The Structural Basis of Coenzyme A Recycling in a Bacterial Organelle

Bacterial Microcompartments (BMCs) are proteinaceous organelles that encapsulate critical segments of autotrophic and heterotrophic metabolic pathways; they are functionally diverse and are found across 23 different phyla. The majority of catabolic BMCs (metabolosomes) compartmentalize a common core of enzymes to metabolize compounds via a toxic and/or volatile aldehyde intermediate. The core enzyme phosphotransacylase (PTAC) recycles Coenzyme A and generates an acyl phosphate that can serve as an energy source. The PTAC predominantly associated with metabolosomes (PduL) has no sequence homology to the PTAC ubiquitous among fermentative bacteria (Pta). Here, we report two high-resolution PduL crystal structures with bound substrates. The PduL fold is unrelated to that of Pta; it contains a dimetal active site involved in a catalytic mechanism distinct from that of the housekeeping PTAC. Accordingly, PduL and Pta exemplify functional, but not structural, convergent evolution. The PduL structure, in the context of the catalytic core, completes our understanding of the structural basis of cofactor recycling in the metabolosome lumen.     

Potential role of muscarinic receptors in schizophrenia

The role of muscarinic receptors in schizophrenia was investigated using the muscarinic agonist PTAC. PTAC was highly selective for muscarinic receptors, was a partial agonist at muscarinic M2/M4 receptors and an antagonist at M1, M3 and M5 receptors. PTAC was highly active in animal models predictive of antipsychotic behavior including inhibition of conditioned avoidance responding in rats and blockade of apomorphine-induced climbing behavior in mice. d-Amphetamine-induced Fos expression in rat nucleus accumbens was inhibited by PTAC, thus directly demonstrating the ability of PTAC to modulate DA activity. In electrophysiological studies in rats, PTAC acutely inhibited the firing of A10 DA cells and after chronic administration decreased the number of spontaneously firing DA cells in the A10 brain area. However, PTAC did not appreciably alter the firing of A9 DA cells. Thus, PTAC appears to have novel antipsychotic-like activity and these data suggest that muscarinic compounds such as PTAC may represent a new class of antipsychotic agents.     

Partial tetra-allel crosses

Summary The construction of partical tetra-allel crosses (PTAC) is considered using BIB and PBIB designs with blocks of size four. It is shown how this can lead to certain types of balanced designs. An explicit procedure is given for constructing circulant PTAC's. The analysis of PTAC's — estimation of general effects of the lines involved and analysis of variance — is illustrated in terms of an example.

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Zusammenfassung In dieser Arbeit wird die Konstruktion von unvollständigen Tetra-Allelen (PTAC) mit Hilfe von unvollständigen Blockversuchsplänen (BIB- und PBIB-Plänen) betrachtet. Es wird gezeigt, wie dies zu gewissen ausgewogenen Plänen führen kann. Eine Methode zur Konstruktion von zirkularen PTAC's wird explizit angegeben. Die Analyse von PTAC's, d.h. das Schätzen von allgemeinen Effekten der betrachteten Linien und die Varianzanalyse, wird an einem Beispiel erläutert.

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Part of this work was supported by National Science Foundation Grant NSF-16491.

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Journal Paper No. J-5831 of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project No. 890.     

In vivo evidence for involvement of a 58 kDa component of nuclear pore-targeting complex in nuclear protein import.

We recently showed that a nuclear location signal (NLS)-containing karyophile forms a stable complex with cytoplasmic components for nuclear pore-targeting The complex, termed nuclear pore-targeting complex (PTAC), contained two essential proteins of 54 and 90 kDa, respectively, as estimated by electrophoresis. In this study, we found that the 54 kDa component of PTAC is the mouse homologue of Xenopus importin (m-importin). Cytoplasmic injection of the antibodies raised against recombinant m-importin showed an inhibitory effect on nuclear import of a karyophile in living mammalian cells. A portion of cytoplasmically injected antibodies migrated rapidly into the nucleus, indicating dynamic movement of this protein across the nuclear envelope. Moreover, the injected antibodies co-precipitated the karyophile, in an NLS-dependent manner, with endogenous m-importin in the cytoplasm. These results provide in vivo evidence that m-importin is involved in nuclear protein import through association with a NLS in the cytoplasm before nuclear pore binding.     

Inheritance of zingiberene in Lycopersicon

Summary The simple mating designs provide unbiased estimates for genetic components of variance (additive genetic variance and dominance variance) under the assumption of no epistatic effect. There is empirical evidence, however, that suggests the existence of epistatic gene effects. The triallel and double cross mating designs permit the estimation of epistatic gene effects. A systematic and mathematical approach is suggested for the estimation of variance components based on the alternate model for triallel mating design.     

