Accumulation of d-Glucose from Pentoses by Metabolically Engineered Escherichia coli

Escherichia coli that is unable to metabolize d-glucose (with knockouts in ptsG, manZ, and glk) accumulates a small amount of d-glucose (yield of about 0.01 g/g) during growth on the pentoses d-xylose or l-arabinose as a sole carbon source. Additional knockouts in the zwf and pfkA genes, encoding, respectively, d-glucose-6-phosphate 1-dehydrogenase and 6-phosphofructokinase I (E. coli MEC143), increased accumulation to greater than 1 g/liter d-glucose and 100 mg/liter d-mannose from 5 g/liter d-xylose or l-arabinose. Knockouts of other genes associated with interconversions of d-glucose-phosphates demonstrate that d-glucose is formed primarily by the dephosphorylation of d-glucose-6-phosphate. Under controlled batch conditions with 20 g/liter d-xylose, MEC143 generated 4.4 g/liter d-glucose and 0.6 g/liter d-mannose. The results establish a direct link between pentoses and hexoses and provide a novel strategy to increase carbon backbone length from five to six carbons by directing flux through the pentose phosphate pathway.     

Stable changes to calcium fluxes in mitochondria isolated from rat livers perfused with α-adrenergic agonists and with glucagon

1. Human uterine cervical stroma was found to contain a Ca²⁺-independent neutral proteinase against casein and N-benzoyl-dl-arginine p-nitroanilide (Bz-dl-Arg-Nan). This enzyme was tightly bound to an insoluble material (20000g pellet) and was solubilized by high concentrations of NaCl or KCl. High concentrations of them in the reaction system, however, inhibited reversibly the activity of this enzyme. 2. The neutral proteinase was partially purified by extraction with NaCl, gel filtration on Sephadex G-200 and affinity chromatography on casein–Sepharose. 3. The optimal pH of this partially purified enzyme was 7.4–8.0 against casein and Bz-dl-Arg-Nan. The molecular weight of the enzyme was found to be about 1.4×10⁵ by gel filtration on Sephadex G-200. 4. The enzyme was significantly inhibited by di-isopropyl phosphorofluoridate (0.1mm). High concentration of phenylmethanesulphonyl fluoride (5mm), 7-amino-1-chloro-3-l-tosylamidoheptan-2-one (0.5mm), antipain (10μm) or leupeptin (10μm) was also found to be inhibitory, but chymostatin (40μg/ml), soya-bean trypsin inhibitor (2.5mg/ml), human plasma (10%, v/v), p-chloromercuribenzoate (1mm), EDTA (10mm) and 1-chloro-4-phenyl-3-l-tosylamidobutan-2-one (1mm) had no effect on the enzyme. 5. The neutral proteinase hydrolysed casein, Bz-dl-Arg-Nan and heat-denatured collagen, but was inactive towards native collagen and several synthetic substrates, such as 4-phenylazobenzyloxycarbonyl-Pro-Leu-Gly-Pro-d-Arg, 3-carboxypropionyl-Ala-Ala-Ala p-nitroanilide and 2,4-dinitrophenyl-Pro-Gln-Gly-Ile-Ala-Gly-Gln-d-Arg, and also proteoglycan. The enzyme did not act as a plasminogen activator. 6. These properties suggested that a neutral proteinase in the human uterine cervix was different from enzymes previously reported.     

Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers

The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.PEDIGREE-BASED prediction of genetic values based on the additive infinitesimal model (Fisher 1918) has played a central role in genetic improvement of complex traits in plants and animals. Animal breeders have used this model for predicting breeding values either in a mixed model (best linear unbiased prediction, BLUP) (Henderson 1984) or in a Bayesian framework (Gianola and Fernando 1986). More recently, plant breeders have incorporated pedigree information into linear mixed models for predicting breeding values (Crossa et al. 2006, 2007; Oakey et al. 2006; Burgueño et al. 2007; Piepho et al. 2007).The availability of thousands of genome-wide molecular markers has made possible the use of genomic selection (GS) for prediction of genetic values (Meuwissen et al. 2001) in plants (e.g., Bernardo and Yu 2007; Piepho 2009; Jannink et al. 2010) and animals (Gonzalez-Recio et al. 2008; VanRaden et al. 2008; Hayes et al. 2009; de los Campos et al. 2009a). Implementing GS poses several statistical and computational challenges, such as how models can cope with the curse of dimensionality, colinearity between markers, or the complexity of quantitative traits. Parametric (e.g., Meuwissen et al. 2001) and semiparametric (e.g., Gianola et al. 2006; Gianola and van Kaam 2008) methods address these problems differently.In standard genetic models, phenotypic outcomes, , are viewed as the sum of a genetic value, , and a model residual, ; that is, . In parametric models for GS, is described as a regression on marker covariates (j = 1,  …  , p molecular markers) of the form , such that(or , in matrix notation), where is the regression of on the jth marker covariate .Estimation of via multiple regression by ordinary least squares (OLS) is not feasible when p > n. A commonly used alternative is to estimate marker effects jointly using penalized methods such as ridge regression (Hoerl and Kennard 1970) or the Least Absolute Shrinkage and Selection Operator (LASSO) (Tibshirani 1996) or their Bayesian counterpart. This approach yields greater accuracy of estimated genetic values and can be coupled with geostatistical techniques commonly used in plant breeding to model multienvironments trials (Piepho 2009).In ridge regression (or its Bayesian counterpart) the extent of shrinkage is homogeneous across markers, which may not be appropriate if some markers are located in regions that are not associated with genetic variance, while markers in other regions may be linked to QTL (Goddard and Hayes 2007). To overcome this limitation, many authors have proposed methods that use marker-specific shrinkage. In a Bayesian setting, this can be implemented using priors of marker effects that are mixtures of scaled-normal densities. Examples of this are methods Bayes A and Bayes B of Meuwissen et al. (2001) and the Bayesian LASSO of Park and Casella (2008).An alternative to parametric regressions is to use semiparametric methods such as reproducing kernel Hilbert spaces (RKHS) regression (Gianola and van Kaam 2008). The Bayesian RKHS regression regards genetic values as random variables coming from a Gaussian process centered at zero and with a (co)variance structure that is proportional to a kernel matrix K (de los Campos et al. 2009b); that is, , where , are vectors of marker genotypes for the ith and jth individuals, respectively, and is a positive definite function evaluated in marker genotypes. In a finite-dimensional setting this amounts to modeling the vector of genetic values, , as multivariate normal; that is, where is a variance parameter. One of the most attractive features of RKHS regression is that the methodology can be used with almost any information set (e.g., covariates, strings, images, graphs). A second advantage is that with RKHS the model is represented in terms of n unknowns, which gives RKHS a great computational advantage relative to some parametric methods, especially when p ≫ n.This study presents an evaluation of several methods for GS, using two extensive data sets. One contains phenotypic records of a series of wheat trials and recently generated genomic data. The other data set pertains to international maize trials in which different traits were measured in maize lines evaluated under severe drought and well-watered conditions.     

