Solution Self-Assembly and Adsorption at the Air-Water Interface of the Monorhamnose and Dirhamnose Rhamnolipids and Their Mixtures

The self-assembly in solution and adsorption at the air-water interface, measured by small-angle neutron scattering, SANS, and neutron reflectivity, NR, of the monorhamnose and dirhamnose rhamnolipids (R1, R2) and their mixtures, are discussed. The production of the deuterium-labeled rhamnolipids (required for the NR studies) from a Pseudomonas aeruginosa culture and their separation into the pure R1 and R2 components is described. At the air-water interface, R1 and R2 exhibit Langmuir-like adsorption isotherms, with saturated area/molecule values of about 60 and 75 Å(2), respectively. In R1/R2 mixtures, there is a strong partitioning of R1 to the surface and R2 competes less favorably because of the steric or packing constraints of the larger R2 dirhamnose headgroup. In dilute solution (<20 mM), R1 and R2 form small globular micelles, L(1), with aggregation numbers of about 50 and 30, respectively. At higher solution concentrations, R1 has a predominantly planar structure, L(α) (unilamellar, ULV, or bilamellar, BLV, vesicles) whereas R2 remains globular, with an aggregation number that increases with increasing surfactant concentration. For R1/R2 mixtures, solutions rich in R2 are predominantly micellar whereas solutions rich in R1 have a more planar structure. At an intermediate composition (60 to 80 mol % R1), there are mixed L(α)/L(1) and L(1)/L(α) regions. However, the higher preferred curvature associated with R2 tends to dominate the mixed R1/R2 microstructure and its associated phase behavior

Mixing Behavior of the Biosurfactant, Rhamnolipid, with a Conventional Anionic Surfactant, Sodium Dodecyl Benzene Sulfonate

The use of small angle neutron scattering, SANS, neutron reflectivity, NR, and surface tension to study the mixing properties of the biosurfactant rhamnolipid with a conventional anionic surfactant, sodium dodecyl 6-benzene sulfonate, LAS, is reported. The monorhamnose rhamnolipid, R1, mixes close to ideally with LAS at the air-water interface, whereas for mixtures of LAS with the dirhamnose rhamnolipid, R2, the LAS strongly partitions to the air-water interface relative to R2, probably because of the steric hindrance of the larger R2 headgroup. These trends in the binary mixtures are also reflected in the ternary R1/R2/LAS mixtures. However, for these ternary mixtures, there is also a pronounced synergy in the total adsorption, which reaches a maximum for a LAS/rhamnolipid mole ratio of about 0.6 and a R1/R2 mol ratio of about 0.5, an effect which is not observed in the binary mixtures. In solution, the R1/LAS mixtures form relatively small globular micelles, L(1), at low surfactant concentrations (<20 mM), more planar structures (lamellar, L(α), unilamellar/multilamellar vesicles, ulv/mlv) are formed at higher surfactant concentrations for R1 and LAS rich compositions, and a large mixed phase (L(α)/L(1) and L(1)/L(α)) region forms at intermediate surfactant compositions. In contrast, for the R2/LAS mixtures, the higher preferred curvature of R2 dominates the phase behavior. The predominant microstructure is in the form of small globular micelles, except for solution compositions rich in LAS (>80 mol % LAS) where more planar structures are formed. For the ternary mixtures, there is an evolution in the resulting phase behavior from one dominated by L(1) (R2 rich) to one dominated by planar structures, L(α), (R1, LAS rich), and which strongly depends upon the LAS/rhamnolipid and R1/R2 mole ratio

ExpressionPlot: a web-based framework for analysis of RNA-Seq and microarray gene expression data

RNA-Seq and microarray platforms have emerged as important tools for detecting changes in gene expression and RNA processing in biological samples. We present ExpressionPlot, a software package consisting of a default back end, which prepares raw sequencing or Affymetrix microarray data, and a web-based front end, which offers a biologically centered interface to browse, visualize, and compare different data sets. Download and installation instructions, a user's manual, discussion group, and a prototype are available at http://expressionplot.com/ webcite.ALS Therapy Allianc

Effect of Mono and Di-rhamnolipids on Biofilms Pre-formed by Bacillus subtilis BBK006.

