Enhancing Degradation of Low Density Polyethylene Films by Curvularia lunata SG1 Using Particle Swarm Optimization Strategy

Abstract

In the present study, artificial neural network (ANN) modelling coupled with particle swarm optimization (PSO) algorithm was used to optimize the process variables for enhanced low density polyethylene (LDPE) degradation by Curvularia lunata SG1. In the non-linear ANN model, temperature, pH, contact time and agitation were used as input variables and polyethylene bio-degradation as the output variable. Further, on application of PSO to the ANN model, the optimum values of the process parameters were as follows: pH = 7.6, temperature = 37.97 °C, agitation rate = 190.48 rpm and incubation time = 261.95 days. A comparison between the model results and experimental data gave a high correlation coefficient (RANN2=0.999). Significant enhancement of LDPE bio-degradation using C.lunata SG1by about 48 % was achieved under optimum conditions. Thus, the novelty of the work lies in the application of combination of ANN–PSO as optimization strategy to enhance the bio-degradation of LDPE.

Graphical Abstract

Electronic supplementary materialThe online version of this article (doi:10.1007/s12088-015-0522-z) contains supplementary material, which is available to authorized users.     

SFMetrics: an analysis tool for scanning force microscopy images of biomolecules

Background

Scanning force microscopy (SFM) allows direct, rapid and high-resolution visualization of single molecular complexes; irregular shapes and differences in sizes are immediately revealed by the scanning tip in three-dimensional images. However, high-throughput analysis of SFM data is limited by the lack of versatile software tools accessible to SFM users. Most existing SFM software tools are aimed at broad general use: from material-surface analysis to visualization of biomolecules.

Results

We present SFMetrics as a metrology toolbox for SFM, specifically aimed at biomolecules like DNA and proteins, which features (a) semi-automatic high-throughput analysis of individual molecules; (b) ease of use working within MATLAB environment or as a stand-alone application; (c) compatibility with MultiMode (Bruker), NanoWizard (JPK instruments), Asylum (Asylum research), ASCII, and TIFF files, that can be adjusted with minor modifications to other formats.

Conclusion

Assembled in a single user interface, SFMetrics serves as a semi-automatic analysis tool capable of measuring several geometrical properties (length, volume and angles) from DNA and protein complexes, but is also applicable to other samples with irregular shapes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0457-8) contains supplementary material, which is available to authorized users.     

Detecting temporal protein complexes from dynamic protein-protein interaction networks

Background

Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization.

Results

In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points.

Conclusions

Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-335) contains supplementary material, which is available to authorized users.     

MEXPRESS: visualizing expression,DNA methylation and clinical TCGA data

Background

In recent years, increasing amounts of genomic and clinical cancer data have become publically available through large-scale collaborative projects such as The Cancer Genome Atlas (TCGA). However, as long as these datasets are difficult to access and interpret, they are essentially useless for a major part of the research community and their scientific potential will not be fully realized. To address these issues we developed MEXPRESS, a straightforward and easy-to-use web tool for the integration and visualization of the expression, DNA methylation and clinical TCGA data on a single-gene level (http://mexpress.be).

Results

In comparison to existing tools, MEXPRESS allows researchers to quickly visualize and interpret the different TCGA datasets and their relationships for a single gene, as demonstrated for GSTP1 in prostate adenocarcinoma. We also used MEXPRESS to reveal the differences in the DNA methylation status of the PAM50 marker gene MLPH between the breast cancer subtypes and how these differences were linked to the expression of MPLH.

Conclusions

We have created a user-friendly tool for the visualization and interpretation of TCGA data, offering clinical researchers a simple way to evaluate the TCGA data for their genes or candidate biomarkers of interest.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1847-z) contains supplementary material, which is available to authorized users.     

Integrated analyses of DNA methylation and hydroxymethylation reveal tumor suppressive roles of ECM1, ATF5, and EOMES in human hepatocellular carcinoma

Background

Differences in 5-hydroxymethylcytosine, 5hmC, distributions may complicate previous observations of abnormal cytosine methylation statuses that are used for the identification of new tumor suppressor gene candidates that are relevant to human hepatocarcinogenesis. The simultaneous detection of 5-methylcytosine and 5-hydroxymethylcytosine is likely to stimulate the discovery of aberrantly methylated genes with increased accuracy in human hepatocellular carcinoma.