Recharacterization of the mammalian cytosolic type 2 (R)-β-hydroxybutyrate dehydrogenase as 4-oxo-l-proline reductase (EC 1.1.1.104)

Early studies revealed that chicken embryos incubated with a rare analog of l-proline, 4-oxo-l-proline, showed increased levels of the metabolite 4-hydroxy-l-proline. In 1962, 4-oxo-l-proline reductase, an enzyme responsible for the reduction of 4-oxo-l-proline, was partially purified from rabbit kidneys and characterized biochemically. However, only recently was the molecular identity of this enzyme solved. Here, we report the purification from rat kidneys, identification, and biochemical characterization of 4-oxo-l-proline reductase. Following mass spectrometry analysis of the purified protein preparation, the previously annotated mammalian cytosolic type 2 (R)-β-hydroxybutyrate dehydrogenase (BDH2) emerged as the only candidate for the reductase. We subsequently expressed rat and human BDH2 in Escherichia coli, then purified it, and showed that it catalyzed the reversible reduction of 4-oxo-l-proline to cis-4-hydroxy-l-proline via chromatographic and tandem mass spectrometry analysis. Specificity studies with an array of compounds carried out on both enzymes showed that 4-oxo-l-proline was the best substrate, and the human enzyme acted with 12,500-fold higher catalytic efficiency on 4-oxo-l-proline than on (R)-β-hydroxybutyrate. In addition, human embryonic kidney 293T (HEK293T) cells efficiently metabolized 4-oxo-l-proline to cis-4-hydroxy-l-proline, whereas HEK293T BDH2 KO cells were incapable of producing cis-4-hydroxy-l-proline. Both WT and KO HEK293T cells also produced trans-4-hydroxy-l-proline in the presence of 4-oxo-l-proline, suggesting that the latter compound might interfere with the trans-4-hydroxy-l-proline breakdown in human cells. We conclude that BDH2 is a mammalian 4-oxo-l-proline reductase that converts 4-oxo-l-proline to cis-4-hydroxy-l-proline and not to trans-4-hydroxy-l-proline, as originally thought. We also hypothesize that this enzyme may be a potential source of cis-4-hydroxy-l-proline in mammalian tissues.     

Multiple Sclerosis—Current Etiological Concepts

An animal model for acute multiple sclerosis (ms) is experimental allergic encephalomyelitis (eae). eae is produced by intradermal injection of a protein component of central nervous system (cns) myelin. Ultrastructural studies of eae and of a peripheral nerve analog, experimental allergic neuritis (ean), have revealed an orderly sequence of cellular events leading to the destruction and removal of myelin with sparing of axons (primary demyelination). Acute ms has not been studied electron microscopically, but the ultrastructural similarities between ean and a case of acute Landry-Guillain-Barré syndrome, a primary demyelinating disease of the peripheral nervous system, suggest that a similar sequence of events might be found in acute ms. While the pathological findings support a cellmediated or delayed hypersensitivity response, there is also evidence for the pathogenetic role of circulating antibodies. Among such evidence is included the finding that sera from animals with eae and humans with acute ms rapidly produce a reversible block of complex (polysynaptic) electrical activity when applied to cns tissue cultures, which suggests a possible mechanism for transient symptoms in ms. Epidemiological and other studies link ms with a viral cause, although no direct evidence that ms is caused by a virus exists. Viral and immunological mechanisms are not mutually exclusive in considering pathogenetic possibilities for ms, for it can be postulated that a viral infection of the central nervous system acts as a triggering agent for a series of immune responses, including production of a bioelectric blocking antibody and demyelination mediated by sensitized cells, the combination of which ultimately produces the total clinical picture of ms.     

Deficiency in l-Serine Deaminase Interferes with One-Carbon Metabolism and Cell Wall Synthesis in Escherichia coli K-12

Escherichia coli K-12 provided with glucose and a mixture of amino acids depletes l-serine more quickly than any other amino acid even in the presence of ammonium sulfate. A mutant without three 4Fe4S l-serine deaminases (SdaA, SdaB, and TdcG) of E. coli K-12 is unable to do this. The high level of l-serine that accumulates when such a mutant is exposed to amino acid mixtures starves the cells for C₁ units and interferes with cell wall synthesis. We suggest that at high concentrations, l-serine decreases synthesis of UDP-N-acetylmuramate-l-alanine by the murC-encoded ligase, weakening the cell wall and producing misshapen cells and lysis. The inhibition by high l-serine is overcome in several ways: by a large concentration of l-alanine, by overproducing MurC together with a low concentration of l-alanine, and by overproducing FtsW, thus promoting septal assembly and also by overexpression of the glycine cleavage operon. S-Adenosylmethionine reduces lysis and allows an extensive increase in biomass without improving cell division. This suggests that E. coli has a metabolic trigger for cell division. Without that reaction, if no other inhibition occurs, other metabolic functions can continue and cells can elongate and replicate their DNA, reaching at least 180 times their usual length, but cannot divide.The Escherichia coli genome contains three genes, sdaA, sdaB, and tdcG, specifying three very similar 4Fe4S l-serine deaminases. These enzymes are very specific for l-serine for which they have unusually high K_m values (3, 32). Expression of the three genes is regulated so that at least one of the gene products is synthesized under all common growth conditions (25). This suggests an important physiological role for the enzymes. However, why E. coli needs to deaminate l-serine has been a long-standing problem of E. coli physiology, the more so since it cannot use l-serine as the sole carbon source.We showed recently that an E. coli strain devoid of all three l-serine deaminases (l-SDs) loses control over its size, shape, and cell division when faced with complex amino acid mixtures containing l-serine (32). We attributed this to starvation for single-carbon (C₁) units and/or S-adenosylmethionine (SAM). C₁ units are usually made from serine via serine hydroxymethyl transferase (GlyA) or via glycine cleavage (GCV). The l-SD-deficient triple mutant strain is starved for C₁ in the presence of amino acids, because externally provided glycine inhibits GlyA and a very high internal l-serine concentration along with several other amino acids inhibits glycine cleavage. While the parent cell can defend itself by reducing the l-serine level by deamination, this crucial reaction is missing in the ΔsdaA ΔsdaB ΔtdcG triple mutant. We therefore consider these to be “defensive” serine deaminases.The fact that an inability to deaminate l-serine leads to a high concentration of l-serine and inhibition of GlyA is not surprising. However, it is not obvious why a high level of l-serine inhibits cell division and causes swelling, lysis, and filamentation. Serine toxicity due to inhibition of biosynthesis of isoleucine (11) and aromatic amino acids (21) has been reported but is not relevant here, since these amino acids are provided in Casamino Acids.We show here that at high internal concentrations, l-serine also causes problems with peptidoglycan synthesis, thus weakening the cell wall. Peptidoglycan is a polymer of long glycan chains made up of alternating N-acetylglucosamine and N-acetylmuramic acid residues, cross-linked by l-alanyl-γ-d-glutamyl-meso-diaminopimelyl-d-alanine tetrapeptides (1, 28). The glucosamine and muramate residues and the pentapeptide (from which the tetrapeptide is derived) are all synthesized in the cytoplasm and then are exported to be polymerized into extracellular peptidoglycan (2).In this paper, we show that lysis is caused by l-serine interfering with the first step of synthesis of the cross-linking peptide, the addition of l-alanine to uridine diphosphate-N-acetylmuramate. This interference is probably due to a competition between serine and l-alanine for the ligase, MurC, which adds the first l-alanine to UDP-N-acetylmuramate (7, 10, 15). As described here, the weakening of the cell wall by l-serine can be overcome by a variety of methods that reduce the endogenous l-serine pool or counteract the effects of high levels of l-serine.     

Engineering Filamentous Fungi for Conversion of d-Galacturonic Acid to l-Galactonic Acid

d-Galacturonic acid, the main monomer of pectin, is an attractive substrate for bioconversions, since pectin-rich biomass is abundantly available and pectin is easily hydrolyzed. l-Galactonic acid is an intermediate in the eukaryotic pathway for d-galacturonic acid catabolism, but extracellular accumulation of l-galactonic acid has not been reported. By deleting the gene encoding l-galactonic acid dehydratase (lgd1 or gaaB) in two filamentous fungi, strains were obtained that converted d-galacturonic acid to l-galactonic acid. Both Trichoderma reesei Δlgd1 and Aspergillus niger ΔgaaB strains produced l-galactonate at yields of 0.6 to 0.9 g per g of substrate consumed. Although T. reesei Δlgd1 could produce l-galactonate at pH 5.5, a lower pH was necessary for A. niger ΔgaaB. Provision of a cosubstrate improved the production rate and titer in both strains. Intracellular accumulation of l-galactonate (40 to 70 mg g biomass⁻¹) suggested that export may be limiting. Deletion of the l-galactonate dehydratase from A. niger was found to delay induction of d-galacturonate reductase and overexpression of the reductase improved initial production rates. Deletion of the l-galactonate dehydratase from A. niger also delayed or prevented induction of the putative d-galacturonate transporter An14g04280. In addition, A. niger ΔgaaB produced l-galactonate from polygalacturonate as efficiently as from the monomer.     