Different microbial inhibition strategies based on the planktonic bacterial physiology have been known to have limited efficacy on the growth of biofilms communities. This problem can be exacerbated by the emergence of increasingly resistant clinical strains. Biosurfactants have merited renewed interest in both clinical and hygienic sectors due to their potential to disperse microbial biofilms. In this work, we explore the aspects of Bacillus subtilis BBK006 biofilms and examine the contribution of biologically derived surface-active agents (rhamnolipids) to the disruption or inhibition of microbial biofilms produced by Bacillus subtilis BBK006. The ability of mono-rhamnolipids (Rha-C10-C10) produced by Pseudomonas aeruginosa ATCC 9027 and the di-rhamnolipids (Rha-Rha-C14-C14) produced by Burkholderia thailandensis E264, and phosphate-buffered saline to disrupt biofilm of Bacillus subtilis BBK006 was evaluated. The biofilm produced by Bacillus subtilis BBK006 was more sensitive to the di-rhamnolipids (0.4 g/L) produced by Burkholderia thailandensis than the mono-rhamnolipids (0.4 g/L) produced by Pseudomonas aeruginosa ATCC 9027. Rhamnolipids are biologically produced compounds safe for human use. This makes them ideal candidates for use in new generations of bacterial dispersal agents and useful for use as adjuvants for existing microbial suppression or eradication strategies

Genome Expression Pathway Analysis Tool – Analysis and visualization of microarray gene expression data under genomic, proteomic and metabolic context

<p>Abstract</p> <p>Background</p> <p>Regulation of gene expression is relevant to many areas of biology and medicine, in the study of treatments, diseases, and developmental stages. Microarrays can be used to measure the expression level of thousands of mRNAs at the same time, allowing insight into or comparison of different cellular conditions. The data derived out of microarray experiments is highly dimensional and often noisy, and interpretation of the results can get intricate. Although programs for the statistical analysis of microarray data exist, most of them lack an integration of analysis results and biological interpretation.</p> <p>Results</p> <p>We have developed GEPAT, Genome Expression Pathway Analysis Tool, offering an analysis of gene expression data under genomic, proteomic and metabolic context. We provide an integration of statistical methods for data import and data analysis together with a biological interpretation for subsets of probes or single probes on the chip. GEPAT imports various types of oligonucleotide and cDNA array data formats. Different normalization methods can be applied to the data, afterwards data annotation is performed. After import, GEPAT offers various statistical data analysis methods, as hierarchical, k-means and PCA clustering, a linear model based t-test or chromosomal profile comparison. The results of the analysis can be interpreted by enrichment of biological terms, pathway analysis or interaction networks. Different biological databases are included, to give various information for each probe on the chip. GEPAT offers no linear work flow, but allows the usage of any subset of probes and samples as a start for a new data analysis. GEPAT relies on established data analysis packages, offers a modular approach for an easy extension, and can be run on a computer grid to allow a large number of users. It is freely available under the LGPL open source license for academic and commercial users at <url>http://gepat.sourceforge.net</url>.</p> <p>Conclusion</p> <p>GEPAT is a modular, scalable and professional-grade software integrating analysis and interpretation of microarray gene expression data. An installation available for academic users can be found at <url>http://gepat.bioapps.biozentrum.uni-wuerzburg.de</url>.</p

SeqGene: a comprehensive software solution for mining exome- and transcriptome- sequencing data

Abstract Background The popularity of massively parallel exome and transcriptome sequencing projects demands new data mining tools with a comprehensive set of features to support a wide range of analysis tasks. Results SeqGene, a new data mining tool, supports mutation detection and annotation, dbSNP and 1000 Genome data integration, RNA-Seq expression quantification, mutation and coverage visualization, allele specific expression (ASE), differentially expressed genes (DEGs) identification, copy number variation (CNV) analysis, and gene expression quantitative trait loci (eQTLs) detection. We also developed novel methods for testing the association between SNP and expression and identifying genotype-controlled DEGs. We showed that the results generated from SeqGene compares favourably to other existing methods in our case studies. Conclusion SeqGene is designed as a general-purpose software package. It supports both paired-end reads and single reads generated on most sequencing platforms; it runs on all major types of computers; it supports arbitrary genome assemblies for arbitrary organisms; and it scales well to support both large and small scale sequencing projects. The software homepage is http://seqgene.sourceforge.net.</p

A Snapshot of CNVs in the Pig Genome

Recent studies of mammalian genomes have uncovered the extent of copy number variation (CNV) that contributes to phenotypic diversity, including health and disease status. Here we report a first account of CNVs in the pig genome covering part of the chromosomes 4, 7, 14, and 17 already sequenced and assembled. A custom tiling oligonucleotide array was used with a median probe spacing of 409 bp for screening 12 unrelated Duroc boars that are founders of a large family material. After a strict CNV calling pipeline, 37 copy number variable regions (CNVRs) across all four chromosomes were identified, with five CNVRs overlapping segmental duplications, three overlapping pig unigenes and one overlapping a RefSeq pig mRNA. This CNV snapshot analysis is the first of its kind in the porcine genome and constitutes the basis for a better understanding of porcine phenotypes and genotypes with the prospect of identifying important economic traits