sensory cilia are found exclusively at the dendritic ends of sensory neurons. These cilia constitute a highly specialized sensory environment characterized by localized sensory receptors and specific signaling components. Cilium morphology is quite distinctive in many of these cells and likely contributes to their functional specialization (Ward et al. 1975). Recent progress has shed light on the mechanisms that confer this specialization onto more general pan-ciliary pathways (Evans et al. 2006; Mukhopadhyay et al. 2007; Jauregui et al. 2008; Mukhopadhyay et al. 2008; Silverman and Leroux 2009).The genomes of many eukaryotes harbor multiple α- and β-tubulin genes. Two hypotheses, which are not mutually exclusive, have been proposed to account for these paralogs (Cleveland 1987; Wade 2007). At one extreme, different tubulin isotypes might be functionally redundant, such that their minor coding differences are largely irrelevant. According to this model, multiple genes allow the maintenance of a stable pool of available monomers and dimers. The small amount of sequence variation within the α- and β-tubulin families supports this idea, as do studies of functionally redundant mitotic tubulins in C. elegans (Ellis et al. 2004; Lu et al. 2004; Phillips et al. 2004; Lu and Mains 2005). The alternative hypothesis proposes that specific structures, e.g., ciliary axonemes or axonal microtubules, rely on tubulins optimized for specific roles. Support for this idea has come from studies of cultured mammalian neurons (Joshi and Cleveland 1989), Drosophila (Hutchens et al. 1997; Raff et al. 1997), and human tubulins (Vent et al. 2005; Jaglin et al. 2009). In Drosophila, studies of motile sperm flagella have revealed that the sperm-specific β2 tubulin isoform builds not only the specialized motile axoneme but also all other tubulin-based structures (Kemphues et al. 1982). However, sequences both within and outside the axoneme motif in the C-terminal tail of this tubulin isoform are required for the flagellar axoneme, and other closely related β-tubulins cannot support this role (Fuller et al. 1987; Raff et al. 1997; Popodi et al. 2008). Genetic interactions have provided evidence that β2 tubulin heterodimerizes with the α-tubulin 84B (Hays et al. 1989), which also possesses specific functional properties not provided by structurally similar α-tubulins (Hutchens et al. 1997). In C. elegans, a specific role for tubulin isoforms has been described in the six touch receptor neurons. These nonciliated cells harbor unusual 15-filament microtubules composed of dimers of the α-tubulin MEC-12 and the β-tubulin MEC-7. The loss of mec-7 or mec-12, the expression of which is largely restricted to these cells, results in the conversion of 15-filament microtubules to the standard 11-microfilament variety and a commensurate loss of light-touch response (Savage et al. 1989; Fukushige et al. 1999; Bounoutas et al. 2009). Thus experimental support exists for both of these opposing views, and it seems likely that the role of specific tubulin isoforms in regulating microtubule structure and function differs according to cell and organelle type.The C. elegans genome encodes nine α- and six β-tubulin genes (Gogonea et al. 1999). Some of these genes, particularly tba-1, tba-2, tbb-1, and tbb-2, are expressed broadly during embryogenesis and function redundantly in spindle assembly and positioning (Ellis et al. 2004; Lu et al. 2004; Phillips et al. 2004; Lu and Mains 2005). tba-1 and tbb-2 have also been recently shown to be important for axon outgrowth and synaptogenesis (Baran et al. 2010). Several others, including mec-7, mec-12, and the β-tubulin ben-1, have been identified through genetic screens for particular phenotypes, such as touch insensitivity or benzimidazole resistance (Driscoll et al. 1989; Savage et al. 1989; Fukushige et al. 1999). However, the extent to which specific tubulin isoforms are required for structural and functional diversity in the C. elegans nervous system remains unknown. Here, taking advantage of several existing genome-wide data sets, we identify the α-tubulins TBA-6 and TBA-9 and the β-tubulin TBB-4 as strong candidates for tubulins that have roles in sensory cilia. We find that each of these genes are expressed in characteristic, partially overlapping, sets of sensory neurons, where their products localize to ciliary axonemes. While the loss of any one (or all three) of these genes does not abolish ciliogenesis, tubulin mutants exhibit significant defects in the localization of cilium proteins and in some cilium-dependent behavioral responses. Together, our results indicate that specific α- and β-tubulin isoforms are important, although not essential, for the efficient assembly and function of specific classes of C. elegans sensory cilia. Sensory cilia throughout the animal kingdom may therefore employ specific tubulin isoforms to optimize their function. 相似文献