Evaluation and interpretation of results for three cross-over designs

In cross-over designs, individual sequences of treatments are applied to the animals. Within such designs it is possible that every treatment could modify the effect of the subsequent treatment applied to the same animal. We compared three cross-over designs each with three treatments, three periods, and two blocks. This comparison was done with respect to the variance of the estimations of the effects and its biases caused by the interactions between the treatment and the carry over effect of the foregoing treatment. Moreover, different methods of estimating variance components and calculating the degrees of freedom were compared by means of simulation. If the animal variance component is small, then the bias of the REML estimator of the variance components is greater than one of the widespread ANOVA-estimator called 'TYPE3'. But nevertheless, the mean squared error of this estimation is smaller in the case of REML in comparison to ANOVA. Therefore, the REML method should be preferred. For calculating the degrees of freedom, the Kenward-Roger method should be used. After applying this method, the true significance level is almost equal to its required value, but if the Satterthwaite method is used, the true significance level will be too high. If the interaction (treatment × carry over) is ignored in the model although it exists, the standard error of the treatment effect estimation is too great, and, therefore, the true significance level is too small. The methods which have been evaluated are available in the SAS-procedure MIXED (<citeref rid="b9">SAS Institute, 1999a</citeref>). To assist the investigation of cross-over designs by using this software, we developed programs for data management and data analysis. These programs are available from the first author.     

Estimation of Design and Genetic Components of Variance in Double Cross Mating Designs

The present paper deals with the estimation of design and genetic components of variance based on full double crosses and partial tetra-allel crosses. We have presented the explicit experissions for the symmetric sums estimators of design components of variance based on Hinkelmann's model. Consequently we have also presented explicitly the genetic components of variance based on full double crosses and partial tetra-allel crosses. The procedure is also illustrated with the help of a hypothetical data.     

Multiple Modes of Impulsivity in Parkinson's Disease

Cognitive problems are a major factor determining quality of life of patients with Parkinson''s disease. These include deficits in inhibitory control, ranging from subclinical alterations in decision-making to severe impulse control disorders. Based on preclinical studies, we proposed that Parkinson''s disease does not cause a unified disorder of inhibitory control, but rather a set of impulsivity factors with distinct psychological profiles, anatomy and pharmacology. We assessed a broad set of measures of the cognitive, behavioural and temperamental/trait aspects of impulsivity. Sixty adults, including 30 idiopathic Parkinson''s disease patients (Hoehn and Yahr stage I–III) and 30 healthy controls, completed a neuropsychological battery, objective behavioural measures and self-report questionnaires. Univariate analyses of variance confirmed group differences in nine out of eleven metrics. We then used factor analysis (principal components method) to identify the structure of impulsivity in Parkinson''s disease. Four principal factors were identified, consistent with four different mechanisms of impulsivity, explaining 60% of variance. The factors were related to (1) tests of response conflict, interference and self assessment of impulsive behaviours on the Barrett Impulsivity Scale, (2) tests of motor inhibitory control, and the self-report behavioural approach system, (3) time estimation and delay aversion, and (4) reflection in hypothetical scenarios including temporal discounting. The different test profiles of these four factors were consistent with human and comparative studies of the pharmacology and functional anatomy of impulsivity. Relationships between each factor and clinical and demographic features were examined by regression against factor loadings. Levodopa dose equivalent was associated only with factors (2) and (3). The results confirm that impulsivity is common in Parkinson''s disease, even in the absence of impulse control disorders, and that it is not a unitary phenomenon. A better understanding of the structure of impulsivity in Parkinson''s disease will support more evidence-based and effective strategies to treat impulsivity.     

Contribution of genetic effects to genetic variance components with epistasis and linkage disequilibrium

Background

Cockerham genetic models are commonly used in quantitative trait loci (QTL) analysis with a special feature of partitioning genotypic variances into various genetic variance components, while the F_∞ genetic models are widely used in genetic association studies. Over years, there have been some confusion about the relationship between these two type of models. A link between the additive, dominance and epistatic effects in an F_∞ model and the additive, dominance and epistatic variance components in a Cockerham model has not been well established, especially when there are multiple QTL in presence of epistasis and linkage disequilibrium (LD).

