ALBERT

Beschreibung: Background: A novel herbal formulation LI10903F, alternatively known as LOWAT was developed based on its ability to inhibit adipogenesis and lipogenesis in 3T3-L1 adipocytes model. The clinical efficacy and tolerability of LI10903F were evaluated in an eight-week, randomized, double-blind, placebo-controlled, clinical trial in 50 human subjects with body mass index (BMI) between 30 and 40 kg/m2 (clinical trial registration number: ISRCTN37381706). Participants were randomly assigned to either a placebo or LI10903F group. Subjects in the LI10903F group received 300 mg of herbal formulation thrice daily, while subjects in the placebo group received 300 mg of placebo capsules thrice daily. All subjects were provided a standard diet (2,000 kcal daily) and participated in a moderate exercise of 30 min walk for five days a week. Additionally, the safety of this herbal formulation was evaluated by a series of acute, sub-acute toxicity and genotoxicity studies in animals and cellular models. Results: After eight weeks of supplementation, statistically significant net reductions in body weight (2.49 kg; p=0.00005) and BMI (0.96 kg/m2; p=0.00004) were observed in the LI10903F group versus placebo group. Additionally, significant increase in serum adiponectin concentration (p=0.0076) and significant decrease in serum ghrelin concentration (p=0.0066) were found in LI10903F group compared to placebo group. Adverse events were mild and were equally distributed between the two groups. Interestingly, LI10903F showed broad spectrum safety in a series of acute, sub-acute toxicity and genotoxicity studies. Conclusions: Results from the current research suggest that LI10903F or LOWAT is well-tolerated, safe and effective for weight management.

Beschreibung: Background: The -493G/T polymorphism in the microsomal triglyceride transfer protein (MTP) gene is associated with lower serum low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) levels and longevity in several populations, but the results are inconsistent in different racial/ethnic groups. The current study was to investigate the plausible association of MTP -493G/T polymorphism with serum lipid levels and longevity in Zhuang long-lived families residing in Bama area, a famous home of longevity in Guangxi, China. Methods: The MTP -493G/T was genotyped by PCR-restriction fragment length polymorphism in 391 Bama Zhuang long-lived families (BLF, n = 1467, age 56.60 +/- 29.43 years) and four control groups recruited from Bama and out-of-Bama area with or without a familial history of exceptional longevity: Bama non-long-lived families (BNLF, n = 586, age 44.81 +/- 26.83 years), Bama non-Zhuang long-lived families (BNZLF, n = 444, age 52.09 +/- 31.91 years), Pingguo long-lived families (PLF, n = 658, age 50.83 +/- 30.30 years), and Pingguo non-long-lived families (PNLF, n = 539, age 38.74 +/- 24.69 years). Correlation analyses between genotypes and serum lipid levels and longevity were then performed. Results: No particularly favorable lipoprotein and clinical phenotypes were seen in BLF as compared to general families in the same area. Instead, the levels of total cholesterol (TC), TG, LDL-C, and the prevalence of dyslipidemia were significantly higher in the three Bama families as compared to the two non-Bama families (P 〈 0.01 for all). There were no differences in the allelic and genotypic frequencies among the tested cohorts (P 〉 0.05 for all), but the TT genotype tended to enrich in the three long-lived cohorts from both areas. In addition, the individuals harboring TT genotype exhibited lower LDL-C and TC levels in the overall populations and Bama populations with a region- and sex-specific pattern. Multiple linear regression analyses unraveled that LDL-C levels were correlated with genotypes in Bama combined population, BNLF, and the total population (P 〈 0.05 for each) but not in Pingguo populations; TC and HDL-C levels were correlated with genotypes in Bama combined population and BLF, respectively (P 〈 0.05 for each). Conclusions: MTP -493G/T polymorphism may play an important role in fashioning the serum lipid profiles of Bama populations, despite no direct association between MTP -493G/T and longevity was detected.

Beschreibung: Background: RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. Results: In this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs the unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially. Conclusions: This csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.

Beschreibung: Background: Copy number variations (CNVs) are genomic structural variants that are found in healthy populations and have been observed to be associated with disease susceptibility. Existing methods for CNV detection are often performed on a sample-by-sample basis, which is not ideal for large datasets where common CNVs must be estimated by comparing the frequency of CNVs in the individual samples. Here we describe a simple and novel approach to locate genome-wide CNVs common to a specific population, using human ancestry as the phenotype. Results: We utilized our previously published Genome Alteration Detection Analysis (GADA) algorithm to identify common ancestry CNVs (caCNVs) and built a caCNV model to predict population structure. We identified a 73 caCNV signature using a training set of 225 healthy individuals from European, Asian, and African ancestry. The signature was validated on an independent test set of 300 individuals with similar ancestral background. The error rate in predicting ancestry in this test set was 2% using the 73 caCNV signature. Among the caCNVs identified, several were previously confirmed experimentally to vary by ancestry. Our signature also contains a caCNV region with a single microRNA (MIR270), which represents the first reported variation of microRNA by ancestry. Conclusions: We developed a new methodology to identify common CNVs and demonstrated its performance by building a caCNV signature to predict human ancestry with high accuracy. The utility of our approach could be extended to large case--control studies to identify CNV signatures for other phenotypes such as disease susceptibility and drug response.

Beschreibung: Background The robust identification of isotope patterns originating from peptides being analyzed through mass spectrometry (MS) is often significantly hampered by noise artifacts and the interference of overlappingpatterns arising e.g. from post-translational modifications. As the classification of the recorded data points into either 'noise' or 'signal' lies at the very root of essentially every proteomic application, the quality of the automated processing of mass spectra can significantly influence the way the data might be interpreted within a given biological context.Results We propose non-negative least squares/non-negative least absolute deviation regression to fit a raw spectrum by templates imitating isotope patterns. In a carefully designed validation scheme, we show that the method exhibits excellent performance in pattern picking. It is demonstrated that the method is able to disentangle complicated overlaps of patterns. Conclusions: We find that regularization is not necessary to prevent overfitting and that thresholding is an effective and user-friendly way to perform feature selection. The proposed method avoids problems inherent in regularization-based approaches, comes with a set of well-interpretable parameters whose default configuration is shown to generalize well without the need for fine-tuning, and is applicable to spectra of different platforms. The R package IPPD implements the method and is available from the Bioconductor platform (http://bioconductor.fhcrc.org/help/bioc-views/devel/bioc/html/IPPD.html).

Beschreibung: After publication of this manuscript [Lipids in Health and Disease 2012, 11:95], we noted that one of the monounsaturated fatty acid isomers had been mislabelled in the results, and in figure 2A and table 2.In the Results and discussion, the sentence, "Concerning plasma MUFA levels, long-chain MUFA C20:1 (n-9 and n-7) and C22:1 (n-11 and n-9) peaked at 2 hr post-ingestion (Figure 2a and b)" should read, "Concerning plasma MUFA levels, long-chain MUFA C20:1 (n-11, n-9 and n-7) and C22:1 (n-11 and n-9) peaked at 2 hr post-ingestion (Figure 2a and b)".The figure and table have also been corrected (figure 1, table 1).We apologize for this error, which was due to a misreading of the gas chromatography reading.

Beschreibung: Background: The inference of homologies among DNA sequences, that is, positions in multiple genomes that share a common evolutionary origin, is a crucial, yet difficult task facing biologists. Its computational counterpart is known as the multiple sequence alignment problem. There are various criteria and methods available to perform multiple sequence alignments, and among these, the minimization of the overall cost of the alignment on a phylogenetic tree is known in combinatorial optimization as the Tree Alignment Problem. This problem typically occurs as a subproblem of the Generalized Tree Alignment Problem, which looks for the tree with the lowest alignment cost among all possible trees. This is equivalent to the Maximum Parsimony problem when the input sequences are not aligned, that is, when phylogeny and alignments are simultaneously inferred. Results: For large data sets, a popular heuristic is Direct Optimization (DO). DO provides a good tradeoff between speed, scalability, and competitive scores, and is implemented in the computer program POY. All other (competitive) algorithms have greater time complexities compared to DO. Here, weintroduce and present experiments a new algorithm Affine-DO to accommodate the indel (alignment gap) models commonly used in phylogenetic analysis of molecular sequence data. Affine-DO has the same time complexity as DO, but is correctly suited for the affine gap edit distance. We demonstrateits performance with more than 330,000 experimental tests. These experiments show that the solutions of Affine-DO are close to the lower bound inferred from a linear programming solution. Moreover, iterating over a solution produced using Affine-DO shows little improvement. Conclusions: Our results show that Affine-DO is likely producing near-optimal solutions, with approximations within 10% for sequences with small divergence, and within 30% for random sequences, for which Affine-DO produced the worst solutions. The Affine-DO algorithm has the necessary scalability andoptimality to be a significant improvement in the real-world phylogenetic analysis of sequence data.

Beschreibung: Background: Time-course gene expression data such as yeast cell cycle data may be periodically expressed. To cluster such data,currently used Fourier series approximations of periodic gene expressions have been found not to be sufficientlyadequate to model the complexity of the time-course data, partly due to their ignoring the dependence between theexpression measurements over time and the correlation among gene expression profiles. We further investigatethe advantages and limitations of available models in the literature and propose a new mixture model withautoregressive random effects of the first order for the clustering of time-course gene-expression profiles. Somesimulations and real examples are given to demonstrate the usefulness of the proposed models. Results: We illustrate the applicability of our new model using synthetic and real time-course datasets. We show that ourmodel outperforms existing models to provide more reliable and robust clustering of time-course data. Our modelprovides superior results when genetic profiles are correlated. It also gives comparable results when the correlationbetween the gene profiles is weak. In the applications to real time-course data, relevant clusters of co-regulatedgenes are obtained, which are supported by gene-function annotation databases. Conclusions: Our new model under our extension of the EMMIX-WIRE procedure is more reliable and robust for clusteringtime-course data because it adopts a random effects model that allows for the correlation among observations atdifferent time points. It postulates gene-specific random effects with an auto-correlation variance structure thatmodels coregulation within the clusters The developed R package is flexible in its specification of the randomeffectsthrough user-input parameters that enables improved modelling and consequent clustering of time-coursedata.

Beschreibung: Background: 454 pyrosequencing is a commonly used massively parallel DNA sequencing technology with a wide variety of application fields such as epigenetics, metagenomics and transcriptomics. A well-known problem of this platform is its sensitivity to base-calling insertion and deletion errors, particularly in the presence of long homopolymers. In addition, the base-call quality scores are not informative with respect to whether an insertion or a deletion error is more likely. Surprisingly, not much effort has been devoted to the development of improved base-calling methods and more intuitive quality scores for this platform. Results: We present HPCall, a 454 base-calling method based on a weighted Hurdle Poisson model. HPCall uses a probabilistic framework to call the homopolymer lengths in the sequence by modeling well-known 454 noise predictors. Base-calling quality is assessed based on estimated probabilities for each homopolymer length, which are easily transformed to useful quality scores. Conclusions: Using a reference data set of the Escherichia coli K-12 strain, we show that HPCall produces superior quality scores that are very informative towards possible insertion and deletion errors, while maintaining a base-calling accuracy that is better than the current one. Given the generality of the framework, HPCall has the potential to also adapt to other homopolymer-sensitive sequencing technologies.

Beschreibung: Background: Multivariate approaches have been successfully applied to genome wide association studies. Recently, a Partial Least Squares (PLS) based approach was introduced for mapping yeast genotype-phenotype relations, where background information such as gene function classification, gene dispensability, recent or ancient gene copy number variations and the presence of premature stop codons or frameshift mutations in reading frames, were used post hoc to explain selected genes. One of the latest advancement in PLS named L-Partial Least Squares (L-PLS), where 'L' presents the used data structure, enables the use of background information at the modeling level. Here, a modification of L-PLS with variable importance on projection (VIP) was implemented using a stepwise regularized procedure for gene and background information selection. Results werecompared to PLS-based procedures, where no background information was used. Results: Applying the proposed methodology to yeast Saccharomyces cerevisiae data, we found the relationship between genotype-phenotype to have improved understandability. Phenotypic variations were explained by the variations of relatively stable genes and stable background variations. The suggested procedure provides an automatic way for genotype-phenotype mapping. The selected phenotype influencing genes were evolving 29% faster than non-influential genes, and the current results are supported by a recently conducted study. Further power analysis on simulated data verified that the proposed methodology selects relevant variables. Conclusions: A modification of L-PLS with VIP in a stepwise regularized elimination procedure can improve the understandability and stability of selected genes and background information. The approach is recommended for genome wide association studies where background information is available.

Beschreibung: Background: Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble? Results: The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity. Conclusion: Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

Beschreibung: Background: Co-expression measures are often used to define networks among genes. Mutual information (MI) is often used as a generalized correlation measure. It is not clear how much MI adds beyond standard (robust) correlation measures or regression model based association measures. Further, it is important to assess what transformations of these and other co-expression measures lead to biologically meaningful modules (clusters of genes). Results: We provide a comprehensive comparison between mutual information and several correlation measures in 8 empirical data sets and in simulations. We also study different approaches for transforming an adjacency matrix, e.g. using the topological overlap measure. Overall, we confirm close relationships between MI and correlation in all data sets which reflects the fact that most gene pairs satisfy linear or monotonic relationships. We discuss rare situations when the two measures disagree. We also compare correlation and MI based approaches when it comes to defining co-expression network modules. We show that a robust measure of correlation (the biweight midcorrelation transformed via the topological overlap transformation) leads to modules that are superior to MI based modules and maximal information coefficient (MIC) based modules in terms of gene ontology enrichment. We present a function that relates correlation to mutual information which can be used to approximate the mutual information from the corresponding correlation coefficient. We propose the use of polynomial or spline regression models as an alternative to MI for capturing non-linear relationships between quantitative variables. Conclusions: The biweight midcorrelation outperforms MI in terms of elucidating gene pairwise relationships. Coupled with the topological overlap matrix transformation, it often leads to more significantly enriched co-expression modules. Spline and polynomial networks form attractive alternatives to MI in case of non-linear relationships. Our results indicate that MI networks can safely be replaced by correlation networks when it comes to measuring co-expression relationships in stationary data.

Beschreibung: Background: Mitochondrial DNA damage, increased production of reactive oxygen species and progressive respiratory chain dysfunction, together with increased deposition of cholesterol and cholesteryl esters, are hallmarks of atherosclerosis. This study investigated the role of mitochondrial function in regulation of macrophage cholesterol efflux to apolipoprotein A-I, by the addition of established pharmacological modulators of mitochondrial function. Methods: Murine RAW 264.7 macrophages were treated with a range of concentrations of resveratrol, antimycin, dinitrophenol, nigericin and oligomycin, and changes in viability, cytotoxicity, membrane potential and ATP, compared with efflux of [3H]cholesterol to apolipoprotein (apo) A-I. The effect of oligomycin treatment on expression of genes implicated in macrophage cholesterol homeostasis were determined by quantitative polymerase chain reaction, and immunoblotting, relative to the housekeeping enzyme, Gapdh, and combined with studies of this molecule on cholesterol esterification, de novo lipid biosynthesis, and induction of apoptosis. Significant differences were determined using analysis of variance, and Dunnett's or Bonferroni post t-tests, as appropriate. Results: The positive control, resveratrol (24 h), significantly enhanced cholesterol efflux to apoA-I at concentrations 〉=30 muM. By contrast, cholesterol efflux to apoA-I was significantly inhibited by nigericin (45 %; p

Beschreibung: Background: Illumina BeadArray technology includes non specific negative control features that allow a precise estimation of the background noise. As an alternative to the background subtraction proposed in BeadStudio which leads to an important loss of information by generating negative values, a background correction method modeling the observed intensities as the sum of the exponentially distributed signal and normally distributed noise has been developed. Nevertheless, Wang and Ye (2012) display a kernel-based estimator of the signal distribution on Illumina BeadArrays and suggest that a gamma distribution would represent a better modeling of the signal density. Hence, the normal-exponential modeling may not be appropriate for Illumina data and background corrections derived from this model may lead to wrong estimation. Results: We propose a more flexible modeling based on a gamma distributed signal and a normal distributed background noise and develop the associated background correction, implemented in the R-package NormalGamma. Our model proves to be markedly more accurate to model Illumina BeadArrays: on the one hand, it is shown on two types of Illumina BeadChips that this model offers a more correct fit of the observed intensities. On the other hand, the comparison of the operating characteristics of several background correction procedures on spike-in and on normal-gamma simulated data shows high similarities, reinforcing the validation of the normal-gamma modeling. The performance of the background corrections based on the normal-gamma and normal-exponential models are compared on two dilution data sets, through testing procedures which represent various experimental designs. Surprisingly, we observe that the implementation of a more accurate parametrisation in the model-based background correction does not increase the sensitivity. These results may be explained by the operating characteristics of the estimators: the normal-gamma background correction offers an improvement in terms of bias, but at the cost of a loss in precision. Conclusions: This paper addresses the lack of fit of the usual normal-exponential model by proposing a more flexible parametrisation of the signal distribution as well as the associated background correction. This new model proves to be considerably more accurate for Illumina microarrays, but the improvement in terms of modeling does not lead to a higher sensitivity in differential analysis. Nevertheless, this realistic modeling makes way for future investigations, in particular to examine the characteristics of pre-processing strategies.