Results

Here, we performed ultra-performance liquid chromatography/tandem mass spectrometry and single-base high-throughput sequencing, Hydroxymethylation and Methylation Sensitive Tag sequencing, HMST-seq, to synchronously measure these two modifications in human hepatocellular carcinoma samples. After identification of differentially methylated and hydroxymethylated genes in human hepatocellular carcinoma, we integrate DNA copy-number alterations, as determined using array-based comparative genomic hybridization data, with gene expression to identify genes that are potentially silenced by promoter hypermethylation.

Conclusions

We report a high enrichment of genes with epigenetic aberrations in cancer signaling pathways. Six genes were selected as tumor suppressor gene candidates, among which, ECM1, ATF5 and EOMES are confirmed via siRNA experiments to have potential anti-cancer functions.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0533-9) contains supplementary material, which is available to authorized users.     

Antihelminthic benzimidazoles potentiate navitoclax (ABT-263) activity by inducing Noxa-dependent apoptosis in non-small cell lung cancer (NSCLC) cell lines

Background

Evasion of apoptosis is a hallmark of cancer cells. One mechanism to deregulate the apoptotic pathway is by upregulation of the anti-apoptotic Bcl-2 family members. Navitoclax (ABT-263) is a Bcl-2/Bcl-x_L inhibitor that restores the ability of cancer cells to undergo apoptosis.

Methods

In this study we performed a high-throughput screen with 640 FDA-approved drugs to identify potential therapeutic combinations with navitoclax in a non-small cell lung cancer (NSCLC) cell line.

Results

Other than a panel of cancer compounds such as doxorubicin, camptothecin, and docetaxel, four antihelminthic compounds (benzimidazoles) potentiated navitoclax activity. Treatment with benzimidazoles led to induction of the pro-apoptotic protein Noxa at the mRNA and protein level. Noxa binds and antagonizes antiapoptotic protein Mcl-1. siRNA-mediated knock-down of Noxa completely rescued benzimidazole-potentiated navitoclax activity. In addition, inhibiting caspase 3 and 9 partially rescued benzimidazole-potentiated navitoclax activity.

Conclusions

We have identified compounds and mechanisms which potentiate navitoclax activity in lung cancer cell lines. Further validation of the benzimidazole-potentiated navitoclax effect in vivo is required to evaluate the potential for translating this observation into clinical benefit.

Electronic supplementary material

The online version of this article (doi:10.1186/s12935-014-0151-3) contains supplementary material, which is available to authorized users.     

Docosahexaenoic Acid for Reading, Cognition and Behavior in Children Aged 7-9 Years: A Randomized, Controlled Trial (The DOLAB Study)

Background

Omega-3 fatty acids are dietary essentials, and the current low intakes in most modern developed countries are believed to contribute to a wide variety of physical and mental health problems. Evidence from clinical trials indicates that dietary supplementation with long-chain omega-3 may improve child behavior and learning, although most previous trials have involved children with neurodevelopmental disorders such as attention-deficit/hyperactivity disorder (ADHD) or developmental coordination disorder (DCD). Here we investigated whether such benefits might extend to the general child population.

Objectives

To determine the effects of dietary supplementation with the long-chain omega-3 docosahexaenoic acid (DHA) on the reading, working memory, and behavior of healthy schoolchildren.

Design

Parallel group, fixed-dose, randomized, double-blind, placebo-controlled trial (RCT).

Setting

Mainstream primary schools in Oxfordshire, UK (n = 74).

Participants

Healthy children aged 7–9 years initially underperforming in reading (≤33^rd centile). 1376 invited, 362 met study criteria.

Intervention

600 mg/day DHA (from algal oil), or taste/color matched corn/soybean oil placebo.

Main Outcome Measures

Age-standardized measures of reading, working memory, and parent- and teacher-rated behavior.

Results

ITT analyses showed no effect of DHA on reading in the full sample, but significant effects in the pre-planned subgroup of 224 children whose initial reading performance was ≤20^th centile (the target population in our original study design). Parent-rated behavior problems (ADHD-type symptoms) were significantly reduced by active treatment, but little or no effects were seen for either teacher-rated behaviour or working memory.

Conclusions

DHA supplementation appears to offer a safe and effective way to improve reading and behavior in healthy but underperforming children from mainstream schools. Replication studies are clearly warranted, as such children are known to be at risk of low educational and occupational outcomes in later life.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT01066182","term_id":"NCT01066182"}}NCT01066182 and Controlled-Trials.com ISRCTN99771026     

Methylation-capture and Next-Generation Sequencing of free circulating DNA from human plasma

Background

Free circulating DNA (fcDNA) has many potential clinical applications, due to the non-invasive way in which it is collected. However, because of the low concentration of fcDNA in blood, genome-wide analysis carries many technical challenges that must be overcome before fcDNA studies can reach their full potential. There are currently no definitive standards for fcDNA collection, processing and whole-genome sequencing. We report novel detailed methodology for the capture of high-quality methylated fcDNA, library preparation and downstream genome-wide Next-Generation Sequencing. We also describe the effects of sample storage, processing and scaling on fcDNA recovery and quality.