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Min Xu Hargeet K. Brar Sehiza Grosic Reid G. Palmer Madan K. Bhattacharyya 《Genetics》2010,184(1):53-63
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* () in T that encodes a flavonoid 3′ hydroxylase (F3′H) ( EU190440Zabala 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. 相似文献
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S. D. Berry S. R. Davis E. M. Beattie N. L. Thomas A. K. Burrett H. E. Ward A. M. Stanfield M. Biswas A. E. Ankersmit-Udy P. E. Oxley J. L. Barnett J. F. Pearson Y. van der Does A. H. K. MacGibbon R. J. Spelman K. Lehnert R. G. Snell 《Genetics》2009,182(3):923-926
β-Carotene biochemistry is a fundamental process in mammalian biology. Aberrations either through malnutrition or potentially through genetic variation may lead to vitamin A deficiency, which is a substantial public health burden. In addition, understanding the genetic regulation of this process may enable bovine improvement. While many bovine QTL have been reported, few of the causative genes and mutations have been identified. We discovered a QTL for milk β-carotene and subsequently identified a premature stop codon in bovine β-carotene oxygenase 2 (BCO2), which also affects serum β-carotene content. The BCO2 enzyme is thereby identified as a key regulator of β-carotene metabolism.THE metabolism of β-carotene to form vitamin A is nutritionally important, and vitamin A deficiency remains a significant public health burden. Genetic variation may underlie individual differences in β-carotene metabolism and contribute to the etiology of vitamin A deficiency. Within an agricultural species, genetic variation provides opportunity for production improvements, disease resistance, and product specialization options. We have previously shown that natural genetic variation can be successfully used to inform bovine breeding decisions (Grisart et al. 2002; Blott et al. 2003). Despite numerous reports of quantitative trait loci (QTL), few causative mutations have been identified. We discovered a QTL for milk β-carotene content and report here the identification of a mutation in the bovine β-carotene oxygenase 2 (BCO2) gene responsible for this QTL. The mutation, which results in a premature stop codon, supports a key role for BCO2 in β-carotene metabolism.The QTL trial consisted of a Holstein-Friesian × Jersey cross in an F2 design and a half-sibling family structure (Spelman et al. 2001). Six F1 sires and 850 F2 female progeny formed the trial herd. To construct the genetic map, the pedigree (including the F1 sires, F1 dams, F2 daughters, and selected F0 grandsires: n = 1679) was genotyped, initially with 237 microsatellite markers, and subsequently, with 6634 SNP markers (Affymetrix Bovine 10K SNP GeneChip). A wide range of phenotypic measures relating to growth and development, health and disease, milk composition, fertility, and metabolism were scored on the F2 animals from birth to 6 years of age.To facilitate the discovery of QTL and genes regulating β-carotene metabolism, milk concentration of β-carotene was measured during week 6 of the animals'' second lactation (n = 651). Using regression methodology in a half-sib model (Haley et al. 1994; Baret et al. 1998), a QTL on bovine chromosome 15 (P < 0.0001; Figure 1A) was discovered. The β-carotene QTL effect on chromosome 15 was also significant (P < 0.0001) at two additional time points, in months 4 and 7 of lactation. Three of the six F1 sire families segregated for the QTL, suggesting that these three F1 sires would be heterozygous for the QTL allele (“Q”). To further define the most likely region within the QTL that would harbor the causative mutation, we undertook association mapping, using the 225 SNP markers that formed the chromosome 15 genetic map (Figure 1A). One SNP (“PAR351319”) was more closely associated with the β-carotene phenotype than any other marker (P = 2.522E−18). This SNP was located beneath the QTL peak. Further, the SNP was heterozygous in the three F1 sires that segregated for the QTL, and homozygous in the remaining three sires. On this basis, we hypothesized that the milk β-carotene phenotype would differ between animals on the basis of the genotype of SNP PAR351319.Open in a separate windowFigure 1.