Beschreibung: Background: In many types of cancer, prostaglandin E2 (PGE2) is associated with tumour related processes including proliferation, migration, angiogenesis and apoptosis. However in gliomas the role of this prostanoid is poorly understood. Here, we report on the proliferative, migratory, and apoptotic effects of PGE1, PGE2 and Ibuprofen (IBP) observed in the T98G human glioma cell line in vitro. Methods: T98G human glioma cells were treated with IBP, PGE1 or PGE2 at varying concentrations for 24--72 hours. Cell proliferation, mitotic index and apoptotic index were determined for each treatment. Caspase-9 and caspase-3 activity was measured using fluorescent probes in live cells (FITC-LEHD-FMK and FITC-DEVD-FMK respectively). The migratory capacity of the cells was quantified using a scratch migration assay and a transwell migration assay. Results: A significant decrease was seen in cell number (54%) in the presence of 50 muM IBP. Mitotic index and bromodeoxyuridine (BrdU) incorporation were also decreased 57% and 65%, respectively, by IBP. The apoptotic index was increased (167%) and the in situ activity of caspase-9 and caspase-3 was evident in IBP treated cells. The inhibition of COX activity by IBP also caused a significant inhibition of cell migration in the monolayer scratch assay (74%) and the transwell migration assay (36%).In contrast, the presence of exogenous PGE1 or PGE2 caused significant increases in cell number (37% PGE1 and 45% PGE2). When mitotic index was measured no change was found for either PG treatment. However, the BrdU incorporation rate was significantly increased by PGE1 (62%) and to a greater extent by PGE2 (100%). The apoptotic index was unchanged by exogenous PGs. The addition of exogenous PGs caused an increase in cell migration in the monolayer scratch assay (43% PGE1 and 44% PGE2) and the transwell migration assay (28% PGE1 and 68% PGE2). Conclusions: The present study demonstrated that treatments which alter PGE1 and PGE2 metabolism influence the proliferative and apoptotic indices of T98G glioma cells. The migratory capacity of the cells was also significantly affected by the change in prostaglandin metabolism. Modifying PG metabolism remains an interesting target for future studies in gliomas.

Beschreibung: Background: The objectives of the present study were to investigate the efficacy of the mixed culture of Lactobacillus acidophilus (DSM 20242), Bifidobacterium bifidum (DSM 20082) and Lactobacillus helveticus (CK60) in the fermentation of maize and the evaluation of the effect of the fermented meal on the lipid profile of rats. Methods: Rats were randomly assigned to 3 groups and each group placed on a Diet A (high fat diet into which a maize meal fermented with a mixed culture of Lb acidophilus (DSM 20242), B bifidum (DSM 20082) and Lb helveticus (CK 60) was incorporated), B (unfermented high fat diet) or C (commercial rat chow) respectively after the first group of 7 rats randomly selected were sacrificed to obtain the baseline data. Thereafter 7 rats each from the experimental and control groups were sacrificed weekly for 4 weeks and the plasma, erythrocytes, lipoproteins and organs of the rats were assessed for cholesterol, triglyceride and phospholipids. Results: Our results revealed that the mixed culture of Lb acidophilus (DSM 20242), B bifidum (DSM 20082) and Lb helveticus (CK 60) were able to grow and ferment maize meal into 'ogi' of acceptable flavour. In addition to plasma and hepatic hypercholesterolemia and hypertriglyceridemia, phospholipidosis in plasma, as well as cholesterogenesis, triglyceride constipation and phospholipidosis in extra-hepatic tissues characterized the consumption of unfermented hyperlipidemic diets. However, feeding the animals with the fermented maize diet reversed the dyslipidemia. Conclusion: The findings of this study indicate that consumption of mixed culture lactic acid bacteria (Lb acidophilus (DSM 20242), Bifidobacterium bifidum (DSM 20082) and Lb helveticus (CK 60) fermented food results in the inhibition of fat absorption. It also inhibits the activity of HMG CoA reductase. This inhibition may be by feedback inhibition or repression of the transcription of the gene encoding the enzyme via activation of the sterol regulatory element binding protein (SREBP) transcription factor. It is also possible that consumption of fermented food enhances conversion of cholesterol to bile acids by activating cholesterol-7alpha-hydroxylase.

Beschreibung: Background: Administration of androgens decreases plasma concentrations of high-density lipid cholesterol (HDL-C). However, the mechanisms by which androgens mediate lipid metabolism remain unknown. This present study used HepG2 cell cultures and ovariectomized C57BL/6 J mice to determine whether apolipoprotein M (ApoM), a constituent of HDL, was affected by dihydrotestosterone (DHT). Methods: HepG2 cells were cultured in the presence of either DHT, agonist of protein kinase C (PKC), phorbol-12-myristate-13-acetate (PMA), blocker of androgen receptor flutamide together with different concentrations of DHT, or DHT together with staurosporine at different concentrations for 24 hrs. Ovariectomized C57BL/6 J mice were treated with DHT or vehicle for 7d or 14d and the levels of plasma ApoM and livers ApoM mRNA were measured. The mRNA levels of ApoM, ApoAI were determined by real-time RT-PCR. ApoM and ApoAI were determined by western blotting analysis. Results: Addition of DHT to cell culture medium selectively down-regulated ApoM mRNA expression and ApoM secretion in a dose-dependent manner. At 10 nM DHT, the ApoM mRNA levels were about 20% lower than in untreated cells and about 40% lower at 1000 nM DHT than in the control cells. The secretion of ApoM into the medium was reduced to a similar extent. The inhibitory effect of DHT on ApoM secretion was not blocked by the classical androgen receptor blocker flutamide but by an antagonist of PKC, Staurosporine. Agonist of PKC, PMA, also reduced ApoM. At 0.5 muM PMA, the ApoM mRNA levels and the secretion of ApoM into the medium were about 30% lower than in the control cells. The mRNA expression levels and secretion of another HDL-associated apolipoprotein AI (ApoAI) were not affected by DHT. The levels of plasma ApoM and liver ApoM mRNA of DHT-treated C57BL/6 J mice were lower than those of vehicle-treated mice. Conclusions: DHT directly and selectively down-regulated the level of ApoM mRNA and the secretion of ApoM by protein kinase C but independently of the classical androgen receptor.

Beschreibung: Background: Although statins (STs) are drugs of first choice in hypercholesterolemic patients, especially in those at high cardiovascular risk, some of them are intolerant to STs or refuse treatment with these drugs. In view of this, we have evaluated the lipid-lowering effect of a nutraceutical pill containing berberine (BBR) and of ezetimibe, as alternative treatments, in monotherapy or in combination, in 228 subjects with primary hypercholesterolemia (HCH), with history of STs intolerance or refusing STs treatment. In addition, since PCSK9 was found up-regulated by STs dampening their effect through an LDL receptors (LDLRs) degradation, and BBR suppressed PCSK9 expression in cellular studies, we supplemented the stable lipid-lowering therapy of 30 genotype-confirmed Familial Hypercholesterolemia heterozygotes (HeFH) with BBR, searching for a further plasma cholesterol reduction. Plasma lipid pattern was evaluated at baseline and during treatments. Results: In HCH subjects the nutraceutical pill resulted more effective than EZE in lowering LDL cholesterol (-31.7% vs -25.4%, P 〈 0.001) and better tolerated. On treatment, LDL-C level below 3.36 mmol/L (

Beschreibung: Background: Biologists are elucidating complex collections of genetic regulatory data for multiple organisms. Software is needed for such regulatory network data. Results: The Pathway Tools software provides a comprehensive environment for manipulating molecular regulatory interactions that integrates regulatory data with an organism's genome and metabolic network. The Pathway Tools regulation ontology captures transcriptional and translational regulation, substrate-level regulation of enzyme activity, post-translational modifications, and regulatory pathways. Curated collections of regulatory data are available for Escherichia coli, Bacillus subtilis, and Shewanella oneidensis. Regulatory visualizations include a novel diagram that sum- marizes all regulatory influences on a gene; a transcription-unit diagram, and an interactive visualization of a full transcriptional regulatory network that can be painted with gene expression data to probe correlations between gene expression and regulatory mechanisms. We introduce a novel type of enrichment analysis that asks whether a gene-expression dataset is over-represented for known regulators. We present algorithms for ranking the degree of regulatory influence of genes , and for computing the net positive and negative regulatory influences on a gene.

Beschreibung: Background: Inverted repeat genes encode precursor RNAs characterized by hairpin structures. These RNA hairpins are then metabolized by biosynthetic pathways to produce functional small RNAs. In eukaryotic genomes, short non-autonomous transposable elements can have similar size and hairpin structures as non-coding precursor RNAs. This resemblance leads to problems annotating small RNAs.MethodWe mapped all microRNA precursors from miRBASE to several genomes and studied the repetition and dispersion of the corresponding loci. We then searched for repetitive elements overlapping these loci. Results: We developed an automatic method called ncRNAclassifier to classify pre-ncRNAs according to their relationship with transposable elements (TEs). We show there is a correlation between the number of scattered occurrences of ncRNA precursor candidates is correlated with the presence of TEs. We applied ncRNAclassifier on six chordate genomes and report our findings. Among the 1,426 human and 721 mouse pre-miRNAs of miRBase, we identified 235 and 68 mis-annotated pre-miRNAs respectively corresponding completely to TEs. Conclusions: We provide a tool enabling the identification of repetitive elements in precursor ncRNA sequences. ncRNAclassifier is available at http://EvryRNA.ibisc.univ-evry.fr

Beschreibung: Background: Partly because of functional genomics, there has been a major paradigm shift from solely thinking of skeletal muscle as contractile machinery to an understanding that it can have roles in paracrine and endocrine functions. Physical inactivity is an established risk factor for some blood clotting disorders. The effects of inactivity during sitting are most alarming when a person develops the enigmatic condition in the legs called deep venous thrombosis (DVT) or "coach syndrome," caused in part by muscular inactivity. The goal of this study was to determine if skeletal muscle expresses genes with roles in hemostasis and if their expression level was responsive to muscular inactivity such as prolonged sitting. Methods: Microarray analyses were performed on skeletal muscle samples from rats and humans to identify genes associated with hemostatic function that were significantly expressed above background based on multiple probe sets with perfect and mismatch sequences. Furthermore, we determined if any of these genes were responsive to models of physical inactivity. Multiple criteria were used to determine differential expression including significant expression above background, fold change, and non-parametric statistical tests. Results: These studies demonstrate skeletal muscle tissue expresses at least 17 genes involved in hemostasis. These include the fibrinolytic factors tetranectin, annexin A2, and tPA; the anti-coagulant factors TFPI, protein C receptor, PAF acetylhydrolase; coagulation factors, and genes necessary for the posttranslational modification of these coagulation factors such as vitamin K epoxide reductase. Of special interest, lipid phosphate phosphatase-1 (LPP1/PAP2A), a key gene for degrading prothrombotic and proinflammatory lysophospholipids, was suppressed locally in muscle tissue within hours after sitting in humans; this was also observed after acute and chronic physical inactivity conditions in rats, and exercise was relatively ineffective at counteracting this effect. Conclusions: These findings suggest that skeletal muscle may play an important role in hemostasis and that muscular inactivity may contribute to hemostatic disorders not only because of the slowing of blood flow per se, but also potentially because of the contribution from genes expressed locally in muscles, such as LPP1.

Beschreibung: Background: New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences. Results: We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as "Sifting Families," or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology--based analyses. Conclusions: We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/).

Beschreibung: Background: Surgery is the mainstay therapy for HPV-induced laryngeal papillomatosis (LP) and adjuvant therapies are palliative at best. Research revealed that conjugated-linoleic acid (CLA) may improve the outcome of virally-induced diseases. The effects of ClarinolTM G-80 (CLA) and high oleic safflower oil (HOSF) on children with LP (concomitant with surgery) were evaluated.DesignA randomized, double-blinded, crossover and reference-oil controlled trial was conducted at a South African medical university. Study components included clinical, HPV type/load and lymphocyte/cytokine analyses, according to routine laboratory methods.ParticipantsOverall: ten children enrolled; eight completed the trial; five remained randomized; seven received CLA first; all treatments remained double-blinded.InterventionChildren (4 to 12 years) received 2.5 ml p/d CLA (8 weeks) and 2.5 ml p/d HOSF (8 weeks) with a washout period (6 weeks) in-between. The one-year trial included a post-treatment period (30 weeks) and afterwards was a one-year follow-up period.Main outcome measuresChanges in numbers of surgical procedures for improved disease outcome, total/anatomical scores (staging system) for papillomatosis prevention/viral inhibition, and lymphocyte/cytokine counts for immune responses between baselines and each treatment/end of trial were measured.FindingsAfter each treatment all the children were in remission (no surgical procedures); after the trial two had recurrence (surgical procedures in post-treatment period); after the follow-up period three had recurrence (several surgical procedures) and five recovered (four had no surgical procedures). Effects of CLA (and HOSF to a lesser extent) were restricted to mildly/moderately aggressive papillomatosis. Children with low total scores (seven/less) and reduced infections (three/less laryngeal sub-sites) recovered after the trial. No harmful effects were observed. The number of surgical procedures during the trial (n6/available records) was significantly lower [(p 0.03) (95 % CI 1.1; 0)]. Changes in scores between baselines and CLA treatments (n8) were significantly lower: total scores [(p 0.02) (95 % CI 30.00; 0.00)]; anatomical scores [(p 0.008) (95 % CI 33.00: -2.00)]. Immune enhancement could not be demonstrated. Conclusions: These preliminary case and group findings pave the way for further research on the therapeutic potential of adjuvant CLA in the treatment of HPV-induced LP.

Beschreibung: Background: Cholesteryl ester transfer protein (CETP) plays a major role in lipid metabolism, but studies on the association of CETP polymorphisms with risks of cardiovascular disease are inconsistent. This study investigated whether the CETP gene I405V and Taq1B polymorphisms modified subclinical atherosclerosis in an asymptomatic Brazilian population sample. Methods: The polymorphisms were analyzed using polymerase chain reaction in 207 adult volunteers. Serum lipid profiles, oxLDL Ab titers, C-reactive protein and tumor necrosis factor-alpha concentrations and CETP and phospholipid transfer protein (PLTP) activities were determined, and common carotid artery intima-media thickness (cIMT) was measured using ultrasonography. Results: No differences in cIMT were observed between the presence or absence of the minor B2 and V alleles in either polymorphism. However, inverse correlations between mean cIMT and CETP activity in the presence of these polymorphisms were observed, and positive correlations of these polymorphisms with PLTP activity and oxLDL Ab titers were identified. Moreover, logistic multivariate analysis revealed that the presence of the B2 allele was associated with a 5.1-fold (CI 95%, OR: 1.26 -- 21.06) increased risk for cIMT, which was equal and above the 66th percentile and positively interacted with age. However, no associations with the V allele or CETP and PLTP activities were observed. Conclusions: None of the studied parameters, including CETP activity, explained the different relationships between these polymorphisms and cIMT, suggesting that other non-determined factors were affected by the genotypes and related to carotid atherosclerotic disease.