Results

Use of serum versus plasma, and storage of blood prior to separation resulted in genomic DNA contamination, likely due to leukocyte lysis. Methylated fcDNA fragments were isolated from 5 donors using a methyl-binding protein-based protocol and appear as a discrete band of ~180 bases. This discrete band allows minimal sample loss at the size restriction step in library preparation for Next-Generation Sequencing, allowing for high-quality sequencing from minimal amounts of fcDNA. Following sequencing, we obtained 37×10⁶-86×10⁶ unique mappable reads, representing more than 50% of total mappable reads. The methylation status of 9 genomic regions as determined by DNA capture and sequencing was independently validated by clonal bisulphite sequencing.

Conclusions

Our optimized methods provide high-quality methylated fcDNA suitable for whole-genome sequencing, and allow good library complexity and accurate sequencing, despite using less than half of the recommended minimum input DNA.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-476) contains supplementary material, which is available to authorized users.     

Gene set enrichment analysis for multiple continuous phenotypes

Background

Gene set analysis (GSA) methods test the association of sets of genes with phenotypes in gene expression microarray studies. While GSA methods on a single binary or categorical phenotype abounds, little attention has been paid to the case of a continuous phenotype, and there is no method to accommodate correlated multiple continuous phenotypes.

Result

We propose here an extension of the linear combination test (LCT) to its new version for multiple continuous phenotypes, incorporating correlations among gene expressions of functionally related gene sets, as well as correlations among multiple phenotypes. Further, we extend our new method to its nonlinear version, referred as nonlinear combination test (NLCT), to test potential nonlinear association of gene sets with multiple phenotypes. Simulation study and a real microarray example demonstrate the practical aspects of the proposed methods.

Conclusion

The proposed approaches are effective in controlling type I errors and powerful in testing associations between gene-sets and multiple continuous phenotypes. They are both computationally effective. Naively (univariately) analyzing a group of multiple correlated phenotypes could be dangerous. R-codes to perform LCT and NLCT for multiple continuous phenotypes are available at http://www.ualberta.ca/~yyasui/homepage.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-260) contains supplementary material, which is available to authorized users.     

Priming Immunization with DNA Augments Immunogenicity of Recombinant Adenoviral Vectors for Both HIV-1 Specific Antibody and T-Cell Responses

Background

Induction of HIV-1-specific T-cell responses relevant to diverse subtypes is a major goal of HIV vaccine development. Prime-boost regimens using heterologous gene-based vaccine vectors have induced potent, polyfunctional T cell responses in preclinical studies.

Methods

The first opportunity to evaluate the immunogenicity of DNA priming followed by recombinant adenovirus serotype 5 (rAd5) boosting was as open-label rollover trials in subjects who had been enrolled in prior studies of HIV-1 specific DNA vaccines. All subjects underwent apheresis before and after rAd5 boosting to characterize in depth the T cell and antibody response induced by the heterologous DNA/rAd5 prime-boost combination.

Results

rAd5 boosting was well-tolerated with no serious adverse events. Compared to DNA or rAd5 vaccine alone, sequential DNA/rAd5 administration induced 7-fold higher magnitude Env-biased HIV-1-specific CD8⁺ T-cell responses and 100-fold greater antibody titers measured by ELISA. There was no significant neutralizing antibody activity against primary isolates. Vaccine-elicited CD4⁺ and CD8⁺ T-cells expressed multiple functions and were predominantly long-term (CD127⁺) central or effector memory T cells and that persisted in blood for >6 months. Epitopes mapped in Gag and Env demonstrated partial cross-clade recognition.

Conclusion

Heterologous prime-boost using vector-based gene delivery of vaccine antigens is a potent immunization strategy for inducing both antibody and T-cell responses.

Trial Registration

ClinicalTrails.gov {"type":"clinical-trial","attrs":{"text":"NCT00102089","term_id":"NCT00102089"}}NCT00102089, {"type":"clinical-trial","attrs":{"text":"NCT00108654","term_id":"NCT00108654"}}NCT00108654     

ANGSD: Analysis of Next Generation Sequencing Data

Background

High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously.

Results

We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods.