—Discovery of BCO2 mutation affecting milk β-carotene concentration. (A) The β-carotene QTL on bovine chromosome 15 (P < 0.0001) is shown by the red line. The maximum F-value at 21 cM was 7.15. The 95% confidence interval is shown by the shaded box. The association of each marker with milk β-carotene is shown by the blue dots, and the association of the BCO2 genotype is shown by the green diamond. A total of 233 informative markers (8 microsatellite markers and 225 single nucleotide polymorphisms) were included on the genetic map for BTA15. QTL detection was conducted using regression methodology in a line of descent model (Haley et al. 1994) and a half-sib model (Baret et al. 1998). Threshold levels were determined at the chromosomewide level using permutation testing (Churchill and Doerge 1998) and confidence intervals estimated using bootstrapping (Visscher et al. 1996). (B) The haplotypes of 10 representative animals for “QQ” and “qq” are shown for the SNP markers encompassing the SNP (“PAR351319”) most closely associated with the milk β-carotene phenotype. Light and dark gray boxes represent homozygous SNPs, while white boxes represent heterozygous SNPs. The genes present within the defined region are also shown. (C) The mutation in the bovine BCO2 gene is shown. The structure of the BCO2 gene is indicated by the horizontal bar, with vertical bars representing exons 1–12. The A > G mutation in exon 3 (red) causes a premature termination codon at amino acid position 80. (D) The mean concentration of β-carotene in the milk fat of “QQ,” “Qq,” and “qq” cows is shown. β-Carotene was measured by absorbance at 450 nm as previously described (Winkelman et al. 1999). Data are means ± SEM. The statistical significance was determined using ANOVA (***P < 0.0001; n = 651).We then made the following assumptions: that the effect of the QTL was additive, that the Q allele was present in the dam population, allowing the occurrence of homozygous (“QQ”) offspring, and that the QTL was caused by a single mutation, acting with a dominant effect on the milk β-carotene phenotype. Haplotypes encompassing the PAR351319 SNP were determined in the F2 offspring. A comparison of the phenotypic effect of homozygous Q, heterozygous and homozygous q individuals revealed that indeed, animals with the “QQ” genotype had a higher concentration of milk β-carotene than animals with the “qq” genotype (Figure 1D). We predicted that the region of homozygosity was likely to contain the causative gene and mutation. The extent of this region and the candidate genes contained within it are shown in Figure 1B. A total of 10 genes with known function, including BCO2, were located within the region. This information, combined with knowledge of the role BCO2 plays in β-carotene metabolism in other species (Kiefer et al. 2001), made BCO2 a good positional candidate for the QTL. We therefore sequenced the entire coding region (12 exons, ) of the BCO2 gene in each of the six F1 sires. An A > G mutation, which was heterozygous in the three F1 sires that segregated for the QTL, was discovered in exon three, 240 bp from the translation initiation site ( NC_007313.3Figure 1C). The three remaining sires were homozygous for the G allele, which encodes the 530-amino-acid BCO2 protein (). The A allele creates a premature stop codon resulting in a truncated protein of 79 amino acids. To determine whether this mutation was associated with the QTL, the remainder of the pedigree was genotyped. The BCO2 genotype was significantly associated with the milk β-carotene phenotype (P = 8.195E−29) The AA genotype (referred to as BCO2−/−) was present in 3.4% (n = 28) of the F2 population. The AG and GG genotypes (subsequently referred to as BCO2−/+ and BCO2+/+, respectively) were present in 32.8% (n = 269) and 63.8% (n = 523), respectively, of the F2 population.The effect of the premature stop codon on milk β-carotene content was striking. BCO2−/− cows produced milk with 78 and 55% more β-carotene than homozygous (GG) and heterozygous (AG) wild-type animals, respectively (P < 0.0001; NP_001101987Figure 2A). Consequently, the yellow color of the milk fat varied greatly (Figure 2B). The genotype effect on milk β-carotene content was similar at the other two time points measured during lactation (78 and 68% more β-carotene in milk from BCO2−/− cows compared to BCO2+/+ cows; data not shown).Open in a separate windowFigure 2.