Beschreibung: Background: Chromosome conformation capture experiments result in pairwise proximity measurements between chromosome locations in a genome, and they have been used to construct three-dimensional models of genomic regions, chromosomes, and entire genomes. These models can be used to understand long-range gene regulation, chromosome rearrangements, and the relationships between sequence and spatial location. However, it is unclear whether these pairwise distance constraints provide sufficient information to embed chromatin in three dimensions. A priori, it is possible that an infinite number of embeddings are consistent with the measurements due to a lack of constraints between some regions. It is therefore necessary to separate regions of the chromatin structure that are sufficiently constrained from regions with measurements that do not provide enough information to reconstruct the embedding. Results: We present a new method based on graph rigidity to assess the suitability of experiments for constructingplausible three-dimensional models of chromatin structure. Underlying this analysis is a new, efficient, andaccurate algorithm for finding sufficiently constrained (rigid) collections of constraints in three dimensions, aproblem for which there is no known efficient algorithm. Applying the method to four recent chromosomeconformation experiments, we find that, for even stringently filtered constraints, a large rigid component spansmost of the measured region. Filtering highlights higher-confidence regions, and we find that the organizationof these regions depends crucially on short-range interactions. Conclusions: Without performing an embedding or creating a frequency-to-distance mapping, our proposed approachestablishes which substructures are supported by a sufficient framework of interactions. It also establishes thatinteractions from recent highly filtered genome-wide chromosome conformation experiments provide anadequate set of constraints for embedding. Pre-processing experimentally observed interactions with thismethod before relating chromatin structure to biological phenomena will ensure that hypothesized correlationsare not driven by the arbitrary choice of a particular unconstrained embedding. The software for identifyingrigid components is GPL-Licensed and available for download at http://cbcb.umd.edu/kingsford-group/starfish.

Beschreibung: Background: Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. Results: GOBA - Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. Conclusions: The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Beschreibung: Background: The University of California, Santa Cruz (UCSC) genome database is among the most used sources of genomic annotation in human and other organisms. The database offers an excellent web-based graphical user interface (the UCSC genome browser) and several means for programmatic queries. A simple application programming interface (API) in a scripting language aimed at the biologist was however not yet available. Here, we present the Ruby UCSC API, a library to access the UCSC genome database using Ruby. Results: The API is designed as a BioRuby plug-in and built on the ActiveRecord 3 framework for the object-relational mapping, making writing SQL statements unnecessary. The current version of the API supports databases of all organisms in the UCSC genome database including human, mammals, vertebrates, deuterostomes, insects, nematodes, and yeast.The API uses the bin index---if available---when querying for genomic intervals. The API also supports genomic sequence queries using locally downloaded *.2bit files that are not stored in the official MySQL database. The API is implemented in pure Ruby and is therefore available in different environments and with different Ruby interpreters (including JRuby). Conclusions: Assisted by the straightforward object-oriented design of Ruby and ActiveRecord, the Ruby UCSC API will facilitate biologists to query the UCSC genome database programmatically. The API is available through the RubyGem system. Source code and documentation are available at https://github.com/misshie/bioruby-ucsc-api/ under the Ruby license. Feedback and help is provided via the website at http://rubyucscapi.userecho.com/.

Beschreibung: Background: Cardiomyocytes apoptosis is an important contributor to myocardial dysfunction and heart failure. Adiponectin has cardioprotective effects, potential mechanisms behind it are not clear in cardiomyocytes. The purpose of the study was to investigate whether adiponectin can block palmitate-induced apoptosis and the underlying biochemical mechanism in H9c2 cells. Methods: H9c2 cells were treated with palmitate presence or absence of 2.5 mug/mL globular adiponectin. The effect on the cell viability of H9c2 cells was evaluated using MTT assay, and cell apoptosis was determined by Hoechst 33342 staining. Protein expression was measured using the western blot method. Results: Our results showed that the palmitate treatment induced apoptosis in H9c2 cells, which was associated with increasing the level of cleaved caspase-3 and cleaved PARP. Meanwhile, palmitate-induced apoptosis increased the protein level of p-ERK1/2, and decreased the protein level of p-Akt significantly. However, levels of both of these proteins were restored to the normal when pretreated with adiponectin, and followed with the decrease of cleaved caspase-3 and cleaved PARP. In line with these results, the protective effect of adiponectin can be blocked by PI3K/Akt inhibitor LY294002, and palmitate-induced apoptosis can be attenuated by ERK1/2 inhibitor U0126. Conclusions: Taken together, the present study demonstrated that adiponectin protects H9c2 cells from palmitate-induced apoptosis via PI3K/Akt and ERK1/2 signaling pathways. Our results reveal a link between adiponectin and cardiomyocytes apoptosis, suggesting that adioponectin may be a promising therapeutic for the treatment of lipotoxicity cardiomyopathy.

Beschreibung: Background: Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays. Results: LO-BaFL filtering and DE testing accurately predicted previously validated DE genes in a published experiment on coronary artery disease (CAD). Filtering healthy control data from the sALS and CAD studies with LO-BaFL resulted in highly correlated expression levels across many genes. After bioinformatics analysis, twelve genes from the sALS DE gene list were selected for independent testing using qRT-PCR assays. High-quality RNA from six healthy Control and six sALS samples yielded the predicted differential expression for 7 genes: TARDBP, SKIV2L2, C12orf35, DYNLT1, ACTG1, B2M, and ILKAP. Four of the seven have been previously described in sALS studies, while ACTG1, B2M and ILKAP appear in the context of this disease for the first time. Supplementary material can be accessed at: http://webpages.uncc.edu/~cbaciu/LO-BaFL/supplementary_data.html Conclusion: LO-BaFL predicts DE results that are broadly similar to those of other methods. The small healthy control cohort in the sALS study is a reasonable foundation for predicting DE genes. Modifying the BaFL pipeline allowed us to remove noise and systematic errors, improving the power of this study, which had a small sample size. Each bioinformatics approach revealed DE genes not predicted by the other; subsequent PCR assays confirmed seven of twelve candidates, a relatively high success rate.

Beschreibung: Background: With the advent of next-generation sequencing (NGS) technologies, full cDNA shotgun sequencing has become a major approach in the study of transcriptomes, and several different protocols in 454 sequencing have been invented. As each protocol uses its own short DNA tags or adapters attached to the ends of cDNA fragments for labeling or sequencing, different contaminants may lead to mis-assembly and inaccurate sequence products. Results: We have designed and implemented a new program for raw sequence cleaning in a graphical user interface and a batch script. The cleaning process consists of several modules including barcode trimming, sequencing adapter trimming, amplification primer trimming, poly-A tail trimming, vector screening and low quality region trimming. These modules can be combined based on various sequencing applications. Conclusions: ESTclean is a software package not only for cleaning cDNA sequences, but also for helping to develop sequencing protocols by providing summary tables and figures for sequencing quality control in a graphical user interface. It outperforms in cleaning read sequences from complicated sequencing protocols which use barcodes and multiple amplification primers.

Beschreibung: Background: While the genetics of diploid inheritance are well studied and software for linkage mapping, haplotyping and QTL analysis are available, for tetraploids the available tools are limited. In order to develop such tools it would be helpful if simulated populations based on a variety of models of the tetraploid meiosis would be available. Results: Here we present PedigreeSim, a software package that simulates meiosis in both diploid and tetraploid species and uses this to simulate pedigrees and cross populations. For tetraploids a variety of models can be used, including both bivalent and quadrivalent formation, varying degrees of preferential pairing of hom(oe)ologous chromosomes, different quadrivalent configurations and more. Simulation of quadrivalent meiosis results as expected in double reduction and recombination between more than two hom(oe)ologous chromosomes. The results are shown to match theoretical predictions. Conclusions: This is the first simulation software that implements all features of meiosis in tetraploids. It allows to generate data for tetraploid and diploid populations, and to investigate different models of tetraploid meiosis. The software and manual are available from http://www.plantbreeding.wur.nl/UK/software_pedigreeSim.html and as Additional files 1, 2, 3 and 4 with this publication.

Beschreibung: The objective of the study is a comparative evaluation of flavone isolated from Mucuna pruriens and coumarin isolated from Ionidium suffruticosum was assessed for the hypolipidemic activity in rats fed with high fat diet. The acute toxicity study was found that flavone (M.pruriens) and coumarin (I.suffruticosum) are safe up to 100mg/kg, so one tenth of this dose (10mg/kg) was consider as a evaluation dose. High fat diet group of rats showed significant (p

Beschreibung: Background: Sharing of data about variation and the associated phenotypes is a critical need, yet variant information can be arbitrarily complex, making a single standard vocabulary elusive and re-formatting difficult. Complex standards have proven too time-consuming to implement. Results: The GEN2PHEN project addressed these difficulties by developing a comprehensive data model for capturing biomedical observations, Observ-OM, and building the VarioML format around it. VarioML pairs a simplified open specification for describing variants, with a toolkit for adapting the specification into one's own research workflow. Straightforward variant data can be captured, federated, and exchanged with no overhead; more complex data can be described, without loss of compatibility. The open specification enables push-button submission to gene variant databases (LSDB's) e.g., the Leiden Open Variation Database, using the Cafe Variome data publishing service, while VarioML bidirectionally transforms data between XML and web-application code formats, opening up new possibilities for open source web applications building on shared data. A Java implementation toolkit makes VarioML easily integrated into biomedical applications. VarioML is designed primarily for LSDB data submission and transfer scenarios, but can also be used as a standard variation data format for JSON and XML document databases and user interface components. Conclusions: VarioML is a set of tools and practices improving the availability, quality, and comprehensibility of human variation information. It enables researchers, diagnostic laboratories, and clinics to share that information with ease, clarity, and without ambiguity.

Beschreibung: Background: We consider the problem of finding the maximum frequent agreement subtrees (MFASTs) in a collection ofphylogenetic trees. Existing methods for this problem often do not scale beyond datasets with around 100taxa. Our goal is to address this problem for datasets with over a thousand taxa and hundreds of trees. Results: We develop a heuristic solution that aims to find MFASTs in sets of many, large phylogenetic trees. Ourmethod works in multiple phases. In the first phase, it identifies small candidate subtrees from the set of inputtrees which serve as the seeds of larger subtrees. In the second phase, it combines these small seeds to buildlarger candidate MFASTs. In the final phase, it performs a post-processing step that ensures that we find afrequent agreement subtree that is not contained in a larger frequent agreement subtree. We demonstrate thatthis heuristic can easily handle data sets with 1000 taxa, greatly extending the estimation of MFASTs beyondcurrent methods. Conclusions: Although this heuristic does not guarantee to find all MFASTs or the largest MFAST, it found the MFAST inall of our synthetic datasets where we could verify the correctness of the result. It also performed well on largeempirical data sets. Its performance is robust to the number and size of the input trees. Overall, this methodprovides a simple and fast way to identify strongly supported subtrees within large phylogenetic hypotheses.

Beschreibung: Background: Currently, there is no open-source, cross-platform and scalable framework for coalescent analysis in population genetics. There is no scalable GUI based user application either. Such a framework and application would not only drive the creation of more complex and realistic models but also make them truly accessible. Results: As a first attempt, we built a framework and user application for the domain of exact calculations in coalescent analysis. The framework provides an API with the concepts of model, data, statistic, phylogeny, gene tree and recursion. Infinite-alleles and infinite-sites models are considered. It defines pluggable computations such as counting and listing all the ancestral configurations and genealogies and computing the exact probability of data. It can visualize a gene tree, trace and visualize the internals of the recursion algorithm for further improvement and attach dynamically a number of output processors. The user application defines jobs in a plug-in like manner so that they can be activated, deactivated, installed or uninstalled on demand. Multiple jobs can be run and their inputs edited. Job inputs are persisted across restarts and running jobs can be cancelled where applicable. Conclusions: Coalescent theory plays an increasingly important role in analysing molecular population genetic data. Models involved are mathematically difficult and computationally challenging. An open-source, scalable framework that lets users immediately take advantage of the progress made by others will enable exploration of yet more difficult and realistic models. As models become more complex and mathematically less tractable, the need for an integrated computational approach is obvious. Object oriented designs, though has upfront costs, are practical now and can provide such an integrated approach.

Beschreibung: Background: A dietary supplement containing a blend of 170 mg of N-oleyl-phosphatidylethanolamine (NOPE) and 100 mg of epigallocatechin-3-gallate (EGCG) has been shown to improve compliance to low caloric diets. Considering the cost of dietary ingredients, many manufacturers attempt to determine the lowest efficacious dose. Thus, the purpose of this study was to evaluate the efficacy of 8-weeks of supplementation with a daily intake of 120 mg of NOPE and 105 mg of EGCG in conjunction with a low caloric diet and regular, moderate exercise on dietary compliance in healthy, overweight adults. An additional purpose was to examine the effect of this supplement/diet/exercise paradigm on changes in body composition, sensation of appetite, mood and severity of binge eating. Methods: Fifty healthy, overweight (BMI 〉 25 m[bullet operator]kg2) men (15) and women (35) (SUP; n = 25; 32.7 +/- 13.75 y; BMI = 33.4 +/- 6.2; PLA; n = 25, 34.3 +/- 12.7 years; BMI = 33.2 +/- 6.8) were recruited for a double-blind, placebo controlled study. Each volunteer was randomly assigned to either the supplement (SUP; n = 25) or placebo group (PLA; n = 25). Based upon a self-reported 3-day dietary recall all volunteers were recommended a 500 kcal or 30% (maximum of 1000 kcal) reduction in caloric intake. Volunteers were also encouraged to exercise 30 minutes per day, three times per week. Results: Subjects in SUP were significantly more compliant (x2 = 3.86, p = 0.049) in maintaining a low caloric diet at week 4, but this was not able to be maintained through the 8-week study. In addition, a significant difference in mood, feelings of fatigue and confusion were noted between the groups at week 4, but again not maintained by week 8 where only feelings of tension were improved. No differences between groups (p 〉 0.05) were observed for body mass, body composition, feelings of hunger, and binge eating after eight weeks. Conclusion: Supplementing with a combination of 120 mg of NOPE and 105 mg of EGCG does appear to enhance compliance to a low caloric diet and improve mood for 4 --weeks, but loses its effectiveness by week 8.