Results

In this paper, we further explore the differences and links between the F_∞ and Cockerham models. First, we show that the Cockerham type models are allelic based models with a special modification to correct a confounding problem. Several important moment functions, which are useful for partition of variance components in Cockerham models, are also derived. Next, we discuss properties of the F_∞ models in partition of genotypic variances. Its difference from that of the Cockerham models is addressed. Finally, for a two-locus biallelic QTL model with epistasis and LD between the loci, we present detailed formulas for calculation of the genetic variance components in terms of the additive, dominant and epistatic effects in an F_∞ model. A new way of linking the Cockerham and F_∞ model parameters through their coding variables of genotypes is also proposed, which is especially useful when reduced F_∞ models are applied.

Conclusion

The Cockerham type models are allele-based models with a focus on partition of genotypic variances into various genetic variance components, which are contributed by allelic effects and their interactions. By contrast, the F_∞ regression models are genotype-based models focusing on modeling and testing of within-locus genotypic effects and locus-by-locus genotypic interactions. When there is no need to distinguish the paternal and maternal allelic effects, these two types of models are transferable. Transformation between an F_∞ model's parameters and its corresponding Cockerham model's parameters can be established through a relationship between their coding variables of genotypes. Genetic variance components in terms of the additive, dominance and epistatic genetic effects in an F_∞ model can then be calculated by translating formulas derived for the Cockerham models.

     

A new u-statistic with superior design sensitivity in matched observational studies

Summary In an observational or nonrandomized study of treatment effects, a sensitivity analysis indicates the magnitude of bias from unmeasured covariates that would need to be present to alter the conclusions of a naïve analysis that presumes adjustments for observed covariates suffice to remove all bias. The power of sensitivity analysis is the probability that it will reject a false hypothesis about treatment effects allowing for a departure from random assignment of a specified magnitude; in particular, if this specified magnitude is “no departure” then this is the same as the power of a randomization test in a randomized experiment. A new family of u‐statistics is proposed that includes Wilcoxon's signed rank statistic but also includes other statistics with substantially higher power when a sensitivity analysis is performed in an observational study. Wilcoxon's statistic has high power to detect small effects in large randomized experiments—that is, it often has good Pitman efficiency—but small effects are invariably sensitive to small unobserved biases. Members of this family of u‐statistics that emphasize medium to large effects can have substantially higher power in a sensitivity analysis. For example, in one situation with 250 pair differences that are Normal with expectation 1/2 and variance 1, the power of a sensitivity analysis that uses Wilcoxon's statistic is 0.08 while the power of another member of the family of u‐statistics is 0.66. The topic is examined by performing a sensitivity analysis in three observational studies, using an asymptotic measure called the design sensitivity, and by simulating power in finite samples. The three examples are drawn from epidemiology, clinical medicine, and genetic toxicology.     

PduL is an evolutionarily distinct phosphotransacylase involved in B12-dependent 1,2-propanediol degradation by Salmonella enterica serovar typhimurium LT2

Salmonella enterica degrades 1,2-propanediol (1,2-PD) in a coenzyme B(12)-dependent manner. Previous enzymatic assays of crude cell extracts indicated that a phosphotransacylase (PTAC) was needed for this process, but the enzyme involved was not identified. Here, we show that the pduL gene encodes an evolutionarily distinct PTAC used for 1,2-PD degradation. Growth tests showed that pduL mutants were unable to ferment 1,2-PD and were also impaired for aerobic growth on this compound. Enzyme assays showed that cell extracts from a pduL mutant lacked measurable PTAC activity in a background that also carried a pta mutation (the pta gene was previously shown to encode a PTAC enzyme). Ectopic expression of pduL corrected the growth defects of a pta mutant. PduL fused to eight C-terminal histidine residues (PduL-His(8)) was purified, and its kinetic constants were determined: the V(max) was 51.7 +/- 7.6 micromol min(-1) mg(-1), and the K(m) values for propionyl-PO(4)(2-) and acetyl-PO(4)(2-) were 0.61 and 0.97 mM, respectively. Sequence analyses showed that PduL is unrelated in amino acid sequence to known PTAC enzymes and that PduL homologues are distributed among at least 49 bacterial species but are absent from the Archaea and Eukarya.     

The Design and Use of Variance Component Models in the Analysis of Human Quantitative Pedigree Data

The use of variance components and multivariate linear models in genetics applications has a long history that dates back to (at least) Fisher's seminal 1918 paper “The correlation between relatives on the supposition of Mendelian inheritance” [Phil. Trans. 52: 399–433]. Although extensions and elaborations of Fisher's insights have been offered in recent times, relatively few studies exist which examine the theoretical and operational properties variance component models possess in complicated genetic analysis settings. In this paper variance component models, as well as some of their properties (e.g., power, efficiency, and sample size considerations) are discussed in the context of each of the following genetic analysis settings: 1. the detection of general polygenic additive and dominance effects; 2. the detection of genetic effects in the presence of environmental effects (and vice versa); 3. the detection of pleiotropic gene action; 4. aspects of the detection of genotype by environment interaction; and 5. sequential tests for general hypotheses framed in the context of settings 1 through 4. Exposition of the proposed methods and results are facilitated through a special emphasis placed on pedigree covariance structure modeling.     

