An estimation of sonic velocities in shale using clay and silt fractions from the elemental capture spectroscopy log

Anisotropic differential effective medium approach is used to simulate elastic properties of shales from elastic properties and volume fractions of clay and silt constituents. Anisotropic elastic coefficients of the wet clay pack are assumed to be independent of mineralogy and to be linearly dependent on clay packing density (CPD), a fraction of clay in an individual wet clay pack. Simulated compressional and shear velocities normal to the bedding plane and are shown to be in a good agreement with measured sonic velocities. Further, elastic coefficients of shales, and, calculated from the log sonic velocities, calibrated porosity and clay fraction obtained from the mineralogy tool are used to invert for elastic constants of clays, C33 and C44. The obtained elastic coefficients of clays show lower scatter than the original elastic coefficients of shales. The noticeable increase of the clay elastic coefficients with the depth increase is shown to result from the positive trend of the CPD with depth. Being interpolated to the same CPD = 0.8, elastic coefficients of clays show no depth dependency. Our findings show that the CPD and silt fraction are the key parameters that can be used for successful modelling of elastic properties of shales

Differential in vivo tumorigenicity of distinct subpopulations from a luminal-like breast cancer xenograft

Intratumor heterogeneity caused by genetic, phenotypic or functional differences between cancer cell subpopulations is a considerable clinical challenge. Understanding subpopulation dynamics is therefore central for both optimization of existing therapy and for development of new treatment. The aim of this study was to isolate subpopulations from a primary tumor and by comparing molecular characteristics of these subpopulations, find explanations to their differing tumorigenicity. Cell subpopulations from two patient derived in vivo models of primary breast cancer, ER+ and ER-, were identified. EpCAM+ cells from the ER+ model gave rise to tumors independently of stroma cell support. The tumorigenic fraction was further divided based on SSEA-4 and CD24 expression. Both markers were expressed in ER+ breast cancer biopsies. FAC-sorted cells based on EpCAM, SSEA-4 and CD24 expression were subsequently tested for differences in functionality by in vivo tumorigenicity assay. Three out of four subpopulations of cells were tumorigenic and showed variable ability to recapitulate the marker expression of the original tumor. Whole genome expression analysis of the sorted populations disclosed high similarity in the transcriptional profiles between the tumorigenic populations. Comparing the non-tumorigenic vs the tumorigenic populations, 44 transcripts were, however, significantly differentially expressed. A subset of these, 26 identified and named genes, highly expressed in the non-tumorigenic population, predicted longer overall survival (N = 737, p<0.0001) and distant metastasis free survival (DMFS) (N = 1379, p<0.0001) when performing Kaplan-Meier survival analysis using the GOBO online database. The 26 gene set correlated with longer DMFS in multiple breast cancer subgroups. Copy number profiling revealed no aberrations that could explain the observed differences in tumorigenicity. This study emphasizes the functional variability among cell populations that are otherwise genomically similar, and that the risk of breast cancer recurrence can only be eliminated if the tumorigenic abilities in multiple cancer cell subpopulations are inhibited

Integrated study of copy number states and genotype calls using high-density SNP arrays

We propose a statistical framework, named genoCN, to simultaneously dissect copy number states and genotypes using high-density SNP (single nucleotide polymorphism) arrays. There are at least two types of genomic DNA copy number differences: copy number variations (CNVs) and copy number aberrations (CNAs). While CNVs are naturally occurring and inheritable, CNAs are acquired somatic alterations most often observed in tumor tissues only. CNVs tend to be short and more sparsely located in the genome compared with CNAs. GenoCN consists of two components, genoCNV and genoCNA, designed for CNV and CNA studies, respectively. In contrast to most existing methods, genoCN is more flexible in that the model parameters are estimated from the data instead of being decided a priori. GenoCNA also incorporates two important strategies for CNA studies. First, the effects of tissue contamination are explicitly modeled. Second, if SNP arrays are performed for both tumor and normal tissues of one individual, the genotype calls from normal tissue are used to study CNAs in tumor tissue. We evaluated genoCN by applications to 162 HapMap individuals and a brain tumor (glioblastoma) dataset and showed that our method can successfully identify both types of copy number differences and produce high-quality genotype calls

