Investigating the utility of combining Phi 29 whole genome amplification and highly multiplexed single nucleotide polymorphism BeadArray (TM) genotyping

Background: Sustainable DNA resources and reliable high-throughput genotyping methods are required for large-scale, long-term genetic association studies. In the genetic dissection of common disease it is now recognised that thousands of samples and hundreds of thousands of markers, mostly single nucleotide polymorphisms (SNPs), will have to be analysed. In order to achieve these aims, both an ability to boost quantities of archived DNA and to genotype at low costs are highly desirable. We have investigated Phi29 polymerase Multiple Displacement Amplification (MDA)-generated DNA product (MDA product), in combination with highly multiplexed BeadArray(TM) genotyping technology. As part of a large-scale BeadArray genotyping experiment we made a direct comparison of genotyping data generated from MDA product with that from genomic DNA (gDNA) templates. Results: Eighty-six MDA product and the corresponding 86 gDNA samples were genotyped at 345 SNPs and a concordance rate of 98.8% was achieved. The BeadArray sample exclusion rate, blind to sample type, was 10.5% for MDA product compared to 5.8% for gDNA. Conclusions: We conclude that the BeadArray technology successfully produces high quality genotyping data from MDA product. The combination of these technologies improves the feasibility and efficiency of mapping common disease susceptibility genes despite limited stocks of gDNA samples

Identification of microRNAs with regulatory potential using a matched microRNA-mRNA time-course data

Over the past decade, a class of small RNA molecules called microRNAs (miRNAs) has been shown to regulate gene expression at the post-transcription stage. While early work focused on the identification of miRNAs using a combination of experimental and computational techniques, subsequent studies have focused on identification of miRNA-target mRNA pairs as each miRNA can have hundreds of mRNA targets. The experimental validation of some miRNAs as oncogenic has provided further motivation for research in this area. In this article we propose an odds-ratio (OR) statistic for identification of regulatory miRNAs. It is based on integrative analysis of matched miRNA and mRNA time-course microarray data. The OR-statistic was used for (i) identification of miRNAs with regulatory potential, (ii) identification of miRNA-target mRNA pairs and (iii) identification of time lags between changes in miRNA expression and those of its target mRNAs. We applied the OR-statistic to a cancer data set and identified a small set of miRNAs that were negatively correlated to mRNAs. A literature survey revealed that some of the miRNAs that were predicted to be regulatory, were indeed oncogenic or tumor suppressors. Finally, some of the predicted miRNA targets have been shown to be experimentally valid

Quantifying vertical mixing in estuaries

© 2008 The Authors. This is an open-access article distributed under the terms of the Creative Commons Attribution Noncommercial License. The definitive version was published in Environmental Fluid Mechanics 8 (2008): 495-509, doi:10.1007/s10652-008-9107-2.Estuarine turbulence is notable in that both the dissipation rate and the buoyancy frequency extend to much higher values than in other natural environments. The high dissipation rates lead to a distinct inertial subrange in the velocity and scalar spectra, which can be exploited for quantifying the turbulence quantities. However, high buoyancy frequencies lead to small Ozmidov scales, which require high sampling rates and small spatial aperture to resolve the turbulent fluxes. A set of observations in a highly stratified estuary demonstrate the effectiveness of a vessel-mounted turbulence array for resolving turbulent processes, and for relating the turbulence to the forcing by the Reynolds-averaged flow. The observations focus on the ebb, when most of the buoyancy flux occurs. Three stages of mixing are observed: (1) intermittent and localized but intense shear instability during the early ebb; (2) continuous and relatively homogeneous shear-induced mixing during the mid-ebb, and weakly stratified, boundary-layer mixing during the late ebb. The mixing efficiency as quantified by the flux Richardson number Rf was frequently observed to be higher than the canonical value of 0.15 from Osborn (J Phys Oceanogr 10:83–89, 1980). The high efficiency may be linked to the temporal–spatial evolution of shear instabilities.The funding for this research was obtained from ONR Grant N00014-06-1-0292 and NSF Grant OCE-0729547

Plasma bioavailability and changes in PBMC gene expression after treatment of ovariectomized rats with a commercial soy supplement

The health effects of soy supplementation in (post)menopausal women are still a controversial issue. The aim of the present study was to establish the effect of the soy isoflavones (SIF) present in a commercially available supplement on ovariectomized rats and to investigate whether these rats would provide an adequate model to predict effects of SIF in (post)menopausal women. Two dose levels (i.e. 2 and 20. mg/kg b.w.) were used to characterize plasma bioavailability, urinary and fecal concentrations of SIF and changes in gene expression in peripheral blood mononuclear cells (PBMC). Animals were dosed at 0 and 48. h and sacrificed 4. h after the last dose. A clear dose dependent increase of SIF concentrations in plasma, urine and feces was observed, together with a strong correlation in changes in gene expression between the two dose groups. All estrogen responsive genes and related biological pathways (BPs) that were affected by the SIF treatment were regulated in both dose groups in the same direction and indicate beneficial effects. However, in general no correlation was found between the changes in gene expression in rat PBMC with those in PBMC of (post)menopausal women exposed to a comparable dose of the same supplement. The outcome of this short-term study in rats indicates that the rat might not be a suitable model to predict effects of SIF in humans. Although the relative exposure period in this rat study is comparable with that of the human study, longer repetitive administration of rats to SIF may be required to draw a final conclusion on the suitability of the rat a model to predict effects of SIF in humans