—Effect of BCO2 genotype on milk β-carotene content. (A) The mean concentration of β-carotene in the milk fat of BCO2−/−, BCO2−/+, and BCO2+/+ cows is shown. β-Carotene was measured by absorbance at 450 nm as previously described (Winkelman et al. 1999). Data are means ± SEM. The statistical significance was determined using ANOVA (***P < 0.0001; n = 651). (B) The effect of the BCO2 genotype on milk fat color is illustrated.No adverse developmental or health affects as a result of the A allele were observed at any stage throughout the lifespan of the animals. The BCO2−/− cows were fertile and milk yield was normal throughout lactation. Interestingly, quantitative real-time PCR showed fourfold lower levels of the BCO2 mRNA in liver tissue from BCO2−/− cows (data not shown).β-Carotene and vitamin A (retinol) concentrations were also measured in serum, liver, and adipose tissue samples, and vitamin A concentration was measured in milk samples from 14 F2 cows of each genotype. Serum β-carotene concentration was higher in BCO2−/− cows compared to the heterozygous and homozygous wild-type cows (P = 0.003; Figure 3A). Thus, the effect of the mutation on β-carotene concentration was similar for both milk and serum, showing that this effect was not confined to the mammary gland. Vitamin A concentration was higher in serum from BCO2−/− cows (P = 0.001; Figure 3B); however, the concentration did not differ in milk (13.1 μg/g fat vs. 14.1 μg/g fat for BCO2−/− and BCO2+/+ cows, respectively; P > 0.1). Liver β-carotene concentration did not differ between genotype groups (Figure 3C), but liver vitamin A was lower in BCO2−/− cows compared to BCO2+/+ cows (P < 0.03; Figure 3D). β-Carotene and vitamin A concentration did not differ between the genotype groups in adipose tissue (data not shown), suggesting tissue-specific effects of the BCO2 enzyme.Open in a separate windowFigure 3.—Effect of the BCO2 genotypes on concentration of β-carotene (A and C), and retinol (B and D), in serum (A and B), and liver (C and D). Subcutaneous adipose tissue biopsies (∼500 mg tissue), liver biopsies (∼100 mg tissue), and serum samples (10 ml) were taken from a subset of 42 cows (14 animals each BCO2−/−, BCO2−/+, and BCO2+/+ genotypes). β-Carotene and retinol measurements were determined using HPLC with commercial standards, on the basis of a published method (Hulshof et al. 2006). Data shown are means ± SEM. Significant differences are indicated by asterisks (*P < 0.05; **P < 0.01; ANOVA, n = 14 per genotype).While previous studies have shown a key role for β-carotene 15, 15′ monooxygenase (BCMO1) in catalyzing the symmetrical cleavage of β-carotene to vitamin A (von Lintig and Vogt 2000; von Lintig et al. 2001; Hessel et al. 2007) similar evidence for the role of the BCO2 enzyme in β-carotene metabolism is lacking. The physiological relevance of BCO2 has therefore been a topic of debate (Wolf 1995; Lakshman 2004; Wyss 2004). BCO2 mRNA and protein have been detected in several human tissues (Lindqvist et al. 2005), and the in vitro cleavage of β-carotene to vitamin A has been demonstrated (Kiefer et al. 2001; Hu et al. 2006). Our results provide in vivo evidence for BCO2-mediated conversion of β-carotene to vitamin A. BCO2−/− cows had more β-carotene in serum and milk and less vitamin A in liver, the main storage site for this vitamin.Our results show that a simple genetic test will allow the selection of cows for milk β-carotene content. Thus, milk fat color may be increased or decreased for specific industrial applications. Market preference for milk fat color varies across the world. Further, β-carotene enriched dairy foods may assuage vitamin A deficiency. Milk may be an ideal food for delivery of β-carotene, which is fat soluble and most efficiently absorbed in the presence of a fat component (Ribaya-Mercado 2002).In conclusion, we have discovered a naturally occurring premature stop codon in the bovine BCO2 gene strongly suggesting a key role of BCO2 in β-carotene metabolism. This discovery has industrial applications in the selection of cows producing milks with β-carotene content optimized for specific dairy products or to address a widespread dietary deficiency. More speculatively, it would be interesting to investigate possible effects of BCO2 variation in humans on the etiology of vitamin A deficiency. 相似文献