Increase in Quantitative Variation After Exposure to Environmental Stresses and/or Introduction of a Major Mutation: G × E Interaction and Epistasis or Canalization?

Why does phenotypic variation increase upon exposure of the population to environmental stresses or introduction of a major mutation? It has usually been interpreted as evidence of canalization (or robustness) of the wild-type genotype; but an alternative population genetic theory has been suggested by J. Hermisson and G. Wagner: “the release of hidden genetic variation is a generic property of models with epistasis or genotype–environment interaction.” In this note we expand their model to include a pleiotropic fitness effect and a direct effect on residual variance of mutant alleles. We show that both the genetic and environmental variances increase after the genetic or environmental change, but these increases could be very limited if there is strong pleiotropic selection. On the basis of more realistic selection models, our analysis lends further support to the genetic theory of Hermisson and Wagner as an interpretation of hidden variance.A common experimental observation in quantitative genetics is a higher phenotypic variance for quantitative traits in populations that carry a major mutation or are exposed to environmental stresses (e.g., heat shock) (Scharloo 1991; for a recent review see Gibson and Dworkin 2004). Part of the added variance must be genetic because the population responds to artificial selection. The lower variability of the wild type than that of the mutants has been interpreted as evidence for robustness or canalization (Waddington 1957): that is, under the new condition the magnitudes of gene effects across all trait loci increase relative to the original condition. The importance of canalization has been recognized for a long time and has been the subject of renewed interest recently (see de Visser et al. 2003 and Hansen 2006 for reviews).An alternative population genetic theory has been proposed by Hermisson and Wagner (2004), who suggest that the increase in genetic variance V_G after the change in environmental conditions or genetic background is a generic property of the population, with no need to introduce canalization (Waddington 1957). The theory appears simple. Under mutation–selection balance (MSB), the mutant alleles are at a selective disadvantage and there is a negative correlation between frequencies and effects of mutations: mutant alleles of small effects on the trait segregate at intermediate frequencies. After the change in genetic or environmental background, gene effects consequently change due to G × E interaction or epistasis, which reduces the negative correlation because genes that were previously of small effects and at intermediate frequencies may now have large effects. That is, the frequencies of alleles are determined by the previous MSB, while their new effects are at least partly determined by the new conditions. The genetic variance will therefore increase.Hermisson and Wagner (2004) found that the predicted increase in genetic variance can be substantial; however, the predicted increase is highly sensitive to the population size and can increase without bound with increasing population size (see their Figure 2 and Equation 16). Genetic variance would enlarge with the population size within a small population (Lynch and Hill 1986; Weber and Diggins 1990), but becomes insensitive to the population size within large populations (Falconer and Mackay 1996, Chap. 20). Hence the unbounded increase under the novel environmental condition appears to us as a downside of their theory, even though the predicted increase can be reduced if the changed environmental condition is not novel but there is previous adaptation to it (see their Figure 3).Open in a separate windowFigure 2.—Influence of the pleiotropic effect (s_p) on the increase of genetic variance Δ_G in units of the interaction parameter ξ for a “typical” situation with strength of stabilizing selection ω² = 0.1μ², mutation rate λ = 0.1 per haploid genome per generation, and population size N_e = 10⁶. The allelic pleiotropic effect on fitness and its variance effect on the trait independently follow gamma distributions with shape parameters β_s and β_v, respectively. The mean of a² across loci is E(v) = E(a²) = 10⁻⁴μ².Open in a separate windowOpen in a separate windowFigure 3.—Influence of shapes of distributions of mutational effects on (a) the variances at mutation–selection balance and (b) their increases after the genetic or environmental change. The squares represent the genetic variance and its increase and the triangles the environmental variance and its increase. The mutation rate is λ= 0.1 per haploid genome per generation, the population size is N_e = 10⁹, and the strength of real stabilizing selection is ω² = 0.1μ². Allelic effects on trait value (a), fitness (s), and residual variance (b) are assumed to be independently distributed such that v = a² follows a gamma () distribution with mean 10⁻⁴μ², s follows gamma (β_s) with mean s_p = 0.05, and b follows gamma (β_b) with mean 10⁻⁴μ².The basic model that Hermisson and Wagner (2004) employed is that the quantitative trait is under real stabilizing selection and mutant alleles have effects on the focal trait only by changing its so-called locus genetic variance. At the mutation–real stabilizing selection balance, some mutants can segregate at intermediate frequencies because of their small effects and therefore weak selection; and there are more such mutants the more strongly leptokurtic is the distribution of effects at individual loci. The unbounded increase of Hermisson and Wagner (2004) results from such a gene-frequency distribution; but it has been shown (see Barton and Turelli 1989; Falconer and Mackay 1996; Lynch and Walsh 1998) that solely stabilizing selection, whether modeled with a Gaussian (Kimura 1965) or a house of-cards approximation (Turelli 1984) or even the generalized form of Hermisson and Wagner (2004) (i.e., their Equation 14), cannot provide a satisfactory explanation for the high levels of genetic variance observed in natural populations under realistic values of mutation and selection parameters.A common observation is that one trait is controlled by many genes and one gene can influence many traits; i.e., pleiotropy is ubiquitous (Barton and Turelli 1989; Barton and Keightley 2002; Mackay 2004; Ostrowski et al. 2005). Recent detailed studies suggest that pleiotropy calculated as the number of phenotypic traits affected varies considerably among quantitative trait loci (QTL) (Cooper et al. 2007; Albert et al. 2008; Kenney-Hunt et al. 2008; Wagner et al. 2008). Such pleiotropic effects must influence the magnitude of the variance. Though some genes have little effect on the focal trait, they almost certainly affect other traits and therefore are not neutral. The inclusion of pleiotropic effects on fitness strengthens the overall selection on mutant alleles and, assuming such pleiotropic effects are mainly deleterious, maintains them at low frequencies. The genetic variance for a trait is therefore likely to be maintained at lower levels than that under only real stabilizing selection on the trait alone (Tanaka 1996). Although the gene-frequency distribution is much more extreme under this joint model, the relevant rate of mutation is genomewide and hence is much larger than that where mutation affects only the focal trait as is assumed in the real stabilizing selection model (Turelli 1984; Falconer and Mackay 1996). Taking into account empirical knowledge of mutation parameters, a combination of both pleiotropic and real stabilizing selection appears to be a plausible mechanism for the maintenance of quantitative genetic variance (Zhang et al. 2004). If pleiotropic selection is much stronger than real stabilizing selection, the association between frequency and effect of mutant alleles is weaker than that for a real stabilizing selection model. Further, if overall selection is stronger than recurrent mutation, the frequency distribution of mutant alleles will be extreme. Under those situations, the increase of genetic variance after the genetic or environmental change will be kept at lower levels than that of Hermisson and Wagner (2004), and hence the unbounded increase could be avoided.Further, Hermisson and Wagner (2004) assume that the environmental variance is not under genetic control (i.e., the variance of phenotypic value given genotypic value is the same for all genotypes) and therefore is not subject to change. This assumption conflicts with the increasingly accumulating empirical data that indicate otherwise (Zhang and Hill 2005; Mulder et al. 2007 for reviews). Direct experimental evidence is available that mutation can directly affect environmental variance, V_E (Whitlock and Fowler 1999; Mackay and Lyman 2005), and Baer (2008) provides what is perhaps the first clear demonstration that mutations increase environmental variances, on the basis of data for body size and productivity of Caenorhabditis elegans, and finds that the magnitudes of the increases are of the same order as those in the genetic variance.As real stabilizing selection on phenotype favors genotypes possessing low V_E (Gavrilets and Hastings 1994; Zhang and Hill 2005), a mutant that contributes little to V_E is more favored by stabilizing selection than one that contributes a lot. With all else being the same, mutants with small effect on V_E thus segregate at relatively high frequencies at MSB. That is, there is a negative correlation between the effect on V_E and the frequency of mutant genes. After the genetic or environmental change, some mutants that were previously of small effects on V_E have large effects due to G × E interaction or epistasis while their frequencies remain roughly the same as in the previous MSB. This certainly increases environmental variance.In this note, we first assume that mutant alleles can affect only the mean value of a focal quantitative trait and otherwise affect fitness through their pleiotropic effects (Zhang et al. 2004) and try to answer the following questions: How will the conclusion of Hermisson and Wagner (2004) be affected by taking into account the pleiotropic effect of mutants? Can the “unbounded increase” be avoided? We then further assume that mutant alleles can also directly affect the environmental variance of the focal trait (Zhang and Hill 2008) and investigate how both V_G and V_E change following the genetic or environmental change in the population.     