T cells and T cell tumors efficiently generate antigen-specific cytotoxic T cell immunity when modified with an NKT ligand

Various Invariant NKT (iNKT) cell ligands have been shown as potent adjuvants in boosting T cell reactivates to antigens on professional APC. Non-professional APC, such as T cells, also co-expressing MHC class I and CD1d, have been unattractive cell vaccine carriers due to their poor immunogenicity. Here, we report that T cells as well as T cell lymphoma can efficiently generate antigen-specific cytotoxic T lymphocytes (CTL) responses in mice in vivo, when formulated to present iNKT ligand α-galactosylceramide (αGC) on their surface CD1d. Vaccination with αGC-pulsed EG-7 T-cell lymphoma induced tumor-specific CTL response and suppressed the growth of EG-7 in a CD8 T cell-dependent manner. Injection of αGC-loaded CD4 T cells in mice efficiently activated iNKT cells in vivo. While T cells loaded with a class I-restricted peptide induced proliferation but not effector differentiation of antigen-specific CD8 T cells, injection of T cells co-pulsed with αGC strongly induced IFNγ and Granzyme B expression in T cells and complete lysis of target cells in vivo. Presentation of αGC and peptide on the same cells was required for optimal CTL response and vaccinating T cells appeared to directly stimulate both iNKT and cytotoxic CD8 T cells. Of note, the generation of this cytotoxic T cell response was independent of IL-4, IFNγ, IL-12, IL-21 and costimulation. Our data indicate that iNKT cell can license a non-professional APC to directly trigger antigen-specific cytotoxic T cell responses, which provides an alternative cellular vaccine strategy against tumors

The rice StMADS11-like genes OsMADS22 and OsMADS47 cause floral reversions in Arabidopsis without complementing the svp and agl24 mutants

During floral induction and flower development plants undergo delicate phase changes which are under tight molecular control. MADS-box transcription factors have been shown to play pivotal roles during these transition phases. SHORT VEGETATIVE PHASE (SVP) and AGAMOUS LIKE 24 (AGL24) are important regulators both during the transition to flowering and during flower development. During vegetative growth they exert opposite roles on floral transition, acting as repressor and promoter of flowering, respectively. Later during flower development they act redundantly as negative regulators of AG expression. In rice, the orthologues of SVP and AGL24 are OsMADS22, OsMADS47, and OsMADS55 and these three genes are involved in the negative regulation of brassinosteroid responses. In order to understand whether these rice genes have maintained the ability to function as regulators of flowering time in Arabidopsis, complementation tests were performed by expressing OsMADS22 and OsMADS47 in the svp and agl24 mutants. The results show that the rice genes are not able to complement the flowering-time phenotype of the Arabidopsis mutants, indicating that they are biologically inactive in Arabidopsis. Nevertheless, they cause floral reversions, which mimic the SVP and AGL24 floral overexpressor phenotypes. Yeast two-hybrid analysis suggests that these floral phenotypes are probably the consequence of protein interactions between OsMADS22 and OsMADS47 and other MADS-box proteins which interfere with the formation of complexes required for normal flower development

Pharmacological levels of withaferin A (Withania somnifera) trigger clinically relevant anticancer effects specific to triple negative breast cancer cells

Withaferin A (WA) isolated from Withania somnifera (Ashwagandha) has recently become an attractive phytochemical under investigation in various preclinical studies for treatment of different cancer types. In the present study, a comparative pathway-based transcriptome analysis was applied in epithelial-like MCF-7 and triple negative mesenchymal MDA-MB-231 breast cancer cells exposed to different concentrations of WA which can be detected systemically in in vivo experiments. Whereas WA treatment demonstrated attenuation of multiple cancer hallmarks, the withanolide analogue Withanone (WN) did not exert any of the described effects at comparable concentrations. Pathway enrichment analysis revealed that WA targets specific cancer processes related to cell death, cell cycle and proliferation, which could be functionally validated by flow cytometry and real-time cell proliferation assays. WA also strongly decreased MDA-MB-231 invasion as determined by single-cell collagen invasion assay. This was further supported by decreased gene expression of extracellular matrix-degrading proteases (uPA, PLAT, ADAM8), cell adhesion molecules (integrins, laminins), pro-inflammatory mediators of the metastasis-promoting tumor microenvironment (TNFSF12, IL6, ANGPTL2, CSF1R) and concomitant increased expression of the validated breast cancer metastasis suppressor gene (BRMS1). In line with the transcriptional changes, nanomolar concentrations of WA significantly decreased protein levels and corresponding activity of uPA in MDA-MB-231 cell supernatant, further supporting its anti-metastatic properties. Finally, hierarchical clustering analysis of 84 chromatin writer-reader-eraser enzymes revealed that WA treatment of invasive mesenchymal MDA-MB-231 cells reprogrammed their transcription levels more similarly towards the pattern observed in non-invasive MCF-7 cells. In conclusion, taking into account that sub-cytotoxic concentrations of WA target multiple metastatic effectors in therapy-resistant triple negative breast cancer, WA-based therapeutic strategies targeting the uPA pathway hold promise for further (pre)clinical development to defeat aggressive metastatic breast cancer

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset