Refinement of the spatial structure of the gramicidin A ion channel]

The spatial structure of the gramicidin A (GA) transmembrane ion-channel was refined on the base of cross-peak volumes measured in NOESY spectra (mixing time tau m = 100 and 200 ms). The refinement methods included the comparison of experimental cross-peak volumes with those calculated for low-energy GA conformations, dynamic averaging of the low-energy conformation set and restrained energy minimization. Accuracy of the spatial structure determination was estimated by the penalty function Fr defined as a root mean square deviation of interproton distances corresponding to the calculated and experimental cross-peak volumes. As the initial conformation we used the right-handed pi 6,3 LD pi 6,3 LD helix established on the base of NMR data regardless of the cross-peak volumes. The conformation is in a good agreement with NOE cross-peak volumes (Fr 0.2 to 0.5 A depending on NOESY spectrum). For a number of NOEs formed by the side chain protons, distances errors were found as much as 0.5-2.0 A. Restrained energy minimization procedure had little further success. However some of these errors were eliminated by the change in torsional angle chi 2 of D-Leu12 and dynamic averaging of the Val7 side chain conformations. Apparently, majority of deviations of the calculated and experimental cross-peak volumes are due to the intramolecular mobility of GA and cannot be eliminated within the framework of rigid globule model. In summary the spatial structure of GA ion-channel can be thought as a set of low-energy conformations, differing by the side chain torsion angles chi 1 Val7 and chi 2 D-Leu4 and D-Leu10 and the orientation of the C-terminal ethanolamine group. Root mean square differences between the atomic coordinates of conformations are in the range of 0.3-0.8 A.     

Gibberellin Biosynthesis in Maize. Metabolic Studies with GA(15), GA(24), GA(25), GA(7), and 2,3-Dehydro-GA(9)

[17-(14)C]-Labeled GA(15), GA(24), GA(25), GA(7), and 2,3-dehydro-GA(9) were separately injected into normal, dwarf-1 (d1), and dwarf-5 (d5) seedlings of maize (Zea mays L.). Purified radioactive metabolites from the plant tissues were identified by full-scan gas chromatography-mass spectrometry and Kovats retention index data. The metabolites from GA(15) were GA(44), GA(19), GA(20), GA(113), and GA(15)-15,16-ene (artifact?). GA(24) was metabolized to GA(19), GA(20), and GA(17). The metabolites from GA(25) were GA(17), GA(25) 16alpha,17-H(2)-17-OH, and HO-GA(25) (hydroxyl position not determined). GA(7) was metabolized to GA(30), GA(3), isoGA(3) (artifact?), and trace amounts of GA(7)-diene-diacid (artifact?). 2,3-Dehydro-GA(9) was metabolized to GA(5), GA(7) (trace amounts), 2,3-dehydro-GA(10) (artifact?), GA(31), and GA(62). Our results provide additional in vivo evidence of a metabolic grid in maize (i.e. pathway convergence). The grid connects members of a putative, non-early 3,13-hydroxylation branch pathway to the corresponding members of the previously documented early 13-hydroxylation branch pathway. The inability to detect the sequence GA(12) --> GA(15) --> GA(24) --> GA(9) indicates that the non-early 3,13-hydroxylation pathway probably plays a minor role in the origin of bioactive gibberellins in maize.     

Characterization of activity and binding mode of glycyrrhetinic acid derivatives inhibiting 11β-hydroxysteroid dehydrogenase type 2

Modulation of intracellular glucocorticoid availability is considered as a promising strategy to treat glucocorticoid-dependent diseases. 18β-Glycyrrhetinic acid (GA), the biologically active triterpenoid metabolite of glycyrrhizin, which is contained in the roots and rhizomes of licorice (Glycyrrhiza spp.), represents a well-known but non-selective inhibitor of 11β-hydroxysteroid dehydrogenases (11β-HSDs). However, to assess the physiological functions of the respective enzymes and for potential therapeutic applications selective inhibitors are needed. In the present study, we applied bioassays and 3D-structure modeling to characterize nine 11β-HSD1 and fifteen 11β-HSD2 inhibiting GA derivatives. Comparison of the GA derivatives in assays using cell lysates revealed that modifications at the 3-hydroxyl and/or the carboxyl led to highly selective and potent 11β-HSD2 inhibitors. The data generated significantly extends our knowledge on structure-activity relationship of GA derivatives as 11β-HSD inhibitors. Using recombinant enzymes we found also potent inhibition of mouse 11β-HSD2, despite significant species-specific differences. The selected GA derivatives potently inhibited 11β-HSD2 in intact SW-620 colon cancer cells, although the rank order of inhibitory potential differed from that obtained in cell lysates. The biological activity of compounds was further demonstrated in glucocorticoid receptor (GR) transactivation assays in cells coexpressing GR and 11β-HSD1 or 11β-HSD2. 3D-structure modeling provides an explanation for the differences in the selectivity and activity of the GA derivatives investigated. The most potent and selective 11β-HSD2 inhibitors should prove useful as mechanistic tools for further anti-inflammatory and anti-cancer in vitro and in vivo studies. Article from the Special issue on Targeted Inhibitors.     

Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

MOTIVATION: Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the class. BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes). RESULTS: We have shown that BMA selects smaller numbers of relevant genes (compared with other methods) and achieves a high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any number of classes, and outputs posterior probabilities for the selected genes and models. Our selected models typically consist of only a few genes. The combination of high accuracy, small numbers of genes and posterior probabilities for the predictions should make BMA a powerful tool for developing diagnostics from expression data. AVAILABILITY: The source codes and datasets used are available from our Supplementary website.     

Nonlinearities and asymmetries of the human cardiovagal baroreflex

To determine whether an approach such as the modified Oxford technique can consistently produce data that reveal the nonlinear nature of the cardiovagal baroreflex and to ascertain whether the model parameters provide unique insight into baroreflex function, we retrospectively examined 91 baroreflex trials (38 subjects, 27 men and 11 women, ages 22-72 yr). The modified Oxford technique (bolus sodium nitroprusside followed by bolus phenylephrine) was used to perturb blood pressure, and the resulting systolic blood pressure-R-R interval responses were plotted and modeled using a linear, a four-parameter symmetric, and a five-parameter asymmetric model. Several issues, such as the effect of data averaging, various approaches to gain estimation, and the predictive value of model parameters, were examined during reflex modeling. Sigmoid models accounted for a greater amount of the variance than did the linear model: linear r2=0.81+/-0.01, four-parameter r2=0.90+/-0.08, and five-parameter r2=0.90+/-0.08 (P<0.05, linear vs. sigmoid models). Data averaging did not affect model fits. Although the four gain estimates (linear remodel, 1st derivative, peak, and set point) were statistically related, the set point gain was significantly lower than other estimates (P<0.05). Subgroup comparisons between young and older healthy subjects revealed differences in all indexes of cardiovagal baroreflex gain, as well as R-R interval operating range and curvature parameters. In conclusion, the modified Oxford technique consistently reveals the nonlinear nature of the human cardiovagal baroreflex. Moreover, of the parameters produced by the symmetric sigmoid model, only the response range provides unique information beyond that of reflex gain.     

Point of care testing system via enzymatic method for the rapid, efficient assay of glycated albumin

The ratio of glycated albumin to albumin concentration in serum is termed the glycated albumin (GA) value. The GA value provides a time-averaged index of the state of glycemic control for the previous 2 weeks. In this study, a dry chemistry system (GA monitor) via an enzymatic method was proposed in order to provide a GA value measurement for point of care testing (POCT). The GA monitor was made from three devices a set of test-tapes, a test-strip and an optical analyzer. A GA test-tape, a ketoamine test-tape and an albumin test-tape were enclosed in the fabricated test-strip. Time-course changes of the optical characteristics were evaluated using the test-strip. It was found that the three test tapes must be enclosed in the test-strip to create a dry chemistry system for small sample volumes (20 microl). A temperature control unit, which could hold the temperature of the GA test-tape at 45 degrees C and at 25 degrees C for the other two types of test-tape was incorporated into the optical analyzer. With the GA test-tape held separately and controlled at 45 degrees C, the analytical time decreased to one-third of the time taken for the three tapes at 25 degrees C. The analytical accuracy of the three types of test-tape showed favorable results, with R2 values of 0.96-0.98 and coefficients of variation (CV) of 2.4-6.8%. Compared with a commercially available liquid chemistry system, the analytical accuracy of the GA monitor exhibited a relatively favorable linearity of R=0.82. According to these results, a new GA value analytical system was realized, in which the GA value could be assayed within five minutes using only 20 microl of blood sample with a disposable test-strip. This system could potentially be used for clinical purposes as test equipment for rapid and efficient POCT.     