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We consider neutral evolution of a large population subject to changes in its population size. For a population with a time-variable carrying capacity we study the distribution of the total branch lengths of its sample genealogies. Within the coalescent approximation we have obtained a general expression—Equation 20—for the moments of this distribution with a given arbitrary dependence of the population size on time. We investigate how the frequency of population-size variations alters the total branch length.MODELS for gene genealogies of biological populations often assume a constant, time-independent population size N. This is the case for the Wright–Fisher model (Fisher 1930; Wright 1931), for the Moran model (Moran 1958), and for their representation in terms of the coalescent (Kingman 1982). In real biological populations, by contrast, the population size changes over time. Such fluctuations may be due to catastrophic events (bottlenecks) and subsequent population expansions or just reflect the randomness in the factors determining the population dynamics. Many authors have argued that genetic variation in a population subject to size fluctuations may nevertheless be described by the Wright–Fisher model, if one replaces the constant population size in this model by an effective population size of the form(1)where Nl stands for the population size in generation l. The harmonic average in Equation 1 is argued to capture the significant effect of catastrophic events on patterns of genetic variation in a population: if, for example, a population went through a recent bottleneck, a large fraction of individuals in a given sample would originate from few parents. This in turn would lead to significantly reduced genetic variation, parameterized by a small value of Neff. (See, e.g., Ewens 1982 for a review of different measures of the effective population size and Sjödin et al. 2005 and Wakeley and Sargsyan 2009 for recent developments of this concept.)The concept of an effective population size has been frequently used in the literature, implicitly assuming that the distribution of neutral mutations in a large population of fluctuating size is identical to the distribution in a Wright–Fisher model with the corresponding constant effective population size given by Equation 1. However, recently it was shown that this is true only under certain circumstances (Kaj and Krone 2003; Nordborg and Krone 2003; Jagers and Sagitov 2004). It is argued by Sjödin et al. (2005) that the concept of an effective population size is appropriate when the timescale of fluctuations of Nl is either much smaller or much larger than the typical time between coalescent events in the sample genealogy. In these limits it can be proved that the distribution of the sample genealogies is exactly given by that of the coalescent with a constant, effective population size.More importantly, it follows from these results that, in populations with variable size, the coalescent with a constant effective population size is not always a valid approximation for the sample genealogies. Deviations between the predictions of the standard coalescent model and empirical data are frequently observed, and there are a number of different statistical tests quantifying the corresponding discrepancies (see, for example, Tajima 1989, Fu and Li 1993, and Zeng et al. 2006). The analysis of such deviations is of crucial importance in understanding, for example, human genetic history (Garrigan and Hammer 2006). But while there is a substantial amount of work numerically quantifying deviations, often in terms of a single number, little is known about their qualitative origins and their effect upon summary statistics in the population in question.The question is thus to understand the effect of population-size fluctuations on the patterns of genetic variation, in particular for the case where the scale of the population-size fluctuations is comparable to the time between coalescent events in the ancestral tree. As is well known, many empirical measures of genetic variation can be computed from the total branch length of the sample genealogy (the expected number of single-nucleotide polymorphisms, for example, is proportional to the average total branch length).The aim of this article is to analyze the distribution of the scaled total branch length Tn for a sample genealogy in a population of fluctuating size, as illustrated in Figure 1. For the genealogy of n ≥ 2 lineages sampled at the present time, the expression ⌊NTn⌋ gives the total branch length in terms of generations. Here ⌊Nt⌋ is the largest integer ≤Nt, and the scaling factor N is a suitable measure of the number of genes in the population and serves as a counterpart of the constant generation size of the standard Wright–Fisher model.Open in a separate windowFigure 1.