Construction of biosensor systems for determining the pathophysiological potential of carrageenan variants.

In vitro systems for monitoring safety of nutritional additives are desirable for high-throughput screenings and as a substitute for animal models. Carrageenan (CGN) is a sulfated polysaccharide widely used as a thickener and texturizer in human nutrition and is intensely discussed regarding its pathophysiological potential. Low molecular weight (lm) variants of CGN are considered to exert more profound pathophysiological effects in vivo than high molecular weight (hm) variants. We used a systematic approach to construct reporter systems allowing distinction between CGN-variants with different pathophysiological potential. Reporter systems utilizing segments of the CGN-responsive DMBT1 promoter did not display substantial activity in SW620 cells of intestinal epithelial origin. Genome-wide profiling revealed stronger qualitative and quantitative changes in global gene activities for hm-CGN than for lm-CGN (824 versus 91 genes; -6.64 to 22.33-fold for hm-CGN versus the range of -2.65 to 2.96-fold for lm-CGN). Reporter systems with segments of the IL-8 promoter showed a specific activation in response to hm-sulfated polysaccharides with lower pathophysiological potential in vivo and provided a better classification of CGN-variants than cytotoxicity assays in vitro. IL-8 reporter systems can be used for discerning between the effects of sulfated polysaccharides in vivo. Our data further provide initial insights into the molecular mechanisms that may play a role in the different effects of CGN-variants.

Down-regulation of HLA Class I and NKG2D ligands through a concerted action of MAPK and DNA methyltransferases in colorectal cancer cells.

Most malignant features of cancer cells are triggered by activated oncogenes and the loss of tumor suppressors due to mutation or epigenetic inactivation. It is still unclear, to what extend the escape of emerging cancer cells from recognition and elimination by the immune system is determined by similar mechanisms. We compared the transcriptomes of HCT116 colorectal cancer cells deficient in DNA methyltransferases (DNMTs) and of cells, in which the RAS pathway as the major growth-promoting signaling system is blocked by inhibition of MAPK. We identified the MHC Class I genes HLA-A1/A2 and the ULBP2 gene encoding 1 of the 8 known ligands of the activating NK receptor NKG2D among a cluster of immune genes up-regulated under the conditions of both DNMT-deficiency and MEK-inhibition. Bisulphite sequencing analyses of HCT116 with DNMT deficiency or after MEK-inhibition showed that de-methylation of the ULPB2 promoter correlated with its enhanced surface expression. The HLA-A promoters were not methylated indicating that components of the HLA assembly machinery were also suppressed in DNMT-deficient and MEK-inhibited cells. Increased HLA-A2 surface expression was correlated with enhanced recognition and lysis by A2-specific CTL. On the contrary, elevated ULBP2 expression was not reflected by enhanced recognition and lysis by NK cells. Cosuppression of HLA Class I and NKG2D ligands and genes encoding peptide transporters or proteasomal genes mediates a strong functional link between RAS activation, DNMT activity and disruption of the antigen presenting system controlling immune recognition in colorectal cancer cells.

Differential genomic and proteomic profiling of glioblastoma cells exposed to terpyridineplatinum(II) complexes.

Terpyridineplatinum(II) complexes (TPCs) efficiently inhibit the proliferation of glioblastoma cells in vitro and have been tested successfully in a rodent glioblastoma model. Apart from intercalation with DNA, the major mechanism of action of TPCs is a very potent and specific interaction with the human selenoprotein thioredoxin reductase (TrxR). TrxR plays a crucial role in cellular redox homeostasis and protection against oxidative damage. In many malignant cells the thioredoxin system is upregulated, promoting tumor growth and progression. Thus, the thioredoxin system has been proposed to be an attractive target for cancer therapy. This study gives the first comprehensive overview of the effects of TPCs on the transcriptome and proteome of glioblastoma cells. We reveal that under TPC treatment, mechanisms countersteering TrxR inhibition are activated in parallel to DNA-damage-responsive pathways. TPC pressure results in long-term compensatory upregulation of TrxR expression. In parallel, p53 is activated, leading to a range of regulations typical for cell-cycle-arrested cells such as upregulation of CDKN1A, induction of GADD45, inhibition of eIF5A maturation, and reduced phosphorylation of stathmin. We also show that TPCs induce endoplasmic reticulum stress, as they activate the unfolded protein response. This profiling study provides a thorough insight into the spectrum of cellular events resulting from specific TrxR inhibition and characterizes the TPC mode of action.

