Mutation in Bovine β-Carotene Oxygenase 2 Affects Milk Color

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 = 2.522^E−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, {"type":"entrez-nucleotide","attrs":{"text":"NC_007313.3","term_id":"194719391","term_text":"NC_007313.3"}}NC_007313.3) 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 (Figure 1C). The three remaining sires were homozygous for the G allele, which encodes the 530-amino-acid BCO2 protein ({"type":"entrez-protein","attrs":{"text":"NP_001101987","term_id":"241666456","term_text":"NP_001101987"}}NP_001101987). 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 = 8.195^E−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; Figure 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.     

The Spontaneous Appearance Rate of the Yeast Prion [PSI+] and Its Implications for the Evolution of the Evolvability Properties of the [PSI+] System

Epigenetically inherited aggregates of the yeast prion [PSI+] cause genomewide readthrough translation that sometimes increases evolvability in certain harsh environments. The effects of natural selection on modifiers of [PSI+] appearance have been the subject of much debate. It seems likely that [PSI+] would be at least mildly deleterious in most environments, but this may be counteracted by its evolvability properties on rare occasions. Indirect selection on modifiers of [PSI+] is predicted to depend primarily on the spontaneous [PSI+] appearance rate, but this critical parameter has not previously been adequately measured. Here we measure this epimutation rate accurately and precisely as 5.8 × 10⁻⁷ per generation, using a fluctuation test. We also determine that genetic “mimics” of [PSI+] account for up to 80% of all phenotypes involving general nonsense suppression. Using previously developed mathematical models, we can now infer that even in the absence of opportunities for adaptation, modifiers of [PSI+] are only weakly deleterious relative to genetic drift. If we assume that the spontaneous [PSI+] appearance rate is at its evolutionary optimum, then opportunities for adaptation are inferred to be rare, such that the [PSI+] system is favored only very weakly overall. But when we account for the observed increase in the [PSI+] appearance rate in response to stress, we infer much higher overall selection in favor of [PSI+] modifiers, suggesting that [PSI+]-forming ability may be a consequence of selection for evolvability.THE yeast phenotype [PSI+] is characterized by prion aggregates of the protein Sup35. Cells are in either a [psi−] (normal) or [PSI+] state, depending on the absence or presence of the prion aggregates (Figure 1, a and b). Sup35 prion aggregates replicate in a similar fashion to mammalian prions but are cytoplasmic and, as such, the prion state is cytoplasmically inherited (Wickner et al. 1995).Open in a separate windowFigure 1.—Comparison between the three possible modes ([PSI+], genetic mimic, point mutation revertant) of the expression of 3′-UTR sequences in yeast. (a) The normal [psi−] phenotypic state; (b) the [PSI+] prion causes readthrough and low-level expression of 3′-UTRs across multiple genes, appearing at rate m_PSI; (c) a genetic mimic of [PSI+] such as the sal3-4 mutant of Sup35 (Eaglestone et al. 1999) appearing at rate m_mimic not reversible by the application of guanidine hydrochloride; (d) a point mutation in a single stop codon at rate μ_point, leading to incorporation of formerly 3′-UTR into a single coding sequence. (e) [PSI+] can act as a “stop-gap” mechanism, buying a lineage more time to acquire one or more adaptive stop codon readthrough point mutations. When this genetic assimilation is complete, [PSI+] can revert to [psi−] (Masel and Bergman 2003; Griswold and Masel 2009).When not part of an aggregate, Sup35 helps mediate translation termination in yeast (Stansfield et al. 1995b; Zhouravleva et al. 1995). Sup35 molecules that are incorporated into nonfunctional prion aggregates are presumably not available for translation termination, which can lead to the translation of stop codons by near-cognate tRNAs (Figure 1b) (Tuite and Mclaughlin 1982; Pure et al. 1985; Lin et al. 1986). This partial loss of Sup35 function leads to an increased frequency of readthrough translation of 3′-untranslated regions (3′-UTR) across all genes (Figure 1b). This increase is modest in wild-type yeast, from an average readthrough rate of 0.3% in [psi−] cells up to 1% in [PSI+] cells (Firoozan et al. 1991). Some [PSI+] yeast strains grow faster than [psi−] controls in certain harsh environments, suggesting that readthrough translation of some 3′-UTRs may be adaptive in certain conditions (True and Lindquist 2000; Joseph and Kirkpatrick 2008). This directly shows that [PSI+]-mediated capacitance may increase evolvability in the laboratory. [PSI+]-mediated phenotypes have a complex genetic basis, involving multiple loci (True et al. 2004).As an epigenetically inherited protein aggregate, [PSI+] can easily be lost after some generations (Cox et al. 1980). This returns the lineage to its normal [psi−] state and restores translation fidelity. If a subset of revealed phenotypic variation is adaptive, it may have lost its dependence on [PSI+] by this time (True et al. 2004). This process of genetic assimilation may, for example, involve one or more point mutations in stop codons, increasing readthrough up to 100% (Figure 1e) (Griswold and Masel 2009). This leaves the yeast with a new adaptive trait and with no permanent load of other, deleterious variation.In general, stop codons can be lost either directly through point mutations or indirectly through upstream indels. This leads to novel coding sequence coming from in-frame and out-of-frame 3′-UTRs, respectively. [PSI+] is expected to facilitate only the former, while mutation bias favors the latter. Yeasts show a much higher ratio of in-frame to out-of-frame 3′-UTR incorporation events than mammals do (Giacomelli et al. 2007), confirming a role for [PSI+] in capacitance-mediated evolvability in natural populations.The adaptive evolution both of evolvability in general (Sniegowski and Murphy 2006; Lynch 2007; Pigliucci 2008) and of capacitance in particular (Dickinson and Seger 1999; Wagner et al. 1999; Partridge and Barton 2000; Brookfield 2001; Pal 2001; Meiklejohn and Hartl 2002; Ruden et al. 2003) is highly controversial. In general, any costs of evolvability are borne in the present, while the benefits lie in the future, making it difficult for natural selection to favor an evolvability allele. For example, mutation rates seem to be set according to a trade-off between metabolic cost (favoring higher mutation rates) and the avoidance of deleterious effects (favoring lower mutation rates) (Sniegowski et al. 2000). The fact that mutation creates variation, the ultimate source of evolvability, is merely a fortuitous consequence of the metabolic cost of fidelity.Previous theoretical population genetic studies have, however, suggested that modifier alleles promoting the formation of [PSI+] might, unlike mutator alleles, be favored for their evolvability properties (King and Masel 2007; Masel et al. 2007; Griswold and Masel 2009; Masel and Griswold 2009). These models depend, however, on a number of parameter estimates. In particular, a number of predictions depend on the spontaneous rate of [PSI+] formation (Masel and Griswold 2009).

[PSI+] appearance rates and the fluctuation test:

The most widely cited spontaneous appearance rate for [PSI+] is m_PSI ∼ 10⁻⁷–10⁻⁵, on the basis of experiments by Lund and Cox (1981). This estimate was calculated as the proportion of colonies scored as [PSI+] after growth over multiple generations from a single founding [psi−] clone. If [PSI+] happens to appear in the first generation of growth, this leads to a “jackpot” event with only one switching event, but many [PSI+] colonies. The proportion of colonies scored as [PSI+] therefore yields a systematic overestimation of the [PSI+] appearance rate.Various implementations of the fluctuation test (Luria and Delbrück 1943) can address such effects. The mutation rate experiment is replicated many times using independent populations, and a Luria–Delbrück distribution is fitted to the results across all replicates. In a simulation study, Stewart (1994) examined a number of estimators of the underlying Luria–Delbrück distribution and found that the maximum-likelihood estimator performed the best.Originally developed to study mutation rates, the fluctuation test can also be used for estimating epimutation rates. Fluctuation tests have been used to estimate the rate of gene silencing in Chinese hamster ovary cells (Holliday and Ho 1998) and in the yeast Schizosaccharomyces pombe (Singh and Klar 2002). However, fluctuation tests do not appear to be used routinely for epimutation rate estimates. For example, although the rates of spontaneous appearance and disappearance of [ISP+], a prion-like element in yeast, have been measured using the fluctuation test (Volkov et al. 2002), to the best of our knowledge there are no published estimates of the spontaneous rate of [PSI+] appearance as measured using a fluctuation test. Although results from the fluctuation test can be confounded by reverse epimutation, or back-switching, this is an issue only if the rate of back-switching is very high, e.g., 10⁻¹–10⁻² per generation (Saunders et al. 2003). This is not the case for [PSI+], for which the reverse epimutation rate (loss of [PSI+]) is <2 × 10⁻⁴ (Tank et al. 2007).

Other [PSI+]-like phenotypes, including genetic mimics:

[PSI+] causes partial loss of Sup35 function, leading to elevated rates of translational readthrough at all stop codons (Figure 1b). There are many other spontaneous changes, presumably mutations, that also lead to elevated translational readthrough (Lund and Cox 1981). Mutations that affect readthrough at all stop codons (Figure 1c) (sometimes called “[PSI+]-like”) can be considered as genetic “mimics” because they produce the same phenotype as the Sup35 aggregate, but are generally not epigenetically inherited. A specific example of such a genetic mimic was characterized by Eaglestone et al. (1999), who identified the sal3-4 point mutation in the SUP35 gene. This leads to a defect in the Sup35 protein structure rendering the termination process less efficient (Eaglestone et al. 1999). The sal3-4 mutant can therefore be considered a partial loss-of-function genetic mimic of [PSI+], since it generates the same readthrough phenotype. Translation termination could also potentially be impaired through other point mutations or deletions, for example, in either the SUP35 or the SUP45 gene (Stansfield et al. 1995a) or in a tRNA that mutates to recognize stop codons at a higher rate. The presence of genetic mimics, whose effects are less reversible than those of [PSI+], can affect the evolution of the evolvability properties of the [PSI+] system such as its epimutation rate (Lancaster and Masel 2009). Note that genetic mimics are quite different from much rarer point mutations that convert stop codons into coding sequence (Figure 1d), resulting in readthrough at a single gene rather than multiple genes.Here we performed experiments to obtain accurate and precise estimates of the baseline appearance rates of both [PSI+] and [PSI+]-like phenotypes in permissive laboratory conditions, excluding stop codon point mutations that affect only a single gene. Our estimates are superior to previous estimates, since we use the fluctuation test. We consider the consequences of these estimates for the evolution of the [PSI+] system.     

Rescue of a Dominant Mutant With RNA Interference

Maize Mucronate1 is a dominant floury mutant based on a misfolded 16-kDa γ-zein protein. To prove its function, we applied RNA interference (RNAi) as a dominant suppressor of the mutant seed phenotype. A γ-zein RNAi transgene was able to rescue the mutation and restore normal seed phenotype. RNA interference prevents gene expression. In most cases, this is used to study gene function by creating a new phenotype. Here, we use it for the opposite purpose. We use it to reverse the creation of a mutant phenotype by restoring the normal phenotype. In the case of the maize Mucronate1 (Mc1) phenotype, interaction of a misfolded protein with other proteins is believed to be the basis for the Mc1 phenotype. If no misfolded protein is present, we can reverse the mutant to the normal phenotype. One can envision using this approach to study complex traits and in gene therapy.TRANSLUCENT or vitreous maize kernels are harder and able to sustain stronger mechanical strength during harvesting, transportation, and storage. There is a direct link between a vitreous seed phenotype and the type of storage proteins in the seed, collectively called zeins in maize. Zeins, encoded by a multigene family, constitute >60% of all maize seed proteins. They are classified into four groups (α-, β-, γ-, and δ-zein) on the basis of their structures (Esen 1987). Zeins are specifically synthesized in the endosperm ∼10 days after pollination (DAP) and deposited into protein bodies (Wolf et al. 1967; Burr and Burr 1976; Lending and Larkins 1992). Irregularly shaped protein bodies are found in floury or opaque kernel phenotypes (Coleman et al. 1997; Kim et al. 2004, 2006; Wu et al. 2010; Wu and Messing 2010). The terms “floury” and “opaque” were originally created on the basis of the genetic behaviors of the mutant allele causing the soft kernel texture. The floury mutants behave as semidominant or dominant mutants, as floury1 and floury2 do, while the opaque mutants are recessive, as opaque1 and opaque2 are (Hayes and East 1915; Lindstrom 1923; Emerson et al. 1935; Maize Genetics Cooperation 1939). Similar to floury2 with a single mutation in the signal peptide of a 22-kDa α-zein resulting in an unprocessed protein (Coleman et al. 1995), De*-B30 produces an unprocessed 19-kDa α-zein (Kim et al. 2004). It was hypothesized that the two mutant proteins with an unprocessed signal peptide are misfolded and docked in the membranes of the rough endoplasmic reticulum (RER), blocking the deposition of other zein proteins (Coleman et al. 1995; Kim et al. 2004). In Mucronate1 (Mc1), a 38-bp deletion in the C terminus of the 16-kDa γ-zein (γ16-zein) gene resulted in a frameshift and a protein with a different amino-acid tail. This modified 16-kDa γ-zein (Δγ16-zein) has altered solubility properties, which would explain the formation of irregular protein bodies. Because De*-B30 and Mc1 are semidominant and dominant, respectively, they belong to the floury mutant class.The γ-zein genes (γ27-zein and γ16-zein) are homologous copies because maize underwent allotetraploidization and both gene copies have been retained during diploidization (Xu and Messing 2008). The two γ-zeins and the 15-kDa β-zein have a redundant function in stabilizing protein-body formation (Wu and Messing 2010). Knockdown of both γ-zeins with a single RNA interference (RNAi) construct conditioned only partial opacity in the crown, the top of the kernel, as opposed to the remainder or gown area of the kernel. Consistent with its light kernel phenotype, protein bodies in such a γ-zein RNAi (γRNAi) mutant exhibited a slight alteration in morphology. This phenotype is clearly distinguishable from the Mc1 phenotype, which is far more severe. Therefore, if Mc1 is caused by a misfolded chimeric 16-kDa γ-zein, preventing its expression should restore normal kernel phenotype. Indeed, a simple cross of Mc1 with a maize line carrying the γRNAi transgene produced a non-floury phenotype, providing an example of RNAi as a dominant suppressor of a dominant phenotype and as a general tool in marker rescue.