Sodium alginate decreases the permeability of intestinal mucus

In the small intestine the nature of the environment leads to a highly heterogeneous mucus layer primarily composed of the MUC2 mucin. We set out to investigate whether the soluble dietary fibre sodium alginate could alter the permeability of the mucus layer. The alginate was shown to freely diffuse into the mucus and to have minimal effect on the bulk rheology when added at concentrations below 0.1%. Despite this lack of interaction between the mucin and alginate, the addition of alginate had a marked effect on the diffusion of 500 nm probe particles, which decreased as a function of increasing alginate concentration. Finally, we passed a protein stabilised emulsion through a simulation of oral, gastric and small intestinal digestion. We subsequently showed that the addition of 0.1% alginate to porcine intestinal mucus decreased the diffusion of fluorescently labelled lipid present in the emulsion digesta. This reduction may be sufficient to reduce problems associated with high rates of lipid absorption such as hyperlipidaemia

The effects of linkage disequilibrium in large scale SNP datasets for MDR

<p>Abstract</p> <p>Background</p> <p>In the analysis of large-scale genomic datasets, an important consideration is the power of analytical methods to identify accurate predictive models of disease. When trying to assess sensitivity from such analytical methods, a confounding factor up to this point has been the presence of linkage disequilibrium (LD). In this study, we examined the effect of LD on the sensitivity of the Multifactor Dimensionality Reduction (MDR) software package.</p> <p>Results</p> <p>Four relative amounts of LD were simulated in multiple one- and two-locus scenarios for which the position of the functional SNP(s) within LD blocks varied. Simulated data was analyzed with MDR to determine the sensitivity of the method in different contexts, where the sensitivity of the method was gauged as the number of times out of 100 that the method identifies the correct one- or two-locus model as the best overall model. As the amount of LD increases, the sensitivity of MDR to detect the correct functional SNP drops but the sensitivity to detect the disease signal and find an indirect association increases.</p> <p>Conclusions</p> <p>Higher levels of LD begin to confound the MDR algorithm and lead to a drop in sensitivity with respect to the identification of a direct association; it does not, however, affect the ability to detect indirect association. Careful examination of the solution models generated by MDR reveals that MDR can identify loci in the correct LD block; though it is not always the functional SNP. As such, the results of MDR analysis in datasets with LD should be carefully examined to consider the underlying LD structure of the dataset.</p

The Role of the Mucus Barrier in Digestion

Mucus forms a protective layer across a variety of epithelial surfaces. In the gastrointestinal (GI) tract, the barrier has to permit the uptake of nutrients, while excluding potential hazards, such as pathogenic bacteria. In this short review article, we look at recent literature on the structure, location, and properties of the mammalian intestinal secreted mucins and the mucus layer they form over a wide range of length scales. In particular, we look at the structure of the gel-forming glycoprotein MUC2, the primary intestinal secreted mucin, and the influence this has on the properties of the mucus layer. We show that, even at the level of the protein backbone, MUC2 is highly heterogeneous and that this is reflected in the networks it forms. It is evident that a combination of charge and pore size determines what can diffuse through the layer to the underlying gut epithelium. This information is important for the targeted delivery of bioactive molecules, including nutrients and pharmaceuticals, and for understanding how GI health is maintained