Spending degrees of freedom in a poor economy: A case study of building a sightability model for moose in northeastern Minnesota

Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys. Like many models in wildlife and ecology, sightability models are typically developed from small observational datasets with many candidate predictors. Aggressive model-selection methods are often employed to choose a best model for prediction and effect estimation, despite evidence that such methods can lead to overfitting (i.e., selected models may describe random error or noise rather than true predictor–response curves) and poor predictive ability. We used moose (Alces alces) sightability data from northeastern Minnesota (2005–2007) as a case study to illustrate an alternative approach, which we refer to as degrees-of-freedom (df) spending: sample-size guidelines are used to determine an acceptable level of model complexity and then a pre-specified model is fit to the data and used for inference. For comparison, we also constructed sightability models using Akaike's Information Criterion (AIC) step-down procedures and model averaging (based on a small set of models developed using df-spending guidelines). We used bootstrap procedures to mimic the process of model fitting and prediction, and to compute an index of overfitting, expected predictive accuracy, and model-selection uncertainty. The index of overfitting increased 13% when the number of candidate predictors was increased from three to eight and a best model was selected using step-down procedures. Likewise, model-selection uncertainty increased when the number of candidate predictors increased. Model averaging (based on R = 30 models with 1–3 predictors) effectively shrunk regression coefficients toward zero and produced similar estimates of precision to our 3-df pre-specified model. As such, model averaging may help to guard against overfitting when too many predictors are considered (relative to available sample size). The set of candidate models will influence the extent to which coefficients are shrunk toward zero, which has implications for how one might apply model averaging to problems traditionally approached using variable-selection methods. We often recommend the df-spending approach in our consulting work because it is easy to implement and it naturally forces investigators to think carefully about their models and predictors. Nonetheless, similar concepts should apply whether one is fitting 1 model or using multi-model inference. For example, model-building decisions should consider the effective sample size, and potential predictors should be screened (without looking at their relationship to the response) for missing data, narrow distributions, collinearity, potentially overly influential observations, and measurement errors (e.g., via logical error checks). © 2011 The Wildlife Society.     

Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method.

MOTIVATION: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. METHODS: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. RESULTS: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. AVAILABILITY: The method is available at http://dir.niehs.nih.gov/microarray/datamining CONTACT: LI3@niehs.nih.gov     

Gluconic acid production from sucrose in an airlift reactor using a multi-enzyme system

Sucrose from sugarcane is produced in abundance in Brazil, which provides an opportunity to manufacture other high-value products. Gluconic acid (GA) can be produced by multi-enzyme conversion of sucrose using the enzymes invertase, glucose oxidase, and catalase. In this process, one of the byproducts is fructose, which has many commercial applications. This work concerns the batch mode production of GA in an airlift reactor fed with sucrose as substrate. Evaluation was made of the influence of temperature and pH, as well as the thermal stability of the enzymes. Operational conditions of 40 °C and pH 6.0 were selected, based on the enzymatic activity profiles and the thermal stabilities. Under these conditions, the experimental data could be accurately described by kinetic models. The maximum yield of GA was achieved within 3.8 h, with total conversion of sucrose and glucose and a volumetric productivity of around 7.0 g L^?1 h^?1.     

Predicting climate-induced range shifts: model differences and model reliability

Predicted changes in the global climate are likely to cause large shifts in the geographic ranges of many plant and animal species. To date, predictions of future range shifts have relied on a variety of modeling approaches with different levels of model accuracy. Using a common data set, we investigated the potential implications of alternative modeling approaches for conclusions about future range shifts and extinctions. Our common data set entailed the current ranges of 100 randomly selected mammal species found in the western hemisphere. Using these range maps, we compared six methods for modeling predicted future ranges. Predicted future distributions differed markedly across the alternative modeling approaches, which in turn resulted in estimates of extinction rates that ranged between 0% and 7%, depending on which model was used. Random forest predictors, a model‐averaging approach, consistently outperformed the other techniques (correctly predicting >99% of current absences and 86% of current presences). We conclude that the types of models used in a study can have dramatic effects on predicted range shifts and extinction rates; and that model‐averaging approaches appear to have the greatest potential for predicting range shifts in the face of climate change.     

Simple Sequence Repeat Analysis of Genetic Diversity in Primary Core Collection of Peach （Prunus persica）

In this study, the genetic diversity of 51 cultivars in the primary core collection of peach （Prunus persica （L.） Batsch） was evaluated by using simple sequence repeats （SSRs）. The phylogenetic relationships and the evolutionary history among different cultivars were determined on the basis of SSR data. Twenty-two polymorphic SSR primer pairs were selected, and a total of 111 alleles were identified in the 51 cultivars, with an average of 5 alleles per locus. According to traditional Chinese classification of peach cultivars, the 51 cultivars in the peach primary core collection belong to six variety groups. The SSR analysis revealed that the levels of the genetic diversity within each variety group were ranked as Sweet peach 〉 Crisp peach 〉 Flat peach 〉 Nectarine 〉 Honey Peach 〉 Yellow fleshed peach. The genetic diversity among the Chinese cultivars was higher than that among the introduced cultivars. Cluster analysis by the unweighted pair group method with arithmetic averaging （UPGMA） placed the 51 cultivars into five linkage clusters. Cultivar members from the same variety group were distributed in different UPGMA clusters and some members from different variety groups were placed under the same cluster. Different variety groups could not be differentiated in accordance with SSR markers. The SSR analysis revealed rich genetic diversity in the peach primary core collection, representative of genetic resources of peach.     