High-Level Heterologous Production of d-Cycloserine by Escherichia coli

Previously, we successfully cloned a d-cycloserine (d-CS) biosynthetic gene cluster consisting of 10 open reading frames (designated dcsA to dcsJ) from d-CS-producing Streptomyces lavendulae ATCC 11924. In this study, we put four d-CS biosynthetic genes (dcsC, dcsD, dcsE, and dcsG) in tandem under the control of the T7 promoter in an Escherichia coli host. SDS-PAGE analysis demonstrated that the 4 gene products were simultaneously expressed in host cells. When l-serine and hydroxyurea (HU), the precursors of d-CS, were incubated together with the E. coli resting cell suspension, the cells produced significant amounts of d-CS (350 ± 20 μM). To increase the productivity of d-CS, the dcsJ gene, which might be responsible for the d-CS excretion, was connected downstream of the four genes. The E. coli resting cells harboring the five genes produced d-CS at 660 ± 31 μM. The dcsD gene product, DcsD, forms O-ureido-l-serine from O-acetyl-l-serine (OAS) and HU, which are intermediates in d-CS biosynthesis. DcsD also catalyzes the formation of l-cysteine from OAS and H₂S. To repress the side catalytic activity of DcsD, the E. coli chromosomal cysJ and cysK genes, encoding the sulfite reductase α subunit and OAS sulfhydrylase, respectively, were disrupted. When resting cells of the double-knockout mutant harboring the four d-CS biosynthetic genes, together with dcsJ, were incubated with l-serine and HU, the d-CS production was 980 ± 57 μM, which is comparable to that of d-CS-producing S. lavendulae ATCC 11924 (930 ± 36 μM).     

Glutamate, glutamine, aspartate, asparagine, glucose and ketone-body metabolism in chick intestinal brush-border cells

1. Suspensions of isolated chick jejunal columnar absorptive (brush-border) cells respired on endogenous substrates at a rate 40% higher than that shown by rat brush-border cells. 2. Added d-glucose (5 or 10mm), l-glutamine (2.5mm) and l-glutamate (2.5mm) were the only individual substrates which stimulated respiration by chick cells; l-aspartate (2.5 or 6.7mm), glutamate (6.7mm), glutamine (6.7mm), l-alanine (1 or 10mm), pyruvate (1 or 2mm), l-lactate (5 or 10mm), butyrate (10mm) and oleate (1mm) did not stimulate chick cell respiration; l-asparagine (6.7mm) inhibited slightly; glucose (5mm) stimulated more than did 10mm-glucose. 3. Acetoacetate (10mm) and d-3-hydroxybutyrate (10mm) were rapidly consumed but, in contrast to rat brush-border cells, did not stimulate respiration. 4. Glucose (10mm) was consumed more slowly than 5mm-glucose; the dominant product of glucose metabolism during vigorous respiration was lactate; the proportion of glucose converted to lactate was greater with 10mm- than with 5mm-glucose. 5. Glutamate and aspartate consumption rates decreased, and alanine and glutamine consumption rates increased when their initial concentrations were raised from 2.5 to 6.7 or 10mm. 6. The metabolic fate of glucose was little affected by concomitant metabolism of any one of aspartate, glutamate or glutamine except for an increased production of alanine; the glucose-stimulated respiration rate was unaffected by concomitant metabolism of these individual amino acids. 7. Chick cells produced very little alanine from aspartate and, in contrast to rat cells, likewise produced very little alanine from glutamate or glutamine; in chick cells alanine appeared to be predominantly a product of transmination of pyruvate derived from glucose metabolism. 8. In chick cells, glutamate and glutamine were formed from aspartate (2.5 or 6.7mm); aspartate and glutamine were formed from glutamate (2.5mm) but only aspartate from 6.7mm-glutamate; glutamate was the dominant product formed from glutamine (6.7mm) but aspartate only was formed from 2.5mm-glutamine. 9. Chick brush-border cells can thus both catabolize and synthesize glutamine; glutamine synthesis is always diminished by concomitant metabolism of glucose, presumably by allosteric inhibition of glutamine synthetase by alanine. 10. Proline was formed from glutamine (2.5mm) but not from glutamine (2.5mm)+glucose (5mm) and not from 2.5mm-glutamate; ornithine was formed from glutamine (2.5mm)+glucose (5.0mm) but not from glutamine alone; serine was formed from glutamine (2.5mm)+glucose (5mm) and from these two substrates plus aspartate (2.5mm). 11. Total intracellular adenine nucleotides (22μmol/g dry wt.) remained unchanged during incubation of chick cells with glucose. 12. Intracellular glutathione (0.7–0.8mm) was depleted by 40% during incubation of respiring chick cells without added substrates for 75min at 37°C; partial restoration of the lost glutathione was achieved by incubating cells with l-glutamate+l-cysteine+glycine.     