Analysis of the progeny from the cross of Mc1 and γRNAi mutants:

Mc1 seeds (Stock ID U840I) were requested from the Maize Genetics Cooperation Stock Center. The γRNAi transgenic lines have been reported in previous work (Wu et al. 2010; Wu and Messing 2010). Twelve progeny kernels from the cross of the Mc1 mutant [homozygous for the dominant-negative mutant 16-kDa γ-zein alleles (Δγ16/Δγ16) and heterozygous for the γRNAi line (γRNAi/+)] were dissected at 18 DAP for segregation and mRNA accumulation analyses. For each kernel, the embryo and endosperm were separated for DNA and RNA extraction, respectively. As shown in Figure 1A, five and seven kernels were positive and negative for the amplification of the γRNAi gene with a specific primer set, exemplifying a 1:1 segregation of the γRNAi gene.Open in a separate windowFigure 1.—Segregation analysis of the accumulations of mRNAs and proteins from the cross of the Mc1 mutant and the γRNAi line by RT–PCR and SDS–PAGE. (A) γRNAi gene segregation from progeny (Δγ16/Δγ16 x γRNAi/+) by PCR amplification with a specific primer set (GFPF, ACAACCACTACCTGAGCAC and T35SHindIII, ATTAAGCTTTGCAGGTCACTGGATTTTGG). Kernels 3, 8, 9, 10, and 12 are positive for the γRNAi gene and the rest of them are negative. M, DNA markers from top to bottom band are 3, 2, 1.5, 1.4, and 1 kb. (B) RT–PCR analysis of mRNA accumulation from the normal γ16 and mutant Δγ16 alleles in the endosperms with the genotypes corresponding to the embryos analyzed above. Total RNA was extracted by using TRIzol reagent (Invitrogen). Two micrograms of RNA was digested with DNase I (Invitrogen) and then reverse-transcribed. Twenty-five nanograms of cDNA from each of the twelve endosperms was applied for PCR (25 cycles of 30 sec, 94 °C; 30 sec, 58 °C; and 1 min, 72 °C). A specific primer set (γ16F, ATGAAGGTGCTGATCGTTGC and γ16R, TCAGTAGTAGACACCGCCG) was designed for amplification of the full-length γ16-zein coding sequence (552 bp). The lower band (514 bp) from the mutant Δγ16 allele is 38 bp shorter than that from the normal allele (552 bp). Kernels 3, 8, 9, 10, and 12 with the γRNAi gene accumulated significantly less mRNA compared to those without the γRNAi gene (kernels 1, 2, 4, 5, 6, 7, and 11). BA, hybrid of B × A lines. M, DNA markers from top to bottom are 1 kb, 750 bp, and 500 bp. (C) Profile of zein accumulations of 20 kernels from the progeny as described in the text. The zein extraction method has been described elsewhere (Wu et al. 2009). The Δγ16-zein from Mc1 was not extracted by traditional total-zein extraction protocol (70% ethanol and 2% 2-mercaptoethanol). The γ27- and γ16-zeins were knocked down to a nondetectable level in kernels 1, 2, 3, 5, 7, 10, 12, 13, 16, and 20. In γRNAi-gene segregating progeny (kernels 4, 6, 8, 9, 11, 14, 15, 17, 18, and 19), the γ16-zein from the normal γ16 allele is marked by arrowheads. Protein loaded in each lane was equal to 500 μg fresh endosperm at 18 DAP. The size for each band is indicated by the numbers in the “kDa” columns. BA, hybrid of B × A lines; 1–20, kernels from the progeny described above; M, protein markers from top to bottom are 50, 25, 20, and 15 kDa.Due to the 38-bp deletion in the C terminus of the coding region, the Δγ16 allele is shorter than the normal one (Figure 1B). Therefore, most of Δγ16-zein was in the non-zein fraction. In progeny endosperms of another 20 kernels from the same cross described above segregating for the γRNAi gene, two types of γ16-zeins were synthesized: the normal γ16-zein in the ethanol-soluble zein fraction and the Δγ16-zein in the non-zein fraction. In progeny inheriting the γRNAi gene, the γ27- and γ16-zeins were reduced to nondetectable levels (Figure 1C). Although the Δγ16-zein is not in the ethanol-soluble zein fraction, the level of normal γ16-zein is a good indicator of the accumulation of the Δγ16-zein.

Rescue of protein-body morphologies in the Mc1 mutant:

Regular protein bodies are round with distinct membrane boundaries (Figure 2A) and 1–2 μm in diameter at maturity. In homozygous and heterozygous Mc1 mutants (Δγ16/Δγ16 and Δγ16/+), protein bodies were irregularly shaped, some without discrete boundaries (Figure 2, C and D), which is quite different from the absence of normal γ27- or γ16-zeins in maize endosperm (Figure 2B). Indeed, protein bodies of the Mc1 mutant, blocked in the accumulation of Δγ16-zein, showed morphologies with no discernible difference from those in the γRNAi/+ line (Figure 2, B and E).Open in a separate windowFigure 2.—Transmission electron micrographs of protein bodies. The method has been described elsewhere (Wu and Messing 2010). (A) Nontransgenic BA. (B) γRNAi transgenic line (γRNAi/+). (C) Mc1 (Δγ16/Δγ16). (D) Cross of Mc1 mutant and nontransgenic hybrid of B × A lines (Δγ16/+). (E) Cross of Mc1 mutant (Δγ16/Δγ16) and heterologous γRNAi transgenic line (γRNAi/+). PB, protein body; RER, rough endoplasmic reticulum; CW, cell wall; Mt, mitochondria; SG, starch granule. Bars, 500 nm.

Recovery of floury phenotype in progeny:

On the basis of these observations, it is reasoned that irregularly shaped protein bodies (Figure 2, C and D) in the Mc1 mutant cause the floury phenotype (Figure 3, A and B). Because knockdown of γ-zeins caused opacity only in the crown area (Figure 3C), one could envision that once the irregular protein bodies are restored, the kernel would become vitreous in the gown area of the kernel. Indeed, the progeny ear from the cross of Δγ16/Δγ16 and γRNAi/+ showed a 1:1 ratio of floury and vitreous kernels (Figure 3, D and F), and all kernels were vitreous when the Mc1 mutant was pollinated by a homozygous γRNAi line (Figure 3E).Open in a separate windowFigure 3.—Segregation of vitreous and floury kernels from a progeny ear. (A) Mc1 mutant with Δγ16/Δγ16 genotype. (B) The cross of the Mc1 mutant and the nontransgenic hybrid of B × A lines, showing floury phenotype as in A. (C) γRNAi transgenic line with partial opacity only in the crown area. (D) The cross of the Mc1 mutant (Δγ16/Δγ16) and the heterologous γRNAi transgenic line (γRNAi/+), showing a 1:1 ratio of vitreous and floury kernels. A row in the ear is marked with arrowheads and crosses to indicate vitreous and floury gowns of kernels. (E) Cross of the Mc1 mutant (Δγ16/Δγ16) and the γRNAi homozygous transgenic line (γRNAi/γRNAi), showing all vitreous kernels. (F) Truncated kernel phenotype. (Top) Mc1, cross of Mc1 × BA, and γRNAi transgenic line. (Bottom) Three vitreous and floury kernels from D.

Conclusions:

RNAi can be used to rescue mutations that are dominant negative with a single cross, providing a useful tool in genetic analysis, plant breeding, and potentially in gene therapy in general.     

A Neo-Sex Chromosome That Drives Postzygotic Sex Determination in the Hessian Fly (Mayetiola destructor)

Two nonoverlapping autosomal inversions defined unusual neo-sex chromosomes in the Hessian fly (Mayetiola destructor). Like other neo-sex chromosomes, these were normally heterozygous, present only in one sex, and suppressed recombination around a sex-determining master switch. Their unusual properties originated from the anomalous Hessian fly sex determination system in which postzygotic chromosome elimination is used to establish the sex-determining karyotypes. This system permitted the evolution of a master switch (Chromosome maintenance, Cm) that acts maternally. All of the offspring of females that carry Cm-associated neo-sex chromosomes attain a female-determining somatic karyotype and develop as females. Thus, the chromosomes act as maternal effect neo-W''s, or W-prime (W′) chromosomes, where ZW′ females mate with ZZ males to engender female-producing (ZW′) and male-producing (ZZ) females in equal numbers. Genetic mapping and physical mapping identified the inversions. Their distribution was determined in nine populations. Experimental matings established the association of the inversions with Cm and measured their recombination suppression. The inversions are the functional equivalent of the sciarid X-prime chromosomes. We speculate that W′ chromosomes exist in a variety of species that produce unisexual broods.SEX chromosomes are usually classified as X, Y, Z, or W on the basis of their pattern of segregation and the gender of the heterogametic sex (Ohno 1967). However, when chromosome-based sex determination occurs postzygotically, the same nomenclature confounds important distinctions and may hide interesting evolutionary phenomena. The Hessian fly (Mayetiola destructor), a gall midge (Diptera: Cecidomyiidae) and an important insect pest of wheat, presents an excellent example (Stuart and Hatchett 1988, 1991). In this insect, all of the female gametes and all of the male gametes have the same number of X chromosomes (Figure 1A); no heterogametic sex exists. Nevertheless, Hessian fly sex determination is chromosome based; postzygotic chromosome elimination produces different X chromosome to autosome ratios in somatic cells (male A1A2X1X2/A1A2OO and female A1A2X1X2/A1A2X1X2, where A1 and A2 are the autosomes, X1 and X2 are the X chromosomes, and the paternally derived chromosomes follow the slash) (Stuart and Hatchett 1991; Marin and Baker 1998). Thus, Hessian fly “X” chromosomes are defined by their haploid condition in males, rather than by their segregation in the gametes.Open in a separate windowFigure 1.—Chromosome behavior and sex determination in the Hessian fly. (A) Syngamy (1) establishes the germ-line chromosome constitution: ∼32 maternally derived E chromosomes (represented as a single white chromosome) and both maternally derived (black) and paternally derived (gray) autosomes and X chromosomes. During embryogenesis, while the E chromosomes are eliminated, the paternally derived X chromosomes are either retained (2) or excluded (3) from the presumptive somatic cells. When the paternally derived X chromosomes are retained (2), a female-determining karyotype is established. When they are eliminated (3), a male-determining karyotype is established. Thelygenic mothers carry Cm (white arrow), which conditions all of their offspring to retain the X chromosomes. Recombination occurs during oogenesis (4). All ova contain a full complement of E chromosomes and a haploid complement of autosomes and X chromosomes. Chromosome elimination occurs during spermatogenesis (5). Sperm contain only the maternally derived autosomes and X chromosomes. (B) The segregation of Cm (white dot) on a Hessian fly autosome among monogenic families. Thelygenic females produce broods composed of equal numbers of thelygenic (Cm/−) and arrhenogenic (−/−) females (box 1). Arrhenogenic females produce males (box 2). (C) Matings between monogenic and amphigenic families. Cm (white dot) is dominant to the amphigenic-derived chromosomes (gray dot) and generates all-female offspring (box 3). Amphigenic-derived chromosomes are dominant to the arrhenogenic-derived chromosomes (no dot) and generate offspring of both sexes (box 4).An autosomal, dominant, genetic factor called Chromosome maintenance (Cm) complicates Hessian fly sex determination further (Stuart and Hatchett 1991). Cm has a maternal effect that acts upstream of X chromosome elimination during embryogenesis (Figure 1A). It prevents X chromosome elimination so that all of the offspring of Cm-bearing mothers obtain a female-determining karyotype. Cm-bearing females produce only female offspring and are therefore thelygenic. The absence of Cm usually has the opposite effect; all of the offspring of most Cm-lacking females obtain a male-determining karyotype. These Cm-lacking females produce only male offspring and are therefore arrhenogenic. Like a sex-determining master switch, Cm is usually heterozygous and present in only one sex (Figure 1B). Thus, thelygenic females (Cm/−) are “heterogametic,” as their Cm-containing gametes and Cm-lacking gametes produce thelygenic (Cm/−) and arrhenogenic (−/−) females in a 1:1 ratio. Collectively, thelygenic and arrhenogenic females are called monogenic because they produce unisexual families. However, some Hessian fly females produce broods of both sexes and are called amphigenic. No mating barrier between monogenic and amphigenic families exists (Figure 1C), but amphigenic females have always been found in lower abundance (Painter 1930; Gallun et al. 1961; Stuart and Hatchett 1991). In experimental matings, the inheritance of maternal phenotype was consistent with the segregation of three Cm alleles (Figure 1C): a dominant thelygenic allele, a hypomorphic amphigenic allele, and a null arrhenogenic allele (Stuart and Hatchett 1991).Here we report the genetic and physical mapping of Cm on Hessian fly autosome 1 (A1). Two nonoverlapping inversions were identified that segregated perfectly with Cm. The most distal inversion was present in all thelygenic females examined. The more proximal inversion extended recombination suppression. These observations suggested that successive inversions evolved to suppress recombination around Cm after it arose. The inversions therefore appear to have evolved in response to the forces that shaped vertebrate Y and W chromosomes (Charlesworth 1996; Graves and Shetty 2001; Rice and Chippindale 2001; Carvalho and Clark 2005). We therefore believe the inversion-bearing chromosomes may be classified as maternal effect neo-W''s.     