The validity and reliability of the Socioeconomic Status Instrument for assessing prostate cancer patients

Background: Because of the lack of consistency in the associations of the socioeconomic status (SES) of prostate cancer (PC) patients from diverse racial and ethnic backgrounds with PC health outcomes, I created the Socioeconomic Status Instrument (SESI) from the Demographic and Health Access components of the Behavioral Risk Factor Surveillance System 2004 Questionnaires and the socioeconomic indices of the subjects’ residential counties to better assess the SES of PC patients. Methods: The SESI was tested on 220 consecutive subjects with pathologically confirmed PC at the Veterans Affairs Medical Center in Houston, TX. A team that included an epidemiologist, a validation statistician/health services research scientist, and PC survivors assessed the content validity of the SESI. The construct validity of the SESI was assessed with factor analysis by extracting and analyzing 5 principal components based on the subjects’ individual responses on the assessment: county socioeconomic characteristics, individual socioeconomic characteristics, financial distress, increased domestic burden with limited earnings, and affluence. The internal consistency reliability of the SESI was assessed with Cronbach's alpha coefficients. Results: Based on the reviews of the SESI, all of the initial 10 items were retained. The correlations between individual responses on the SESI were similar to the results of previous studies. The 5 principal components that I assessed accounted for 71.5% of the variance. Factor loadings ranged from 0.66 to 0.98 and communalities ranged from 0.55 to 0.94. County socioeconomic characteristics accounted for 22.6% of the variance, whereas individual socioeconomic characteristics accounted for 14.6% of the variance. The overall Cronbach's alpha coefficient was 0.78. Conclusions: The SESI is valid and reliable. Accurate measurements of the SES of PC patients would provide better guidance for future research and care deliveries.     

Analysis of cytoplasmic and maternal effects I. A genetic model for diploid plant seeds and animals

A genetic model for modified diallel crosses is proposed for estimating variance and covariance components of cytoplasmic, maternal additive and dominance effects, as well as direct additive and dominance effects. Monte Carlo simulations were conducted to compare the efficiencies of minimum norm quadratic unbiased estimation (MINQUE) methods. For both balanced and unbalanced mating designs, MINQUE (0/1), which has 0 for all the prior covariances and 1 for all the prior variances, has similar efficiency to MINQUE(), which has parameter values for the prior values. Unbiased estimates of variance and covariance components and their sampling variances could be obtained with MINQUE(0/1) and jackknifing. A t-test following jackknifing is applicable to test hypotheses for zero variance and covariance components. The genetic model is robust for estimating variance and covariance components under several situations of no specific effects. A MINQUE(0/1) procedure is suggested for unbiased estimation of covariance components between two traits with equal design matrices. Methods of unbiased prediction for random genetic effects are discussed. A linear unbiased prediction (LUP) method is shown to be efficient for the genetic model. An example is given for a demonstration of estimating variance and covariance components and predicting genetic effects.     

A Psychometric Evaluation and Validation of the Preferences Scale

The Preferences Scale (PS) is a new measure of morningness and therefore requires intensive investigation to establish its measurement properties and validity. A confirmatory factor analysis (CFA) of Smith and colleagues (2002) posited model structure was not optimal in both a student (n=731) and working sample (n=218). However, significant differences (p<0.01) were found for times to sleep and wake between morning and evening types. Principal components and reliability analyses were used to develop a 6‐item model comprising 2 factors using a student sample (n=368). Cronbach's α for the PS was 0.73 and the factors explained 61% of the variance. The revised model was replicated via CFA in a separate student sample (n=363). A subsequent CFA confirmed the model structure in the working sample. Cronbach's α was 0.74 and the factors explained 64% of the variance. Significant differences (p<0.01) in self‐reported alertness ratings between morning and evening types were obtained by time‐of‐day. These results provide preliminary support for the PS. The predictive efficacy of the PS requires further validation against a number of health and work‐related variables.     

What can genetic variation tell us about the evolution of senescence?