Identity-by-Descent Matrix Decomposition Using Latent Ancestral Allele Models

Genetic linkage and association studies are empowered by proper modeling of relatedness among individuals. Such relatedness can be inferred from marker and/or pedigree information. In this study, the genetic relatedness among n inbred individuals at a particular locus is expressed as an n × n square matrix Q. The elements of Q are identity-by-descent probabilities, that is, probabilities that two individuals share an allele descended from a common ancestor. In this representation the definition of the ancestral alleles and their number remains implicit. For human inspection and further analysis, an explicit representation in terms of the ancestral allele origin and the number of alleles is desirable. To this purpose, we decompose the matrix Q by a latent class model with K classes (latent ancestral alleles). Let P be an n × K matrix with assignment probabilities of n individuals to K classes constrained such that every element is nonnegative and each row sums to 1. The problem then amounts to approximating Q by PP^T, while disregarding the diagonal elements. This is not an eigenvalue problem because of the constraints on P. An efficient algorithm for calculating P is provided. We indicate the potential utility of the latent ancestral allele model. For representative locus-specific Q matrices constructed for a set of maize inbreds, the proposed model recovered the known ancestry.HIGH-THROUGHPUT techniques allow extensive genotyping of individuals for thousands of SNP markers (Gibbs et al. 2003) and thereby provide accurate information about the genetic diversity within a population at many chromosomal loci. If two individuals within this population carry the same DNA sequence at a locus, and this sequence can be traced to the same common ancestor, the individuals are said to be identical by descent (IBD) for this segment (Chapman and Thompson 2003). Quite often, however, the ancestral source of a chromosomal segment is ambiguous and thus IBD relationships between haplotypes are given as probabilities. Various methods have been described to estimate the IBD probability of pairs of chromosomal segments (Meuwissen and Goddard 2001; Leutenegger et al. 2003). When pedigree relationships are known, these can be included to estimate IBD probabilities (Wang et al. 1995; Heath 1997; George et al. 2000; Meuwissen and Goddard 2000; Besnier and Carlborg 2007).In quantitative genetic analysis we seek to find and characterize associations between the large number of SNPs that are now available for many organisms and phenotypic variation for traits of interest (e.g., grain yield and time to flowering). Many current methods developed for this purpose make use of IBD information. For example, a locus-specific matrix of IBD probabilities can be incorporated into restricted maximum-likelihood (REML) procedures for fine mapping quantitative trait loci (Bink and Meuwissen 2004) as well as for marker-based genetic evaluation (Fernando and Grossman 1989) using mixed models. The IBD matrix takes the role of a covariance matrix in the REML procedure.Other approaches, however, require that chromosome segments (also referred to here as haplotypes or alleles) are assigned to independent ancestors. These approaches include regression approaches with genetic predictors (Malosetti et al. 2006) and Bayesian oligo-allelic approaches that sample the ancestral origin of each chromosomal segment (Heath 1997; Uimari and Sillanpaa 2001; Bink et al. 2008a). In the IBD matrix representation the ancestral alleles and their number remain implicit. For these approaches, the locus-specific matrix of IBD probabilities must therefore be decomposed into a matrix that links the chromosomal segments to independent ancestral alleles. This decomposition is addressed in this article.The individuals that we consider in this article are inbred. For n inbred individuals the IBD matrix at a given chromosomal position is thus n × n, because there is no need to distinguish between identical chromosomes. In diploid, outbred populations, each individual would be represented by two haplotypes (alleles) and the matrix would be 2n × 2n (Fernando and Grossman 1989). This is feasible if any phase ambiguity can be resolved. From now on, the term “individual” thus means chromosomal segment or haplotype. Analogously, ancestor will be shorthand for ancestral allele (ancestral haplotype).We propose two models of IBD matrix decomposition, a simple threshold model (TIBD) and a more sophisticated latent ancestral allele model (LAAM), that provide (1) an estimate of the number of independent ancestral alleles, (2) a concise, easy-to-interpret, summary of the relatedness, (3) an explicit (probabilistic) representation of the descent of alleles, and (4) the ability to sample alleles for each individual from a set of ancestral alleles in such a way that the probability that a pair of individuals shares the same allele corresponds to their IBD probability.The last two features of the model are essential for its use in Bayesian oligo-allelic approaches to quantitative trait locus (QTL) analysis (Uimari and Sillanpaa 2001; Bink et al. 2008a).     

The Elucidation of the Structure of Thermotoga maritima Peptidoglycan Reveals Two Novel Types of Cross-link

Thermotoga maritima is a Gram-negative, hyperthermophilic bacterium whose peptidoglycan contains comparable amounts of l- and d-lysine. We have determined the fine structure of this cell-wall polymer. The muropeptides resulting from the digestion of peptidoglycan by mutanolysin were separated by high-performance liquid chromatography and identified by amino acid analysis after acid hydrolysis, dinitrophenylation, enzymatic determination of the configuration of the chiral amino acids, and mass spectrometry. The high-performance liquid chromatography profile contained four main peaks, two monomers, and two dimers, plus a few minor peaks corresponding to anhydro forms. The first monomer was the d-lysine-containing disaccharide-tripeptide in which the d-Glu-d-Lys bond had the unusual γ→ϵ arrangement (GlcNAc-MurNAc-l-Ala-γ-d-Glu-ϵ-d-Lys). The second monomer was the conventional disaccharide-tetrapeptide (GlcNAc-MurNAc-l-Ala-γ-d-Glu-l-Lys-d-Ala). The first dimer contained a disaccharide-l-Ala as the acyl donor cross-linked to the α-amine of d-Lys in a tripeptide acceptor stem with the sequence of the first monomer. In the second dimer, donor and acceptor stems with the sequences of the second and first monomers, respectively, were connected by a d-Ala⁴-α-d-Lys³ cross-link. The cross-linking index was 10 with an average chain length of 30 disaccharide units. The structure of the peptidoglycan of T. maritima revealed for the first time the key role of d-Lys in peptidoglycan synthesis, both as a surrogate of l-Lys or meso-diaminopimelic acid at the third position of peptide stems and in the formation of novel cross-links of the l-Ala¹(α→α)d-Lys³ and d-Ala⁴(α→α)d-Lys³ types.Peptidoglycan (or murein) is a giant macromolecule whose main function is the protection of the cytoplasmic membrane against the internal osmotic pressure. It is composed of alternating residues of N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc)² cross-linked by short peptides (1). The composition of the peptide stem in nascent peptidoglycan is l-Ala¹-γ-d-Glu²-X³-d-Ala⁴-d-Ala⁵, where X is most often meso-diaminopimelic acid (meso-A₂pm) or l-lysine in Gram-negative and Gram-positive species, respectively (2, 3). In the mature macromolecule, the last d-Ala residue is removed. Cross-linking of the glycan chains generally occurs between the carboxyl group of d-Ala at position 4 of a donor peptide stem and the side-chain amino group of the diamino acid at position 3 of an acceptor peptide stem (4→3 cross-links). Cross-linking is either direct or through a short peptide bridge such as pentaglycine in Staphylococcus aureus (2, 3). The enzymes for the formation of the 4→3 cross-links are active-site serine dd- transpeptidases that belong to the penicillin-binding protein (PBP) family and are the essential targets of β-lactam antibiotics in pathogenic bacteria (4). Catalysis involves the cleavage of the d-Ala⁴-d-Ala⁵ bond of a donor peptide stem and the formation of an amide bond between the carboxyl of d-Ala⁴ and the side chain amine at the third position of an acceptor stem. Transpeptidases of the ld specificity are active-site cysteine enzymes that were shown to act as surrogates of the PBPs in mutants of Enterococcus faecium resistant to β-lactam antibiotics (5). They cleave the X³-d-Ala⁴ bond of a donor stem peptide to form 3→3 cross-links. This alternate mode of cross-linking is usually marginal, although it has recently been shown to predominate in non-replicative “dormant” forms of Mycobacterium tuberculosis (6).Thermotoga maritima is a Gram-negative, extremely thermophilic bacterium isolated from geothermally heated sea floors by Huber et al. (7). A morphological characteristic is the presence of an outer sheath-like envelope called “toga.” Although the organism has received considerable attention for its biotechnological potential, studies about its peptidoglycan are scarce (8–11), and in particular the fine structure of the macromolecule is still unknown. In their initial work, Huber et al. (7) showed that the composition of its peptidoglycan was unusual for a Gram-negative species, because it contained both isomers of lysine and no A₂pm. Recently, we purified and studied the properties of T. maritima MurE (12); this enzyme is responsible for the addition of the amino acid residue at position 3 of the peptide stem (13, 14). We demonstrated that T. maritima MurE added in vitro l- and d-Lys to UDP-MurNAc-l-Ala-d-Glu. Although l-Lys was added in the usual way, yielding the conventional nucleotide UDP-MurNAc-l-Ala-γ-d-Glu-l-Lys containing a d-Glu(γ→α)l-Lys amide bond, the d-isomer was added in an “upside-down” manner, yielding the novel nucleotide UDP-MurNAc-l-Ala-d-Glu(γ→ϵ)d-Lys. We also showed that the d-Lys-containing nucleotide was not a substrate for T. maritima MurF, the subsequent enzyme in the biosynthetic pathway, whereas this ligase catalyzed the addition of dipeptide d-Ala-d-Ala to the l-Lys-containing tripeptide, yielding the conventional UDP-MurNAc-pentapeptide (12).However, both the l-Lys-containing UDP-MurNAc-pentapeptide and d-Lys-containing UDP-MurNAc-tripeptide were used as substrates by T. maritima MraY with comparable efficiencies in vitro (12). This observation implies that the unusual d-Lys-containing peptide stems are likely to be translocated to the periplasmic face of the cytoplasmic membrane and to participate in peptidoglycan polymerization. Therefore, we have determined here the fine structure of T. maritima peptidoglycan and we have shown that l-Lys- and d-Lys-containing peptide stems are both present in the polymer, the latter being involved in the formation of two novel types of peptidoglycan cross-link.     