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

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

A Genetic Screen for Suppressors of a Mutated 5′ Splice Site Identifies Factors Associated With Later Steps of Spliceosome Assembly

Many alleles of human disease genes have mutations within splicing consensus sequences that activate cryptic splice sites. In Caenorhabditis elegans, the unc-73(e936) allele has a G-to-U mutation at the first base of the intron downstream of exon 15, which results in an uncoordinated phenotype. This mutation triggers cryptic splicing at the −1 and +23 positions and retains some residual splicing at the mutated wild-type (wt) position. We previously demonstrated that a mutation in sup-39, a U1 snRNA gene, suppresses e936 by increasing splicing at the wt splice site. We report here the results of a suppressor screen in which we identify three proteins that function in cryptic splice site choice. Loss-of-function mutations in the nonessential splicing factor smu-2 suppress e936 uncoordination through changes in splicing. SMU-2 binds SMU-1, and smu-1(RNAi) also leads to suppression of e936. A dominant mutation in the conserved C-terminal domain of the C. elegans homolog of the human tri-snRNP 27K protein, which we have named SNRP-27, suppresses e936 uncoordination through changes in splicing. We propose that SMU-2, SMU-1, and SNRP-27 contribute to the fidelity of splice site choice after the initial identification of 5′ splice sites by U1 snRNP.PRE-mRNA splicing takes place in a large ribonucleoprotein complex called the spliceosome (Burge et al. 1999). Components of this splicing machinery assemble at conserved signal sequences within the pre-mRNA. The 5′ splice site consensus sequence M₋₃A₋₂G₋₁ | G₊₁U₊₂R₊₃A₊₄G₊₅U₊₆ and the 3′ splice site consensus sequence Y₋₃A₋₂G₋₁ | R₊₁ (M is either A or C; R is a purine, and Y is a pyrimidine) define the limits of the intron. Base-pairing interactions between the 5′ end of the U1 snRNA and the 5′ splice site consensus sequence occur early in spliceosome assembly. It is the nearly invariable GU dinucleotide at the first two positions of the 5′ end of the intron that defines the beginning of the intron. The 5′ consensus sequence is essential but insufficient for splice site selection, as 5′ splice sites with weaker consensus matches may require additional determinants for proper activation (Sanford et al. 2005).Mutations that disrupt the 5′ consensus splice signal can lead to genetic disease in humans (Nelson and Green 1990; Cohen et al. 1994). Approximately 15% of point mutations that cause genetic diseases affect pre-mRNA splicing consensus sequences (Krawczak et al. 1992). For some specific disease genes, as many as 50% of the known heritable alleles alter splicing (Teraoka et al. 1999; Ars et al. 2000; Roca et al. 2003; Pagenstecher et al. 2006). Among all the positions of the 5′ splice site consensus sequence, the highest proportion of human disease mutations occur at the +1G position (Buratti et al. 2007). The fidelity of pre-mRNA splice site choice is largely disrupted by this defect, since this mutation causes splicing at this site to be either abolished or outcompeted by the activation of nearby cryptic 5′ splice sites (Nelson and Green 1990; Cohen et al. 1994). Cryptic splice sites are used only when the wild-type splice donor is disrupted by mutation, as they tend to have very weak splice donor consensus sequences outside of a 5′-GU dinucleotide that defines the beginning of the intron (Roca et al. 2003). Suppression of mutations to the 5′ splice site consensus sequence in vivo has been achieved through the expression of U1 snRNAs containing compensatory base substitutions (Zhuang and Weiner 1986); however, suppression of mutations to the +1 position of the intron using reverse genetic approaches has not been successful (Newman et al. 1985; Nelson and Green 1990; Cohen et al. 1994).We have used a specific allele of the Caenorhabditis elegans unc-73 gene, e936, which contains a G-to-U mutation at the first nucleotide of intron 16 (Steven et al. 1998), as a model for studying cryptic splice site choice (Roller et al. 2000; Zahler et al. 2004). unc-73 encodes a RAC guanine nucleotide exchange factor that is expressed in neurons and is important for axon guidance (Steven et al. 1998). The e936 allele induces the use of three different cryptic 5′ splice sites (Figure 1A). Two of these 5′ splice sites, located at the −1 and +23 positions, define introns beginning with GU. The third 5′ splice site used is at the mutated wild-type (wt) position and is referred to as “wt” since splicing at this site still produces wild-type unc-73 mRNA and protein, even though the intron begins with UU (Roller et al. 2000). Use of either the −1 or the +23 cryptic site causes a shift in the reading frame and loss of gene function. In e936 animals, 90% of the stable messages of unc-73 are out-of-frame, yet the phenotype is not as severe as for other alleles in this gene. This indicates that the 10% of steady-state messages that are in frame have some functional role.Open in a separate windowFigure 1.—(A) Diagram of the unc-73 gene between exons 15 and 16. The positions of the −1 and +23 cryptic 5′ splice sites are indicated by arrows. The intronic e936 (+1G → U) point mutation is highlighted. (B) γ-³²P-labeled RT–PCR results across the cryptic splicing region of unc-73(e936) for different strains. Lanes 1, 2, and 3 are loaded with RT–PCR reactions from wild type (N2), unc-73(e936);sup-39(je5), and unc-73(e936) RNA, respectively. The lines carrying the suppressor alleles and e936 follow in lanes 4–10 as indicated. (C) The unc-73 genomic sequence from exon 15 (uppercase letters) and intron 15 (lowercase letters). The locations of the az23 and e936 mutational substitutions are indicated below. The position of the −9 cryptic splice donor activated in e936az23 is indicated by an arrow above.In a previous genetic screen for extragenic suppressors of e936 movement defects, Way and colleagues identified sup-39 (Run et al. 1996). It was subsequently shown that mutations in sup-39 alter cryptic splice site choice of e936 (Roller et al. 2000). sup-39 encodes a U1 snRNA gene with a compensatory mutation at the position that normally base pairs with the +1G. This allows sup-39 to base pair with an intron with a +1U (Zahler et al. 2004). This dominant suppressor increases usage of the mutated splice site and improves the fraction of in-frame messages from e936 from 10 to 33%, with a dramatic improvement in coordination. A similar mutant U1 snRNA suppressor with a different compensatory substitution, sup-6(st19), was found to suppress the intronic +1G to A transition of unc-13(e309) to allow for splicing at the mutated wild-type site, even though the intron begins with AU instead of GU (Zahler et al. 2004).We are interested in identifying additional factors that play a role in cryptic 5′ splice site choice. To do this, we took advantage of unc-73(e936), in which modest increases in the use of the wt splice site lead to dramatic increases in coordination, as a sensitive screen for changes in cryptic splice site choice. In this article we report that the proteins SMU-1 and SMU-2, which are nonessential factors previously shown to have a role in alternative splicing (Spartz et al. 2004), have a role in selection of cryptic 5′ splice sites. We also report the identification of a new dominant suppressor of cryptic splicing, snrp-27, which encodes a C. elegans homolog of the human tri-snRNP 27K protein.     

Novel Growth Regime of MDCK II Model Tissues on Soft Substrates

It is well established that MDCK II cells grow in circular colonies that densify until contact inhibition takes place. Here, we show that this behavior is only typical for colonies developing on hard substrates and report a new growth phase of MDCK II cells on soft gels. At the onset, the new phase is characterized by small, three-dimensional droplets of cells attached to the substrate. When the contact area between the agglomerate and the substrate becomes sufficiently large, a very dense monolayer nucleates in the center of the colony. This monolayer, surrounded by a belt of three-dimensionally packed cells, has a well-defined structure, independent of time and cluster size, as well as a density that is twice the steady-state density found on hard substrates. To release stress in such dense packing, extrusions of viable cells take place several days after seeding. The extruded cells create second-generation clusters, as evidenced by an archipelago of aggregates found in a vicinity of mother colonies, which points to a mechanically regulated migratory behavior.Studying the growth of cell colonies is an important step in the understanding of processes involving coordinated cell behavior such as tissue development, wound healing, and cancer progression. Apart from extremely challenging in vivo studies, artificial tissue models are proven to be very useful in determining the main physical factors that affect the cooperativity of cells, simply because the conditions of growth can be very well controlled. One of the most established cell types in this field of research is the Madin-Darby canine kidney epithelial cell (MDCK), originating from the kidney distal tube (1). A great advantage of this polarized epithelial cell line is that it retained the ability for contact inhibition (2), which makes it a perfect model system for studies of epithelial morphogenesis.Organization of MDCK cells in colonies have been studied in a number of circumstances. For example, it was shown that in three-dimensional soft Matrigel, MDCK cells form a spherical enclosure of a lumen that is enfolded by one layer of polarized cells with an apical membrane exposed to the lumen side (3). These structures can be altered by introducing the hepatocyte growth factor, which induces the formation of linear tubes (4). However, the best-studied regime of growth is performed on two-dimensional surfaces where MDCK II cells form sheets and exhibit contact inhibition. Consequently, the obtained monolayers are well characterized in context of development (5), mechanical properties (6), and obstructed cell migration (7–9).Surprisingly, in the context of mechanics, several studies of monolayer formation showed that different rigidities of polydimethylsiloxane gels (5) and polyacrylamide (PA) gels (9) do not influence the nature of monolayer formation nor the attainable steady-state density. This is supposedly due to long-range forces between cells transmitted by the underlying elastic substrate (9). These results were found to agree well with earlier works on bovine aortic endothelial cells (10) and vascular smooth muscle cells (11), both reporting a lack of sensitivity of monolayers to substrate elasticity. Yet, these results are in stark contrast with single-cell experiments (12–15) that show a clear response of cell morphology, focal adhesions, and cytoskeleton organization to substrate elasticity. Furthermore, sensitivity to the presence of growth factors that are dependent on the elasticity of the substrate in two (16) and three dimensions (4) makes this result even more astonishing. Therefore, we readdress the issue of sensitivity of tissues to the elasticity of the underlying substrate and show that sufficiently soft gels induce a clearly different tissue organization.We plated MDCK II cells on soft PA gels (Young’s modulus E = 0.6 ± 0.2 kPa), harder PA gels (E = 5, 11, 20, 34 kPa), and glass, all coated with Collagen-I. Gels were prepared following the procedure described in Rehfeldt et al. (17); rigidity and homogeneity of the gels was confirmed by bulk and microrheology (see the Supporting Material for comparison). Seeding of MDCK II cells involved a highly concentrated solution dropped in the middle of a hydrated gel or glass sample. For single-cell experiments, cells were dispersed over the entire dish. Samples were periodically fixed up to Day 12, stained for nuclei and actin, and imaged with an epifluorescence microscope. Details are described in the Supporting Material.On hard substrates and glass it was found previously that the area of small clusters expands exponentially until the movement of the edge cannot keep up with the proliferation in the bulk (5). Consequently, the bulk density increases toward the steady state, whereas the density of the edge remains low. At the same time, the colony size grows subexponentially (5). This is what we denote “the classical regime of growth”. Our experiments support these observations for substrates with E ≥ 5 kPa. Specifically, on glass, colonies start as small clusters of very low density of 700 ± 200 cells/mm² (Fig. 1, A and B), typically surrounded by a strong actin cable (Fig. 1, B and C). Interestingly, the spreading area of single cells (Fig. 1 A) on glass was found to be significantly larger, i.e., (2.0 ± 0.9) × 10⁻³ mm². After Day 4 (corresponding cluster area of 600 ± 100 mm²), the density in the center of the colony reached the steady state with 6,800 ± 500 cells/mm², whereas the mean density of the edge profile grew to 4,000 ± 500 cells/mm². This density was retained until Day 12 (cluster area 1800 ± 100 mm²), which is in agreement with previous work (9).Open in a separate windowFigure 1Early phase of cluster growth on hard substrates. (A) Well-spread single cells, and small clusters with a visible actin cable 6 h after seeding. (B) Within one day, clusters densify and merge, making small colonies. (C) Edge of clusters from panel B.In colonies grown on 0.6 kPa gels, however, we encounter a very different growth scenario. The average spreading area of single cells is (0.34 ± 0.3) × 10⁻³ mm², which is six times smaller than on glass substrates (Fig. 2 A). Clusters of only few cells show that cells have a preference for cell-cell contacts (a well-established flat contact zone can be seen at the cell-cell interface in Fig. 2 A) rather than for cell-substrate contacts (contact zone is diffusive and the shape of the cells appears curved). The same conclusion emerges from the fact that dropletlike agglomerates, resting on the substrate, form spontaneously (Fig. 2 A), and that attempts to seed one single cluster of 90,000 cells fail, resulting in a number of three-dimensional colonies (Fig. 2 A). When the contact area with the substrate exceeds 4.7 × 10⁻³ mm², a monolayer appears in the center of such colonies (Fig. 2 B). The colonies can merge, and if individual colonies are small, the collapse into a single domain is associated with the formation of transient irregular structures (Fig. 2 B). Ultimately, large elliptical colonies (average major/minor axis of e = 1.8 ± 0.6) with a smooth edge are formed (Fig. 2 C), unlike on hard substrates where circular clusters (e = 1.06 ± 0.06) with a ragged edge comprise the characteristic phenotype.Open in a separate windowFigure 2Early phase of cluster growth on soft substrates. (A) Twelve hours after seeding, single cells remain mostly round and small. They are found as individual, or within small, three-dimensional structures (top). The latter nucleate a monolayer in their center (bottom), if the contact area with the substrate exceeds ∼5 × 10⁻³ mm². (B) Irregularly-shaped clusters appear due to merging of smaller droplets. A stable monolayer surrounded by a three-dimensional belt of densely packed cells is clearly visible, even in larger structures. (C) All colonies are recorded on Day 4.Irrespective of cluster size, in the new regime of growth, the internal structure is built of two compartments (Fig. 2 B):

• 1.The first is the edge (0.019 ± 0.05-mm wide), a three-dimensional structure of densely packed cells. This belt is a signature of the new regime because on hard substrates the edge is strictly two-dimensional (Fig. 1 C).
• 2.The other is the centrally placed monolayer with a spatially constant density that is very weakly dependent on cluster size and age (Fig. 3). The mean monolayer density is 13,000 ± 2,000 cells/mm², which is an average over 130 clusters that are up to 12 days old and have a size in the range of 10⁻³ to 10 mm², each shown by a data point in Fig. 3. This density is twice the steady-state density of the bulk tissue in the classical regime of growth.Open in a separate windowFigure 3Monolayer densities in colonies grown on 0.6 kPa substrates, as a function of the cluster size and age. Each cluster is represented by a single data point signifying its mean monolayer density. (Black lines) Bulk and (red dashed lines) edge of steady-state densities from monolayers grown on glass substrates. Error bars are omitted for clarity, but are discussed in the Supporting Material.

Until Day 4, the monolayer is very homogeneous, showing a nearly hexagonal arrangement of cells. From Day 4, however, defects start to appear in the form of small holes (typical size of (0.3 ± 0.1) × 10⁻³ mm²). These could be attributed to the extrusions of viable cells, from either the belt or areas of increased local density in the monolayer (inset in Fig. 4). This suggests that extrusions serve to release stress built in the tissue, and, as a consequence, the overall density is decreased.Open in a separate windowFigure 4Cell nuclei within the mother colony and in the neighboring archipelago of second-generation clusters grown on 0.6 kPa gels at Day 12. (Inset; scale bar = 10 μm) Scar in the tissue, a result of a cell-extrusion event. (Main image; scale bar = 100 μm) From the image of cell nuclei (left), it is clear that there are no cells within the scar, whereas the image of actin (right) shows that the cytoplasm of the cells at the edge has closed the hole.Previous reports suggest that isolated MDCK cells undergo anoikis 8 h after losing contact with their neighbors (18). However, in this case, it appears that instead of dying, the extruded cells create new colonies, which can be seen as an archipelago surrounding the mother cluster (Fig. 4). The viability of off-cast cells is further evidenced by the appearance of single cells and second-generation colonies with sizes varying over five orders of magnitude, from Day 4 until the end of the experiment, Day 12. Importantly, no morphological differences were found in the first- and second-generation colonies.In conclusion, we show what we believe to be a novel phase of growth of MDCK model tissue on soft PA gels (E = 0.6 kPa) that, to our knowledge, despite previous similar efforts (9), has not been observed before. This finding is especially interesting in the context of elasticity of real kidneys, for which a Young’s modulus has been found to be between 0.05 and 5 kPa (19,20). This coincides with the elasticity of substrates studied herein, and opens the possibility that the newly found phase of growth has a particular biological relevance. Likewise, the ability to extrude viable cells may point to a new migratory pathway regulated mechanically by the stresses in the tissue, the implication of which we hope to investigate in the future.     