Beschreibung: Background: Clinical Bioinformatics is currently growing and is based on the integration of clinical and omics data aiming at the development of personalized medicine. Thus the introduction of novel technologies able to investigate the relationship among clinical states and biological machineries may help the development of this field. For instance the Affymetrix DMET platform (drug metabolism enzymes and transporters) is able to study the relationship among the variation of the genome of patients and drug metabolism, detecting SNPs (Single Nucleotide Polymorphism) ongenes related to drug metabolism. This may allow for instance to find genetic variants in patients which present different drug responses, in pharmacogenomics and clinical studies. Despite this, there is currently a lack in the development of open-source algorithms and tools for the analysis of DMET data. Existing software tools for DMET data generally allow only the preprocessing of binary data (e.g. the DMET-Console provided by Affymetrix) and simple data analysis operations, but do not allow to test the association of the presence of SNPs with the response to drugs. Results: We developed DMET-Analyzer a tool for the automatic association analysis among the variation of the patient genomes and the clinical conditions of patients, i.e. the different response to drugs. The proposed system allows: (i) to automatize the workflow of analysis of DMET-SNP data avoiding the use of multiple tools; (ii) the automatic annotation of DMET-SNP data and the search in existing databases of SNPs (e.g. dbSNP), (iii) the association of SNP with pathway through the search in PharmaGKB, a major knowledge base for pharmacogenomic studies. DMET-Analyzer has a simple graphical user interface that allows users (doctors/biologists) to upload and analyse DMET files produced by Affymetrix DMET-Console in an interactive way. The effectiveness and easy use of DMET Analyzer is demonstrated through different case studies regarding the analysis of clinical datasets produced in the University Hospital of Catanzaro, Italy. Conclusion: DMET Analyzer is a novel tool able to automatically analyse data produced by the DMET-platform in case-control association studies. Using such tool user may avoid wasting time in the manual execution of multiple statistical tests avoiding possible errors and reducing the amount of time needed for a whole experiment. Moreover annotations and the direct link to external databases may increase the biological knowledge extracted. The system is freely available for academic purposes at: https://sourceforge.net/projects/dmetanalyzer/files/

Beschreibung: Background: The Hedgehog Signaling Pathway is one of signaling pathways that are very important toembryonic development. The participation of inhibitors in the Hedgehog Signal Pathway cancontrol cell growth and death, and searching novel inhibitors to the functioning of thepathway are in a great demand. As the matter of fact, effective inhibitors could provideefficient therapies for a wide range of malignancies, and targeting such pathway in cellsrepresents a promising new paradigm for cell growth and death control. Current researchmainly focuses on the syntheses of the inhibitors of cyclopamine derivatives, which bindspecifically to the Smo protein, and can be used for cancer therapy. While quantitativelystructure-activity relationship (QSAR) studies have been performed for these compounds among different cell lines, none of them have achieved acceptable results in the prediction ofactivity values of new compounds. In this study, we proposed a novel collaborative QSARmodel for inhibitors of the Hedgehog Signaling Pathway by integration the information frommultiple cell lines. Such a model is expected to substantially improve the QSAR ability fromsingle cell lines, and provide useful clues in developing clinically effective inhibitors andmodifications of parent lead compounds for target on the Hedgehog Signaling Pathway. Results: In this study, we have presented: (1) a collaborative QSAR model, which is used to integrateinformation among multiple cell lines to boost the QSAR results, rather than only a singlecell line QSAR modeling. Our experiments have shown that the performance of our model issignificantly better than single cell line QSAR methods; and (2) an efficient feature selectionstrategy under such collaborative environment, which can derive the commonly importantfeatures related to the entire given cell lines, while simultaneously showing their specificcontributions to a specific cell-line. Based on feature selection results, we have proposedseveral possible chemical modifications to improve the inhibitor affinity towards multipletargets in the Hedgehog Signaling Pathway. Conclusions: Our model with the feature selection strategy presented here is efficient, robust, and flexible,and can be easily extended to model large-scale multiple cell line/QSAR data. The data andscripts for collaborative QSAR modeling are available in the Additional file 1.

Beschreibung: Background: Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation.In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows.Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources.We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations.ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided.These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlappinng, gene-model-centric genomic loci.Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations.Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis. Results: ParsEval is capable of analyzing annotations for large eukaryotic genomes on typical desktop or laptop hardware.In comparison to existing methods, ParsEval exhibits a considerable performance improvement, both in terms of runtime and memory consumption.Reports from ParsEval can provide relevant biological insights into the gene structure annotations being compared. Conclusions: Implemented in C, ParseEval provides the quickest and most feature-rich solution for genome annotation comparison to date.The source code is freely available (under an ISC license) at http://parseval.sourceforge.net/.

Beschreibung: Background: Web-based synteny visualization tools are important for sharing data and revealing patterns of complicated genome conservation and rearrangements. Such tools should allow biologists to upload genomic data for their own analysis. This requirement is critical because individual biologists are generating large amounts of genomic sequences that quickly overwhelm any centralized web resources to collect and display all those data. Recently, we published a web-based synteny viewer, GSV, which was designed to satisfy the above requirement. However, GSV can only compare two genomes at a given time. Extending the functionality of GSV to visualize multiple genomes is important to meet the increasing demand of the research community. Results: We have developed a multi-Genome Synteny Viewer (mGSV). Similar to GSV, mGSV is a web-based tool that allows users to upload their own genomic data files for visualization. Multiple genomes can be presented in a single integrated view with an enhanced user interface. Users can navigate through all the selected genomes in either pairwise or multiple viewing mode to examine conserved genomic regions as well as the accompanying genome annotations. Besides serving users who manually interact with the web server, mGSV also provides Web Services for machine-to-machine communication to accept data sent by other remote resources. The entire mGSV package can also be downloaded for easy local installation. Conclusions: mGSV significantly enhances the original functionalities of GSV. A web server hosting mGSV is provided at http://cas-bioinfo.cas.unt.edu/mgsv.

Beschreibung: Background: Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application Results: This paper describes a database and related software tools that allow testing of a given structure based methods on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. Conclusions: The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database.

Beschreibung: Background: Protein-protein, cell-signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent. Results: Genes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect input lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user's PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs separating diseases into two categories. Conclusions: Genes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.

Beschreibung: Background: Due to hybridization events in evolution, studying two different genes of a set of species may yieldtwo related but different phylogenetic trees for the set of species. In this case, we want to combine the two phylogenetic trees into a hybridization network with the fewest hybridization events. This leads to three computational problems, namely, the problem of computing the minimum size of a hybridization network, the problem of constructing one minimum hybridization network, and the problem of enumerating a representative set of minimum hybridization networks. The previously best software tools for these problems (namely, Chen and Wang's HybridNet and Albrecht et al.'s Dendroscope 3) run very slowly for large instances that cannot be reduced to relatively small instances. Indeed, when the minimum size of a hybridization network of two given trees are larger than 23 and the problem for the trees cannot be reduced to relatively smaller independent subproblems, then HybridNet almost always takes longer than 1 day and Dendroscope 3 often fails to complete. Thus, a faster software tool for the problems is in need. Results: We develop a software tool in ANSI C, named FastHN, for the following problems: Computing the minimum size of a hybridization network, constructing one minimum hybridization network, and enumerating a representative set of minimum hybridization networks. We obtain FastHN by refining HybridNet with three ideas. The first idea is to preprocess the input trees so that the trees become smaller or the problem becomes to solve two or more relatively smaller independent subproblems. The second idea is to use a fast algorithm for computing rSPR distance of two given phylognetic trees to cut more branches of the search tree in the exhaustive-search stage of the algorithm. The third idea is that during the exhaustive-search stage of the algorithm, we find two sibling leaves in one of the two forests (obtained from the given trees by cutting some edges) such that they are as far as possible in the other forest. As the result, FastHN always runs much faster than HybridNet. Unlike Dendroscope 3, FastHN is a single-threaded program. Despite this disadvantage, our experimental data shows that FastHN runs substantially faster than the multi-threaded Dendroscope 3 on a PC with multiple cores. Indeed, FastHN can finish within 16 minutes (on average on a Windows-7 (x64) desktop PC with i7-2600 CPU) even if the minimum size of a hybridization network of two given trees is about 25, the trees each have 100 leaves, and the problem for the input trees cannot be reduced to two or more independent subproblems via cluster reductions. It is also worth mentioning that like HybridNet, FastHN does not use much memory (indeed, the amount of memory is at most quadratic in the input size). In contrast, Dendroscope 3 uses a huge amount of memory. Executables of FastHN for Windows XP (x86), Windows 7 (x64), Linux, and Mac OS are available. Conclusions: For both biological datasets and simulated datasets, our experimental results show that FastHN runs substantially faster than HybridNet and Dendroscope 3. The superiority of FastHN in speed over the previous tools becomes more significant as the hybridization number becomes larger. In addition, FastHN uses much less memory than Dendroscope 3 and uses the same amount of memory as HybridNet.

Beschreibung: Background: Ongoing innovation in phylogenetics and evolutionary biology has been accompanied by a proliferation of software tools, data formats, analytical techniques and web servers. This brings with it the challenge of integrating phylogenetic and other related biological data found in a wide variety of formats, and underlines the need for reusable software that can read, manipulate and transform this information into the various forms required to build computational pipelines. Results: We built a Python software library for working with phylogenetic data that is tightly integrated with Biopython, a broad-ranging toolkit for computational biology. Our library, Bio.Phylo, is highly interoperable with existing libraries, tools and standards, and is capable of parsing common file formats for phylogenetic trees, performing basic transformations and manipulations, attaching rich annotations, and visualizing trees. We unified the modules for working with the standard file formats Newick, NEXUS and phyloXML behind a consistent and simple API, providing a common set of functionality independent of the data source. Conclusions: Bio.Phylo meets a growing need in bioinformatics for working with heterogeneous types of phylogenetic data. By supporting interoperability with multiple file formats and leveraging existing Biopython features, this library simplifies the construction of phylogenetic workflows. We also provide examples of the benefits of building a community around a shared open-source project. Bio.Phylo is included with Biopython, available through the Biopython website, http://biopython.org.

Beschreibung: Background: The increased use of multi-locus data sets for phylogenetic reconstruction has increased the need todetermine whether a set of gene trees significantly deviate from the phylogenetic patterns of other genes.Such unusual gene trees may have been influenced by other evolutionary processes such as selection, geneduplication, or horizontal gene transfer. Results: Motivated by this problem we propose a nonparametric goodness-of-fit test for two empirical distributionsof gene trees, and we developed the software GeneOut to estimate a p-value for the test. Our approachmaps trees into a multi-dimensional vector space and then applies support vector machines (SVMs) tomeasure the separation between two sets of pre-defined trees. We use a permutation test to assess thesignificance of the SVM separation. To demonstrate the performance of GeneOut, we applied it to thecomparison of gene trees simulated within different species trees across a range of species tree depths.Applied directly to sets of simulated gene trees with large sample sizes, GeneOut was able to detect verysmall differences between two set of gene trees generated under different species trees. Our statistical testcan also include tree reconstruction into its test framework through a variety of phylogenetic optimalitycriteria. When applied to DNA sequence data simulated from different sets of gene trees, results in the formof receiver operating characteristic (ROC) curves indicated that GeneOut performed well in the detectionof differences between sets of trees with different distributions in a multi-dimensional space. Furthermore, itcontrolled false positive and false negative rates very well, indicating a high degree of accuracy. Conclusions: The non-parametric nature of our statistical test provides fast and efficient analyses, and makes it anapplicable test for any scenario where evolutionary or other factors can lead to trees with differentmulti-dimensional distributions. The software GeneOut is freely available under the GNU public license.

Beschreibung: Background: Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study. Results: The results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the characterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors. Conclusions: Our best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect.

Beschreibung: Background: A scientific name for an organism can be associated with almost all biological data. Name identification is an important step in many text mining tasks aiming to extract useful information from biological, biomedical and biodiversity text sources. A scientific name acts as an important metadata element to link biological information. Results: We present NetiNeti (Name Extraction from Textual Information-Name Extraction for Taxonomic Indexing), a machine learning based approach for recognition of scientific names including the discovery of new species names from text that will also handle misspellings, OCR errors and other variations in names. The system generates candidate names using rules for scientific names and applies probabilistic machine learning methods to classify names based on structural features of candidate names and features derived from their contexts. NetiNeti can also disambiguate scientific names from other names using the contextual information. We evaluated NetiNeti on legacy biodiversity texts and biomedical literature (MEDLINE). NetiNeti performs better (precision = 98.9 % and recall = 70.5 %) compared to a popular dictionary based approach (precision = 97.5 % and recall = 54.3 %) on a 600-page biodiversity book that was manually marked by an annotator. On a small set of PubMed Central's full text articles annotated with scientific names, the precision and recall values are 98.5 % and 96.2 % respectively. NetiNeti found more than 190,000 unique binomial and trinomial names in more than 1,880,000 PubMed records when used on the full MEDLINE database. NetiNeti also successfully identifies almost all of the new species names mentioned within web pages. Additionally, we present the comparison results of various machine learning algorithms on our annotated corpus. Naive Bayes and Maximum Entropy with Generalized Iterative Scaling (GIS) parameter estimation are the top two performing algorithms. Conclusions: We present NetiNeti, a machine learning based approach for identification and discovery of scientific names. The system implementing the approach can be accessed at http://namefinding.ubio.org

Beschreibung: Background: Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets. Results: MetNet Online is a web-based portal that provides access to a regulatory and metabolic plant pathway database. The database and portal integrate Arabidopsis, soybean (Glycine max) and grapevine (Vitis vinifera) data. Pathways are enriched with known or predicted information on sub cellular location. MetNet Online enables pathways, interactions and entities to be browsed or searched by multiple categories such as sub cellular compartment, pathway ontology, and GO term. In addition to this, the "My MetNet" feature allows registered users to bookmark content and track, import and export customized lists of entities. Users can also construct custom networks using existing pathways and/or interactions as building blocks. Conclusion: The site can be reached at http://www.metnetonline.org. Extensive video tutorials on how to use the site are available through http://www.metnetonline.org/tutorial/.

Beschreibung: Background: Atherosclerosis is a chronic degenerative disease of the arteries and is thought to be one of the most common causes of death globally. In recent years, the functions of adventitial fibroblasts in the development of atherosclerosis and tissue repair have gained increased interests. LPS can increase the morbidity and mortality of atherosclerosis-associated cardiovascular disease. Although LPS increases neointimal via TLR4 activation has been reported, how LPS augments atherogenesis through acting on adventitial fibroblasts is still unknown. Here we explored lipid deposition within adventitial fibroblasts mediated by lipopolysaccharide (LPS) to imitate inflammatory conditions. Results: In our study, LPS enhanced lipid deposition by the up-regulated expression of adipose differentiation-related protein (ADRP) as the silencing of ADRP abrogated lipid deposition in LPS-activated adventitial fibroblasts. In addition, pre-treatment with anti-Toll-like receptor 4 (TLR4) antibody diminished the LPS-induced lipid deposition and ADRP expression. Moreover, LPS induced translocation of nuclear factor-kappaB (NF-kappaB), which could markedly up-regulate lipid deposition as pre-treatment with the NF-kappaB inhibitor, PDTC, significantly reduced lipid droplets. In addition, the lowering lipid accumulation was accompanied with the decreased ADRP expression. Furthermore, LPS-induced adventitial fibroblasts secreted more monocyte chemoattractant protein (MCP-1), compared with transforming growth factor-beta1 (TGF-beta1). Conclusions: Taken together, these results suggest that LPS promotes lipid accumulation via the up-regulation of ADRP expression through TLR4 activated downstream of NF-kappaB in adventitial fibroblasts. Increased levels of MCP-1 released from LPS-activated adventitial fibroblasts and lipid accumulation may accelerate monocytes recruitment and lipid-laden macrophage foam cells formation. Here, our study provides a new explanation as to how bacterial infection contributes to the pathological process of atherosclerosis.

Beschreibung: Background: Dysregulation of imprinted genes, which are expressed in a parent-of-origin-specific manner, plays an important role in various human diseases, such as cancer and behavioral disorder. To date, however, fewer than 100 imprinted genes have been identified in the human genome. The recent availability of high-throughput technology makes it possible to have large-scale prediction of imprinted genes. Here we propose a Bayesian model (dsPIG) to predict imprinted genes on the basis of allelic expression observed in mRNA-Seq data of independent human tissues. Results: Our model (dsPIG) was capable of identifying imprinted genes with high sensitivity and specificity and a low false discovery rate when the number of sequenced tissue samples was fairly large, according to simulations. By applying dsPIG to the mRNA-Seq data, we predicted 94 imprinted genes in 20 cerebellum samples and 57 imprinted genes in 9 diverse tissue samples with expected low false discovery rates. We also assessed dsPIG using previously validated imprinted and non-imprinted genes. With simulations, we further analyzed how imbalanced allelic expression of non-imprinted genes or different minor allele frequencies affected the predictions of dsPIG. Interestingly, we found that, among biallelically expressed genes, at least 18 genes expressed significantly more transcripts from one allele than the other among different individuals and tissues. Conclusion: With the prevalence of the mRNA-Seq technology, dsPIG has become a useful tool for analysis of allelic expression and large-scale prediction of imprinted genes. For ease of use, we have set up a web service and also provided an R package for dsPIG at http://www.shoudanliang.com/dsPIG/.