Allele-specific disparity in breast cancer

Background In a cancer cell the number of copies of a locus may vary due to amplification and deletion and these variations are denoted as copy number alterations (CNAs). We focus on the disparity of CNAs in tumour samples, which were compared to those in blood in order to identify the directional loss of heterozygosity. Methods We propose a numerical algorithm and apply it to data from the Illumina 109K-SNP array on 112 samples from breast cancer patients. B-allele frequency (BAF) and log R ratio (LRR) of Illumina were used to estimate Euclidian distances. For each locus, we compared genotypes in blood and tumour for subset of samples being heterozygous in blood. We identified loci showing preferential disparity from heterozygous toward either the A/B-allele homozygous (allelic disparity). The chi-squared and Cochran-Armitage trend tests were used to examine whether there is an association between high levels of disparity in single nucleotide polymorphisms (SNPs) and molecular, clinical and tumour-related parameters. To identify pathways and network functions over-represented within the resulting gene sets, we used Ingenuity Pathway Analysis (IPA). Results To identify loci with a high level of disparity, we selected SNPs 1) with a substantial degree of disparity and 2) with substantial frequency (at least 50% of the samples heterozygous for the respective locus). We report the overall difference in disparity in high-grade tumours compared to low-grade tumours (p-value < 0.001) and significant associations between disparity in multiple single loci and clinical parameters. The most significantly associated network functions within the genes represented in the loci of disparity were identified, including lipid metabolism, small-molecule biochemistry, and nervous system development and function. No evidence for over-representation of directional disparity in a list of stem cell genes was obtained, however genes appeared to be more often altered by deletion than by amplification. Conclusions Our data suggest that directional loss and amplification exist in breast cancer. These are highly associated with grade, which may indicate that they are enforced with increasing number of cell divisions. Whether there is selective pressure for some loci to be preferentially amplified or deleted remains to be confirmed

Genes harbouring susceptibility SNPs are differentially expressed in the breast cancer subtypes

Recently, genome-wide association studies of breast cancer revealed single nucleotide polymorphisms (SNPs) in five genes with novel association to susceptibility. While there is little doubt that the novel susceptibility markers produced from such highly powered studies are true, the mechanisms by which they cause the susceptibility remain undetermined. We have looked at the expression levels of the identified genes in tumours and found that they are highly significantly differentially expressed between the five established breast cancer subtypes. Also, a significant association between SNPs in these genes and their expression in tumours was seen as well as a significantly different frequency of the SNPs between the subtypes. This suggests that the observed genes are associated with different breast cancer subtypes, and may exert their effect through their expression in the tumours. Thus, future studies stratifying patients by their molecular subtypes may give much more power to classic case control studies, and genes of no or borderline significance may appear to be high-penetrant for certain subtypes and, therefore, be identifiable

Deciphering the genome repertoire of Pseudomonas sp. M1 toward β-Myrcene biotransformation

Pseudomonas sp. M1 is able to mineralize several unusual substrates of natural and xenobiotic origin, contributing to its competence to thrive in different ecological niches. In this work, the genome of M1 strain was resequenced by Illumina MiSeq to refine the quality of a published draft by resolving the majority of repeat-rich regions. In silico genome analysis led to the prediction of metabolic pathways involved in biotransformation of several unusual substrates (e.g., plant-derived volatiles), providing clues on the genomic complement required for such biodegrading/biotransformation functionalities. Pseudomonas sp. M1 exhibits a particular sensory and biotransformation/biocatalysis potential toward β-myrcene, a terpene vastly used in industries worldwide. Therefore, the genomic responsiveness of M1 strain toward β-myrcene was investigated, using an RNA sequencing approach. M1 cells challenged with β-myrcene(compared with cells grown in lactate) undergo an extensive alteration of the transcriptome expression profile, including 1,873 genes evidencing at least 1.5-fold of altered expression (627 upregulated and 1,246 downregulated), toward β-myrcene-imposed molecular adaptation and cellular specialization. A thorough data analysis identified a novel 28-kb genomic island, whose expression was strongly stimulated in β-myrcene-supplemented medium, that is essential for β-myrcene catabolism. This island includes β-myrcene-induced genes whose products are putatively involved in 1) substrate sensing, 2) gene expression regulation, and 3) β-myrcene oxidation and bioconversion of β-myrcene derivatives into central metabolism intermediates. In general, this locus does not show high homology with sequences available in databases and seems to have evolved through the assembly of several functional blocks acquired from different bacteria, probably, at different evolutionary stages.Acknowledgments This work was supported by FEDER through POFC— COMPETE and by national funds from Foundation for Science and Technology (Portugal) through the projects PEst-C/BIA/UI4050/2011, PTDC/EBB-BIO/104980/2008 and PTDC/BIA-MIC/113733/09, and through a PhD grant (grant number SFRH/BD/76894/2011) to P.S.-C.info:eu-repo/semantics/publishedVersio

Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as ‘fold-difference’ results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample