The transcriptome's drugable frequenters

Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer

Ovarian cancer (OC) is one of the most lethal cancers among all gynecological malignancies. An effective and non-invasive screening approach is needed urgently to reduce high mortality rate. The purpose of this study was to identify the salivary protein signatures (SPS) for non-invasive detection of ovarian cancer. Differentially expressed SPS were identified by fluorescence-based 2D-DIGE coupled with MALDI/TOF-MS. The expression levels of three differential proteins (Lipocalin-2, indoleamine-2, 3-dioxygenase1 (IDO1) and S100A8) were validated using western blotting and ELISA. Immunohistochemistry and qRT-PCR were performed in an independent cohort of ovarian tumor tissues. 25 over expressed and 19 under expressed (p<0.05) proteins between healthy controls and cancer patients were identified. Lipocalin-2, IDO1 and S100A8 were selected for initial verification and successfully verified by immunoassay. Diagnostic potential of the candidate biomarkers was evaluated by ROC analysis. The selected biomarkers were further validated by immunohistochemistry in an independent cohort of ovarian tissues. The global expression of selected targets was also analyzed by microarray and validated using qRT-PCR to strengthen our hypothesis. Tumor secreted proteins identified by 'dual-omics' strategy, whose concentration are significantly high in ovarian cancer patients have obvious potential to be used as screening biomarker after large scale validation.

The effect of inhibition of PP1 and TNFα signaling on pathogenesis of SARS coronavirus

BACKGROUND

The complex interplay between viral replication and host immune response during infection remains poorly understood. While many viruses are known to employ anti-immune strategies to facilitate their replication, highly pathogenic virus infections can also cause an excessive immune response that exacerbates, rather than reduces pathogenicity. To investigate this dichotomy in severe acute respiratory syndrome coronavirus (SARS-CoV), we developed a transcriptional network model of SARS-CoV infection in mice and used the model to prioritize candidate regulatory targets for further investigation.

RESULTS

We validated our predictions in 18 different knockout (KO) mouse strains, showing that network topology provides significant predictive power to identify genes that are important for viral infection. We identified a novel player in the immune response to virus infection, Kepi, an inhibitory subunit of the protein phosphatase 1 (PP1) complex, which protects against SARS-CoV pathogenesis. We also found that receptors for the proinflammatory cytokine tumor necrosis factor alpha (TNFα) promote pathogenesis, presumably through excessive inflammation.

CONCLUSIONS

The current study provides validation of network modeling approaches for identifying important players in virus infection pathogenesis, and a step forward in understanding the host response to an important infectious disease. The results presented here suggest the role of Kepi in the host response to SARS-CoV, as well as inflammatory activity driving pathogenesis through TNFα signaling in SARS-CoV infections. Though we have reported the utility of this approach in bacterial and cell culture studies previously, this is the first comprehensive study to confirm that network topology can be used to predict phenotypes in mice with experimental validation.

Analysing microarray data in drug discovery using systems biology

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.

Quantitative proteomics and transcriptomics addressing the estrogen receptor subtype-mediated effects in T47D breast cancer cells exposed to the phytoestrogen genistein

The present study addresses, by transcriptomics and quantitative stable isotope labeling by amino acids in cell culture (SILAC)-based proteomics, the estrogen receptor α (ERα) and β (ERβ)-mediated effects on gene and protein expression in T47D breast cancer cells exposed to the phytoestrogen genistein. Using the T47D human breast cancer cell line with tetracycline-dependent ERβ expression (T47D-ERβ), the effect of a varying intracellular ERα/ERβ ratio on genistein-induced gene and protein expression was characterized. Results obtained reveal that in ERα-expressing T47D-ERβ cells with inhibited ERβ expression genistein induces transcriptomics and proteomics signatures pointing at rapid cell growth and migration by dynamic activation of cytoskeleton remodeling. The data reveal an interplay between integrins, focal adhesion kinase, CDC42, and actin cytoskeleton signaling cascades, occurring upon genistein treatment, in the T47D-ERβ breast cancer cells with low levels of ERα and no expression of ERβ. In addition, data from our study indicate that ERβ-mediated gene and protein expression counteracts ERα-mediated effects because in T47D-ERβ cells expressing ERβ and exposed to genistein transcriptomics and proteomics signatures pointing at a clear down-regulation of cell growth and induction of cell cycle arrest and apoptosis were demonstrated. These results suggest that ERβ decreases cell motility and metastatic potential as well as cell survival of the breast cancer cell line. It is concluded that the effects of genistein on proteomics and transcriptomics end points in the T47D-ERβ cell model are comparable with those reported previously for estradiol with the ultimate estrogenic effect being dependent on the relative affinity for both receptors and on the receptor phenotype (ERα/ERβ ratio) in the cells or tissue of interest.

The GeneTrawler®: mapping potential drug targets in human and rat tissues

Expression data are an important element of target identification and validation. The authors have established an automated high-throughput method based on real time quantitative polymerase chain reaction, called the GeneTrawler, for the characterization of pharmaceutical targets on an annotated collection of human tissues. The authors have conducted a variability analysis of the system, which demonstrates that the majority of the variability between expression levels determined is due to biologic variation between samples, rather than technical variation due to imprecision of the method. Gene expression maps, generated with this carefully controlled system provide a large, reliable, consistent data set. The authors have used this system to characterize the expression of > 100 genes, and here they show the expression profile of SUR1 in order to illustrate its use. The authors were able to confirm SUR1 expression in the lung, which was suggested on the basis of pharmacologic experiments but has not previously been confirmed by mRNA detection. The data also show SUR1 expression in tissues that have been associated with some of the side effects seen with SUR1 modulators. This and other examples demonstrate that the GeneTrawler is useful to gauge the suitability of a prospective therapeutic target, to fully exploit a known drug target, or to identify and help validate new hypothetical druggable targets to fuel drug discovery pipelines.

Quantitative proteomic and genomic profiling reveals metastasis-related protein expression patterns in gastric cancer cells

Gastric cancer is a leading cause of death worldwide, and patients have an overall 5-year survival rate of less than 10%. Using quantitative proteomic techniques together with microarray chips, we have established comprehensive proteome and transcriptome profiles of the metastatic gastric cancer TMC-1 cells and the noninvasive gastric cancer SC-M1 cell. Our qualitative protein profiling strategy offers the first comprehensive analysis of the gastric cancer cell proteome, identifying 926 and 909 proteins from SC-M1 and TMC-1 cells, respectively. Cleavable isotope-coded affinity tagging analysis allows quantitation of a total of 559 proteins (with a protein false-positive rate of <0.005), and 240 proteins were differentially expressed (>1.3-fold) between the SC-M1 and TMC-1 cells. We identified numerous proteins not previously associated with gastric cancer. Notably, a large subset of differentially expressed proteins was associated with tumor metastasis, including proteins functioning in cell-cell and cell-extracellular matrix (cell-ECM) adhesion, cell motility, proliferation, and tumor immunity. Gene expression profiling by DNA microarray revealed differential expression (of >2-fold) of about 1000 genes. The weak correlation observed between protein and mRNA profiles highlights the important complementarities of DNA microarray and proteomics approaches. These comparative data enabled us to map the disease-perturbed cell-cell and cell-ECM adhesion and Rho GTPase-mediated cytoskeletal pathways. Further validation of a subset of genes suggests the potential use of vimentin and galectin 1 as markers for metastasis. We demonstrate that combining proteomic and genomic approaches not only provides a rapid, robust, and sensitive platform to elucidate the molecular mechanisms underlying gastric cancer metastasis but also may identify candidate diagnostic markers and therapeutic targets.

Microarray technology in obstetrics and gynecology: a guide for clinicians

Microarrays can be constructed with dozens to millions of probes on their surface to allow high-throughput analyses of many biologic processes to be performed simultaneously on the same sample. Microarrays are now widely used for gene expression analysis, deoxyribonucleic acid resequencing, single-nucleotide polymorphism genotyping, and comparative genomic hybridization. Microarray technology is accelerating research in many fields and now microarrays are moving into clinical application. This review discusses the emerging role of microarrays in molecular diagnostics, pathogen detection, oncology, and pharmacogenomics.

Present and future of rapid and/or high-throughput methods for nucleic acid testing

BACKGROUND

Behind the success of 'completing' the human genome project was a more than 30-year history of technical innovations for nucleic acid testing.

METHODS

Discovery of specific restriction endonucleases and reverse transcriptase was followed shortly by the development of the first diagnostic nucleic acid tests in the early 1970s. Introduction of Southern, Northern and dot blotting and DNA sequencing later in the 1970s considerably advanced the diagnostic capabilities. Nevertheless, it was the discovery of the polymerase chain reaction (PCR) in 1985 that led to an exponential growth in molecular biology and the introduction of practicable nucleic acid tests in the routine laboratory. The past two decades witnessed a continuing explosion of technological innovations in molecular diagnostics. In addition to classic PCR and reverse transcriptase PCR, numerous variations of PCR and alternative amplification techniques along with an ever-increasing variety of detection chemistries, closed tube (homogeneous) assays, and automated systems were developed. Discovery of real-time quantitative PCR and the development of oligonucleotide microarrays, the 'DNA chip', in the 1990s heralded the beginning of another revolution in molecular biology and diagnostics that is still in progress.