Estimating Breeding Values With Molecular Relatedness and Reconstructed Pedigrees in Natural Mating Populations of Common Sole,Solea Solea

Captive populations where natural mating in groups is used to obtain offspring typically yield unbalanced population structures with highly skewed parental contributions and unknown pedigrees. Consequently, for genetic parameter estimation, relationships need to be reconstructed or estimated using DNA marker data. With missing parents and natural mating groups, commonly used pedigree reconstruction methods are not accurate and lead to loss of data. Relatedness estimators, however, infer relationships between all animals sampled. In this study, we compared a pedigree relatedness method and a relatedness estimator (“molecular relatedness”) method using accuracy of estimated breeding values. A commercial data set of common sole, Solea solea, with 51 parents and 1953 offspring (“full data set”) was used. Due to missing parents, for 1338 offspring, a pedigree could be reconstructed with 10 microsatellite markers (“reduced data set”). Cross-validation of both methods using the reduced data set showed an accuracy of estimated breeding values of 0.54 with pedigree reconstruction and 0.55 with molecular relatedness. Accuracy of estimated breeding values increased to 0.60 when applying molecular relatedness to the full data set. Our results indicate that pedigree reconstruction and molecular relatedness predict breeding values equally well in a population with skewed contributions to families. This is probably due to the presence of few large full-sib families. However, unlike methods with pedigree reconstruction, molecular relatedness methods ensure availability of all genotyped selection candidates, which results in higher accuracy of breeding value estimation.To estimate genetic parameters, additive genetic relationships between individuals are inferred from known pedigrees (Falconer and Mackay 1996; Lynch and Walsh 1997). However, in natural populations (Ritland 2000; Thomas et al. 2002) and in captive species where natural mating in groups is used to obtain offspring (Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009) pedigrees are reconstructed. In these populations there is no control on mating structure, and typically unbalanced population structures with highly skewed parental contributions are obtained (Bekkevold et al. 2002; Brown et al. 2005; Fessehaye et al. 2006; Blonk et al. 2009). To reconstruct pedigrees, parental allocation methods are often used (Marshall et al. 1998; Avise et al. 2002; Duchesne et al. 2002). These methods require that all parents be known. For situations where parental information is not available, numerous DNA-marker-based methods for estimating molecular relatedness have been developed (Lynch 1988; Queller and Goodnight 1989; Ritland 2000; Toro et al. 2002). These relatedness estimators determine relationship values between individuals on a continuous scale. Evaluation of relatedness estimators within real and simulated data in both plants and animals (e.g., see Van de Casteele et al. 2001 ; Milligan 2003; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008) has generally focused on bias and sampling error of estimated genetic variances or relatedness values. Relatively little attention has been paid to their efficiency for estimation of breeding values.Two types of relatedness estimators are currently available: method-of-moments estimators and maximum-likelihood estimators. Method-of-moments estimators (e.g., Queller and Goodnight 1989; Li et al. 1993; Ritland 1996; Lynch and Ritland 1999; Toro et al. 2002) determine relationships while calculating sharing of alleles between pairs in different ways. A variant of method-of-moments estimators is the transformation of continuous relatedness values to categorical genealogical relationships using “explicit pedigree reconstruction” (Fernández and Toro 2006) or thresholds (Rodríguez-Ramilo et al. 2007). However, correlations of transformed coancestries with known genealogical coancestries are low (Rodríguez-Ramilo et al. 2007). Several studies have compared different method-of-moments estimators but none revealed one single best estimator (Van de Casteele et al. 2001; Oliehoek et al. 2006; Rodríguez-Ramilo et al. 2007; Bink et al. 2008).Maximum-likelihood (ML) approaches classify animals into a limited number of relationship classes (Mousseau et al. 1998; Thomas et al. 2002; Wang 2004; Herbinger et al. 2006; Anderson and Weir 2007). For each pair a likelihood to fall into a possible relatedness class (e.g., full sib vs. unrelated) is calculated given its genotype and phenotype. ML techniques combined with a Markov chain Monte Carlo approach reconstruct groups with specific relationships jointly and are therefore more efficient than other ML approaches. To minimize standard errors, all discussed ML methods require balanced population structures, large sibling groups, and a large variance of relatedness (Thomas et al. 2002; Wang 2004; Anderson and Weir 2007). Therefore, these methods may not be suitable for natural mating systems.Unlike parental allocation methods, a benefit from relatedness estimators is that essentially all selection candidates are maintained for breeding value estimation, even with missing parents. The question is, however, whether such relatedness estimators also give accurate breeding values to perform selection.In this study, we test suitability of a relatedness estimator to obtain breeding values in a population of common sole, Solea solea (n = 1953) obtained by natural mating. First, we estimate breeding values using pedigree relatedness of animals for which a pedigree could be reconstructed (using parental allocation). This data set (n = 1338) is further referred to as “reduced data set.” We compare results with estimated breeding values using a simple but robust method-of-moments relatedness estimator: “molecular relatedness” (Toro et al. 2002, 2003). Next, we estimate breeding values using molecular relatedness in the full data set (n = 1953). Results show that accuracies of estimated breeding values obtained with molecular relatedness and pedigree relatedness are comparable. Accuracy increases when breeding values are estimated with molecular relatedness in the full data set. This implies that a molecular relatedness estimator can be used to estimate breeding values in captive natural mating populations.     

The Role of a d-Mannosyl-Lipid as an Intermediate in the Synthesis of Polysaccharide in Phaseolus aureus Seedlings

Particulate preparations from Phaseolus aureus produce a d-mannosyl-lipid when treated with GDP-d-mannose. This lipid complex appears to be an active d-mannose donor, and some investigators have proposed that its role might be an obligatory intermediate in mannan synthesis of higher plants. When the partially purified d-mannosyl-lipids, isotopically labeled in the d-mannose moiety, were treated with particulate enzymes under a variety of conditions, a negligible amount of material was produced that behaved as a polysaccharide. Endogenous, particle-bound d-mannosyl-¹⁴C-lipid prepared from P. aureus particles readily transferred d-mannose to GDP to yield GDP-d-mannose and was hydrolyzed to free d-mannose when treated briefly with 0.01 n HCl at 100 C. The d-mannosyl-lipid, therefore, exhibits active d-mannose transfer potential in its endogenous state. When endogenous glycosyl-lipid was incubated in the absence of GDP-d-mannose-¹⁴C, little or no polysaccharide was produced. It was, instead, slowly degraded to d-mannose. Addition of several different unlabeled sugar nucleotides had no effect on the results. Our studies to date, therefore, offer no evidence that the mannosyl-lipid is an obligatory precursor of polysaccharide.     