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

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

Mathematical Modeling-Guided Evaluation of Biochemical,Developmental, Environmental,and Genotypic Determinants of Essential Oil Composition and Yield in Peppermint Leaves

We have previously reported the use of a combination of computational simulations and targeted experiments to build a first generation mathematical model of peppermint (Mentha × piperita) essential oil biosynthesis. Here, we report on the expansion of this approach to identify the key factors controlling monoterpenoid essential oil biosynthesis under adverse environmental conditions. We also investigated determinants of essential oil biosynthesis in transgenic peppermint lines with modulated essential oil profiles. A computational perturbation analysis, which was implemented to identify the variables that exert prominent control over the outputs of the model, indicated that the essential oil composition should be highly dependent on certain biosynthetic enzyme concentrations [(+)-pulegone reductase and (+)-menthofuran synthase], whereas oil yield should be particularly sensitive to the density and/or distribution of leaf glandular trichomes, the specialized anatomical structures responsible for the synthesis and storage of essential oils. A microscopic evaluation of leaf surfaces demonstrated that the final mature size of glandular trichomes was the same across all experiments. However, as predicted by the perturbation analysis, differences in the size distribution and the total number of glandular trichomes strongly correlated with differences in monoterpenoid essential oil yield. Building on various experimental data sets, appropriate mathematical functions were selected to approximate the dynamics of glandular trichome distribution/density and enzyme concentrations in our kinetic model. Based on a χ² statistical analysis, simulated and measured essential oil profiles were in very good agreement, indicating that modeling is a valuable tool for guiding metabolic engineering efforts aimed at improving essential oil quality and quantity.The essential oil distilled from peppermint (Mentha × piperita) leaves is used in numerous consumer products (e.g. chewing gum, toothpaste, and mouthwash), as a flavor in the confectionary and pharmaceutical industries, and as a source of active ingredients for aromatherapy. Peppermint oil consists primarily of p-menthane-type monoterpenes, with smaller amounts of other monoterpenes and very minor quantities of sesquiterpenes (Rohloff, 1999). The essential oil is synthesized and accumulated in specialized anatomical structures called peltate glandular trichomes (Gershenzon et al., 1989; McCaskill et al., 1992). These trichomes contain secretory cells, arranged in an eight-celled disc, which are responsible for the synthesis of the oil. Nascent essential oil is secreted into an emerging cavity formed by the separation of a preformed layer of cuticular material (Amelunxen, 1965). Over the last two decades, the entire complement of genes and enzymes involved in the peppermint monoterpenoid essential oil biosynthetic pathway has been characterized (for review, see Croteau et al., 2005).Transgenic peppermint plants have been generated in efforts aimed at modulating essential oil yield and composition. Mahmoud and Croteau (2001) reported that, by overexpressing the gene encoding 1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR), oil yield increases (compared with wild-type plants) of up to 50% were observed. Antisense suppression of the (+)-menthofuran synthase (MFS) gene led to a dramatic decrease in the amounts of the undesirable side product (+)-menthofuran (elite transgenic line designated MFS7a; Mahmoud and Croteau, 2001). A slight increase in overall monoterpene yields was reported for transgenic plants with increased expression levels of the gene encoding (−)-limonene synthase (LS; Diemer et al., 2001), whereas only negligible effects on yield were detected in an independent study (Krasnyansky et al., 1999). Transgenic plants overexpressing the gene coding for (−)-limonene 3-hydroxylase (L3H) did not accumulate increased levels of the recombinant protein, and the composition and yield of the essential oils were the same as in wild-type controls; however, cosuppression of the L3H gene resulted in a vastly increased accumulation of the intermediate (−)-limonene, without notable effects on oil yield (elite transgenic line designed L3H20; Mahmoud et al., 2004).Mathematical modeling can be a powerful tool to support metabolic engineering efforts, including those performed with peppermint. Stoichiometric modeling only requires knowledge of the topology of reactions in the pathway and inputs/outputs. This is a particularly useful approach to determine flux distributions and the systemic characteristics of metabolic networks (for review, see Llaneras and Picó, 2008). When experimental designs supporting metabolic and isotopic steady state are employed, isotope labeling data can be utilized for the development of quantitative flux maps of metabolic pathways (for review, see Libourel and Shachar-Hill, 2008). For dynamic systems, kinetic modeling is regarded as the generally most suitable method (McNeil et al., 2000; Poolman et al., 2004; Bruggeman and Westerhoff, 2006; Rios-Estepa and Lange, 2007; Mendes et al., 2009). Building on the rich body of published data on the enzymology and physiology of the peppermint monoterpene pathway (for review, see Croteau et al., 2005), we recently developed a first generation kinetic model to simulate the dynamics of peppermint monoterpene composition (Rios-Estepa et al., 2008). Modeling indicated that the monoterpene profiles observed in leaves of plants grown under low-light conditions could be explained if one assumed that (+)-menthofuran, a dead-end side product, acted as a heretofore unknown competitive inhibitor against (+)-pulegone, the primary substrate of the branch point enzyme (+)-pulegone reductase (PR; Fig. 1). Follow-up biochemical studies established that this prediction was correct (Rios-Estepa et al., 2008), thus illustrating the utility of an approach that integrates mathematical modeling with experimental testing.Open in a separate windowFigure 1.Outline of p-menthane monoterpene biosynthesis in peppermint glandular trichomes. The following enzymes are involved in this pathway: 1, 1-deoxy-d-xylulose 5-phosphate synthase; 2, 1-deoxy-d-xylulose 5-phosphate reductoisomerase; 3, 2C-methyl-d-erythritol 4-phosphate cytidyltransferase; 4, 4-(cytidine 5′-diphospho)-2C-methyl-d-erythritol kinase; 5, 2C-methyl-d-erythritol 2,4-cyclodiphosphate synthase; 6, (E)-4-hydroxy-3-methyl-but-2-enyl diphosphate synthase; 7, (E)-4-hydroxy-3-methyl-but-2-enyl diphosphate reductase; 8, isopentenyl diphosphate isomerase; 9, geranyl diphosphate synthase; 10, (−)-limonene synthase; 11, (−)-limonene 3-hydroxylase; 12, (−)-trans-isopiperitenol dehydrogenase; 13, (−)-trans-isopiperitenone reductase; 14, (+)-cis-isopulegone isomerase; 15, (+)-menthofuran synthase; 16a, (+)-pulegone reductase [(−)-menthone-forming activity]; 16b, (+)-pulegone reductase [(+)-isomenthone-forming activity]; 17a, (−)-menthone:(−)-menthol reductase [(−)-menthol-forming activity]; 17b, (−)-menthone:(−)-menthol reductase [(+)-neoisomenthol-forming activity]; 18a, (−)-menthone:(+)-neomenthol reductase [(+)-neomenthol-forming activity]; 18b, (−)-menthone:(+)-neomenthol reductase [(+)-isomenthol-forming activity]. The subcellular compartmentation of p-menthane metabolic enzymes is color coded as follows: Cyt (blue), cytosol; ER (orange), endoplasmic reticulum; Lpl (green), leucoplasts; Mit (red), mitochondria. The inhibitory effects of (+)-menthofuran on (+)-pulegone reductase and geranyl diphosphate on isopentenyl diphosphate isomerase are indicated by red arcs with orthogonal red lines. Names of selected metabolites are shown in the colors that are used to indicate the corresponding profiles in Figures 2 to 5​5​​.As part of this study, a computational perturbation analysis was used to predict factors with the potentially greatest impacts on peppermint essential oil yield and composition (specific biosynthetic enzymes and the density of oil-synthesizing trichomes). To test these modeling predictions experimentally, we first acquired biometric data with peppermint plants grown under several environmental conditions known to adversely affect oil accumulation (Burbott and Loomis, 1967; Clark and Menary, 1980) and the transgenic line MFS7a, for which an altered essential oil profile had been reported earlier (Mahmoud and Croteau, 2001). Building on these experimental data sets, we then developed a second generation model that accounts for biochemical, developmental, environmental, and genotypic factors of essential oil formation. This updated model was then used to simulate monoterpenoid essential oil profiles for the transgenic line MFS7a grown under low-light environmental stress conditions and the transgenic line L3H20, which had previously been shown to have vastly reduced expression levels of the gene encoding L3H. In both cases, simulated and measured monoterpene patterns were very similar, indicating that mathematical modeling has great potential for guiding efforts aimed at developing peppermint lines with high oil yields and favorable composition, even under adverse environmental conditions.     

Retrogenes Reveal the Direction of Sex-Chromosome Evolution in Mosquitoes

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

The Total Branch Length of Sample Genealogies in Populations of Variable Size

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 N_l 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 N_eff. (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 N_l 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 T_n 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 ⌊NT_n⌋ 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(T_n) of the total branch lengths T_n 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/N_eff. 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 T_n 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, K_l = 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.     

Surprising Fitness Consequences of GC-Biased Gene Conversion: I. Mutation Load and Inbreeding Depression

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 (

Epithelial Polarity: Interactions Between Junctions and Apical–Basal Machinery

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

Big steps toward understanding dynein

Dynein is a microtubule-based molecular motor that is involved in various biological functions, such as axonal transport, mitosis, and cilia/flagella movement. Although dynein was discovered 50 years ago, the progress of dynein research has been slow due to its large size and flexible structure. Recent progress in understanding the force-generating mechanism of dynein using x-ray crystallography, cryo-electron microscopy, and single molecule studies has provided key insight into the structure and mechanism of action of this complex motor protein.It has been 50 years since dynein was discovered and named by Ian Gibbons as a motor protein that drives cilia/flagella bending (Gibbons, 1963; Gibbons and Rowe, 1965). In the mid-1980s, dynein was also found to power retrograde transport in neurons (Paschal and Vallee, 1987). Subsequently, the primary amino acid sequence of the cytoplasmic dynein heavy chain, which contains the motor domain, was determined from the cDNA sequence (Mikami et al., 1993; Zhang et al., 1993). Like other biological motors, such as kinesins and myosins, the amino acid sequence of the dynein motor domain is well conserved. There are 16 putative genes that encode dynein heavy chains in the human genome (Yagi, 2009). Among these is one gene encoding cytoplasmic dynein heavy chain and one encoding retrograde intraflagellar transport dynein heavy chain, while the rest encode for heavy chains of axonemal dyneins. Most of the genes encoding the human dynein heavy chain have a counterpart in Chlamydomonas reinhardtii, which suggests that their functions are conserved from algae to humans.Dynein is unique compared with kinesin and myosin because dynein molecules form large molecular complexes. For example, one axonemal outer arm dynein molecule of C. reinhardtii is composed of three dynein heavy chains, two intermediate chains, and more than ten light chains (King, 2012). Mammalian cytoplasmic dynein consists of two heavy chains and several smaller subunits (Fig. 1 A; Vallee et al., 1988; Allan, 2011). The cargoes of cytoplasmic dynein are various membranous organelles, including lysosomes, endosomes, phagosomes, and the Golgi complex (Hirokawa, 1998). It is likely that one cytoplasmic dynein heavy chain can adapt to diverse cargos and functions by changing its composition.Open in a separate windowFigure 1.Atomic structures of cytoplasmic dynein. (A) Schematic structure of cytoplasmic dynein complex, adapted from Allan (2011). (B) The primary structure of cytoplasmic dynein. (C and D) The atomic model of D. discoideum cytoplasmic dynein motor domain (PDB accession no. 3VKG) overlaid on a microtubule (EMDB-5193; Sui and Downing, 2010) according to the orientation determined by Mizuno et al. (2007) (C) Side view. (D) View from the plus end of microtubule. (E) Schematic domain structure of dynein.Dynein must have a distinct motor mechanism from kinesin and myosin, because it belongs to the AAA+ family of proteins and does not have the conserved amino acid motifs, called the switch regions, present in kinesins, myosins, and guanine nucleotide-binding proteins (Vale, 1996). Therefore, studying dynein is of great interest because it will reveal new design principles of motor proteins. This review will focus on the mechanism of force generation by cytoplasmic and axonemal dynein heavy chains revealed by recent structural and biophysical studies.

Anatomy of dynein

To understand the chemomechanical cycle of dynein based on its molecular structure, it is important to obtain well-diffracting crystals and build accurate atomic models. Recently, Kon and colleagues determined the crystal structures of Dictyostelium discoideum cytoplasmic dynein motor domain, first at 4.5-Å resolution (Kon et al., 2011), and subsequently at 2.8 Å (without the microtubule binding domain) and 3.8-Å (wild type) resolution (Kon et al., 2012). Carter and colleagues also determined the crystal structures of the Saccharomyces cerevisiae (yeast) cytoplasmic dynein motor domain, first at 6-Å resolution (Carter et al., 2011), and later at 3.3–3.7-Å resolution (Schmidt et al., 2012). According to these crystal structures as well as previous EM studies, the overall structure of the dynein heavy chain is divided into four domains: tail, linker, head, and stalk (Fig. 1, B–E). Simply put, each domain carries out one essential function of a motor protein: the tail is the cargo binding domain, the head is the site of ATP hydrolysis, the linker is the mechanical amplifier, and the stalk is the track-binding domain.The tail, which is not part of the motor domain and is absent from crystal structures, is located at the N-terminal ∼1,400 amino acid residues and involved in cargo binding (gray in Fig. 1, B and E). The next ∼550 residues comprise the “linker” (pink in Fig. 1, B–E), which changes its conformation depending on the nucleotide state (Burgess et al., 2003; Kon et al., 2005). This linker domain was first observed by negative staining EM in combination with single particle analysis of dynein c, an isoform of inner arm dynein from C. reinhardtii flagella (Burgess et al., 2003). According to the crystal structures, the linker is made of bundles of α-helices and lies across the AAA+ head domain, forming a 10-nm-long rod-like structure (Fig. 1, C and D). Recent class averaged images of D. discoideum cytoplasmic dynein show that the linker domain is stiff along its entire length when undocked from the head (Roberts et al., 2012). The head (motor) domain of dynein is composed of six AAA+ (ATPase associated with diverse cellular activities) modules (Neuwald et al., 1999; color-coded in Fig. 1, B–E). Although many AAA+ family proteins are a symmetric homohexamer (Ammelburg et al., 2006), the AAA+ domains of dynein are encoded by a single heavy chain gene and form an asymmetric heterohexamer. Among the six AAA+ domains, hydrolysis at the first AAA domain mainly provides the energy for dynein motility (Imamula et al., 2007; Kon et al., 2012). The hexameric ring has two distinct faces: the linker face and the C-terminal face. The linker face is slightly convex and the linker domain lies across this side (Fig. 1 D, left side). The other side of the ring has the C-terminal domain (Fig. 1 D, right side).The stalk domain of dynein was identified as the microtubule-binding domain (MTBD; Gee et al., 1997). It emanates from the C-terminal face of AAA4 and is composed of antiparallel α-helical coiled-coil domain (yellow in Fig. 1, B–E). The tip of the stalk is the actual MTBD. Interestingly, the crystal structures revealed another antiparallel α-helical coiled coil that emerges from AAA5 (orange in Fig. 1, B–E), and this region is called the buttress (Carter et al., 2011) or strut (Kon et al., 2011), which was also observed as the bifurcation of the stalk by negative-staining EM (Burgess et al., 2003; Roberts et al., 2009). The tip of the buttress/strut is in contact with the middle of the stalk and probably works as a mechanical reinforcement of the stalk.

