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The aim of the present study was to search for the differential gene expression and measure the serum level of a number of biochemical parameters in the cold Zheng (CZ) and non-cold Zheng (NCZ) in patients receiving hemodialysis. Hemodialysis (HD) patients were randomly selected from the CZ and NCZ groups. The between-group differences in gene expression were assessed using complementary DNA (cDNA) microarray. Differential gene expression was further validated by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Our results demonstrated that the up-regulation of the inflammation-associated genes, ALOX5AP, S100A8 and S100A12, down-regulation of the genes related to immunity (DEFA4), metabolism (GNG11, PYGB, PRKAR2B), and growth/proliferation (HSF2, DDR2, TK1) were found in the CZ group. Furthermore, the CZ HD patients had significantly lower serum albumin levels compared with their NCZ counterparts (3.31 ± 0.08 g/dL versus 4.18 ± 0.12 g/dL). It appears reasonable to conclude that up-regulated inflammatory-gene expression (ALOX5AP, S100A8 and S100A12) may play an important role in CZ HD patients.

It is essential to explore the molecular therapeutic effect of warm and tonic herb treatment for individuals with typical kidney-yang deficiency. In this report, we have identified members of a family with a history of suffering from cold and kidney-yang deficiency syndromes. First, we have employed the accumulated scores of the 40-items clinical scoring indicators for kidney-yang deficiency and cold syndromes to clinically assess the presence or absence of the deficiency for 15 family members. We then proceeded to compare the gene expression profiles of RNA isolated from blood samples, prior to and post-herbal treatment, of a sibling (brother and younger sister) that are suffering from the deficiency using cDNA microarrays with 18816 genes. Following treatments with the warming and tonic herb, the accumulated clinical scores obtained from the 40-items clinical scoring indicators were compared to those obtained pre-treatment. It was observed that the accumulated clinical scores were reduced by 1/3 for the brother and 2/3 for his younger sister following the treatments. Furthermore, we have demonstrated that the level of gene expression for a total of 33 genes at pre-treatment was modulated after treatments with the warming and tonic herb and correlated well with the clinical improvements of their syndromes. These results suggest that the combination of gene profiling and the accumulated clinical scores obtained from the 40-items clinical scoring indicators may provide an accurate clinical assessment and a way to monitor the therapeutic efficacy of the warming and tonic herb treatment.

Microarrays are widely used to study changes in gene expression in diseases. In this paper, we use this technology to discover gene expression patterns in the cold syndrome in Chinese medicine. We identify differentially expressed genes and extracted gene modules that are enriched with differentially expressed genes in the cold syndrome by analyzing cDNA samples, which are purified from blood taken from a pedigree. Our results suggest that the cold syndrome might be caused by the physiological imbalance and/or the disorder of metabolite processes. The study confirms the hypotheses about molecular pathways responsible to human metabolic-related diseases.

Several of ten thousands functional genes control the growth, genetics, and behavior of living organisms by regulating different gene expressions. The genes in a normal cell control the process of cell growth, differentiation, reproduction, and apoptosis via multiple steps of interactive regulation mechanism. The mechanism of gene regulation is a very important process in human beings. If there is something wrong in the gene regulation mechanism, it may cause some diseases such as cancer. It is very difficult to identify the regulatory relations among genes in human genome. Traditional biological research methods consume huge amount of time and man strength to do this work. In recent years, with the rapid development of microarray technologies, cDNA can be used to analyze the changes of gene expressions in different cells in a high throughput manner. In this paper, we propose a novel bioinformatics approach to predict possible cancerous genes based on a so-called regulation truth table (RTT) of genes. The RTT of two genes is constructed using the differential expressions of cDNA microarray data for tumor and normal tissues. The differences in regulatory relations of genes for tumor and normal tissues are adopted to identify possible cancerous genes.

The phenomenon of dedifferentiation typically occurs in soft tissue sarcomas where a low grade or well-differentiated tumor shows an abrupt transformation to a high-grade sarcoma without lineage specificity. The biological behavior and metastatic potential of these tumors is dictated by the dedifferentiated phenotype. Tumor material was available from two dedifferentiated liposarcomas. We performed cDNA microarray analysis of a dedifferentiated liposarcoma in which the atypical lipomatous/well-differentiated and dedifferentiated portions were grossly distinct, to find differentially expressed genes in the dedifferentiated component compared to the well-differentiated component. There were 100 differentially expressed genes, both up- and down-regulated in the high grade sarcoma. In addition, we performed RT-PCR on selected genes in both cases to confirm the microarray findings. We discuss the expression patterns of these genes in comparison to other studies in the literature.