The Conserved miR-51 microRNA Family Is Redundantly Required for Embryonic Development and Pharynx Attachment in Caenorhabditis elegans

A major question about cytokinesis concerns the role of the septin proteins, which localize to the division site in all animal and fungal cells but are essential for cytokinesis only in some cell types. For example, in Schizosaccharomyces pombe, four septins localize to the division site, but deletion of the four genes produces only a modest delay in cell separation. To ask if the S. pombe septins function redundantly in cytokinesis, we conducted a synthetic-lethal screen in a septin-deficient strain and identified seven mutations. One mutation affects Cdc4, a myosin light chain that is an essential component of the cytokinetic actomyosin ring. Five others cause frequent cell lysis during cell separation and map to two loci. These mutations and their dosage suppressors define a signaling pathway (including Rho1 and a novel arrestin) for repairing cell-wall damage. The seventh mutation affects the poorly understood RNA-binding protein Scw1 and severely delays cell separation when combined either with a septin mutation or with a mutation affecting the septin-interacting, anillin-like protein Mid2, suggesting that Scw1 functions in a pathway parallel to that of the septins. Taken together, our results suggest that the S. pombe septins participate redundantly in one or more pathways that cooperate with the actomyosin ring during cytokinesis and that a septin defect causes septum defects that can be repaired effectively only when the cell-integrity pathway is intact.THE fission yeast Schizosaccharomyces pombe provides an outstanding model system for studies of cytokinesis (McCollum and Gould 2001; Balasubramanian et al. 2004; Pollard and Wu 2010). As in most animal cells, successful cytokinesis in S. pombe requires an actomyosin ring (AMR). The AMR begins to assemble at the G₂/M transition and involves the type II myosin heavy chains Myo2 and Myp2 and the light chains Cdc4 and Rlc1 (Wu et al. 2003). Myo2 and Cdc4 are essential for cytokinesis under all known conditions, Rlc1 is important at all temperatures but essential only at low temperatures, and Myp2 is essential only under stress conditions. As the AMR constricts, a septum of cell wall is formed between the daughter cells. The primary septum is sandwiched by secondary septa and subsequently digested to allow cell separation (Humbel et al. 2001; Sipiczki 2007). Because of the internal turgor pressure of the cells, the proper assembly and structural integrity of the septal layers are essential for cell survival.Septum formation involves the β-glucan synthases Bgs1/Cps1/Drc1, Bgs3, and Bgs4 (Ishiguro et al. 1997; Le Goff et al. 1999; Liu et al. 1999, 2002; Martín et al. 2003; Cortés et al. 2005) and the α-glucan synthase Ags1/Mok1 (Hochstenbach et al. 1998; Katayama et al. 1999). These synthases are regulated by the Rho GTPases Rho1 and Rho2 and the protein kinase C isoforms Pck1 and Pck2 (Arellano et al. 1996, 1997, 1999; Nakano et al. 1997; Hirata et al. 1998; Calonge et al. 2000; Sayers et al. 2000; Ma et al. 2006; Barba et al. 2008; García et al. 2009b). The Rho GTPases themselves appear to be regulated by both GTPase-activating proteins (GAPs) and guanine-nucleotide-exchange factors (GEFs) (Nakano et al. 2001; Calonge et al. 2003; Iwaki et al. 2003; Tajadura et al. 2004; Morrell-Falvey et al. 2005; Mutoh et al. 2005; García et al. 2006, 2009a,b). In addition, septum formation and AMR function appear to be interdependent. In the absence of a normal AMR, cells form aberrant septa and/or deposit septal materials at random locations, whereas a mutant defective in septum formation (bgs1) is also defective in AMR constriction (Gould and Simanis 1997; Le Goff et al. 1999; Liu et al. 1999, 2000). Both AMR constriction and septum formation also depend on the septation initiation network involving the small GTPase Spg1 (McCollum and Gould 2001; Krapp and Simanis 2008). Despite this considerable progress, many questions remain about the mechanisms and regulation of septum formation and its relationships to the function of the AMR.One major question concerns the role(s) of the septins. Proteins of this family are ubiquitous in fungal and animal cells and typically localize to the cell cortex, where they appear to serve as scaffolds and diffusion barriers for other proteins that participate in a wide variety of cellular processes (Longtine et al. 1996; Gladfelter et al. 2001; Hall et al. 2008; Caudron and Barral 2009). Despite the recent progress in elucidating the mechanisms of septin assembly (John et al. 2007; Sirajuddin et al. 2007; Bertin et al. 2008; McMurray and Thorner 2008), the details of septin function remain obscure. However, one prominent role of the septins and associated proteins is in cytokinesis. Septins concentrate at the division site in every cell type that has been examined, and in Saccharomyces cerevisiae (Hartwell 1971; Longtine et al. 1996; Lippincott et al. 2001; Dobbelaere and Barral 2004) and at least some Drosophila (Neufeld and Rubin 1994; Adam et al. 2000) and mammalian (Kinoshita et al. 1997; Surka et al. 2002) cell types, the septins are essential for cytokinesis. In S. cerevisiae, the septins are required for formation of the AMR (Bi et al. 1998; Lippincott and Li 1998). However, this cannot be their only role, because the AMR itself is not essential for cytokinesis in this organism (Bi et al. 1998; Korinek et al. 2000; Schmidt et al. 2002). Moreover, there is no evidence that the septins are necessary for AMR formation or function in any other organism. A further complication is that in some cell types, including most Caenorhabditis elegans cells (Nguyen et al. 2000; Maddox et al. 2007) and some Drosophila cells (Adam et al. 2000; Field et al. 2008), the septins do not appear to be essential for cytokinesis even though they localize to the division site.S. pombe has seven septins, four of which (Spn1, Spn2, Spn3, and Spn4) are expressed in vegetative cells and localize to the division site shortly before AMR constriction and septum formation (Longtine et al. 1996; Berlin et al. 2003; Tasto et al. 2003; Wu et al. 2003; An et al. 2004; Petit et al. 2005; Pan et al. 2007; Onishi et al. 2010). Spn1 and Spn4 appear to be the core members of the septin complex (An et al. 2004; McMurray and Thorner 2008), and mutants lacking either of these proteins do not assemble the others at the division site. Assembly of a normal septin ring also depends on the anillin-like protein Mid2, which colocalizes with the septins (Berlin et al. 2003; Tasto et al. 2003). Surprisingly, mutants lacking the septins are viable and form seemingly complete septa with approximately normal timing. These mutants do, however, display a variable delay in separation of the daughter cells, suggesting that the septins play some role(s) in the proper completion of the septum or in subsequent processes necessary for cell separation (Longtine et al. 1996; An et al. 2004; Martín-Cuadrado et al. 2005).It is possible that the septins localize to the division site and yet are nonessential for division in some cell types because their role is redundant with that of some other protein(s) or pathway(s). To explore this possibility in S. pombe, we screened for mutations that were lethal in combination with a lack of septins. The results suggest that the septins cooperate with the AMR during cytokinesis and that, in the absence of septin function, the septum is not formed properly, so that an intact system for recognizing and repairing cell-wall damage becomes critical for cell survival.     

High Sensitivity to Auxin is a Common Feature of Hairy Root

The responses to auxin of Lycopersicon esculentum roots transformed by (T_l+T_r)-DNA of the Ri plasmid of agropine-type Agrobacterium rhizogenes strain 15834 and Catharanthus trichophyllus roots transformed by the (T_l+T_r)-DNA, and by T_l- or T_r- DNA alone of the same bacterial strain were compared to that of their normal counterparts. The transmembrane electrical potential difference of root protoplasts was measured as a function of the concentration of exogenous naphthalene acetic acid. The sensitivity to auxin expressed by this response was shown to be independent of the measurement conditions and of the basal polarization of isolated protoplasts. According to this electrical response, as well as to the modulation by auxin of proton excretion by root tips and root tip elongation, roots transformed by (T_l+T_r) DNA are 100 to 1000 times more sensitive to exogenous auxin than normal roots, as is the case with normal and transformed roots from Lotus corniculatus (WH Shen, A Petit, J Guern, J Tempé [1988] Proc Natl Acad Sci USA 85: 3417-3421). Further-more, transformed roots of C. trichophyllus are not modified in their sensitivity to fusicoccin, illustrating the specificity of the modification of the auxin sensitivity. Roots transformed by the T_r-DNA alone showed the same sensitivity to auxin as normal roots, whereas the roots transformed by the T_l-DNA alone exhibited an auxin sensitivity as high as the roots transformed by (T_l+T_r)-DNA. It was concluded that the high sensitivity to auxin is controlled by the T_l-DNA in agropine type Ri plasmids.     