Beschreibung: Background: Given that acne is a rare condition in societies with higher consumption of omega-3 (n-3) relative to omega-6 (n-6) fatty acids, supplementation with n-3 may suppress inflammatory cytokine production and thereby reduce acne severity. Methods: 13 individuals with inflammatory acne were given three grams of fish oil containing 930 mg of EPA to their unchanged diet and existing acne remedies for 12 weeks. Acne was assessed using an overall severity grading scale, total inflammatory lesion counts, and colorimetry.FindingsThere was no significant change in acne grading and inflammatory counts at week 12 compared to baseline. However, there was a broad range of response to the intervention on an individual basis. The results showed that acne severity improved in 8 individuals, worsened in 4, and remained unchanged in 1. Interestingly, among the individuals who showed improvement, 7 were classified as having moderate to severe acne at baseline, while 3 of the 4 whose acne deteriorated were classified as having mild acne. Conclusion: There is some evidence that fish oil supplementation is associated with an improvement in overall acne severity, especially for individuals with moderate to severe acne. Divergent responses to fish oil in our pilot study indicates that dietary and supplemental lipids are worthy of further investigation in acne.

Beschreibung: Modern analytical methods in biology and chemistry useseparation techniques coupled to sensitive detectors, such as gaschromatography-mass spectrometry (GC-MS) and liquid chromatography-massspectrometry (LC-MS). These hyphenated methods provide high-dimensionaldata. Comparing such data manually to find corresponding signals is a laborioustask, as each experiment usually consists of thousands of individual scans, eachcontaining hundreds or even thousands of distinct signals.In order to allow for successful identification of metabolites or proteinswithin such data, especially in the context of metabolomics and proteomics, anaccurate alignment and matching of corresponding features between two or moreexperiments is required. Such a matching algorithm should capture fluctuationsin the chromatographic system which lead to non-linear distortions on the timeaxis, as well as systematic changes in recorded intensities.Many different algorithms for the retention time alignment of GC-MS and LC-MSdata have been proposed and published, but all of them focus either on aligningpreviously extracted peak features or on aligning and comparing the complete rawdata containing all available features. Results: In this paper we introduce two algorithms for retentiontime alignment of multiple GC-MS datasets: multiple alignment bybidirectional best hits peak assignment and cluster extension (BiPACE) andcenter-star multiple alignment by pairwise partitioned dynamic time warping(CeMAPP-DTW). We show how the similarity-based peak group matchingmethod BiPACE may be used for multiple alignment calculation individually and how it can be usedas a preprocessing step for the pairwise alignments performed by CeMAPP-DTW. We evaluate thealgorithms individually and in combination on a previously published small GC-MS dataset studying the Leishmania parasite and on a larger GC-MS dataset studying grains of wheat (Triticum aestivum). Conclusions: We have shown that BiPACE achieves very high precision and recall anda very low number of false positive peak assignments on both evaluation datasets. CeMAPP-DTW finds a high number of true positives when executed on its own,but achieves even better results when BiPACE is used to constrain its search space. The source code of both algorithms is included in the OpenSource software framework Maltcms, which is available from http://maltcms.sf.net. The evaluation scripts of the present study are available from the same source.

Beschreibung: Background: Biomedical processes can provide essential information about the (mal-) functioning of an organism and are thus frequently represented in biomedical terminologies and ontologies, including the GO Biological Process branch. These processes often need to be described and categorised in terms of their attributes, such as rates or regularities. The adequate representation of such process attributes has been a contentious issue in bio-ontologies recently; and domain ontologies have correspondingly developed ad hoc workarounds that compromise interoperability and logical consistency. Results: We present a design pattern for the representation of process attributes that is compatible with upper ontology frameworks such as BFO and BioTop. Our solution rests on two key tenets: firstly, that many of the sorts of process attributes which are biomedically interesting can be characterised by the ways that repeated parts of such processes constitute, in combination, an overall process; secondly, that entities for which a full logical definition can be assigned do not need to be treated as primitive within a formal ontology framework. We apply this approach to the challenge of modelling and automatically classifying examples of normal and abnormal rates and patterns of heart beating processes, and discuss the expressivity required in the underlying ontology representation language. We provide full definitions for process attributes at increasing levels of domain complexity. Conclusions: We show that a logical definition of process attributes is feasible, though limited by the expressivity of DL languages so that the creation of primitives is still necessary. This finding may endorse current formal upper-ontology frameworks as a way of ensuring consistency, interoperability and clarity.

Beschreibung: No description available

Beschreibung: Background: Epidemiological studies have suggested the benefits of omega-3 polyunsaturated fatty acids (n-3 PUFAs) on cardiovascular health, but only limited data are available describing n-3 PUFA regulated pathways in humans. The aim of this study was to investigate the effects of n-3 PUFA administration on whole genome expression profiles in the blood of normo- and dyslipidemic subjects. Methods: Differentially expressed genes were detected after four hours, one week and twelve weeks of supplementation with either fish oil (FO) or corn oil in normo- and dyslipidemic men using whole genome microarrays. Results: Independent of the oil, a significantly higher number of genes was regulated in dyslipidemic subjects compared to normolipidemic subjects. Pathway analyses discovered metabolisms dominantly affected by FO after twelve weeks of supplementation, including the lipid metabolism, immune system and cardiovascular diseases. Several pro-inflammatory genes, in particular, were down-regulated in dyslipidemic subjects, indicating the immune-modulatory and anti-inflammatory capability of FO and its bioactive FAs, eicosapentaenoic acid and docosahexaenoic acid. Conclusions: This is the first study showing significant differences in gene expression profiles between normo- and dyslipidemic men after FO supplementation. Further studies need to clarify the exact role of n-3 PUFAs in pathways and metabolisms which were identified as being regulated after FO supplementation in this study.Trial registrationClinicalTrials.gov (ID: NCT01089231)

Beschreibung: Background: The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment. Results: The novel angiogenesis module integrated into the agent-based tumor model is based on a set of reaction--diffusion equations that describe the spatio-temporal evolution of the distributions of micro-environmental factors such as glucose, oxygen, TGFalpha, VEGF and fibronectin. These molecular species regulate tumor growth during angiogenesis. Each tumor cell is equipped with an EGFR signaling pathway linked to a cell-cycle pathway to determine its phenotype. EGFR TKIs are delivered through the blood vessels of tumor microvasculature and the response to treatment is studied. Conclusions: Our simulations demonstrated that entire tumor growth profile is a collective behaviour of cells regulated by the EGFR signaling pathway and the cell cycle. We also found that angiogenesis has a dual effect under TKI treatment: on one hand, through neo-vasculature TKIs are delivered to decrease tumor invasion; on the other hand, the neo-vasculature can transport glucose and oxygen to tumor cells to maintain their metabolism, which results in an increase of cell survival rate in the late simulation stages.

Beschreibung: Background: Next-generation sequencing technologies have become important tools for genome-wide studies. However, the quality scores that are assigned to each base have been shown to be inaccurate. If the quality scores are used in downstream analyses, these inaccuracies can have a significant impact on the results. Results: Here we present ReQON, a tool that recalibrates the base quality scores from an input BAM file of aligned sequencing data using logistic regression. ReQON also generates diagnostic plots showing the effectiveness of the recalibration. We show that ReQON produces quality scores that are both more accurate, in the sense that they more closely correspond to the probability of a sequencing error, and do a better job of discriminating between sequencing errors and non-errors than the original quality scores. We also compare ReQON to other available recalibration tools and show that ReQON is less biased and performs favorably in terms of quality score accuracy. Conclusion: ReQON is an open source software package, written in R and available through Bioconductor, for recalibrating base quality scores for next-generation sequencing data. ReQON produces a new BAM file with more accurate quality scores, which can improve the results of downstream analysis, and produces several diagnostic plots showing the effectiveness of the recalibration.

Beschreibung: Background: Quantitative analysis of changes in dendritic spine morphology has become an interesting issue in contemporary neuroscience.However, the diversity in dendritic spines population might seriously influence the results of measurements in which their morphology is studied,the detection of differences in spine morphology between control and test group is often compromised by the number of dendritic spines taken for analysis. In order to estimate how severe is such an impact we have performed Monte Carlo simulations examining various experimental setups and statistical approaches. The confocal images of dendritic spines from hippocampal dissociated cultures have been used to create a set of variables exploited as the simulation resources. Results: The tabulated results of simulations are given, providing the number of dendritic spines required for the detection of hidden morphological differences between control and test group, in spine head-width, length and area. It turns out that this is the head-width among these three variables, where the changes are most easily detected. Simulation of changes occurring in a subpopulation of spines reveal the strong dependenceof detectability on the statistical approach applied. The analysis based on comparison of percentage of spines in subclasses is less sensitive thanthe direct comparison of relevant variables describing spines morphology. Conclusions: We evaluated the sampling aspect and effect of systematic morphological variation on detecting the differences in spine morphology.Provided results may serve as a guideline in selecting the number of samples to be studied in a planned experiment. Our simulations might be a step towards the development of a standardized method of quantitative comparison of dendritic spines morphology, in which different sources of errors are considered.

Beschreibung: Background: Traditionally various human diseases of kidneys, hormonal imbalance and sexual diseases are treated with Launaea procumbens (L). In the present study protective effects of methanolic extract of Launaea procumbens (LPME) was evaluated against CCl4-induced oxidative damages in rat testis. Methods: To examine the protective effects of Launaea procumbens on testis against oxidative stress of carbon tetrachloride in male rat, 30 male albino rats were equally divided into 5 groups (6 rats). First group was given standard diet and drinking water. Second group received CCl4 3 ml/kg intraperitoneally (30 % in olive oil). Third and forth were given orally 100; 200 mg/kg b.w., in 99.8 % dimethyl sulphooxide (DMSO), Launaea procumbens methanolic extracts (LPME) after 48 h of CCl4 treatment twice a week and sixth group received only LPME in DMSO at a dose of 200 mg/kg b.w., for four weeks. Protective effects of Launaea procumbens were observed on sperm concentration, motility and morphology, serum reproductive hormonal level, activity of antioxidant enzymes, lipid peroxidation (TBARS) and DNA damages. Results: Results of the present study revealed that treatment of CCl4 significantly (p 〈 0.01) reduced sperm concentration and motility comparatively to controls. Level of testosterone, luteinizing hormone and follicle stimulating hormone, were depleted markedly (p

Beschreibung: Since the amounts of arachidonic acid (AA) and EPA in food may have implications for human health, we investigated whether a small change in chicken feed influenced the blood lipid concentration in humans ingesting the chicken. Forty-six young healthy volunteers (age 19--29) were randomly allocated into two groups in a double-blind dietary intervention trial, involving ingestion of about 160 g chicken meat per day for 4 weeks. The ingested meat was either from chickens given a feed concentrate resembling the commercial chicken feed, containing 4% soybean oil (SO), or the meat was from chickens given a feed where the soybean oil had been replaced by 2% rapeseed oil plus 2% linseed oil (RLO).Serum total cholesterol, LDL and HDL cholesterol, triacylglycerols, serum phospholipid fatty acid concentration, blood pressure, body weight and C-reactive protein were determined at baseline and post-intervention. In subjects consuming chicken meat from the RLO group there was a significantly (p 〈 0.001) increased concentration of EPA in serum phospholipids, and a reduced ratio between AA and EPA. The participants that had a low% of EPA + DHA in serum phospholipids (less than 4.6%), all increased their% of EPA + DHA after the four week intervention period when consuming the RLO chicken. No significant response differences in cholesterol, triacylglycerol, C-reactive protein, body weight or blood pressure were observed between the groups. This trial demonstrates that a simple change in chicken feed can have beneficial effects on amount of EPA and the AA/EPA ratio in human serum phospholipids.

Beschreibung: Background: To compare the predictive ability of adolescent lipoprotein classification using the National Examination Survey (NHANES) cut points and those of the National Cholesterol Education Program (NCEP) for predicting abnormal levels in adulthood.MethodFrom 1032 adolescents, aged 14--19 years, participants of the Tehran Lipid and Glucose Study, all lipid measures were determined at baseline and again after 6 years. Multivariable Odds Ratios (ORs) were calculated for borderline and high categories of lipids to predict dyslipidemia in adulthood, considering the normal level as a reference. Area under the receiving characteristics curve (AUC) was used to assess the predictive ability of each adolescent lipid classification.ResultApplying the NCEP classification, the prevalences of high total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), triglycerides and low high density lipoprotein cholesterol (HDL-C) in males were 12.1%, 12.9%, 26.1% and 34.2% respectively; in females the corresponding prevalences were 15.4%, 17.9%, 21.4% and 25.0%, respectively. Using NHANES cut points, the prevalence of high TC, LDL-C and triglycerides were lower, than those defined by NCEP; the ORs of high categories of lipids (defined by NHANES) were higher than ORs based on the NECP classification, except for HDL-C. For all lipid measures, both classifications had similar predictive abilities, except for TC/HDL-C, which had higher predictive power applying the NHANES classification rather than the NCEP one (AUC 71% vs. 68%, respectively). Conclusion: No differences were found between NCEP and NHANES classifications for prediction of adult dyslipidemia, except for TC/HDL-C. Because of their simple application, NCEP cut points can be used in clinical settings.

Beschreibung: Background: Food security is an issue that has come under renewed scrutiny amidst concerns that substantial yield increases in cereal crops are required to feed the world's booming population. Wheat is of fundamental importance in this regard being one of the three most important crops for both human consumption and livestock feed; however, increase in crop yields have not kept pace with the demands of a growing world population. In order to address this issue, plant breeders require new molecular tools to help them identify genes for important agronomic traits that can be introduced into elite varieties. Studies of the genome using next-generation sequencing enable the identification of molecular markers such as single nucleotide polymorphisms that may be used by breeders to identify and follow genes when breeding new varieties. The development and application of next-generation sequencing technologies has made the characterisation of SNP markers in wheat relatively cheap and straightforward. There is a growing need for the widespread dissemination of this information to plant breeders.DescriptionCerealsDB is an online resource containing a range of genomic datasets for wheat (Triticum aestivum) that will assist plant breeders and scientists to select the most appropriate markers for marker assisted selection. CerealsDB includes a database which currently contains in excess of 100,000 putative varietal SNPs, of which several thousand have been experimentally validated. In addition, CerealsDB contains databases for DArT markers and EST sequences, and links to a draft genome sequence for the wheat variety Chinese Spring. Conclusion: CerealsDB is an open access website that is rapidly becoming an invaluable resource within the wheat research and plant breeding communities.

Beschreibung: Background: One of the crucial steps in regulation of gene expression is the binding of transcription factor(s) to specific DNA sequences. Knowledge of the binding affinity and specificity at a structural level between transcription factors and their target sites has important implications in our understanding of the mechanism of gene regulation. Due to their unique functions and binding specificity, there is a need for a transcription factor-specific, structure-based database and corresponding web service to facilitate structural bioinformatics studies of transcription factor-DNA interactions, such as development of knowledge-based interaction potential, transcription factor-DNA docking, binding induced conformational changes, and the thermodynamics of protein-DNA interactions.DescriptionTFinDit is a relational database and a web search tool for studying transcription factor-DNA interactions. The database contains annotated transcription factor-DNA complex structures and related data, such as unbound protein structures, thermodynamic data, and binding sequences for the corresponding transcription factors in the complex structures. TFinDit also provides a user-friendly interface and allows users to either query individual entries or generate datasets through culling the database based on one or more search criteria. Conclusions: TFinDit is a specialized structural database with annotated transcription factor-DNA complex structures and other preprocessed data. We believe that this database/web service can facilitate the development and testing of TF-DNA interaction potentials and TF-DNA docking algorithms, and the study of protein-DNA recognition mechanisms.