Conclusions

The open source c/c++ program ANGSD is available at http://www.popgen.dk/angsd. The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0356-4) contains supplementary material, which is available to authorized users.     

mAPKL: R/ Bioconductor package for detecting gene exemplars and revealing their characteristics

Background

So far many algorithms have been proposed towards the detection of significant genes in microarray analysis problems. Several of those approaches are freely available as R-packages though their engagement in gene expression analysis by non-bioinformaticians is usually a frustrating task. Besides, only some of those packages offer a complete suite of tools starting from initial data import and ending to analysis report. Here we present an R/Bioconductor package that implements a hybrid gene selection method along with a bunch of functions to facilitate a thorough and convenient gene expression profiling analysis.

Results

mAPKL is an open-source R/Bioconductor package that implements the mAP-KL hybrid gene selection method. The advantage of this method is that selects a small number of gene exemplars while achieving comparable classification results to other well established algorithms on a variety of datasets and dataset sizes. The mAPKL package is accompanied with extra functionalities including (i) solid data import; (ii) data sampling following a user-defined proportion; (iii) preprocessing through several normalization and transformation alternatives; (iv) classification with the aid of SVM and performance evaluation; (v) network analysis of the significant genes (exemplars), including degree of centrality, closeness, betweeness, clustering coefficient as well as the construction of an edge list table; (vi) gene annotation analysis, (vii) pathway analysis and (viii) auto-generated analysis reporting.

Conclusions

Users are able to run a thorough gene expression analysis in a timely manner starting from raw data and concluding to network characteristics of the selected gene exemplars. Detailed instructions and example data are provided in the R package, which is freely available at Bioconductor under the GPL-2 or later license http://www.bioconductor.org/packages/3.1/bioc/html/mAPKL.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0719-5) contains supplementary material, which is available to authorized users.     

dbVOR: a database system for importing pedigree,phenotype and genotype data and exporting selected subsets

Background

When studying the genetics of a human trait, we typically have to manage both genome-wide and targeted genotype data. There can be overlap of both people and markers from different genotyping experiments; the overlap can introduce several kinds of problems. Most times the overlapping genotypes are the same, but sometimes they are different. Occasionally, the lab will return genotypes using a different allele labeling scheme (for example 1/2 vs A/C). Sometimes, the genotype for a person/marker index is unreliable or missing. Further, over time some markers are merged and bad samples are re-run under a different sample name. We need a consistent picture of the subset of data we have chosen to work with even though there might possibly be conflicting measurements from multiple data sources.

Results

We have developed the dbVOR database, which is designed to hold data efficiently for both genome-wide and targeted experiments. The data are indexed for fast retrieval by person and marker. In addition, we store pedigree and phenotype data for our subjects. The dbVOR database allows us to select subsets of the data by several different criteria and to merge their results into a coherent and consistent whole. Data may be filtered by: family, person, trait value, markers, chromosomes, and chromosome ranges. The results can be presented in columnar, Mega2, or PLINK format.

Conclusions

dbVOR serves our needs well. It is freely available from https://watson.hgen.pitt.edu/register. Documentation for dbVOR can be found at https://watson.hgen.pitt.edu/register/docs/dbvor.html.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0505-4) contains supplementary material, which is available to authorized users.     

SASI-Seq: sample assurance Spike-Ins,and highly differentiating 384 barcoding for Illumina sequencing

Background

A minor but significant fraction of samples subjected to next-generation sequencing methods are either mixed-up or cross-contaminated. These events can lead to false or inconclusive results. We have therefore developed SASI-Seq; a process whereby a set of uniquely barcoded DNA fragments are added to samples destined for sequencing. From the final sequencing data, one can verify that all the reads derive from the original sample(s) and not from contaminants or other samples.

Results

By adding a mixture of three uniquely barcoded amplicons, of different sizes spanning the range of insert sizes one would normally use for Illumina sequencing, at a spike-in level of approximately 0.1%, we demonstrate that these fragments remain intimately associated with the sample. They can be detected following even the tightest size selection regimes or exome enrichment and can report the occurrence of sample mix-ups and cross-contamination.As a consequence of this work, we have designed a set of 384 eleven-base Illumina barcode sequences that are at least 5 changes apart from each other, allowing for single-error correction and very low levels of barcode misallocation due to sequencing error.

Conclusion

SASI-Seq is a simple, inexpensive and flexible tool that enables sample assurance, allows deconvolution of sample mix-ups and reports levels of cross-contamination between samples throughout NGS workflows.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-110) contains supplementary material, which is available to authorized users.