Amino Acid Isomerization in the Production of l-Phenylalanine from d-Phenylalanine by Bacteria

To establish an advantageous method for the production of l-amino acids, microbial isomerization of d- and dl-amino acids to l-amino acids was studied. Screening experiments on a number of microorganisms showed that cell suspensions of Pseudomonas fluorescens and P. miyamizu were capable of isomerizing d- and dl-phenylalanines to l-phenylalanine. Various conditions suitable for isomerization by these organisms were investigated. Cells grown in a medium containing d-phenylalanine showed highest isomerization activity, and almost completely converted d- or dl-phenylalanine into l-phenylalanine within 24 to 48 hr of incubation. Enzymatic studies on this isomerizing system suggested that the isomerization of d- or dl-phenylalanine is not catalyzed by a single enzyme, “amino acid isomerase,” but the conversion proceeds by a two step system as follows: d-pheylalanine is oxidized to phenylpyruvic acid by d-amino acid oxidase, and the acid is converted to l-phenylalanine by transamination or reductive amination.     

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

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

Topologies of the Conditional Ancestral Trees and Full-Likelihood-Based Inference in the General Coalescent Tree Framework

The general coalescent tree framework is a family of models for determining ancestries among random samples of DNA sequences at a nonrecombining locus. The ancestral models included in this framework can be derived under various evolutionary scenarios. Here, a computationally tractable full-likelihood-based inference method for neutral polymorphisms is presented, using the general coalescent tree framework and the infinite-sites model for mutations in DNA sequences. First, an exact sampling scheme is developed to determine the topologies of conditional ancestral trees. However, this scheme has some computational limitations and to overcome these limitations a second scheme based on importance sampling is provided. Next, these schemes are combined with Monte Carlo integrations to estimate the likelihood of full polymorphism data, the ages of mutations in the sample, and the time of the most recent common ancestor. In addition, this article shows how to apply this method for estimating the likelihood of neutral polymorphism data in a sample of DNA sequences completely linked to a mutant allele of interest. This method is illustrated using the data in a sample of DNA sequences at the APOE gene locus.THE interest in analyzing polymorphism data in contemporary samples of DNA sequences under various evolutionary scenarios creates a demand to design computationally tractable full-likelihood-based inference methods. For an evolutionary scenario of interest, an ancestral-mutation model can be used to design such a method. The ancestral-mutation model for a sample of DNA sequences at a nonrecombining locus is a combination of two processes: one is an ancestral process that traces the lineages of the sample back in time until the most recent common ancestor, constructing an ancestral tree for the sample. The second is a mutation process that is superimposed on the ancestral tree. The complexities of ancestral-mutation models make the design of such methods challenging. Full data are used instead of summary statistics, which can result in loss of important information in the data (see Felsenstein 1992; Donnelly and Tavaré 1995). In addition, current methods use specific features of the underlying ancestral-mutation models, so they lose flexibility to be applicable to other ancestral-mutation models.More specifically, Griffiths and Tavaré (1994c, 1995) and Kuhner et al. (1995) developed full-likelihood-based inference methods for neutral polymorphisms at a nonrecombining locus. They used the combinations of the standard coalescent (Kingman 1982a,b,c; Hudson 1983; Tajima 1983) with the finite-sites or infinite-sites (Watterson 1975) models as ancestral-mutation models. Stephens and Donnelly (2000) designed an importance sampling method to estimate the full likelihood of the data using the same settings for the ancestral-mutation models. Hobolth et al. (2008) provided another importance sampling scheme restricted to the infinite-sites model. The last two methods are computationally more efficient than the first two methods, but they lose flexibility to be applicable to ancestral models without standard coalescent features with independent coalescence waiting times, such as the coalescent processes with exponential growth (Slatkin and Hudson 1991; Griffiths and Tavaré 1994b).To incorporate the coalescent processes with exponential growth, Kuhner et al. (1998) and Griffiths and Tavaré (1994a, 1999) extended their previous methods. For example, the method of Griffiths and Tavaré (1994a, 1999) allows one to consider ancestral models based on coalescent processes with variable population sizes. Coop and Griffiths (2004) modified this inference method and made it applicable for analyzing full polymorphism data in a sample of DNA sequences from a nonrecombining locus completely linked to a mutant allele of interest, either neutral or under selection. Additionally, ancestral models have been developed for this type of sample, where the mutant allele is either neutral (Griffiths and Tavaré 1998, 2003; Wiuf and Donnelly 1999; Stephens 2000) or under selection (Slatkin and Rannala 1997; Stephens and Donnelly 2003). The ancestral model of Slatkin and Rannala (1997) is part of a family of ancestral models derived by Thompson (1975), Nee et al. (1994), and Rannala (1997), using a linear birth–death process as an evolutionary process in a population. Although all the ancestral models mentioned above differ in their properties and evolutionary scenarios, they are part of the general coalescent tree framework (Griffiths and Tavaré 1998). Therefore, a computationally tractable full-likelihood-based inference method based on this general framework is of great interest.For a sample of n sequences, an ancestral model in the general coalescent tree framework is described as a bifurcating rooted tree with n − 1 internal nodes and n leaves, where the internal nodes are coalescent events that happen one at a time. The tree is a combination of two independent components: the topology and the branch lengths. The topology of the tree is constructed going backward in time by combining two randomly chosen ancestral lineages of the sample at each node; the branch lengths of the tree are defined by the joint distribution function of the coalescence waiting times. Note that any density function for coalescence waiting times can define an ancestral model in the general coalescent tree framework.The n leaves (and the sequences in the sample) are labeled from 1 to n; and the n − 1 internal nodes of the ancestral tree are labeled from 1 to n − 1 (in order of occurrence of the coalescent events backward in time). Thus, the topology of an ancestral tree is a leaf-labeled bifurcating rooted tree with totally ordered interior vertices. These trees are called topological trees.When using the general coalescent tree framework and the infinite-sites model, an evolutionary process that generates polymorphism data in a sample of DNA sequences can be described in the following way. An ancestral tree is constructed, as described above, and mutations are added independently on different branches of the ancestral tree as Poisson processes with equal rates, θ/2, in which θ is the mutation rate at the locus. Then, at the mutation events, the ancestral sequences of the sample are changed according to the infinite-sites model; that is, each mutation occurs at a site of an ancestral sequence at which no previous mutations occurred. Thus, these changes define polymorphism data.Naively, this probabilistic framework can be used to estimate the likelihood of the full observed data in a sample of n sequences. That is, data sets are simulated independently as described above and each simulated data set is compared to the observed data. The proportion of the simulated data sets that match the observed data is an estimate of the likelihood of the observed data. Although this approach provides an estimate for the likelihood of the observed data, this method is computationally infeasible, because the topologies of the ancestral trees of the generated data sets are sampled from the space of all the possible topological trees with n leaves. This space has size n!(n − 1)!/2ⁿ⁻¹ (Edwards 1970), which is huge for moderate values of n. The topologies of the ancestral trees of the generated data sets that match the observed data represent a small portion of that space. Thus, designing a method that samples topologies of the ancestral trees from this subspace can make the method computationally tractable.On the basis of this idea, I use the general coalescent tree framework with the infinite-sites model to develop a computationally tractable full-likelihood-based inference method for polymorphisms in DNA sequences at a nonrecombining locus. First, an exact sampling scheme for topologies of the conditional ancestral trees is developed. This method has some computational limitations, so to overcome these limitations a second scheme based on an importance sampling is provided. These sampling schemes are combined with Monte Carlo integrations to estimate the likelihood of the full data, the ages of the mutations in the sample, and the time of the most recent common ancestor of the sample. I describe an application of this method for neutral polymorphism data in a sample of DNA sequences at a nonrecombining locus that is completely linked to a mutant allele of interest, either neutral or under selection. The method is illustrated using the data in a sample of DNA sequences at the APOE gene locus from Fullerton et al. (2000).