Quantitative genetic approaches have been developed that allow researchers to determine which of two mechanisms, mutation accumulation (MA) or antagonistic pleiotropy (AP), best explain observed variation in patterns of senescence using classical quantitative genetic techniques. These include the creation of mutation accumulation lines, artificial selection experiments and the partitioning of genetic variances across age classes. This last strategy has received the lion''s share of empirical attention. Models predict that inbreeding depression (ID), dominance variance and the variance among inbred line means will all increase with age under MA but not under those forms of AP that generate marginal overdominance. Here, we show that these measures are not, in fact, diagnostic of MA versus AP. In particular, the assumptions about the value of genetic parameters in existing AP models may be rather narrow, and often violated in reality. We argue that whenever ageing-related AP loci contribute to segregating genetic variation, polymorphism at these loci will be enhanced by genetic effects that will also cause ID and dominance variance to increase with age, effects also expected under the MA model of senescence. We suggest that the tests that seek to identify the relative contributions of AP and MA to the evolution of ageing by partitioning genetic variance components are likely to be too conservative to be of general value.     

Phytophthora infestans Field Isolates from Gansu Province,China are Genetically Highly Diverse and Show a High Frequency of Self Fertility

The genetic diversity of 85 isolates of Phytophthora infestans collected in 2007 from Gansu province in China was determined and compared with 21 isolates collected before 2004. Among them, 70 belonged to the A1 mating type and 15 were self‐fertile (SF). The mitochondrial DNA haplotypes revealed both Ia (25%) and IIa (75%) haplotypes. Metalaxyl resistance occurred with high frequency (54%) in Gansu. Simple sequence repeat (SSR) genotyping revealed 26 genotypes (13 from the Tianshui region) among the 85 isolates, and 18 genotypes among the 21 isolates collected before 2004, without overlap in genotypes detected in the two groups. Cluster analysis showed clear subdivisions within the different mating type isolates. Among Gansu's isolates, Nei's and Shannon's diversity indices were highest in isolates collected in Tianshui where both A1 and SF isolates were found. Analysis of molecular variance of isolates from Gansu indicated that 51% and 49% of the variance was explained by within‐area and among‐area variance, respectively. The results suggest that the occurrence of SF isolates increases the risk of sexual reproduction, the formation of oospore as initial inocula in the field, and affects the genotypic diversity in the population.     

Very low levels of direct additive genetic variance in fitness and fitness components in a red squirrel population

A trait must genetically correlate with fitness in order to evolve in response to natural selection, but theory suggests that strong directional selection should erode additive genetic variance in fitness and limit future evolutionary potential. Balancing selection has been proposed as a mechanism that could maintain genetic variance if fitness components trade off with one another and has been invoked to account for empirical observations of higher levels of additive genetic variance in fitness components than would be expected from mutation–selection balance. Here, we used a long‐term study of an individually marked population of North American red squirrels (Tamiasciurus hudsonicus) to look for evidence of (1) additive genetic variance in lifetime reproductive success and (2) fitness trade‐offs between fitness components, such as male and female fitness or fitness in high‐ and low‐resource environments. “Animal model” analyses of a multigenerational pedigree revealed modest maternal effects on fitness, but very low levels of additive genetic variance in lifetime reproductive success overall as well as fitness measures within each sex and environment. It therefore appears that there are very low levels of direct genetic variance in fitness and fitness components in red squirrels to facilitate contemporary adaptation in this population.     

A Note on RAO's Inclusion Probability Proportional to size Sampling Scheme

A sufficient condition that the variance of HORVITZ -THOMPSON estimator for RAO 's (1965) inclusion probability proportional to sizes sampling scheme of selecting two units is uniformly smaller than that of RAO , HARTLEY and COCHRAN (1962) estimator has been obtained.     

Relationships Between Kendall's Coefficient of Concordance and a Nonparametric Measure of Phenotypic Stability with Implications for the Consistency in Rankings as Affected by Variance Components

For a two-way classification with rows (= genotypes) and columns (= environments) relationships between Kendall's coefficient of concordance W and a nonparametric measure of phenotypic stability S (= variance among the ranks over the environments) are presented. This provides an analogy between Wricke's ecovalence and the sum of squares of genotype-environment interactions on the parametric side and S and Kendall's W on the nonparametric side: S is a genotype's contribution to the discordance (1 — W) in the data set, while Wricke's ecovalence is a genotype's contribution to the genotype-environment interaction sum of squares. Genotype x environment interactions may lead, but must not necessarily lead to non-identical rank orders of the genotypes in different environments (crossover versus noncrossover interactions). When does interaction become rank interaction? The similarity of the rank orders (measured by W) can be approximately expressed by the ratio of the genotypic variance and sum of genotypic variance plus residual variance. For the consistency of rankings this relation leads to an approximate test of significance which is based on variance components. Finally, a numerical example is given using grain yield data of 20 genotypes (varieties) of winter wheat in 10 environments (locations) from German registration trials.