Detection of coregulation in differential gene expression profiles

Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.     

Microcultures of lactic acid bacteria: characterization and selection of strains,optimization of nutrients and gallic acid concentration

Eighteen lactic acid bacteria (LAB) strains, isolated from coffee pulp silages were characterized according to both growth and gallic acid (GA) consumption. Prussian blue method was adapted to 96-well microplates to quantify GA in LAB microcultures. Normalized data of growth and GA consumption were used to characterize strains into four phenotypes. A number of 5 LAB strains showed more than 60% of tolerance to GA at 2 g/l; whereas at 10 g/l GA growth inhibition was detected to a different extent depending on each strain, although GA consumption was observed in seven studied strains (>60%). Lactobacillus plantarum L-08 was selected for further studies based on its capacity to degrade GA at 10 g/l (97%). MRS broth and GA concentrations were varied to study the effect on growth of LAB. Cell density and growth rate were optimized by response surface methodology and kinetic analysis. Maximum growth was attained after 7.5 h of cultivation, with a dilution factor of 1–1/2 and a GA concentration between 0.625 and 2.5 g/l. Results indicated that the main factor affecting LAB growth was GA concentration. The main contribution of this study was to propose a novel adaptation of a methodology to characterize and select LAB strains with detoxifying potential of simple phenolics based on GA consumption and tolerance. In addition, the methodology presented in this study integrated the well-known RSM with an experimental design based on successive dilutions.     

Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data

Functional data analysis techniques provide an alternative way of representing movement and movement variability as a function of time. In particular, the registration of functional data provides a local normalization of time functions. This normalization transforms a set of curves, records of repeated trials, yielding a new set of curves that only vary in terms of amplitude. Therefore, main events occur at the "same time" for all transformed curves and interesting features of individual recordings remain after averaging processes. This paper presents an application of the registration process to the analysis of the vertical forces exerted on the ground by both feet during the sit-to-stand movement. This movement is particularly interesting in functional evaluations related to balance control, lower extremity dysfunction or low-back pain.     

Development of a regional macroinvertebrate index for large river bioassessment

Large river bioassessment protocols lag far behind those of wadeable streams and often rely on fish assemblages of individual rivers. We developed a regional macroinvertebrate index and assessed relative condition of six large river tributaries to the upper Mississippi and Ohio rivers, Midwest USA. In 2004 and 2005, benthic macroinvertebrates, water chemistry, and habitat data were collected from randomly selected sites on each of the St. Croix, Wisconsin, Minnesota, Scioto, Wabash, and Illinois rivers. We first identified the human disturbance gradient using principal components analysis (PCA) of abiotic data. From the PCA, least disturbed sites showed strong separation from stressed sites along a gradient contrasting high water clarity, canopy cover, habitat scores, and plant-based substrates at one end and higher conductivity and nutrient concentrations at the other. Evaluation of 97 benthic metrics identified those with good range, responsiveness, and relative scope of impairment, as well as redundancies with other metrics. The final index was composed of Diptera taxa richness, EPT taxa richness, Coleoptera taxa richness, percent oligochaete and leech taxa, percent collector-filterer individuals, predator taxa richness, percent burrower taxa, tolerant taxa richness, and percent facultative individuals. Each of the selected metrics was scored using upper and lower thresholds based on all sites, and averaging across the nine metric scores, we obtained the Non-wadeable Macroinvertebrate Assemblage Condition Index (NMACI). The NMACI showed a strong response to disturbance using a validation data set and was highly correlated with non-metric multidimensional scaling (NMDS) ordination axes of benthic taxa. The cumulative distribution function of index scores for each river showed qualitative differences in condition among rivers. NMACI scores were highest for the federally protected St. Croix River and lowest for the Illinois River. Other rivers were intermediate and generally reflected the mixture of land use types within individual basins. Use of regional reference sites, though setting a high level of expectation, provides a valuable frame of reference for the potential of large river benthic communities that will aid management and restoration efforts.     

Methods of weighted averaging of ECG signals using Bayesian inference and criterion function minimization

Averaging signals in time domain is one of the main methods of noise attenuation in biomedical signal processing in case of systems producing repetitive patterns such as electrocardiographic (ECG) acquisition systems. This paper presents a comprehensive study of weighted averaging of ECG signal. Presented methods use criterion function minimization, partitioning of input set of data in the time domain as well as Bayesian and empirical Bayesian framework. The existing methods are described together with their extensions. Performance of all presented methods is experimentally evaluated and compared with the traditional averaging by using arithmetic mean and well-known weighted averaging methods based on criterion function minimization (WACFM).