Osteosarcomas account for about 20% of all primary bone neoplasms, and affect predominantly adolescents. Important prognostic factors include the presence or absence of metastases at the time of presentation and tumor response to neoadjuvant chemotherapy. Biological markers predicting metastases and chemoresistance are not well characterized. cDNA microarray analysis enables one to examine the expression of thousands of genes in tumor samples. We performed cDNA microarray analysis of histologically low and high grade areas in osteosarcomas to identify gene expression patterns which may depict aggressive behavior. Microarray analysis with 1.2 K cancer array revealed many differentially expressed genes (both upregulated and downregulated), in histologically high grade tumor samples as compared with a low grade sample. Selected up and down regulated markers in the high grade sarcomas were tested in a group of high grade osteosarcomnas (OS) with varying responses to chemotherapy. Of the multiple markers analyzed, ezrin, a member of the ERM family of membrane-cytoskeleton linkers showed an expression pattern statistically significant between tumors with good response to chemotherapy compared with tumors with poor response (p = 0.036). We discuss our findings, with current review of literature.

Both cDNA microarray and spectroscopic data provide indirect information about the chemical compounds present in the biological tissue under consideration. In this paper simple univariate and bivariate measures are used to investigate correlations between both types of high dimensional analyses.

A large dataset of 42 hemp samples on which 3456 cDNA clones and 351 NIR wavelengths have been measured, was analyzed using graphical representations. For this purpose we propose clustered correlation and clustered discrimination images. Large, tissue-related differences are seen to dominate the cDNA-NIR correlation structure but smaller, more difficult to detect, variety-related differences can be found at specific cDNA clone/NIR wavelength combinations.

Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed. However, they fail to identify the genes whose expression patterns do not fit the pre-defined models. Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood. We propose a method called PEM (Partial Energy ratio for Microarray) for the analysis of time course cDNA microarray data. In the PEM method, we assume the gene expressions vary smoothly in the temporal domain. This assumption is comparatively weak and hence the method is general enough to identify genes expressed in unexpected patterns. To identify the differentially expressed genes, a new statistic is developed by comparing the energies of two convoluted profiles. We further improve the statistic for microarray analysis by introducing the concept of partial energy. The PEM statistic is incorporated into the permutation based SAM framework for significance analysis. We evaluated the PEM method with an artificial dataset and two published time course cDNA microarray datasets on yeast. The experimental results show the robustness and the generality of the PEM method. It outperforms the previous versions of SAM and the spline based EDGE approaches in identifying genes of interest, which are differentially expressed in various manner.

cDNA microarray experiments produce expression ratios of thousands of genes across tens of experimental attributes. In this work, development of a robust clustering algorithm and a graph-theoretic model for reverse engineering the gene networks and their implementation using NSGA-II are reported. Clustering results on synthetic datasets as well as on a real life dataset are very encouraging. The clusters for synthetic as well as real life datasets obtained from the robust clustering are used for reverse engineering the gene regulatory networks using the graph-theoretic model inspired by 'small world phenomena'. A set of Pareto-optimal models have been proposed. The models generated for the real life dataset concur with the available biological information. Newer functionalities and interactions are also proposed that concur with the observed cDNA microarray data.

cDNA microarray experiments produce expression ratios of thousands of genes across tens of experimental attributes. In this work, development of a robust clustering algorithm and a graph-theoretic model for reverse engineering the gene networks and their implementation using NSGA-II are reported. Clustering results on synthetic datasets as well as on a real life dataset are very encouraging. The clusters for synthetic as well as real life datasets obtained from the robust clustering are used for reverse engineering the gene regulatory networks using the graph-theoretic model inspired by ‘small world phenomena’. A set of Pareto-optimal models have been proposed. The models generated for the real life dataset concur with the available biological information. Newer functionalities and interactions are also proposed that concur with the observed cDNA microarray data.