—The effect of population-size oscillations on the genealogy of a sample of size n = 17 (schematic). Left, genealogy described by Kingman''s coalescent for a large population of constant size, illustrated by the light blue rectangle; right, sinusoidally varying population size. Coalescence is accelerated in regions of small population sizes and vice versa. This significantly alters the tree and gives rise to changes in the distribution of the number of mutations and of the population homozygosity.A motivating example is given in Figure 2, which shows numerically computed distributions ρ(Tn) of the total branch lengths Tn for a particular population model with a time-dependent carrying capacity. The model is described briefly in the Figure 2 legend and in detail in a model for a population with time-dependent carrying capacity. As Figure 2 shows, the distributions depend in a complex manner on the form of the size changes. We observe that when the frequency of the population-size fluctuations is very small (Figure 2a), the distribution is well described by the standard coalescent result(2)(Hein et al. 2005). When the frequency is very large (Figure 2e), Equation 2 also applies, but with a different time scaling reflecting an effective population size: t on the right-hand side (rhs) in Equation 2 is replaced by t/c with c = N/Neff. Apart from these special limits, however, the form of the distributions appears to depend in a complicated manner upon the frequency of the population-size variation. The observed behavior is caused by the fact that coalescence proceeds faster for smaller population sizes and more slowly for larger population sizes, as illustrated in Figure 1. But the question is how to quantitatively account for the changes shown in Figure 2.Open in a separate windowFigure 2.—Numerically computed distributions of the scaled total branch lengths Tn in genealogies of samples of size n = 10. The model employed in the simulations is outlined in a model for a population with time-dependent carrying capacity. It describes a population subject to a time-varying carrying capacity, Kl = K0(1 + ɛ sin(2πνl)). The frequency of the time changes is determined by ν, and l = 1, 2, 3, … labels discrete generations forward in time. The parameter N = K0 describes the typical population size, which is taken here to be equal to the time-averaged carrying capacity. a–e show for populations with increasingly rapidly oscillating carrying capacity. The dashed red line in a shows that in the limit of low frequencies the standard coalescent result, Equation 2, is obtained. The dashed red line in e shows that also in the limit of large frequencies the standard coalescent result is obtained, but now with an effective population size. The dashed red line in d is a two-parameter distribution, Equation 41, derived in comparison between numerical simulations and coalescent predictions. Further numerical and analytical results on the frequency dependence of the moments of these distributions are shown in Figure 4. Parameter values used: K0 = 10,000, ɛ = 0.9, and r = 1 (see a model for a population with time-dependent carrying capacity for the exact meaning of the intrinsic growth rate r) and (a) νN = 0.001, (b) νN = 0.1, (c) νN = 0.316, (d) νN = 1, and (e) νN = 100.We show in this article that the results of the simulations displayed in Figure 2 are explained by a general expression—Equation 20—for the moments of the distributions shown in Figure 2. Our general result is obtained within the coalescent approximation valid in the limit of large population size. But we find that in most cases, the coalescent approximation works very well down to small population sizes (a few hundred individuals). Our result enables us to understand and quantitatively describe how the distributions shown in Figure 2 depend upon the frequency of the population-size oscillations. It makes possible to determine, for example, how the variance, skewness, and the kurtosis of these distributions depend upon the frequency of demographic fluctuations. This in turn allows us to compute the population homozygosity and to characterize genetic variation in populations with size fluctuations.The remainder of this article is organized as follows. The next section summarizes our analytical results for the moments of the total branch length. Following that, we describe the model employed in the computer simulations. Then, corresponding numerical results are compared to the analytical predictions. And finally, we summarize how population-size fluctuations influence the distribution of total branch lengths and conclude with an outlook. 相似文献
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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.
- 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).
- 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).
- 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.
- 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).