Large scale statistical inference of signaling pathways from RNAi and microarray data.

BACKGROUND: The advent of RNA interference techniques enables the selective silencing of biologically interesting genes in an efficient way. In combination with DNA microarray technology this enables researchers to gain insights into signaling pathways by observing downstream effects of individual knock-downs on gene expression. These secondary effects can be used to computationally reverse engineer features of the upstream signaling pathway. RESULTS: In this paper we address this challenging problem by extending previous work by Markowetz et al., who proposed a statistical framework to score networks hypotheses in a Bayesian manner. Our extensions go in three directions: First, we introduce a way to omit the data discretization step needed in the original framework via a calculation based on p-values instead. Second, we show how prior assumptions on the network structure can be incorporated into the scoring scheme using regularization techniques. Third and most important, we propose methods to scale up the original approach, which is limited to around 5 genes, to large scale networks. CONCLUSION: Comparisons of these methods on artificial data are conducted. Our proposed module network is employed to infer the signaling network between 13 genes in the ER-alpha pathway in human MCF-7 breast cancer cells. Using a bootstrapping approach this reconstruction can be found with good statistical stability. The code for the module network inference method is available in the latest version of the R-package nem, which can be obtained from the Bioconductor homepage.

High expression level of bone degrading proteins as a possible inducer of osteolytic features in pigmented villonodular synovitis.

Protein expression of osteopontin (OPN), osteoprotegerin (OPG), bone sialoprotein (BSP), osteocalcin (OC), RANKL and PTHrP was determined by use of immunohistochemical analysis on tissue arrays (48 cases of PVNS, 20 cases of active (a-RA), non-active rheumatoid arthritis (na-RA), and osteoarthritis (OA)). Additionally, gene expression was analysed using complimentary DNA (cDNA) microarrays. All PVNS cases showed a higher level of both protein and gene expression of RANKL, OPN and BSP in comparison with OA cases. Expression of OPG was not significantly different in PVNS compared to OA. The RANKL/OPG expression ratio was significantly higher in PVNS than in OA. High expressions level of proteins involved in bone degradation in PVNS may promote an intra-osseous propagation of the lesion. This evidence suggests that PVNS might respond to treatment using specific inhibitors of RANKL, OPN and BSP.

Infrared-based protein detection arrays for quantitative proteomics.

The advancement of efficient technologies to comply with the needs of systems biology and drug discovery has so far not received adequate attention. A substantial bottleneck for the time-resolved quantitative description of signaling networks is the limited throughput and the inadequate sensitivity of currently established methods. Here, we present an improved protein microarray-based approach towards the sensitive detection of proteins in the fg-range which is based on signal detection in the near-infrared range. The high sensitivity of the assay permits the specific quantification of proteins derived from as little as only 20,000 cells with an error rate of only 5%. The capacity is limited to the analysis of up to 500 different samples per microarray. Protein abundance is determined qualitatively, and quantitatively, if recombinant protein is available. This novel approach was called IPAQ (infrared-based protein arrays with quantitative readout). IPAQ offers a highly sensitive experimental approach superior to the established standard protein quantification technologies, and is suitable for quantitative proteomics. Employing the IPAQ approach, a detailed analysis of activated signaling networks in biopsy samples and of crosstalk between signaling modules as required in drug discovery strategies can easily be performed.

Parameter estimation for the calibration and variance stabilization of microarray data.

We derive and validate an estimator for the parameters of a transformation for the joint calibration (normalization) and variance stabilization of microarray intensity data. With this, the variances of the transformed intensities become approximately independent of their expected values. The transformation is similar to the logarithm in the high intensity range, but has a smaller slope for intensities close to zero. Applications have shown better sensitivity and specificity for the detection of differentially expressed genes. In this paper, we describe the theoretical aspects of the method. We incorporate calibration and variance-mean dependence into a statistical model and use a robust variant of the maximum-likelihood method to estimate the transformation parameters. Using simulations, we investigate the size of the estimation error and its dependence on sample size and the presence of outliers. We find that the error decreases with the square root of the number of probes per array and that the estimation is robust against the presence of differentially expressed genes. Software is publicly available as an R package through the Bioconductor project (http://www.bioconductor.org).