The chemomechanical cycle of dynein

Based on structural and biochemical data, a putative chemomechanical cycle of dynein is outlined in Fig. 2 (A–E). In the no-nucleotide state, dynein is bound to a microtubule through its stalk domain, and its tail region is bound to cargoes (Fig. 2 A). The crystal structures of yeast dynein are considered to be in this no-nucleotide state. When ATP is bound to the AAA+ head, the MTBD quickly detaches from the microtubule (Fig. 2 B; Porter and Johnson, 1983). The ATP binding also induces “hinging” of the linker from the head (Fig. 2 C). According to the biochemical analysis of recombinant D. discoideum dynein (Imamula et al., 2007), the detachment from the microtubule (Fig. 2, A and B) is faster than the later hinging (Fig. 2, B and C). As a result of these two reactions, the head rotates or shifts toward the minus end of the microtubule (for more discussion about “rotate” versus “shift” see the “Dyneins in the axoneme” section) and the MTBD steps forward. The directionality of stepping seems to be mainly determined by the MTBD, because the direction of dynein movement does not change even if the head domain is rotated relative to the microtubule by insertion or deletion of the stalk (Carter et al., 2008). In the presence of ADP and vanadate, dynein is considered to be in this state (Fig. 2 C).Open in a separate windowFigure 2.Presumed chemomechanical cycle and stepping of dynein. (A–E) Chemomechanical cycle of dynein. The pre- and post-power stroke states are also called the primed and unprimed states, respectively. The registries of the stalk coiled coil are denoted as α and β according to Gibbons et al. (2005). (F and G) Processive movement of kinesin (F) and dynein (G). (F) Hand-over-hand movement of kinesin. A step by one head (red) is always followed by the step of another head (green). The stepping of kinesin is on one protofilament of microtubule. (G) Presumed stepping of dynein. The step size varies and the interhead separation can be large. A step by one head (red) is not always flowed by the step of another head (green). (H) A model of strain-based dynein ATPase activation. (G, top) Without strain, the gap between the AAA1 and AAA2 is open and the motor domain cannot hydrolyze ATP. (G, bottom) Under a strain imposed between MTBD and tail (thin black arrows), the gap becomes smaller (thick black arrows) and turns on ATP hydrolysis by dynein.After the MTBD rebinds to the microtubule at the forward site (Fig. 2 D), release of hydrolysis products from the AAA+ head is activated (Holzbaur and Johnson, 1989) and the hinged linker goes back to the straight conformation (Fig. 2 E; Kon et al., 2005). The crystal structure of D. discoideum dynein is considered to be in the state after phosphate release and before ADP release. This straightening of the linker is considered to be the power-generating step and brings the cargo forward relative to the microtubule.

The MTBD of dynein

As outlined in Fig. 2, the nucleotide state of the head domain may control the affinity of the MTBD to the microtubule. Conversely, the binding of the MTBD to the microtubule should activate the ATPase activity of the head domain. This two-way communication is transmitted through the simple ∼17-nm-long α-helical coiled-coil stalk and the buttress/strut, and its structural basis has been a puzzling question.Currently there are three independent MTBD atomic structures in the Protein Data Bank (PDB): One of the crystal structures of the D. discoideum dynein motor domain contains the MTBD (Fig. 3 A), and Carter et al. (2008) crystallized the MTBD of mouse cytoplasmic dynein fused with a seryl tRNA-synthetase domain (Fig. 3 C). The MTBD structure of C. reinhardtii axonemal dynein was solved using nuclear magnetic resonance (PDB accession no. 2RR7; Fig. 3 B). The MTBD is mostly composed of α-helices and the three structures are quite similar to each other within the globular MTBD (Fig. 3). Note that dynein c has an additional insert at the MTBD–microtubule interface (Fig. 3 B, inset), whose function is not yet clear. The three structures start to deviate from the junction between the MTBD and the coiled-coil region of the stalk (Fig. 3, A–C, blue arrowheads). Particularly, one of the stalk α-helix (CC2) in D. discoideum dynein motor domain appears to melt at the junction with the MTBD (Fig. 3 A, red arrowhead). This structural deviation suggests that the stalk coiled coil at the junction is flexible, which is consistent with the observation by EM (Roberts et al., 2009).Open in a separate windowFigure 3.Atomic models of the MTBD of dynein. (A) D. discoideum cytoplasmic dynein (PDB accession no. 3VKH). (B) C. reinhardtii dynein c (PDB accession no. 2RR7). The inset shows the side view, highlighting the dynein c–specific insert. (C) Mouse cytoplasmic dynein (PDB accession no. 3ERR). (D) Mouse cytoplasmic dynein fit to the MTBD–microtubule complex derived from cryo-EM (PDB accession no. 3J1T). All the MTBD structures were aligned using least square fits and color-coded with a gradient from the N to C terminus. CC1, coiled coil helix 1; CC2, coiled coil helix 2. The blue arrowheads points to the junction between MTBD and the stalk, where a well-conserved proline residue (colored pink) is located. In C and D, two residues (isoleucine 3269 and leucine 3417) are shown as spheres. The two residues form hydrophobic contacts in the β-registry (C), whereas they are separated in the α-registry (D) because of the sliding between the two α-helices (blue and red arrows). Conformational changes observed in the mouse dynein MTBD in complex with a microtubule by cryo-EM are shown by black arrows. Note that the cryo-EM density map does not have enough resolution to observe sliding between CC1 and CC2. The sliding was modeled based on targeted molecular dynamics (Redwine et al., 2012).Various mechanisms have been proposed to explain how the affinity between the MTBD and a microtubule is controlled. Gibbons et al. (2005) proposed “the helix-sliding hypothesis” (for review see Cho and Vale, 2012). In brief, this hypothesis proposes that the sliding between two α-helices CC1 and CC2 (Fig. 3, C and D; blue and red arrows) may control the affinity of this domain to a microtubule. When Gibbons’s classification (Gibbons et al., 2005) of the sliding state is applied to the three MTBD structures, the stalk in the D. discoideum dynein motor domain is in the “α-registry” state (not visible in Fig. 3 A because of the melting of CC2), which corresponds to the strong binding state. However, the mouse cytoplasmic and C. reinhardtii axonemal MTBDs have the “β-registry” stalk (Fig. 3 C), which corresponds to the weak binding state.To observe conformational changes induced by the α-registry and/or microtubule binding, Redwine et al. (2012) solved the structure of mouse dynein MTBD in complex with a microtubule at 9.7-Å resolution using cryo-EM and single particle analysis. The MTBD was coupled with seryl tRNA-synthetase to fix the stalk helix in the α-registry. At this resolution, α-helices are visible, and they used molecular dynamics to fit the crystal structure of mouse MTBD (β-registry) to the cryo-EM density map. According to this result, the first helix H1 moves ∼10 Å to a position that avoids a clash with the microtubule (Fig. 3 D, black arrows). This also induces opening of the stalk helix (CC1). Together with mutagenesis and single-molecule motility assays, Redwine et al. (2012) proposed that this new structure represents the strong binding state. Currently, it is not clear why the MTBD structure of D. discoideum dynein motor domain (α-registry, Fig. 3 A) is not similar to the new α-registry mouse dynein MTBD, and this problem needs to be addressed by further studies.

Structures around the first ATP binding site

Another central question about motor proteins is how Ångstrom-scale changes around the nucleotide are amplified to generate steps >8 nm. For dynein, the interface between the first nucleotide-binding pocket and the linker seem to be the key force-generating element (Fig. 4). The crystal structures of dynein give us clues about how nucleotide-induced conformational changes may be transmitted to and amplified by the linker domain.Open in a separate windowFigure 4.Structures around the first ATP binding site. (A) Schematic domain structure of the head domain. Regions contacting the linker domain are colored purple. (B) AAA submodules surrounding the first nucleotide-binding pocket (PDB accession no. 3VKG, chain A). The linker is connected to AAA1 domain by the “N-loop.” To highlight that the two finger-like structures are protruding, the shadow of the atomic structure has been cast on the plane parallel to the head domain. (C) Interaction between the linker and the two finger-like structures. The pink arrowhead points to the hinge-like structure of the linker. The pink numbers indicates the subdomain of the linker.The main ATP catalytic site is located between AAA1 and AAA2 (Fig. 4, A and B). There are four ADP molecules in the D. discoideum dynein crystal structures, but the first ATP binding site alone drives the microtubule-activated ATPase activity, based on biochemical experiments on dyneins whose ATP binding sites were mutated (Kon et al., 2012).One AAA+ module is composed of a large submodule and a small α submodule (Fig. 4 B). The large α/β submodule is located inside of the ring and the small α submodule is located outside. The large submodule bulges toward the linker face, and the overall ring forms a dome-like shape (Fig. 1 D).The main ATP catalytic site is surrounded by three submodules: AAA1 large α/β, AAA1 small α, and AAA2 large α/β (Fig. 4, A and B). Based on the structural changes of other AAA+ proteins (Gai et al., 2004; Suno et al., 2006; Wendler et al., 2012), the gap between AAA1 and AAA2 modules is expected to open and close during the ATPase cycle.In fact, the size of the gap varies among the dynein crystal structures. The crystal structures of yeast dyneins show a larger gap between AAA1 and AAA2, which might be the reason why no nucleotide was found in the binding pocket. Although Schmidt et al. (2012) soaked the crystals in a high concentration of various nucleotides (up to 25 mM of ATP), no electron densities corresponding to the nucleotide were observed at the first ATP binding site. Among dynein crystal structures, one of D. discoideum dynein (PDB accession no. 3VKH, chain A) has the smallest gap, but it is still considered to be in an “open state” because the arginine finger in the AAA2 module (Fig. 4 B, red) is far from the phosphates of ADP. Because the arginine finger is essential for ATP hydrolysis in other AAA+ proteins (Ogura et al., 2004), the gap is expected to close and the arginine finger would stabilize the negative charge during the transition state of ATP hydrolysis.The presumed open/close conformational change between AAA1 and AAA2 would result in the movement of two “finger-like” structures protruding from the AAA2 large α/β submodule (Fig. 4 B). The two finger-like structures are composed of the H2 insert β-hairpin and preSensor I (PS-I) insert. In D. discoideum dynein crystal structure, the two finger-like structures are in contact with the “hinge-like cleft” of the linker (Fig. 4 C, pink arrowhead). The hinge-like cleft is one of the thinnest parts of the linker, where only one α-helix is connecting between the linker subdomains 2 and 3.In the yeast crystal structures, which have wider gaps between AAA1 and AAA2, the two finger-like structures are not in direct contact with the linker and separated by 18 Å. Instead, the N-terminal region of the linker is in contact with the AAA5 domain (Fig. 4 A). To test the functional role of the linker–AAA5 interaction, Schmidt et al. (2012) mutated a residue involved in the interaction (Phe3446) and found that the mutation resulted in severe motility defects, showing strong microtubule binding and impaired ATPase activities. In D. discoideum dynein crystals, there is no direct interaction between AAA5 and the linker, which suggests that the gap between AAA1 and AAA2 may influence the interaction between the head and linker domain. The contact between the linker and AAA5 may also influence the gap around AAA5, because the gap between AAA5 and AAA6 is large in yeast dynein crystal, whereas the one between AAA4 and AAA5 is large in D. discoideum dynein.The movement of two finger-like structures would induce remodeling of the linker. According to the recent cryo-EM 3D reconstructions of cytoplasmic dynein and axonemal dynein c (Roberts et al., 2012), the linker is visible across the head and there is a large gap between AAA1 and AAA2 in the no-nucleotide state. This linker structure is considered to be the “straight” state (Fig. 2, A and E). In the presence of ADP vanadate, the gap between AAA1 and AAA2 is closed and the N-terminal region of linker is near AAA3, which corresponds to the pre-power stroke “hinged” state (Fig. 2, C and D). The transition from the hinged state to the straight state of the linker is considered to be the force-generating step of dynein.

Processivity of dynein

As the structure of dynein is different from other motor proteins, dynein’s stepping mechanism is also distinct. Both dynein and kinesin are microtubule-based motors and move processively. Based on the single molecule tracking experiment with nanometer accuracy (Yildiz et al., 2004), it is widely accepted that kinesin moves processively by using its two motor domain alternately, called the “hand-over-hand” mechanism. To test whether dynein uses a similar mechanism to kinesin or not, recently Qiu et al. (2012) and DeWitt et al. (2012) applied similar single-molecule approaches to dynein.To observe the stepping, the two head domains of yeast recombinant cytoplasmic dynein were labeled with different colors and the movement of two head domains was tracked simultaneously. If dynein walks by the hand-over-hand mechanism, the step size would be 16 nm and the stepping of one head domain would always be followed by the stepping of another head domain (alternating pattern), and the trailing head would always take a step (Fig. 2 F). Contrary to this prediction, both groups found that the stepping of the head domains is not coordinated when the two head domains are close together. These observations indicated that the chances of a leading or trailing head domain stepping are not significantly different (Fig. 2 G; DeWitt et al., 2012; Qiu et al., 2012).This stepping pattern predicts that the distance between the head domains can be long. In fact, the distance between the two head domains is on average ∼18 ± 11 nm (Qiu et al., 2012) or 28.4 ± 10.7 nm (DeWitt et al., 2012), and as large as ∼50 nm (DeWitt et al., 2012). When the two head domains are separated, there is a tendency where stepping of the trailing head is preferred over that of the forward head.In addition, even though the recombinant cytoplasmic dynein is a homodimer, the two heavy chains do not function equally. While walking along the microtubule, the leading head tends to walk on the right side, whereas the trailing head walks on the left side (DeWitt et al., 2012; Qiu et al., 2012). This arrangement suggests that the stepping mechanism is different between the two heads. In fact, when one of the two dynein heavy chains is mutated to abolish the ATPase activity at AAA1, the heterodimeric dynein still moves processively (DeWitt et al., 2012), with the AAA1-mutated dynein heavy chain remaining mostly in the trailing position. This result clearly demonstrates that allosteric communication between the two AAA1 domains is not required for processivity of dynein. It is likely that the mutated head acts as a tether to the microtubule, as it is known that wild-type dynein can step processively along microtubules under external load even in the absence of ATP (Gennerich et al., 2007).These results collectively show that dynein moves by a different mechanism from kinesin. It is likely that the long stalk and tail allow dynein to move in a more flexible manner.

Dyneins in the axoneme

As mentioned in the introduction, >10 dyneins work in motile flagella and cilia. The core of flagella and cilia is the axoneme, which is typically made of nine outer doublet microtubules and two central pair microtubules (“9 + 2,” Fig. 5 A). The axonemes are found in various eukaryotic cells ranging from the single-cell algae C. reinhardtii to human. Recent extensive cryo-electron tomography (cryo-ET) in combination with genetics revealed the highly organized and complex structures of axonemes that are potentially important for regulating dynein activities (Fig. 5, C and D; Nicastro et al., 2006; Bui et al., 2008, 2009, 2012; Heuser et al., 2009, 2012; Movassagh et al., 2010; Lin et al., 2012; Carbajal-González et al., 2013; Yamamoto et al., 2013).Open in a separate windowFigure 5.Arrangement of axonemal dyneins. (A) The schematic structure of the motile 9 + 2 axoneme, viewed from the base of flagella. (B) Quasi-planar asymmetric movement of the 9 + 2 axoneme typically observed in trachea cilia or in C. reinhardtii flagella. (C and D) 3D structure of a 96-nm repeat of doublet microtubules in the distal/central region of C. reinhardtii flagella (EMDB-2132; Bui et al., 2012). N-DRC, the nexin-dynein regulatory complex; ICLC, intermediate chain/light chain complex. Inner arm dynein subspecies are labeled according to Bui et al. (2012) and Lin et al. (2012). To avoid the confusion with the linker domain of dynein, the structures connecting between outer and inner arm dyneins are labeled as “connecters,” which are normally called “linkers.” Putative ATP binding sites of outer arm dynein determined by biotin-ADP (Oda et al., 2013) are indicated by orange circles. The atomic structure of cytoplasmic dynein is placed into the β-heavy chain of outer arm dynein and its enlarged view is shown in the inset. (D) Two doublet microtubules, viewed from the base of flagella.The basic mechanochemical cycles of axonemal dyneins are believed to be shared with cytoplasmic dynein. Dynein c is an inner arm dynein of C. reinhardtii and used extensively to investigate the conformational changes of dynein, as shown in Fig. 2 (A–E), by combining EM and single-particle analysis (Burgess et al., 2003; Roberts et al., 2012). Structural changes of axonemal dyneins complexed with microtubules are also observed by quick-freeze and deep-etch EM (Goodenough and Heuser, 1982; Burgess, 1995), cryo-EM (Oda et al., 2007), negative-staining EM (Ueno et al., 2008), and cryo-ET (Movassagh et al., 2010). According to these studies, the AAA+ head domains are constrained near the A-tubule in the no-nucleotide state. In the presence of nucleotide, the head domains move closer to the B-tubule and/or the minus end of microtubule, and their appearance becomes heterogeneous, which is consistent with the observation of isolated dynein c that shows greater flexibility between tail and stalk in the ADP/vanadate state (Burgess et al., 2003).One of the controversies about the structural changes of axonemal dyneins is whether their stepping involves “rotation” or “shift” of the head (Fig. 2, B to D). The stalk angle relative to the microtubule seems to be a constant ∼60° irrespective of the nucleotide state (Ueno et al., 2008; Movassagh et al., 2010). This angle is similar to the angle obtained from cryo-EM study of the MTBD–microtubule complex (Redwine et al., 2012). Based on these observations, Ueno et al. (2008) and Movassagh et al. (2010) hypothesize that the “shift” of the head pulls the B-microtubule toward the distal end. However, Roberts et al. (2012) propose that the “rotation” of head and stalk is involved in the stepping based on the docking of dynein c head into an averaged flagella tomogram obtained by Movassagh et al. (2010). This issue needs to be resolved by more reliable and high-resolution data, but these two models may not be mutually exclusive. For example, averaged tomograms may be biased toward the microtubule-bound stalk because tomograms are aligned using microtubules.To interpret these structural changes of axonemal dyneins, docking atomic models of dynein is necessary. According to Roberts et al. (2012), the linker face of inner arm dynein c is oriented outside of axoneme (Fig. 5 D). For outer arm dyneins, we used cryo-EM in combination with biotin-ADP-streptavidin labeling and showed that the ATP binding site, most likely AAA1, is on the left side of the AAA+ head (Fig. 5 C; Oda et al. (2013)). Assuming that the stalks extend out of the plane toward the viewer, the linker face of outer arm dynein is oriented outside of axoneme (Fig. 5 C, inset; and Fig. 5 D). If it were the opposite, the AAA1 would be located on the right side of the AAA+ head. In summary, both inner and outer arm dynein seem to have the same arrangement, with their linker face oriented outside of the axoneme (Fig. 5 D).A unique characteristic of axonemal dyneins is that these dyneins are under precise temporal and spatial control. To generate a planer beating motion (Fig. 5 B), dyneins should be asymmetrically controlled, because the dyneins located on doublets 2–4 drive the effective stroke, whereas the ones on doublets 6–8 drive the recovery stroke (Fig. 5 A). Based on the cryo-ET observation of axonemes, Nicastro et al. (2006) proposed that “linkers” between dyneins provide hard-wiring to coordinate motor activities. Because the linkers in axonemes are distinct structures from the linker domain of dynein, for clarity, here we call them “connecters.” According to the recent cryo-ET of proximal region of C. reinhardtii flagella (Bui et al., 2012), there are in fact asymmetries among nine doublets that are localized to the connecters between outer and inner arm dynein, called the outer-inner dynein (OID) connecters (Fig. 5, A and C). Recently we identified that the intermediate chain 2 (IC2) of outer arm dynein is a part of the OID connecters, and a mutation of the N-terminal region of IC2 affects functions of both outer and inner arm dyneins (Oda et al., 2013), which supports the idea that the connecters between dyneins are involved in axonemal dynein regulation.

Closing remarks

Thanks to the crystal structures, we can now design and interpret experiments such as single molecule assays and EM based on the atomic models of dynein. Our understanding of the molecular mechanism and cellular functions of dyneins will be significantly advanced by these experiments in the near future.One important direction of dynein research is to understand the motor mechanisms closer to the in vivo state. For example, the step sizes of cytoplasmic dynein purified from porcine brain is ∼8 nm independent of load (Toba et al., 2006). This result suggests that intermediate and light chain bound to the dynein heavy chain may modulate the motor activity of dynein. To address such questions, Trokter et al. (2012) reconstituted human cytoplasmic dynein complex from recombinant proteins, although the reconstituted dynein did not show robust processive movement. Further studies are required to understand the movement of cytoplasmic dynein. Similarly, axonemal dyneins should also be studied using mutations in a specific gene that does not affect the overall flagella structure, rather than depending on null mutants that cause the loss of large protein complexes.Detailed full chemomechanical cycle of dynein and its regulation are of great importance. Currently, open/closed states of the gap between AAA1 and AAA2 are not clearly correlated with the chemomechanical cycle of dynein. Soaking dynein crystal with nucleotides showed that the presence of ATP alone is not sufficient to close the gap, at least in the crystal (Schmidt et al., 2012). This result suggests that other factors such as a conformational change of the linker are required. For other motors, ATP hydrolysis is an irreversible chemical step, which is often “gated” by strain. In the case of kinesin, ATP is hydrolyzed by a motor domain only when a forward strain is applied by the other motor domain through the neck linker (Cross, 2004; Kikkawa, 2008). A similar strain-based gating mechanism may play important roles in controlling the dynein ATPase. Upon MTBD binding to the forward binding site, a strain between MTBD and tail would be applied to the dynein molecule. The Y-shaped stalk and strut/buttress under the strain would force the head domain to close the gap between AAA4 and AAA5 (Fig. 2 H). Similarly, the linker under the strain would be hooked onto the two finger-like structures and close the gap between AAA1 and AAA2 (Fig. 2 H). The gap closure then triggers ATP hydrolysis by dynein. This strain-based gating of dynein is consistent with the observation that the rate of nonadvancing backward steps, which would depend on ATP hydrolysis, is increased by load applied to dynein (Gennerich et al., 2007). To explain cilia and flagella movement, the geometric clutch hypothesis has been proposed (Lindemann, 2007), which contends that the forces transverse (t-force) to the axonemal axis act on the dynein to regulate dynein activities. In the axoneme, dynein itself can be the sensor of the t-force by the strain-based gating mechanism. Further experiments are required to test this idea, but the strain-based gating could be a shared property of biological motors.     

Gene Genealogies Strongly Distorted by Weakly Interfering Mutations in Constant Environments

Neutral nucleotide diversity does not scale with population size as expected, and this “paradox of variation” is especially severe for animal mitochondria. Adaptive selective sweeps are often proposed as a major cause, but a plausible alternative is selection against large numbers of weakly deleterious mutations subject to Hill–Robertson interference. The mitochondrial genealogies of several species of whale lice (Amphipoda: Cyamus) are consistently too short relative to neutral-theory expectations, and they are also distorted in shape (branch-length proportions) and topology (relative sister-clade sizes). This pattern is not easily explained by adaptive sweeps or demographic history, but it can be reproduced in models of interference among forward and back mutations at large numbers of sites on a nonrecombining chromosome. A coalescent simulation algorithm was used to study this model over a wide range of parameter values. The genealogical distortions are all maximized when the selection coefficients are of critical intermediate sizes, such that Muller''s ratchet begins to turn. In this regime, linked neutral nucleotide diversity becomes nearly insensitive to N. Mutations of this size dominate the dynamics even if there are also large numbers of more strongly and more weakly selected sites in the genome. A genealogical perspective on Hill–Robertson interference leads directly to a generalized background-selection model in which the effective population size is progressively reduced going back in time from the present.OBSERVED levels of apparently neutral nucleotide diversity (π_n) are typically lower than expected under the assumptions of standard equilibrium theories, and they vary much less among species than do estimates of long-term effective population sizes (Nei and Grauer 1984; Bazin et al. 2006; Nabholz et al. 2008). Many explanations have been proposed for the apparent shortfalls and the lack of proportionality with population size, including (1) complex demographic histories (e.g., recurring population bottlenecks), (2) adaptive selective sweeps (Maynard Smith and Haigh 1974; Gillespie 1999), and (3) selection against deleterious mutations (Charlesworth et al. 1993, 1995; McVean and Charlesworth 2000; Comeron et al. 2008). Of these three possibilities, bottlenecks and sweeps are by far the most frequently mentioned, even though deleterious mutations occur at high rates in all species, regardless of ecological circumstances (Eyre-Walker and Keightley 2007). Here we show that weakly deleterious mutations can distort genealogies in three different ways and dramatically reduce nucleotide diversities in large populations of nonrecombining chromosomes. The mitochondrial genealogies of several species of whale lice (Kaliszewska et al. 2005) are distorted in exactly these ways, and several lines of evidence suggest that bottlenecks and adaptive sweeps are not likely to be the primary causes.Mitochondria have been proposed to be especially sensitive to selective sweeps. Animal mitochondrial genomes contain more than three dozen essential protein and structural RNA genes, so they are large targets for both mutation and selection (Ballard and Whitlock 2004). They do not undergo sexual recombination, so every advantageous mutation that fixes will reduce variation throughout the genome. Mitochondrial nucleotide diversity therefore could depend strongly on rates of environmental change, which could be similar for species with very different population sizes. Indeed, if rates of mitochondrial adaptation were mutation limited, then larger populations might actually experience higher rates of adaptive substitution and as a result show lower average levels of neutral diversity than smaller populations (Gillespie 2000, 2001). This idea was recently invoked to explain the remarkable similarity of average levels of mitochondrial nucleotide diversity among the major animal classes which appear to have very different average population sizes and substantially different average levels of nuclear nucleotide and amino acid diversity (Bazin et al. 2006).Unconditionally deleterious mutations can also depress linked neutral diversity by reducing the effective population size either through (1) background selection against relatively strongly selected mutations (Charlesworth et al. 1993, 1995) or (2) Hill–Robertson interference (Hill and Robertson 1966) among large numbers of relatively weakly selected mutations (reviewed by Comeron et al. 2008). The second of these processes, called “weak-selection Hill–Robertson interference” (wsHRi) by McVean and Charlesworth (2000) and “interference selection” (IS) by Comeron and Kreitman (2002), can shorten genealogies, give them strongly nonneutral branch-length proportions, and skew their topologies (Higgs and Woodcock 1995; Maia et al. 2004).To date, weak interference has mainly been studied by forward simulation, with the aim of assessing its possible effects on patterns of optimal synonymous codon use within eukaryotic nuclear genes and genomes, in the presence of recombination (Comeron and Guthrie 2005; Loewe and Charlesworth 2007; Comeron et al. 2008). In an attempt to understand the striking genealogical distortions seen in whale-louse mitochondria (Kaliszewska et al. 2005), we have developed a structured-coalescent algorithm that accurately models selection of arbitrary strength on a nonrecombining chromosome of finite length. All of the distortions seen in the whale-louse mitochondria are replicated under parameters that might plausibly apply to whale lice and many other animal species, and these distortions scale only weakly with population size.Whale lice are permanent, obligate ectoparasites of cetaceans. They feed on the dead outer surface of their host''s skin, and they appear to be harmless. They are amphipod Crustacea comprising a monophyletic family, Cyamidae, with ∼50 described species in several genera. Three of these species (Cyamus ovalis, C. gracilis, and C. erraticus) occur on right whales (Eubalaena spp.) but not regularly on any other hosts. Most adult right whales carry large populations of all three species.Right whales in the North Pacific, the North Atlantic, and the southern hemisphere have been separated for ∼5 million years, and so have their cyamids (Rosenbaum et al. 2000; Gaines et al. 2005; Kaliszewska et al. 2005). For this reason the right whales in different ocean systems are now considered distinct species (Eubalaena japonica, E. glacialis, and E. australis), and we refer to their cyamids as North Pacific C. ovalis, North Atlantic C. ovalis, southern C. ovalis, and so on, in anticipation that a future revision of the genus Cyamus will recognize them as “triplet” sibling species (3 × 3 = 9 species in all). We studied their mitochondrial population genetics with the initial aim of quantifying patterns of genetic differentiation among the cyamid populations on individual whales within local populations (Kaliszewska et al. 2005).We had reasoned (incorrectly) that the pattern of differentiation among whales might say something about their social interactions, since cyamids can transfer only between whales that are in direct physical contact with each other. We found very low levels of differentiation among whales and to our surprise literally no differentiation among the major southern hemisphere breeding aggregations that calve off the coasts of South America, South Africa, Australia, and New Zealand (Kaliszewska et al. 2005). This absence of population structure seems remarkable by terrestrial standards but is easily explained by modest rates of cyamid exchange among whales within local populations and between the major breeding aggregations, given the enormous sizes of cyamid populations. Right whales are highly gregarious (spending hours per day in social interactions), mobile (traveling thousands of kilometers per year on annual foraging migrations), and mortal (carrying their cyamid populations to the sea floor when they die). Thus cyamids have many opportunities to transfer between whales, and they might be expected to have evolved an inclination to do so when the opportunity presents itself (Hamilton and May 1977).The well-defined ecology of right-whale cyamids allows their population sizes to be estimated directly. The number of adult cyamids per whale (∼500–10,000, varying by species) times the number of whales per ocean (∼50,000–200,000, prior to human exploitation) equals the number of cyamids per species (Kaliszewska et al. 2005). Thus for all three nominal species of right-whale cyamids, long-term census population sizes are expected to have been in the range 2.5 × 10⁷–2 × 10⁹. Given conservative estimates of the per-generation mitochondrial mutation rate, even the lower end of this range predicts levels of synonymous nucleotide diversity at least an order of magnitude larger than those actually seen in the cyamids, which are consistently modest and similar to those seen in typical terrestrial arthropods (Kaliszewska et al. 2005). The three North Atlantic and southern hemisphere sibling-species pairs are strongly reciprocally monophyletic, as illustrated for C. ovalis in Figure 1. This is not expected at mutation–drift equilibrium, given their very large population sizes.Open in a separate windowFigure 1.—Mitochondrial gene genealogies for North Atlantic and southern hemisphere Cyamus ovalis, estimated by UPGMA from partial COI sequences. Left: The intraspecific genealogies coalesce globally at ∼0.5 and 1 MY and share an ancestor at ∼5 MY (Kaliszewska et al. 2005). Center and right: Each intraspecific genealogy is aligned with a generalized skyline plot (Strimmer and Pybus 2001) showing estimates of θ at different times in the past under a piecewise constant model of population size change fit by GENIE 3.0 (Pybus and Rambaut 2002). Three different point estimates of present-day θ are also indicated on the plots (synonymous-site nucleotide diversity π, Watterson''s θ estimated from synonymous sites, and θ estimated jointly with the apparent exponential growth rate by the MCMC coalescent algorithm in LAMARC). Values of Tajima''s D are lower (−1.5 to −1.6) when estimated from the sequences than when estimated from the branch lengths of the trees (−2.1 to −2.4), as expected because multiple substitutions occur at some sites. Similar values of D_T (−2.3 for both species) were obtained from the highest-likelihood trees found by BEAST with a fully parameterized GTR substitution model and a coalescent prior. Those trees have standardized imbalance statistics (−I_S) of −3.7 (southern hemisphere C. ovalis) and −2.2 (North Atlantic C. ovalis). The trees shown here have more extreme values of −I_S (−5.5 and −4.0, respectively), probably as a consequence of artificially pectinate branching orders induced by UPGMA among sets of identical sequences. Slightly different sets of sequences were used to make the two-species genealogy on the left and the single-species genealogies in the center. The long interspecific branches in the two-species tree are based on a multispecies maximum-likelihood analysis involving smaller numbers of much longer (4.1 kb) sequences (Kaliszewskaet al. 2005, Figure 3).These dramatic deficits of variation are not easily explained by population bottlenecks or adaptive selective sweeps. The bottleneck hypothesis is especially problematic because it requires the long-term near extinction of right whales in all three ocean systems. (Short bottlenecks such as those caused by human exploitation of right whales are not expected to have a noticeable effect on cyamid genetic diversity because cyamid populations remain large, and rates of genetic drift low, even when there are few whales.) That all three right whales have survived for millions of years suggests that they have maintained reasonably large population sizes, and the mitochondrial nucleotide diversity of southern right whales is consistent with this assumption (Kaliszewska et al. 2005), as is the nucleotide diversity of a cyamid nuclear gene (described below). Right whales eat copepods and krill, which are relatively close to the base of marine food webs, and right-whale populations are thought to be food limited. Thus a long-term, severe depression of their numbers would also seem to imply a collapse of marine ecosystems worldwide, for which there is no evidence.Several features of cyamid mitochondrial nucleotide diversity are also inconsistent with the bottleneck model and with adaptive sweeps as well. The most obvious of these features is the uniformity of cyamid mitochondrial diversity among species (π = 0.007–0.015 for COI sequences in the seven species surveyed by Kaliszewska et al. 2005). Gene genealogies estimated from these sequences also seem remarkably uniform in total depth, with last common ancestors differing in age by only a factor of 3 and in six of the seven species by less than a factor of 2 (see Kaliszewska et al. 2005, Figure 4). Adaptive sweeps might be expected to occur at roughly random intervals and not to be well coordinated in time among seven species in three different ocean systems. Interspecific coordination of such sweeps (over the whole globe) would seem to be required if they were to be a plausible primary cause of the genealogical shortening.Open in a separate windowFigure 4.—Apparent average effective sizes of ancestral populations, for models with different values of s. Values of N_e are given on the vertical axis (logarithmically scaled). They are estimated from the variance of expected contributions to the present, for the adults of any given generation. The parameters are those of Figure 2 (N = 65,536 = “64k,” μ = 1.5 × 10⁻⁶, L_s = 2048, U = 0.0031). In theory the curve for s = 0 should be perfectly horizontal. The discrepancy appears to be caused by subtle flaws in the shape of the very broad “idealized” simulated distribution that was used in this calculation. The curves for strong-selection cases are perfectly horizontal at times beyond a few thousand generations because the mutation-number distributions are compact, with little stochastic variation. The two lowest curves are those for the selection coefficients (s = 2⁻¹⁰, U/s = 3.2, and s = 2⁻¹¹, U/s = 6.3) that produce the most extreme values of the polymorphism and tree-shape statistics, given the other parameters (Figure 2).In addition to showing too little nucleotide variation, the cyamid mitochondrial genomes show strong and consistent excesses of rare nucleotide states, reflecting the “comb-like” or “star-like” shapes of the genealogies, in which deeper branches tend to be much too short relative to terminal branches (as if the trees had been “squished” from behind). This kind of distortion causes negative values of Tajima''s (1989) D and related statistics. It can be caused by population expansion from a bottleneck or by lineage expansion under positive selection (Kaplan et al. 1989; Slatkin and Hudson 1991; Rogers and Harpending 1992; Bamshad and Wooding 2003). However, the form of branch-length distortion seen in the cyamid genealogies suggests a slow, steady, roughly exponential form of population or lineage growth, not the relatively sudden increases suggested by the bottleneck and selective-sweep hypotheses. Generalized skyline plots (Strimmer and Pybus 2001) describing the histories of population size implied by the shapes of the northern and southern C. ovalis gene genealogies are shown in Figure 1. They are remarkably similar, as are the growth rates and estimates of present-day θ (= 2N_fμ_n) obtained by fitting exponential growth models using the coalescent algorithms in LAMARC (Kuhner et al. 1998, 2004) or BEAST (Drummond and Rambaut 2007).Cyamid populations cannot have grown in numbers as seemingly implied by these analyses. The number of cyamids on each whale appears to be set mainly by microhabitat limitations (e.g., by the area of rough callosity tissue on the head, where C. ovalis and C. gracilis live), and these features of their environment have hardly changed for millions of years, as demonstrated by the strong similarities of northern and southern right whales and their cyamids. Likewise, the numbers of right whales cannot have increased gradually from vanishingly small numbers over several hundred thousand years, for the reasons discussed above.The genealogical signals of “growth” therefore seem likely to be caused by selection. Environmental change is the most obvious potential cause of selection, but the apparent rate of growth seen here is strangely slow—in fact, slower than glacial. The orbitally forced Plio-Pleistocene glacial climate cycles have a major period of ∼100,000 years (Lambert et al. 2008 and references therein), but all seven of the right-whale cyamids for which we have mitochondrial population samples appear to have been “expanding,” more or less continuously, through at least several such cycles. The seemingly fairly consistent rate of branch-length foreshortening seen in the genealogies therefore suggests the action of a process that is relatively homogeneous in time, in addition to being very slow overall.The cyamid mitochondrial genealogies also appear to be topologically skewed, with sister clades too unequal in size, on average. In random bifurcating trees, the distribution of sister-clade sizes is uniform (Yule 1924; Heard 1992; Rogers 1994). Deviations from this null expectation can be quantified by statistics such as Colless''s (1982) index of tree imbalance (Shao and Sokal 1990; Rogers 1996). Our estimates of the cyamid genealogies tend to be excessively imbalanced (Figure 1). Strong topological imbalance is not caused by classic adaptive sweeps or by population growth following a bottleneck, but previous theoretical work has indicated that it can be caused by selection (Higgs and Woodcock 1995; Maia et al. 2004).To summarize, the cyamid mitochondrial genealogies are consistently much too short, too squished, and too skewed, relative to neutral-theory expectations. Owing to several special features of cyamid and right-whale biology, selection seems to be the only plausible explanation for this set of distortions, but conventional adaptive sweeps do not seem likely to be the primary cause. We therefore asked whether weakly deleterious mutations might be sufficient to generate the observed combination of patterns, in the absence of environmental change. Previous work (mentioned above) showed that interference among weak mutations at many sites can strongly affect linked neutral variation, but this work did not fully explore the parameter space relevant to our system or connect all the patterns in a genealogical setting.To address this question we first carried out forward simulations of populations of nonrecombining chromosomes with large numbers of nucleotide positions subject to forward and back mutations with unconditional fitness effects of size s. Large numbers of linked neutral sites were used to estimate genealogies and to calculate population statistics of interest. We found that for a range of intermediate values of s, considerable fitness variation was maintained and all three of the genealogical distortions (and the signal of apparent exponential growth) seen in the cyamid genealogies reached impressively large maxima. However, the computational burden of full forward simulation prevented us from considering realistic parameter values (i.e., large N and small μ), and it was not obvious that extrapolations based on compound parameters (e.g., Nμ and Ns) would work as hoped in all respects (see Comeron et al. 2008). We developed an equivalent coalescent algorithm that accurately reproduces all results of the forward simulations and allows for realistic parameter values. Under parameters relevant to cyamid mitochondria, the distortions of genealogical depth, proportions, and topology can be even more extreme than those seen in the cyamids, and the mean pairwise coalescence times (and resulting neutral nucleotide diversities) associated with maximally distorting intermediate values of s (U_s/s ∼ 5, where U_s is the total genomic mutation rate at sites with selection coefficents of size s) depend only weakly on N.All parameters of this model (including those of the environment) remain constant over time, yet in some respects it displays apparently nonequilibirum behavior. Under weak to intermediate selection (U_s/s > 10), the effective population size appears to become progressively smaller as time recedes into the past, giving rise to the illusion of growth. And in the maximally distorting range of intermediate selection coefficients, the distributions of deleterious mutation numbers and the shapes of genealogies show conspicuous dynamical instability of a form that could be taken to suggest “adaptive evolution” in response to episodes of environmental change. Adaptive mutations contribute importantly to this process, but they are reversions at some of the many sites previously mutated to mildly deleterious states. Subtle patterns of environmental change that converted previously optimal nucleotide states to slightly suboptimal states could give rise to a category of “virtual reversions” that would augment (or even outnumber) simple reversions, and the effects of such a process might well be consistent with the distortions seen in the cyamid mitochondrial genealogies. However, models with no environmental change of any kind appear to explain the observations surprisingly well.     

Lambda Red Recombineering in Escherichia coli Occurs Through a Fully Single-Stranded Intermediate

The phage lambda-derived Red recombination system is a powerful tool for making targeted genetic changes in Escherichia coli, providing a simple and versatile method for generating insertion, deletion, and point mutations on chromosomal, plasmid, or BAC targets. However, despite the common use of this system, the detailed mechanism by which lambda Red mediates double-stranded DNA recombination remains uncertain. Current mechanisms posit a recombination intermediate in which both 5′ ends of double-stranded DNA are recessed by λ exonuclease, leaving behind 3′ overhangs. Here, we propose an alternative in which lambda exonuclease entirely degrades one strand, while leaving the other strand intact as single-stranded DNA. This single-stranded intermediate then recombines via beta recombinase-catalyzed annealing at the replication fork. We support this by showing that single-stranded gene insertion cassettes are recombinogenic and that these cassettes preferentially target the lagging strand during DNA replication. Furthermore, a double-stranded DNA cassette containing multiple internal mismatches shows strand-specific mutations cosegregating roughly 80% of the time. These observations are more consistent with our model than with previously proposed models. Finally, by using phosphorothioate linkages to protect the lagging-targeting strand of a double-stranded DNA cassette, we illustrate how our new mechanistic knowledge can be used to enhance lambda Red recombination frequency. The mechanistic insights revealed by this work may facilitate further improvements to the versatility of lambda Red recombination.OVER the past decade, lambda Red recombination (“recombineering”) has been used as a powerful technique for making precisely defined insertions, deletions, and point mutations in Escherichia coli, requiring as few as 35 bp of homology on each side of the desired alteration (Thomason et al. 2007a; Sharan et al. 2009). With this system, single-stranded DNA (ssDNA) oligonucleotides have been used to efficiently modify E. coli chromosomal targets (Ellis et al. 2001; Costantino and Court 2003), BACs (Swaminathan et al. 2001), and plasmids (Thomason et al. 2007b), as well as to rapidly optimize a metabolic pathway coding for the production of lycopene (Wang et al. 2009). Furthermore, linear double-stranded DNA (dsDNA) recombineering has been used to replace chromosomal genes (Murphy 1998; Murphy et al. 2000), to disrupt gene function (Datsenko and Wanner 2000), and to develop novel cloning methods (Lee et al. 2001; Li and Elledge 2005). Large-scale dsDNA recombineering projects include creating a library of single-gene knockout E. coli strains (Baba et al. 2006) and removing 15% of the genomic material from a single E. coli strain (Posfai et al. 2006). Linear dsDNA recombineering has also been used to insert heterologous genes and entire pathways into the E. coli chromosome (Zhang et al. 1998; Wang and Pfeifer 2008) and BACs (Lee et al. 2001; Warming et al. 2005), including those used for downstream applications in eukaryotes (Chaveroche et al. 2000; Bouvier and Cheng 2009). However, despite the broad use of this method, the mechanism of lambda Red recombination has not achieved scientific consensus, particularly in the case of dsDNA recombination. A clearer understanding of the mechanism underlying this process could suggest ways to improve the functionality, ease, and versatility of lambda Red recombination.Three phage-derived lambda Red proteins are necessary for carrying out dsDNA recombination: Gam, Exo, and Beta. Gam prevents the degradation of linear dsDNA by the E. coli RecBCD and SbcCD nucleases; lambda exonuclease (Exo) degrades dsDNA in a 5′ to 3′ manner, leaving single-stranded DNA in the recessed regions; and Beta binds to the single-stranded regions produced by Exo and facilitates recombination by promoting annealing to the homologous genomic target site (Sawitzke et al. 2007). Current mechanisms claim that Exo binds to both 5′ ends of the dsDNA and degrades in both directions simultaneously to produce a double-stranded region flanked on both sides by 3′ overhangs (Sharan et al. 2009; Szczepanska 2009). However, a comprehensive explanation of how this construct ultimately recombines with the chromosome has not yet been advanced.Initially, it was proposed that this recombination occurs via strand invasion (Thaler et al. 1987). However, it has more recently been shown that strand invasion is unlikely to be the dominant mechanism in the absence of long regions of homology, as recombination remains highly proficient in a recA^- background (Yu et al. 2000). Furthermore, a detailed analysis of lambda Red recombination products showed characteristics consistent with strand annealing rather than a strand invasion model (Stahl et al. 1997). Finally, lambda Red dsDNA recombination has been shown to preferentially target the lagging strand during DNA replication, which suggests strand annealing rather than strand invasion (Lim et al. 2008; Poteete 2008).To explain these results, Court et al. (2002) proposed a strand-annealing model for insertional dsDNA recombination (Figure 1A), in which one single-stranded 3′ end anneals to its homologous target at the replication fork. The replication fork then stalls, due to the presence of a large dsDNA nonhomology (i.e., the insertion cassette). The stalled replication fork is ultimately rescued by the other replication fork traveling in the opposite direction around the circular bacterial chromosome. The other 3′ end of the recombinogenic DNA anneals to the homology region exposed by the second replication fork, forming a crossover structure, which is then resolved by unspecified E. coli enzymes (Court et al. 2002).Open in a separate windowFigure 1.—Previously proposed lambda Red-mediated dsDNA recombination mechanisms. Heterologous dsDNA is shown in green; Exo is an orange oval, and Beta is a yellow oval. In both mechanisms the recombination intermediate is proposed to be a dsDNA core flanked on either side by 3′ ssDNA overhangs. (A) The Court mechanism posits that (1) Beta facilitates annealing of one 3′ overhang to the lagging strand of the replication fork. (2) This replication fork then stalls and backtracks so that the leading strand can template switch onto the synthetic dsDNA. The heterologous dsDNA blocks further replication from this fork. (3) Once the second replication fork reaches the stalled fork, the other 3′ end of the integration cassette is annealed to the lagging strand in the same manner as prior. Finally, the crossover junctions must be resolved by unspecified E. coli enzymes (Court et al. 2002). (B) The Poteete mechanism suggests that (1) Beta facilitates 3′ overhang annealing to the lagging strand of the replication fork and (2) positions the invading strand to serve as the new template for leading-strand synthesis. This structure is resolved by an unspecified host endonuclease (red triangle), and (3) the synthetic dsDNA becomes template for both lagging and leading-strand synthesis. A second template switch must then occur at the other end of the synthetic dsDNA (Poteete 2008). The figure was adapted from the references cited.The Court mechanism was challenged by Poteete (2008), who showed that the dsDNA recombination of a linear lambda phage chromosome occurs readily onto a unidirectionally replicating plasmid, which does not have the second replication fork required by the Court mechanism (Court et al. 2002). Thus, Poteete proposed an alternate mechanism (Poteete 2008), termed “replisome invasion” (Figure 1B), in which a 3′ overhang of the Exo-processed dsDNA first anneals to its complementary sequence on the lagging strand of the recombination target. Subsequently, this overhang displaces the leading strand, thereby serving as the new template for leading-strand synthesis. The resulting structure is resolved by an unspecified endonuclease, after which the recombinogenic DNA becomes the template for the synthesis of both new strands. In the context of recombineering using a linear dsDNA cassette, the author indicates that a second strand-switching event must occur at the other end of the incoming dsDNA.While Poteete''s mechanism addresses some of the weaknesses of the Court mechanism, it remains largely speculative. This mechanism does not identify the endonuclease responsible for resolving the structure after the first template switching event, nor does it explain how the recombinogenic DNA and replication machinery form a new replication fork. Additionally, this template-switching mechanism would have to operate two times in a well-controlled manner, which may not be consistent with the high-recombination frequencies often observed (Murphy et al. 2000) for lambda Red-mediated dsDNA insertion. Finally, little experimental evidence has been advanced to directly support this hypothesis.To address the deficiencies in these mechanisms, we propose that lambda Red dsDNA recombination proceeds via a ssDNA intermediate rather than a dsDNA core flanked by 3′ overhangs (Figure 2). In this mechanism, Exo binds to one of the two dsDNA strands and degrades that strand completely, leaving behind full-length ssDNA. This ssDNA then anneals to its homology target at the lagging strand of the replication fork and is incorporated as part of the newly synthesized strand as if it were an Okazaki fragment. This process is analogous to the accepted mechanism for the lambda Red-mediated recombination of ssDNA oligonucleotides (Court et al. 2002) and, therefore, unifies the mechanisms for ssDNA and dsDNA recombination. Notably, our mechanism uses one replication fork for the incorporation of a full-length heterologous cassette, thereby addressing Poteete''s criticism of the Court mechanism.Open in a separate windowFigure 2.—Lambda Red mediated dsDNA recombination proceeds via a ssDNA intermediate. Instead of a recombination intermediate involving dsDNA flanked by 3′-ssDNA overhangs, we propose that one strand of linear dsDNA is entirely degraded by Exo (orange oval). Beta (yellow oval) then facilitates annealing to the lagging strand of the replication fork in place of an Okazaki fragment. The heterologous region does not anneal to the genomic sequence. This mechanism could account for gene replacement (as shown) or for insertions in which no genomic DNA is removed.The degradation of an entire strand by lambda Exo is feasible, given the highly processive nature of the enzyme (Subramanian et al. 2003). Whereas previously proposed mechanisms assume that both dsDNA ends are degraded approximately simultaneously, our hypothesis implies that some dsDNA molecules will be entirely degraded to ssDNA before a second Exo can bind to the other end. In this article, we demonstrate that single-stranded DNA is a viable recombinogenic intermediate with lagging-strand bias. Furthermore, we show that genetic information from one strand of a recombinogenic dsDNA cassette cosegregates during lambda Red-mediated recombination. These results provide strong support of our proposed mechanism.     

Conformational States of Melittin at a Bilayer Interface

The distribution of peptide conformations in the membrane interface is central to partitioning energetics. Molecular-dynamics simulations enable characterization of in-membrane structural dynamics. Here, we describe melittin partitioning into dioleoylphosphatidylcholine lipids using CHARMM and OPLS force fields. Although the OPLS simulation failed to reproduce experimental results, the CHARMM simulation reported was consistent with experiments. The CHARMM simulation showed melittin to be represented by a narrow distribution of folding states in the membrane interface.Unstructured peptides fold into the membrane interface because partitioned hydrogen-bonded peptide bonds are energetically favorable compared to free peptide bonds (1–3). This folding process is central to the mechanisms of antimicrobial and cell-penetrating peptides, as well as to lipid interactions and stabilities of larger membrane proteins (4). The energetics of peptide partitioning into membrane interfaces can be described by a thermodynamic cycle (Fig. 1). State A is a theoretical state representing the fully unfolded peptide in water, B is the unfolded peptide in the membrane interface, C is the peptide in water, and D is the folded peptide in the membrane. The population of peptides in solution (State C) is best described as an ensemble of folded and unfolded conformations, whereas the population of peptides in State D generally is assumed to have a single, well-defined helicity, as shown in Fig. 1 A (5). Given that, in principle, folding in solution and in the membrane interface should follow the same basic rules, peptides in state D could reasonably be assumed to also be an ensemble. A fundamental question (5) is therefore whether peptides in state D can be correctly described as having a single helicity. Because differentiating an ensemble of conformations and a single conformation may be an impossible experimental task (5), molecular-dynamics (MD) simulations provide a unique high-resolution view of the phenomenon.Open in a separate windowFigure 1Thermodynamic cycles for peptide partitioning into a membrane interface. States A and B correspond to the fully unfolded peptide in solution and membrane interface, respectively. The folded peptide in solution is best described as an ensemble of unfolded and folded conformations (State C). State D is generally assumed to be one of peptides with a narrow range of conformations, but the state could actually be an ensemble of states as in the case of State C.Melittin is a 26-residue, amphipathic peptide that partitions strongly into membrane interfaces and therefore has become a model system for describing folding energetics (3,6–8). Here, we describe the structural dynamics of melittin in a dioleoylphosphatidylcholine (DOPC) bilayer by means of two extensive MD simulations using two different force fields.We extended a 12-ns equilibrated melittin-DOPC system (9) by 17 μs using the Anton specialized hardware (10) with the CHARMM22/36 protein/lipid force field and CMAP correction (11,12) (see Fig. S1 and Fig. S2 in the Supporting Material). To explore force-field effects, a similar system was simulated for 2 μs using the OPLS force field (13) (see Methods in the Supporting Material). In agreement with x-ray diffraction measurements on melittin in DOPC multilayers (14), melittin partitioned spontaneously into the lipid headgroups at a position below the phosphate groups at similar depth as glycerol/carbonyl groups (Fig. 2).Open in a separate windowFigure 2Melittin partitioned into the polar headgroup region of the lipid bilayer. (A) Snapshot of the simulation cell showing two melittin molecules (MLT1 and MLT2, in yellow) at the lipid-water interface. (B) Density cross-section of the simulation cell extracted from the 17-μs simulation. The peptides are typically located below the lipid phosphate (PO4) groups, in a similar depth as the glycerol/carbonyl (G/C) groups.To describe the secondary structure for each residue, we defined helicity by backbone dihedral angles (φ, ψ) within 30° from the ideal α-helical values (–57°, –47°). The per-residue helicity in the CHARMM simulation displays excellent agreement with amide exchange rates from NMR measurements that show a proline residue to separate two helical segments, which are unfolded below Ala⁵ and above Arg²² (15) (Fig. 3 A). In contrast, the OPLS simulation failed to reproduce the per-residue helicity except for a short central segment (see Fig. S3).Open in a separate windowFigure 3Helicity and conformational distribution of melittin as determined via MD simulation. (A) Helicity per residue for MLT1 and MLT2. (B) Corresponding evolution of the helicity. (C) Conformational distributions over the entire 17-μs simulation.Circular dichroism experiments typically report an average helicity of ∼70% for melittin at membrane interfaces (3,6,16,17), but other methods yield average helicities as high as 85% (15,18). Our CHARMM simulations are generally consistent with the experimental results, especially amide-exchange measurements (15); melittin helicity averaged to 78% for MLT1, whereas MLT2 transitioned from 75% to 89% helicity at t ≈ 8 μs, with an overall average helicity of 82% (Fig. 3 B). However, in the OPLS simulation, melittin steadily unfolds over the first 1.3 μs, after which the peptide remains only partly folded, with an average helicity of 33% (see Fig. S3). Similar force-field-related differences in peptide helicity were recently reported, albeit at shorter timescales (19). Although suitable NMR data are not presently available, we have computed NMR quadrupolar splittings for future reference (see Fig. S4).To answer the question asked in this article—whether the conformational space of folded melittin in the membrane interface can be described by a narrow distribution—the helicity distributions for the equilibrated trajectories are shown in Fig. 3 C. Whereas MLT1 in the CHARMM simulation produces a single, narrow distribution of the helicity, MLT2 has a bimodal distribution as a consequence of the folding event at t ≈ 8 μs (Fig. 3 C). We note that CHARMM force fields have a propensity for helix-formation and this transition might therefore be an artifact. We performed a cluster analysis to describe the structure of the peptide in the membrane interface. The four most populated conformations in the CHARMM simulation are shown in Fig. 4. The dominant conformation for both peptides was a helix kinked at G12 and unfolded at the last 5–6 residues of the C-terminus. The folding transition of MLT2 into a complete helix is visible by the 48% occupancy of a fully folded helix.Open in a separate windowFigure 4Conformational clusters of the two melittin peptides (MLT1 and MLT2) from the 17-μs CHARMM simulation in DOPC. Clustering is based on Cα-RMSD with a cutoff criterion of 2 Å.We conclude that the general assumption when calculating folding energetics holds: Folded melittin partitioned into membrane interfaces can be described by a narrow distribution of conformations. Furthermore, extended (several microsecond) simulations are needed to differentiate force-field effects. Although the CHARMM and OPLS simulations would seem to agree for the first few hundred nanoseconds, the structural conclusions differ drastically with longer trajectories, with CHARMM parameters being more consistent with experiments. However, as implied by the difference in substate distributions between MLT1 and MLT2, 17 μs might not be sufficient to observe the fully equilibrated partitioning process. The abrupt change in MLT2 might indicate that the helicity will increase to greater than experimentally observed in a sufficiently long simulation. On the other hand, it could be nothing more than a transient fluctuation. Increased sampling will provide further indicators of convergence of the helix partitioning process.