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Sylvain Glémin 《Genetics》2010,185(3):939-959
GC-biased gene conversion (gBGC) is a recombination-associated process mimicking selection in favor of G and C alleles. It is increasingly recognized as a widespread force in shaping the genomic nucleotide landscape. In recombination hotspots, gBGC can lead to bursts of fixation of GC nucleotides and to accelerated nucleotide substitution rates. It was recently shown that these episodes of strong gBGC could give spurious signatures of adaptation and/or relaxed selection. There is also evidence that gBGC could drive the fixation of deleterious amino acid mutations in some primate genes. This raises the question of the potential fitness effects of gBGC. While gBGC has been metaphorically termed the “Achilles'' heel” of our genome, we do not know whether interference between gBGC and selection merely has practical consequences for the analysis of sequence data or whether it has broader fundamental implications for individuals and populations. I developed a population genetics model to predict the consequences of gBGC on the mutation load and inbreeding depression. I also used estimates available for humans to quantitatively evaluate the fitness impact of gBGC. Surprising features emerged from this model: (i) Contrary to classical mutation load models, gBGC generates a fixation load independent of population size and could contribute to a significant part of the load; (ii) gBGC can maintain recessive deleterious mutations for a long time at intermediate frequency, in a similar way to overdominance, and these mutations generate high inbreeding depression, even if they are slightly deleterious; (iii) since mating systems affect both the selection efficacy and gBGC intensity, gBGC challenges classical predictions concerning the interaction between mating systems and deleterious mutations, and gBGC could constitute an additional cost of outcrossing; and (iv) if mutations are biased toward A and T alleles, very low gBGC levels can reduce the load. A robust prediction is that the gBGC level minimizing the load depends only on the mutational bias and population size. These surprising results suggest that gBGC may have nonnegligible fitness consequences and could play a significant role in the evolution of genetic systems. They also shed light on the evolution of gBGC itself.GC-BIASED gene conversion (gBGC) is increasingly recognized as a widespread force in shaping genome evolution. In different species, gene conversion occurring during double-strand break recombination repair is thought to be biased toward G and C alleles. In heterozygotes, GC alleles undergo a kind of molecular meiotic drive that mimics selection (reviewed in Marais 2003). This process can rapidly increase the GC content, especially around recombination hotspots (Spencer et al. 2006), and, more broadly, can affect genome-wide nucleotide landscapes (Duret and Galtier 2009a). For instance, it is thought to play a role in shaping isochore structure evolution in mammals (Galtier et al. 2001; Meunier and Duret 2004; Duret et al. 2006) and birds (Webster et al. 2006). Direct experimental evidence of gBGC mainly comes from studies in yeast (Birdsell 2002; Mancera et al. 2008; but see Marsolier-Kergoat and Yeramian 2009) and humans (Brown and Jiricny 1987). However, associations between recombination and the nucleotide landscape and frequency spectra biased toward GC alleles provide indirect evidence in very diverse organisms (Organisms Direct evidence Indirect evidence Achille''s heel evidence References Yeast Meiotic segregation bias Mancera et al. (2008) Mitotic and mitotic heteromismatch correction bias Correlation between GC and recombination Birdsell (2002) Mammals Mitotic heteromismatch correction bias Brown and Jiricny (1987) Correlation between GC*/GC and recombination Duret and Arndt (2008); Meunier and Duret (2004) Biased frequency spectrum toward GC alleles Galtier et al. (2001); Spencer et al. (2006) GC bias associated with high dN/dS near recombination hotspot Berglund et al. (2009; Galtier et al. (2009) Birds Correlation between GC and recombination International Chicken Genome Sequencing Consortium (2004) Turtles Correlation between GC and chromosome size Kuraku et al. (2006) Drosophila Correlation between GC and recombination Marais et al. (2003) Biased frequency spectrum toward GC alleles Galtier et al. (2006) Nematodes Correlation between GC and recombination Marais et al. (2001) Grasses Correlation between GC and outcrossing/selfing Glémin et al. (2006) Correlation between GC* and recombination and outcrossing/selfing Outcrossing increases dN/dS for genes with high GC* Haudry et al. (2008) Green algae Correlation between GC and recombination Jancek et al. (2008) Paramecium Correlation between GC and chromosome size Duret et al. (2008)