Beschreibung: Background: Brain-derived neurotrophic factor (BDNF) is a potent neurotrophic factor that is implicated in the regulation of food intake and body weight. Polyunsaturated fatty acids (PUFAs) localised in cell membranes have been shown to alter the levels of BDNF in the brain, suggesting that PUFAs and BDNF could have physical interaction with each other. To decipher the molecular mechanism through which PUFAs modulates BDNF's activity, molecular docking was performed for BDNF with PUFAs and its metabolites, with 4-Methyl Catechol as a control. Results: Inferring from molecular docking studies, lipoxin A4 (LXA4), and a known anti-inflammatory bioactive metabolite derived from PUFAs, with a binding energy of -3.98 Kcal/mol and dissociation constant of 1.2mM showed highest binding affinity for BDNF in comparison to other PUFAs and metabolites considered in the study. Further, the residues Lys 18, Thr 20, Ala 21, Val 22, Phe 46, Glu 48, Lys 50, Lys 58, Thr 75, Gln 77, Arg 97 and Ile 98 form hot point motif, which on interaction enhances BDNF's function. Conclusion: These results suggest that PUFAs and their metabolites especially, LXA4, modulate insulin resistance by establishing a physical interaction with BDNF. Similar interaction(s) was noted between BDNF and resolvins and protectins but were of lesser intensity compared to LXA4.

Beschreibung: Background: High-density oligonucleotide microarray is an appropriate technology for genomic analysis, and is particulary useful in the generation of transcriptional maps, ChIP-on-chip studies and re-sequencing of the genome.Transcriptome analysis of tiling microarray data facilitates the discovery of novel transcripts and the assessment of differential expression in diverse experimental conditions. Although new technologies such as next-generation sequencing have appeared, microarrays might still be useful for the study of small genomes or for the analysis of genomic regions with custom microarrays due to their lower price and good accuracy in expression quantification. Results: Here, we propose a novel wavelet-based method, named ZCL (zero-crossing lines), for the combined denoising and segmentation of tiling signals. The denoising is performed with the classical SUREshrink method and the detection of transcriptionally active regions is based on the computation of the Continuous Wavelet Transform (CWT). In particular, the detection of the transitions is implemented as the thresholding of the zero-crossing lines. The algorithm described has been applied to the public Saccharomyces cerevisiae dataset and it has been compared with two well-known algorithms: pseudo-median sliding window (PMSW) and the structural change model (SCM). As a proof-of-principle, we applied the ZCL algorithm to the analysis of the custom tiling microarray hybridization results of a S. aureus mutant deficient in the sigma B transcription factor. The challenge was to identify those transcripts whose expression decreases in the absence of sigma B. Conclusions: The proposed method archives the best performance in terms of positive predictive value (PPV) while its sensitivity is similar to the other algorithms used for the comparison. The computation time needed to process the transcriptional signals is low as compared with model-based methods and in the same range to those based on the use of filters. Automatic parameter selection has been incorporated and moreover, it can be easily adapted to a parallel implementation. We can conclude that the proposed method is well suited for the analysis of tiling signals, in which transcriptional activity is often hidden in the noise. Finally, the quantification and differential expression analysis of S. aureus dataset have demonstrated the valuable utility of this novel device to the biological analysis of the S. aureus transcriptome.

Beschreibung: Background: A number of software packages are available togenerate DNA multiple sequence alignments (MSAs) evolved undercontinuous-time Markov processes on phylogenetic trees. On the other hand,methods of simulating the DNA MSA directly from the transition matricesdo not exist. Moreover, existing software restricts tothe time-reversible models and it is not optimized togenerate nonhomogeneous data (i.e. placing distinct substitution rates at different lineages). Results: We present the first package designed to generate MSAs evolving under discrete-time Markov processes on phylogenetic trees, directly from probability substitution matrices.Based on the input model and aphylogenetic tree in the Newick format (with branch lengths measuredas the expected number of substitutions per site), thealgorithm produces DNA alignments of desired length.GenNon-h is publicly available for download. Conclusion: The software presented here is an efficient toolto generate DNA MSAs on a given phylogenetic tree.GenNon-h provides the user with the nonstationary or nonhomogeneousphylogenetic data that is well suited for testing complex biologicalhypotheses, exploring the limits of the reconstruction algorithms and their robustness to such models.

Beschreibung: Background: Adiponectin is reported to relate with cardiovascular diseases, we sought to examine whether adiponectin is associated with disease progression of heart failure from hypertension in rats in comparison with other known biomarkers and echocardiographic parameters. Spontaneously hypertensive rats (SHR, n = 35), aged 1 month, were used and followed up to 18 months. High frequency echocardiography was performed both at baseline and every 3 months thereafter. Moreover, serum levels of N-terminal pro-natriuretic peptide (NT-proBNP) and interleukin-6 (IL-6) as well as serum level and tissue expression of adiponectin were determined at the same time as echocardiography. Results: The results clearly demonstrated time-dependent progression of hypertension and heart dysfunction as evidenced by gradually increased left ventricular mass index, NT-proBNP, IL-6 as well as gradually decreased cardiac function as assessed by echocardiography. Meanwhile, tissue and serum adiponectin decreased from 3 months and reached plateau until 12 months in parallel with decreasing of cardiac diastolic function. Thereafter, adiponectin levels increased prior to occurrence of systolic dysfunction. Adiponectin concentration is inversely related with NT-proBNP, IL-6 and E/E[PRIME] (correlation coefficient (r) = [MINUS SIGN]0.756 for NT-proBNP, p 〈 0.001, -0.635 for IL-6, p = 0.002, and [MINUS SIGN]0.626 for E/E[PRIME], p = 0.002, respectively) while positively correlated with E/A and E[PRIME]/A[PRIME] (r = 0.683 for E/A, p = 0.001, 0.671 for E[PRIME]/A[PRIME], p = 0.001, respectively). No difference for adiponectin distribution among visceral adipose tissues was found. Conclusion: Adiponectin through its biphasic serum level is a useful biomarker during transition from diastolic dysfunction to systolic dysfunction.

Beschreibung: Background: Ingestion of glucosylceramide improves transepidermal water loss (TEWL) from the skin, but the underlying mechanism by which a small amount of dietary glucosylceramide can vastly improve skin conditions remains unclear. In a previous report, glucosylceramides were shown to be digested to sphingoids, which were shown to be absorbed through the intestinal epithelium. Based on these observations, we hypothesized that sphingoids are the key molecules facilitating endogenous ceramide production. In this study, we assessed the effect of 4,8-sphingadienine (d18:2) and 4-hydroxy-8-sphingenine (t18:1), derived from konjac glucosylceramide, on stimulating ceramide production. Methods: Konjac glucosylceramide acidolysis was performed using hydrochloric acid; the resulting d18:2 and t18:1 were fractionated by column chromatography. Real-time quantitative RT-PCR was performed to assess the effect of d18:2 and t18:1 on gene expression in normal human epidermal keratinocytes, while their effect on the nuclear receptor, peroxisome proliferator-activated receptor (PPAR)gamma, was measured using a receptor-cofactor assay system. The effect of d18:2 and t18:1 on stimulating ceramide production was evaluated using HPTLC analysis in a 3-dimensional human skin model. Results: We noted the upregulation of genes related to de novo ceramide synthesis as well as of those encoding the elongases of very long-chain fatty acids by d18:2 and t18:1, but not by glucosylceramide and 4-sphingenine. Both these sphingoids also facilitated the expression of PPARbeta/delta and PPARgamma; moreover, they also demonstrated ligand activity for PPARgamma. These results indicated that d18:2 and t18:1 promote the differentiation of keratinocytes. Analysis of the lipids within the 3-dimensional human skin model indicated that treatment with d18:2 and t18:1 not only upregulated gene expression but also increased ceramide production. Conclusions: The sphingoids d18:2 and t18:1 activated genes related to de novo ceramide synthesis and increased ceramide production, whereas glucosylceramide and 4-sphingenine could not. These results suggest that the effect of dietary glucosylceramides on the skin is mediated by d18:2 and t18:1.

Beschreibung: Background: Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development of TVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. Results: In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions: TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

Beschreibung: Background: Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented. Results: Two models are presented in which the efficiency of amplification is based on equilibrium solutions for the annealing phase of the qPCR process. Model 1 assumes annealing of complementary targets strands and annealing of target and primers are both reversible reactions and reach a dynamic equilibrium. Model 2 assumes all annealing reactions are nonreversible and equilibrium is static. Both models include the effect of primer concentration during the annealing phase. Analytic formulae are given for the equilibrium values of all single and double stranded molecules at the end of the annealing step. The equilibrium values are then used in a stepwise method to describe the whole qPCR process. Rate constants of kinetic models are the same for solutions that are identical except for possibly having different initial target concentrations. Analysis of qPCR curves from such solutions are thus analyzed by simultaneous non-linear curve fitting with the same rate constant values applying to all curves and each curve having a unique value for initial target concentration. The models were fit to two data sets for which the true initial target concentrations are known. Both models give better fit to observed qPCR data than other kinetic models present in the literature. They also give better estimates of initial target concentration. Model 1 was found to be slightly more robust than model 2 giving better estimates of initial target concentration when estimation of parameters was done for qPCR curves with very different initial target concentration. Both models may be used to estimate the initial absolute concentration of target sequence when a standard curve is not available. Conclusions: It is argued that the kinetic approach to modeling and interpreting quantitative PCR data has the potential to give more precise estimates of the true initial target concentrations than other methods currently used for analysis of qPCR data. The two models presented here give a unified model of the qPCR process in that they explain the shape of the qPCR curve for a wide variety of initial target concentrations.

Beschreibung: Background: Variations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual. Results: We present a segmentation method named generalized fused lasso (GFL) to reconstruct copy number variant regions, that is based on penalized estimation and is capable of processing multiple signals jointly. Our approach is computationally very attractive and leads to sensitivity and specificity levels comparable to those of state-of-the-art specialized methodologies. We illustrate its applicability with simulated and real data sets. Conclusions: The flexibility of our framework makes it applicable to data obtained with a wide range of technology. Its versatility and speed make GFL particularly useful in the initial screening stages of large data sets.

Beschreibung: Background: Although genome-scale expression experiments are performed routinely in biomedical research, methods ofanalysis remain simplistic and their interpretation challenging. The conventional approach is to compare theexpression of each gene, one at a time, between treatment groups. This implicitly treats the gene expressionlevels as independent, but they are in fact highly interdependent, and exploiting this enables substantialpower gains to be realized. Results: We assume that information on the dependence structure between the expression levels of a set of genes isavailable in the form of a Bayesian network (directed acyclic graph), derived from external resources. Weshow how to analyze gene expression data conditional on this network. Genes whose expression is directlyaffected by treatment may be identified using tests for the independence of each gene and treatment,conditional on the parents of the gene in the network. We apply this approach to two datasets: one from ahepatotoxicity study in rats using a PPAR pathway, and the other from a study of the effects of smoking onthe epithelial transcriptome, using a global transcription factor network. Conclusions: The proposed method is straightforward, simple to implement, gives rise to substantial power gains, andmay assist in relating the experimental results to the underlying biology

Beschreibung: Background: Identifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional. Results: RF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions. Conclusions: While RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

Beschreibung: Background: Alpha-helical transmembrane channel and transporter proteins play vital roles in a diverse range of essential biological processes and are crucial in facilitating the passage of ions and molecules across the lipid bilayer. However, the experimental difficulties associated with obtaining high quality crystals has led to their significant under-representation in structural databases; therefore, computational methods that can identify structural features from sequence alone are of high importance. Results: We present a method capable of automatically identifying pore-lining regions in transmembrane proteins from sequence information alone, which can then be used to determine the pore stoichiometry. By labelling pore-lining residues in crystal structures using geometric criteria, we have trained a support vector machine classifier to predict the likelihood of a transmembrane helix being involved in pore formation. Results from testing this approach under stringent cross-validation indicate that prediction accuracy of 72% is possible, while a support vector regression model is able to predict the number of subunits participating in the pore with 62% accuracy. Conclusion: To our knowledge, this is the first tool capable of identifying such regions and we present the results of applying it to a data set of sequences with available crystal structures. Our method provides a way to characterise pores in transmembrane proteins and may provide valuable insight into routes of therapeutic intervention in a number of important diseases. This software is freely available as source code from:http://bioinfadmin.cs.ucl.ac.uk/downloads/memsat-svm/

Beschreibung: Background: The k-mer hash length is a key factor affecting the output of de novo transcriptome assembly packages using de Bruijn graph algorithms. Assemblies constructed with varying single k-mer choices might result in the loss of unique contiguous sequences (contigs) and relevant biological information. A common solution to this problem is the clustering of single k-mer assemblies. Even though annotation is one of the primary goals of a transcriptome assembly, the success of assembly strategies does not consider the impact of k-mer selection on the annotation output. This study provides an in-depth k-mer selection analysis that is focused on the degree of functional annotation achieved for a non-model organism where no reference genome information is available. Individual k-mers and clustered assemblies (CA) were considered using three representative software packages. Pair-wise comparison analyses (between individual k-mers and CAs) were produced to reveal missing Kyoto Encyclopedia of Genes and Genomes (KEGG) ortholog identifiers (KOIs), and to determine a strategy that maximizes the recovery of biological information in a de novo transcriptome assembly. Results: Analyses of single k-mer assemblies resulted in the generation of various quantities of contigs and functional annotations within the selection window of k-mers (k-19 to k-63). For each k-mer in this window, generated assemblies contained certain unique contigs and KOIs that were not present in the other k-mer assemblies. Producing a non-redundant CA of k-mers 19 to 63 resulted in a more complete functional annotation than any single k-mer assembly. However, a fraction of unique annotations remained (~0.19 to 0.27% of total KOIs) in the assemblies of individual k-mers (k-19 to k-63) that were not present in the non-redundant CA. A workflow to recover these unique annotations is presented. Conclusions: This study demonstrated that different k-mer choices result in various quantities of unique contigs per single k-mer assembly which affects biological information that is retrievable from the transcriptome. This undesirable effect can be minimized, but not eliminated, with clustering of multi-k assemblies with redundancy removal. The complete extraction of biological information in de novo transcriptomics studies requires both the production of a CA and efforts to identify unique contigs that are present in individual k-mer assemblies but not in the CA.

Beschreibung: Background: Today, recognition and classification of sequence motifs and protein folds is a mature field, thanks to the availability of numerous comprehensive and easy to use software packages and web-based services. Recognition of structural motifs, by comparison, is less well developed and much less frequently used, possibly due to a lack of easily accessible and easy to use software. Results: In this paper, we describe an extension of DeepView/Swiss-PdbViewer through which structural motifs may be defined and searched for in large protein structure databases, and we show that common structural motifs involved in stabilizing protein folds are present in evolutionarily and structurally unrelated proteins, also in deeply buried locations which are not obviously related to protein function. Conclusions: The possibility to define custom motifs and search for their occurrence in other proteins permits the identification of recurrent arrangements of residues that could have structural implications. The possibility to do so without having to maintain a complex software / hardware installation on site brings this technology to experts and non-experts alike.

Beschreibung: Background: Clustering DNA sequences into functional groups is an important problem in bioinformatics. We propose a new alignment-free algorithm, mBKM, based on a new distance measure, DMk, for clustering gene sequences. This method transforms DNA sequences into the feature vectors which contain the occurrence, location and order relation of k-tuples in DNA sequence. Afterwards, a hierarchical procedure is applied to clustering DNA sequences based on the feature vectors. Results: The proposed distance measure and clustering method are evaluated by clustering functionally related genes and by phylogenetic analysis. This method is also compared with BlastClust and CD-HIT-EST. The experimental results show our method is effective in classifying DNA sequences with similar biological characteristics and in discovering the underlying relationship among the sequences. Conclusions: We introduced a novel clustering algorithm which is based on a new sequence similarity measure. It is effective in classifying DNA sequences with similar biological characteristics and in discovering the relationship among the sequences.

Beschreibung: Background: Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions. Results: When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task. Conclusions: We conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

Beschreibung: Background: The distance matrix computed from multiple alignments of homologous sequences is widely used by distance-based phylogenetic methods to provide information on the evolution of protein families. This matrix can also be visualized in a low dimensional space by metric multidimensional scaling (MDS). Applied to protein families, MDS provides information complementary to the information derived from tree-based methods. Moreover, MDS gives a unique opportunity to compare orthologous sequence sets because it can add supplementary elements to a reference space. Results: The R package bios2mds (from BIOlogical Sequences to MultiDimensional Scaling) has been designed to analyze multiple sequence alignments by MDS. Bios2mds starts with a sequence alignment, builds a matrix of distances between the aligned sequences, and represents this matrix by MDS to visualize a sequence space. This package also offers the possibility of performing K-means clustering in the MDS derived sequence space. Most importantly, bios2mds includes a function that projects supplementary elements (a.k.a. "out of sample" elements) onto the space defined by reference or "active" elements. Orthologous sequence sets can thus be compared in a straightforward way. The data analysis and visualization tools have been specifically designed for an easy monitoring of the evolutionary drift of protein sub-families. Conclusions: The bios2mds package provides the tools for a complete integrated pipeline aimed at the MDS analysis of multiple sets of orthologous sequences in the R statistical environment. In addition, as the analysis can be carried out from user provided matrices, the projection function can be widely used on any kind of data.

Beschreibung: This study aimed to evaluate the effect of chronic fructose consumption on markers of nonalcoholic fatty liver disease (NAFLD), lipid peroxidation and liver damage in Wistar rats. We separated twenty-eight rats into two groups according to diet: a control group (C) (balanced diet) and a fructose group (F) (fed a diet containing fructose as 60% of the total caloric intake). The animals were fed these diets for 60 d (d 120 to 180). We performed insulin, glucose and fructose tolerance tests as well as a minimum lactate test, for aerobic capacity evaluation, at d 120 and 180. At the end of the experiment, sixteen animals were euthanized, and the following main variables were analysed: aerobic capacity, the serum aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio, serum and liver triglyceride concentrations, serum and liver thiobarbituric acid reactive substance (TBARS) concentrations, serum and liver catalase and superoxide dismutase (SOD) activity and haematoxylin-eosin histology (HE) in hepatocytes. The remaining twelve animals were submitted to an analysis of their hepatic lipogenic rate. The animals fed a fructose-rich diet exhibited a reduction in aerobic capacity, glucose tolerance and insulin sensitivity and increased concentrations of triglycerides and TBARS in the liver. Catalase and SOD activities were reduced in the livers of the fructose-fed animals. In addition, the serum AST/ALT ratio was higher than that of the C group, which indicates hepatic damage, and the damage was confirmed by histology. In conclusion, the fructose-rich diet caused significant liver damage and a reduction in insulin sensitivity in the animals, which could lead to deleterious metabolic effects.

Beschreibung: The present work aims at preparing aqueous suspension of Solid lipid Nanoparticles containing Chitosan (CT) which is a biopolymer that exhibits a number of interesting properties which include controlled drug delivery. Carbamezapine (CBZ) is a lipophilic drug which shows it antiepileptic activity by inactivating sodium channels. The solid lipid Nanoparticles (SLN) of Chitosan-CBZ were prepared by using solvent injection method using ethanol as organic solvent. The prepared SLN formulations exhibited high encapsulation efficiency, high physical stability. The drug incorporated SLNs have demonstrated that the controlled release patterns of the drug for prolonged period. The prepared SLNs were characterized for surface morphology by SEM analysis, entrapment efficiency, zeta potential, FTIR, DSC and in-vitro diffusion studies. The hydrodynamic mean diameter and zeta potential were 168.7 +/-1.8 nm and 28.9 +/-2.0 mV for SLN-chitosan-CBZ respectively. Therefore chitosan-SLN can be good candidates to encapsulate CBZ and to increase its therapeutic efficacy in the treatment of Epilepsy.

Beschreibung: Background: The lipid fraction of rubber (Hevea brasiliensis (kunth. Muell)) seed was extracted and analyzed for toxicological effect. The toxicological compound such as linamarin in rubber seed oil (RSO) extracted using different solvents, such as hexane (RSOh), mixture of chloroform+methanol (RSOchl+mth) and ethanol (RSOeth) were also studied. Various methods analysis such as Fourier transforms infrared spectroscopy (FTIR) and colorimetric methods were carried out to determine the present of such compounds. Results: FTIR spectrum of RSO did not show any presence of cyanide peak. The determination of cyanide by using colorimetric method was demonstrated no response of the cyanide in RSO and didn't show any colored comparing with commercial cyanide which observed blue color. The results showed that no functional groups such as cyanide (CN) associated with linamarin were observed. Toxicological test using rats was also conducted to further confirm the absence of such compounds. RSO did not show any toxic potential to the rats. Bioassay experiments using shrimps had been used as test organisms to evaluate the toxicity of linamarin extract from RSOh, RSOchl+mth and RSOeth and LC50 were found to be (211.70%, 139.40%, and 117.41%, respectively). Conclusions: This can be attributed no hazardous linamarin were found in RSO.

Beschreibung: Background: Resynthesis of triglycerides in enterocytes of the small intestine plays a critical role in the absorption of dietary fat. Acyl-CoA:monoacylglycerol acyltransferase-2 (MGAT2) is highly expressed in the small intestine and catalyzes the synthesis of diacylglycerol from monoacylglycerol and acyl-CoA. To determine the physiological importance of MGAT2 in metabolic disorders and lipid metabolism in the small intestine, we constructed and analyzed Mgat2-deficient mice. Results: In oral fat tolerance test (OFTT), Mgat2-deficient mice absorbed less fat into the circulation. When maintained on a high-fat diet (HFD), Mgat2-deficient mice were protected from HFD-induced obesity and insulin resistance. Heterozygote (Mgat2+/-) mice had an intermediate phenotype between Mgat2+/+ and Mgat2-/- and were partially protected from metabolic disorders. Despite of a decrease in fat absorption in the Mgat2-deficient mice, lipid levels in the feces and small intestine were comparable among the genotypes. Oxygen consumption was increased in the Mgat2-deficient mice when maintained on an HFD. A prominent upregulation of the genes involved in fatty acid oxidation was observed in the duodenum but not in the liver of the Mgat2-deficient mice. Conclusion: These results suggest that MGAT2 has a pivotal role in lipid metabolism in the small intestine, and the inhibition of MGAT2 activity may be a promising strategy for the treatment of obesity-related metabolic disorders.

Beschreibung: Background: Bioactivities of Docosahexaenoic acid (DHA) and Eicosapentaenoic acid (EPA) depend on their chemical forms. The present study was to investigate short term effects of triglyceride (TG), ethyl ester (EE), free fatty acid (FFA) and phospholipid (PL) forms of omega-3 fatty acid (FA) on lipid metabolism in mice, fed high fat or low fat diet.MethodMale Balb/c mice were fed with 0.7% different Omega-3 fatty acid formulation: DHA bound free fatty acid (DHA-FFA), DHA bound triglyceride (DHA-TG), DHA bound ethyl ester (DHA-EE) and DHA bound phospholipid (DHA-PL) for 1 week, with dietary fat levels at 5% and 22.5%. Serum and hepatic lipid concentrations were analyzed, as well as the fatty acid composition of liver and brain.ResultAt low fat level, serum total cholesterol (TC) level in mice fed diets with DHA-FFA, DHA-EE and DHA-PL were significantly lower than that in the control group (P 〈 0.05). Hepatic TG level decreased significantly in mice fed diets with DHA-TG (P 〈 0.05), DHA-EE (P 〈 0.05) and DHA-PL (P 〈 0.05), while TC level in liver was significantly lower in mice fed diets with TG and EE compared with the control group (P 〈 0.05). At high fat level, mice fed diets with DHA-EE and DHA-PL had significantly lower hepatic TC level compared with the control diet (P 〈 0.05). Hepatic PL concentration experienced a significant increase in mice fed the diet with PL at high fat level (P 〈 0.05). Furthermore, both at low and high fat levels, hepatic DHA level significantly increased and AA level significantly decreased in all forms of DHA groups (P 〈 0.05), compared to control groups at two different fat levels, respectively. Additionally, cerebral DHA level in mice fed diets with DHA-FFA, DHA-EE and DHA-PL significantly increased compared with the control at high fat level (P 〈 0.05), but no significant differences were observed among dietary treatments for mice fed diets with low fat level. Conclusion: The present study suggested that not only total dietary fat content but also the molecular forms of omega-3 fatty acids contributed to lipid metabolism in mice. DHA-PL showed effective bioactivity in decreasing hepatic and serum TC, TG levels and increasing omega-3 concentration in liver and brain.

Beschreibung: Background: Cardiovascular disease (CVD) is the number one cause of mortality worldwide and a low high-density lipoprotein cholesterol (HDL-C) level is an important marker of CVD risk. Garlic (Allium sativum) has been widely used in the clinic for treatment of CVD and regulation of lipid metabolism. This study investigated the effects of a high hydrostatic pressure extract of garlic (HEG) on HDL-C level and regulation of hepatic apolipoprotein A-I (apoA-I) gene expression. Methods: Male Sprague-Dawley rats were divided into two groups and maintained on a high-fat control diet (CON) or high-fat control diet supplemented with high hydrostatic pressure extract of garlic (HEG) for 5 weeks. Changes in the expression of genes related to HDL-C metabolism were analyzed in liver, together with biometric and blood parameters. Results: In the HEG group, the plasma levels of triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) were significantly decreased by 19% and 24%, respectively, relative to the control group. The plasma HDL-C level was increased by 59% (P 〈 0.05). The HEG-supplemented diet lowered the atherogenic index (AI) by 45% as compared to the CON group. Hepatic TG and total cholesterol (TC) levels were reduced by 35% and 43%, respectively in comparison with the CON group. The HEG supplementation increased hepatic mRNA levels of apoA-I, which is one of primarily proteins of HDL-C particle (P 〈 0.05). The gene expression of ATP-binding cassette transporter A1 (ABCA1) and lecithin:cholesterol acyltransferase (LCAT), importantly involved in the biogenesis in HDL, were also up-regulated by dietary HEG. Conclusions: These results suggest that HEG ameliorates plasma lipid profiles and attenuates hepatic lipid accumulation in the high-fat fed rats. Our findings provides that the effects of HEG on the increase of the plasma HDL-C level was at least partially mediated by up-regulation of hepatic genes expression such as apoA-I, ABCA1, and LCAT in rats fed a high-fat diet.

Beschreibung: Background: Altered immune function during ageing results in increased production of nitric oxide (NO) and other inflammatory mediators. Recently, we have reported that NO production was inhibited by naturally-occurring proteasome inhibitors (quercetin, delta-tocotrienol, and riboflavin) in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells, and thioglycolate-elicited peritoneal macrophages from C57BL/6 mice. In a continuous effort to find more potent, non-toxic, commercially available, naturally-occurring proteasome inhibitors that suppress inflammation, the present study was carried out to describe the inhibition of NF-kappa B activation and NO, TNF-alpha, IL-6, IL-1beta, and iNOS expression by trans-resveratrol, trans-pterostilbene, morin hydrate, and nicotinic acid in LPS-induced RAW 264.7 cells and thioglycolate-elicited peritoneal macrophages derived from C57BL/6 and BALB/c mice. Results: The present results indicate that resveratrol, pterostilbene, and morin hydrate caused significant inhibition (〉70% to 90%; P 〈 0.02) in the activities of chymotrypsin-like, trypsin-like, and post-acidic (post-glutamase) proteasome sites in RAW 264.7 cells at a dose of only 20 micromolar. These compounds also inhibited the production of NO by RAW-264.7 cells stimulated with LPS alone (〉40%; P 〈 0.05), or LPS + interferon-gamma(IFN-gamma; 〉60%; P 〈 0.02). Furthermore, resveratrol, pterostilbene, morin hydrate, and quercetin suppressed secretion of TNF-alpha (〉40%; P 〈 0.05) in LPS-stimulated RAW 264.7 cells, and suppressed NF-kappa B activation (22% to 45%; P 〈 0.05) in LPS-stimulated HEK293T cells. These compounds also significantly suppressed LPS-induced expression of TNF-alpha, IL-1beta, IL-6, and iNOS genes in RAW 264.7 cells, and also in thioglycolate-elicited peritoneal macrophages from C57BL/6 and BALB/c mice. Conclusions: The present results clearly demonstrate that resveratrol and pterostilbene are particularly potent proteasome inhibitors that suppress expression of genes, and production of inflammatory products in LPS-stimulated RAW 264.7 cells, and macrophages from C57BL/6 and BALB/c mice. Resveratrol and pterostilbene which are present in grapes, blueberries, and red wine, have been implicated as contributing factors to the lower incidence of cardiovascular disease in the French population, despite their relatively high dietary fat intake. Consequently, it appears likely that the beneficial nutritional effects of resveratrol and pterostilbene are due at least in part, to their ability to inhibit NF-kappa B activation by the proteasome, thereby suppressing activation of pro-inflammatory cytokines and iNOS genes, resulting in decreased production of TNF-alpha, IL-1beta, IL-6, and NO levels, in response to inflammatory stimuli. This is the first report demonstrating that resveratrol and pterostilbene act as proteasome inhibitors, thus providing a mechanism for their anti-inflammatory effects.Key Words: Nitric oxide (NO), TNF-alpha, NF-kappa B, cytokines, resveratrol, proteasome inhibitors.

Beschreibung: Background: The effects of exposure to a 50 Hz electric field (EF) on plasma level of triacylglycerol, free fatty acids, total cholesterol and phospholipid and mRNA expression level of diacylglycerol acyltransferase (DGAT) 1 and 2 in liver and intestines from C57BL/6 J mice were studied. Methods: The test was based on comparison between mice post treated with 50 Hz EF of 45 kV/m intensity for 30 min per day for 11 days or without EF. DGATs mRNA expression was analyzed by real-time quantitative polymerase chain reaction. Results: There was no difference in the gene expression level of DGAT1 in liver and intestines. The DGAT2 gene expression level in liver derived from mice treated with EF was significantly lower than those in the control (P 〈 0.001). Both plasma total cholesterol (P 〈 0.01) and phospholipid (P 〈 0.05) in the group exposed to EF were lower than those in the control, but there was no difference in triacylglycerol or free fatty acid levels. Conclusion: Exposure to 50 Hz EF decrease the plasma levels of total cholesterol and phospholipids, and downregulated DGAT2 mRNA expression in liver. The mechanisms for the effects of EF on lipid metabolism are not well understand yet, but altered DGAT2 activity may be involved.

Beschreibung: We describe a system for the automated diagnosis of diabetic retinopathy and glaucoma using fundus and optical coherence tomography (OCT) images. Automatic screening will help the doctors to quickly identify the condition of the patient in a more accurate way. The macular abnormalities caused due to diabetic retinopathy can be detected by applying morphological operations, filters and thresholds on the fundus images of the patient. Early detection of glaucoma is done by estimating the Retinal Nerve Fiber Layer (RNFL) thickness from the OCT images of the patient. The RNFL thickness estimation involves the use of active contours based deformable snake algorithm for segmentation of the anterior and posterior boundaries of the retinal nerve fiber layer. The algorithm was tested on a set of 89 fundus images of which 85 were found to have at least mild retinopathy and OCT images of 31 patients out of which 13 were found to be glaucomatous. The accuracy for optical disk detection is found to be 97.75%. The proposed system therefore is accurate, reliable and robust and can be realized.

Beschreibung: Background: Retinoic acids regulate the reverse cholesterol transport by inducing the ATP binding cassette transporter A1 (ABCA1) dependent cholesterol efflux in macrophages, neuronal as well as intestine cells. In the present study, we aim to test the effect of all trans retinoic acid (ATRA) on ABCA1 expression in human CD4+ T cells and the involvement of cholesterol in ATRA mediated anti-HIV effect. Results: Treatment with ATRA dramatically up-regulated ABCA1 expression in CD4+ T cells in a time and dose dependent manner. The expression of ABCA1 paralleled with increased ABCA1-dependent cholesterol efflux. This induction was dependent on T cell receptor (TCR) signaling and ATRA failed to induce ABCA1 expression in resting T cells. Moreover, ATRA and liver X receptor (LXR) agonist-TO-901317 together had synergistic effect on ABCA1 expression as well as cholesterol efflux. Increased ABCA1 expression was associated with lower cellular cholesterol staining. Cells treated with either ATRA or TO-901317 were less vulnerable to HIV-1 infection. Combination of retinoic acid and TO-901317 further inhibited HIV-1 entry and their inhibitory effects could be reversed by cholesterol replenishment. Methods: ABCA1 RNA and protein were determined by real-time PCR and immuno blot methods in cells treated with ATRA. Cholesterol efflux rate was measured in cells treated with ATRA and TO-901317. Conclusions: ATRA up-regulates ABCA1 expression and cholesterol efflux in CD4+ T cells and combination of ATRA and liver X receptor (LXR) agonist further enhanced these effects. Increased cholesterol efflux contributed to reduced HIV-1 entry, suggesting that anti-HIV effect of ATRA is mediated through ABCA1.

Beschreibung: Background: Two-dimensional polyacrylamide gel electrophoresis (2D PAGE) is commonly used toidentify differentially expressed proteins under two or more experimental or observationalconditions. Wu et al (2009) developed a univariate probabilistic model which was used toidentify differential expression between Case and Control groups, by applying a LikelihoodRatio Test (LRT) to each protein on a 2D PAGE. In contrast to commonly used statisticalapproaches, this model takes into account the two possible causes of missing values in 2DPAGE: either (1) the non-expression of a protein; or (2) a level of expression that falls belowthe limit of detection. Results: We develop a global Bayesian model which extends the previously described model. Unlikethe univariate approach, the model reported here is able treat all differentially expressedproteins simultaneously. Whereas each protein is modelled by the univariate likelihoodfunction previously described, several global distributions are used to model the underlyingrelationship between the parameters associated with individual proteins. These globaldistributions are able to combine information from each protein to give more accurateestimates of the true parameters. In our implementation of the procedure, all parameters arerecovered by Markov chain Monte Carlo (MCMC) integration. The 95% highest posteriordensity (HPD) intervals for the marginal posterior distributions are used to determine whetherdifferences in protein expression are due to differences in mean expression intensities, and/ordifferences in the probabilities of expression. Conclusions: Simulation analyses showed that the global model is able to accurately recover the underlyingglobal distributions, and identify more differentially expressed proteins than the simpleapplication of a LRT. Additionally, simulations also indicate that the probability ofincorrectly identifying a protein as differentially expressed (i.e., the False Discovery Rate) isvery low. The source code is available at https://github.com/stevenhwu/BIDE-2D.

Beschreibung: Background: The identification of gene sets that are significantly impacted in a given condition based on microarray data is acrucial step in current life science research. Most gene set analysis methods treat genes equally, regardless howspecific they are to a given gene set. Results: In this work we propose a new gene set analysis method that computes a gene set score as the mean of absolutevalues of weighted moderated gene t-scores. The gene weights are designed to emphasize the genes appearing infew gene sets, versus genes that appear in many gene sets. We demonstrate the usefulness of the method whenanalyzing gene sets that correspond to the KEGG pathways, and hence we called our method Pathway Analysiswith Down-weighting of Overlapping Genes (PADOG). Unlike most gene set analysis methods which arevalidated through the analysis of 2-3 data sets followed by a human interpretation of the results, the validationemployed here uses 24 different data sets and a completely objective assessment scheme that makes minimalassumptions and eliminates the need for possibly biased human assessments of the analysis results. Conclusions: PADOG significantly improves gene set ranking and boosts sensitivity of analysis using information alreadyavailable in the gene expression profiles and the collection of gene sets to be analyzed. The advantages ofPADOG over other existing approaches are shown to be stable to changes in the database of gene sets to beanalyzed. PADOG was implemented as an R package available at:http://bioinformaticsprb.med.wayne.edu/PADOG/ or www.bioconductor.org.

Beschreibung: Background: Microarray data enables the high-throughput survey of mRNA expression profiles at the genomic level;however, the data presents a challenging statistical problem because of the large number of transcripts withsmall sample sizes that are obtained. To reduce the dimensionality, various Bayesian or empirical Bayeshierarchical models have been developed. However, because of the complexity of the microarray data, nomodel can explain the data fully. It is generally difficult to scrutinize the irregular patterns of expression thatare not expected by the usual statistical gene by gene models. Results: As an extension of empirical Bayes (EB) procedures, we have developed the beta-empirical Bayes (beta-EB)approach based on a beta-likelihood measure which can be regarded as an 'evidence-based' weighted (quasi-)likelihood inference. The weight of a transcript t is described as a power function of its likelihood, f beta(yt|theta).Genes with low likelihoods have unexpected expression patterns and low weights. By assigning low weightsto outliers, the inference becomes robust. The value of beta, which controls the balance between the robustnessand efficiency, is selected by maximizing the predictive beta0-likelihood by cross-validation. The proposedbeta-EB approach identified six significant (p 〈 105) contaminated transcripts as differentially expressed(DE) in normal/tumor tissues from the head and neck of cancer patients. These six genes were all confirmedto be related to cancer; they were not identified as DE genes by the classical EB approach. When applied tothe eQTL analysis of Arabidopsis thaliana, the proposed beta-EB approach identified some potential masterregulators that were missed by the EB approach. Conclusions: The simulation data and real gene expression data showed that the proposed beta-EB method was robustagainst outliers. The distribution of the weights was used to scrutinize the irregular patterns of expression and diagnose the model statistically. When beta-weights outside the range of the predicted distribution wereobserved, a detailed inspection of the data was carried out. The beta-weights described here can be applied toother likelihood-based statistical models for diagnosis, and may serve as a useful tool for transcriptome andproteome studies.

Beschreibung: Background: The emergence of Next Generation Sequencing technologies has made it possible for individual investigators to generate gigabases of sequencing data per week. Effective analysis and manipulation of these data is limited due to large file sizes, so even simple tasks such as data filtration and quality assessment have to be performed in several steps. This requires (potentially problematic) interaction between the investigator and a bioinformatics/computational service provider. Furthermore, such services are often performed using specialized computational facilities. Results: We present a windows-based application, Slim-Filter designed to interactively examine the statistical properties of sequencing reads produced by Illumina Genome Analyzer and to perform a broad spectrum of data manipulation tasks including: filtration of low quality and low complexity reads; filtration of reads containing undesired subsequences (such as parts of adapters and PCR primers used during the sample and sequencing libraries preparation steps); excluding duplicated reads (while keeping each read's copy number information in a specialized data format); and sorting reads by copy numbers allowing for easy access and manual editing of the resulting files. Slim-Filter is organized as a sequence of windows summarizing the statistical properties of the reads. Each data manipulation step has roll-back abilities, allowing for return to previous steps of the data analysis process. Slim-Filter is written in C++ and is compatible with fasta, fastq, and specialized AS file formats presented in this manuscript. Setup files and a user's manual are available for download at the supplementary web site (https://www.bioinfo.uh.edu/Slim_Filter/). Conclusion: The presented windows-based application has been developed with the goal to provide individual investigators with integrated sequencing reads analysis, curation, and manipulation capabilities.

Beschreibung: The paraoxonase (PON) gene family includes three members, PON1, PON2 and PON3, aligned in tandem on chromosome 7 in humans and on chromosome 6 in mice. All PON proteins share considerable structural homology and have the capacity to protect cells from oxidative stress; therefore, they have been implicated in the pathogenesis of several inflammatory diseases, particularly atherosclerosis. The major goal of this review is to highlight the modulation of each of the PONs by infective (bacterial, viral and parasitic) agents, which may shed a light on the interaction between infectious diseases and PONs activities in order to effectively reduce the risk of developing atherosclerosis.

Beschreibung: Background: In high throughput cancer genomic studies, results from the analysis of single datasets often suffer from a lack of reproducibility because of small sample sizes. Integrative analysis can effectively pool and analyze multiple datasets and provides a cost effective way to improve reproducibility. In integrative analysis, simultaneously analyzing all genes profiled may incur high computational cost. A computationally affordable remedy is prescreening, which fits marginal models, can be conducted in a parallel manner, and has low computational cost. Results: An integrative prescreening approach is developed for the analysis of multiple cancer genomic datasets. Simulation shows that the proposed integrative prescreening has better performance than alternatives, particularly including prescreening with individual datasets, an intensity approach and meta-analysis. We also analyze multiple microarray gene profiling studies on liver and pancreatic cancers using the proposed approach. Conclusions: The proposed integrative prescreening provides an effective way to reduce the dimensionality in cancer genomic studies. It can be coupled with existing analysis methods to identify cancer markers.

Beschreibung: Background: Precise DNA-protein interactions play most important and vital role in maintaining the normal physiological functioning of the cell, as it controls many high fidelity cellular processes. Detailed study of the nature of these interactions has paved the way for understanding the mechanisms behind the biological processes in which they are involved. Earlier in 2000, a systematic classification of DNA-protein complexes based on the structural analysis of the proteins was proposed at two tiers, namely groups and families. With the advancement in the number and resolution of structures of DNA-protein complexes deposited in the Protein Data Bank, it is important to revisit the existing classification. Results: On the basis of the sequence analysis of DNA binding proteins, we have built upon the protein centric, two-tier classification of DNA-protein complexes by adding new members to existing families and making new families and groups. While classifying the new complexes, we also realised the emergence of new groups and families. The new group observed was where beta-propeller was seen to interact with DNA. There were 34 SCOP folds which were observed to be present in the complexes of both old and new classifications, whereas 28 folds are present exclusively in the new complexes. Some new families noticed were NarL transcription factor, Z-alpha DNA binding proteins, Forkhead transcription factor, AP2 protein, Methyl CpG binding protein etc. Conclusions: Our results suggest that with the increasing number of availability of DNA-protein complexes in Protein Data Bank, the number of families in the classification increased by approximately three fold. The folds present exclusively in newly classified complexes is suggestive of inclusion of proteins with new function in new classification, the most populated of which are the folds responsible for DNA damage repair. The proposed re-visited classification can be used to perform genome-wide surveys in the genomes of interest for the presence of DNA-binding proteins. Further analysis of these complexes can aid in developing algorithms for identifying DNA-binding proteins and their family members from mere sequence information.

Beschreibung: Background: This study aimed at investigating the oxidative stress ameliorating effect, lipids profile restoration, and the anti-inflammatory effect of Samsum Ant Venom (SAV) in induced endotoxemic male rats, injected with bacterial lipopolysaccharides (LPS). Results: Results revealed that LPS significantly increased the oxidative stress indications in LPS-injected rats. A significant increase of both malondialdehyde (MDA), and advanced oxidative protein products (AOPP), as well as a significant suppression of glutathione were all detected. Treatment with 100 mug/kg dose of SAV significantly restored the oxidative stress normal indications and increased the total glutathione levels. Treatment of the LPS-rats with 100 mug/kg dose of SAV showed a clear anti-inflammatory function; as the histological architecture of the hepatic tissue was partially recovered, along with a valuable decrease in the leukocytes infiltrated the hepatic tissues. Treatment of some rat groups with 600 mug/kg dose of SAV after LPS injection induced a severe endotoxemia that resulted in very high mortality rates. SAV versus the effects of LPS on AKT1, Fas, TNF-alpha and IFN-gamma mRNA expression. SAV was found to significantly lower Fas gene expression comparing to the LPS group and restore the level of IFN-gamma mRNA expression to that of the control group. Conclusion: In conclusion, SAV, at the dose of 100 mug/kg body weight, maintained and restored the oxidative stability, the anti-inflammatory, and the hypolipidemic bioactivity in rats after induced disruption of these parameters by LPS injection. This improvement by SAV was mediated by upregulation of AKT1.

Beschreibung: Background: Seqcrawler takes its roots in software like SRS or Lucegene.It provides an indexing platform to ease the search of data and meta-data in biological banks and it can scale to face the current flow of data.While many biological bank search tools are available on the Internet, mainly provided by large organizations to search in their data, there is a lack of free and open source solution to browse one own set of data with a flexible query system and able to scale from single computer to a cloud system.A personal index platform will help labs and bioinformaticians to search in their meta-data but also to build a larger information system with custom subsets of data. Results: The software is scalable from a single computer to a cloud-based infrastructure.It has been successfully tested in a private cloud with 3 index shards (piece of index) hosting ~400 millions of sequence information (whole GenBank, UniProt, PDB and others) for a total size of 600 GB in a fault tolerant architecture (high-availability). It has also been successfully integrated with software to add extra meta-data from blast results to enhance user's result analysis. Conclusions: Seqcrawler provides a complete open source search and store solution for labs or platforms needing to manage large amount of data/meta-data with a flexible and customizable web interface. All components (search engine, visualization and data storage), though independent, share a common and coherent data system that can be queried with a simple HTTP interface. The solution scales easily and can also provide a high availability infrastructure.

Beschreibung: Background: Chromatin immunoprecipitation combined with high-throughput sequencing (ChIP-Seq) is the most frequently used method to identify the binding sites of transcription factors. Active binding sites can be seen as peaks in enrichment profiles when the sequencing reads are mapped to a reference genome. However, the profiles are normally noisy, making it challenging to identify all significantly enriched regions in a reliable way and with an acceptable false discovery rate. Results: We present the Triform algorithm, an improved approach to automatic peak finding in ChIP-Seq enrichment profiles for transcription factors. The method uses model-free statistics to identify peak-like distributions of sequencing reads, taking advantage of improved peak definition in combination with known characteristics of ChIP-Seq data. Conclusions: Triform outperforms several existing methods in the identification of representative peak profiles in curated benchmark data sets. We also show that Triform in many cases is able to identify peaks that are more consistent with biological function, compared with other methods. Finally, we show that Triform can be used to generate novel information on transcription factor binding in repeat regions, which represents a particular challenge in many ChIP-Seq experiments. The Triform algorithm has been implemented in R, and is available via http://tare.medisin.ntnu.no/triform.

Beschreibung: Background: Previous studies on tumor classification based on gene expression profiles suggest that gene selection plays a key role in improving the classification performance. Moreover, finding important tumor-related genes with the highest accuracy is a very important task because these genes might serve as tumor biomarkers, which is of great benefit to not only tumor molecular diagnosis but also drug development. Results: This paper proposes a novel gene selection method with rich biomedical meaning based on Heuristic Breadth-first Search Algorithm (HBSA) to find as many optimal gene subsets as possible. Due to the curse of dimensionality, this type of method could suffer from over-fitting and selection bias problems. To address these potential problems, a HBSA-based ensemble classifier is constructed using majority voting strategy from individual classifiers constructed by the selected gene subsets, and a novel HBSA-based gene ranking method is designed to find important tumor-related genes by measuring the significance of genes using their occurrence frequencies in the selected gene subsets. The experimental results on nine tumor datasets including three pairs of cross-platform datasets indicate that the proposed method can not only obtain better generalization performance but also find many important tumor-related genes. Conclusions: It is found that the frequencies of the selected genes follow a power-law distribution, indicating that only a few top-ranked genes can be used as potential diagnosis biomarkers. Moreover, the top-ranked genes leading to very high prediction accuracy are closely related to specific tumor subtype and even hub genes. Compared with other related methods, the proposed method can achieve higher prediction accuracy with fewer genes. Moreover, they are further justified by analyzing the top-ranked genes in the context of individual gene function, biological pathway, and protein-protein interaction network.