Peroxynitrite-induced DNA damage in the supF gene: correlation with the mutational spectrum

Tissue inflammation and chronic infection lead to the overproduction of nitric oxide and superoxide. These two species rapidly combine to yield peroxynitrite (ONOO(-)), a powerful oxidizing and nitrating agent that is thought be involved in both cell death and an increased cancer risk observed for inflamed tissues. ONOO(-) has been shown to induce single-strand breaks and base damage in DNA and is mutagenic in the supF gene, inducing primarily G to T transversions clustered at the 5' end of the gene. The mutagenicity of ONOO(-) is believed to result from chemical modifications at guanine nucleobases leading to miscoding DNA lesions. In the present work, we applied a combination of molecular and analytical techniques in an attempt to identify biologically important DNA modifications induced by ONOO(-). pUC19 plasmid treated with ONOO(-) contained single-strand breaks resulting from direct sugar damage at the DNA backbone, as well as abasic sites and nucleobase modifications repaired by Fpg glycosylase. The presence of carbon dioxide in the reaction mixture shifted the ONOO(-) reactivity towards reactions at nucleobases, while suppressing the oxidation of deoxyribose. To further study the chemistry of the ONOO(-) interactions with DNA, synthetic oligonucleotides representing the mutation-prone region of the supF gene were treated with ONOO(-), and the products were analyzed by liquid chromatography-negative ion electrospray ionization mass spectrometry (LC-ESI(-) MS) and tandem mass spectrometry. 8-Nitroguanine (8-nitro-G) was formed in ONOO(-)-treated oligonucleotides in a dose-dependent manner with a maximum at a ratio of [ONOO(-)]: [DNA]=10 and a decline at higher ONOO(-) concentrations, suggesting further reactions of 8-nitro-G with ONOO(-). 8-Nitro-G was spontaneously released from oligonucleotides (t(1/2)=1 h at 37 degrees C) and, when present in DNA, was not recognized by Fpg glycosylase. To obtain more detailed information on ONOO(-)-induced DNA damage, a restriction fragment from the pSP189 plasmid containing the supF gene (135 base pairs) was [32P]-end-labeled and treated with ONOO(-). PAGE analysis of the products revealed sequence-specific lesions at guanine nucleobases, including the sites of mutational "hotspots." These lesions were repaired by Fpg glycosylase and cleaved by hot piperidine treatment, but they were resistant to depurination at 90 degrees C. Since 8-nitro-G is subject to spontaneous depurination, and 8-oxo-guanine is not efficiently cleaved by piperidine, these results suggest that alternative DNA lesion(s) contribute to ONOO(-) mutagenicity. Further investigation of the identities of DNA modifications responsible for the adverse biological effects of ONOO(-) is underway in our laboratory.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Extremely low-frequency electromagnetic fields do not affect DNA damage and gene expression profiles of yeast and human lymphocytes

We studied the effects of extremely low-frequency (50 Hz) electromagnetic fields (EMFs) on peripheral human blood lymphocytes and DBY747 Saccharomyces cerevisiae. Graded exposure to 50 Hz magnetic flux density was obtained with a Helmholtz coil system set at 1, 10 or 100 microT for 18 h. The effects of EMFs on DNA damage were studied with the single-cell gel electrophoresis assay (comet assay) in lymphocytes. Gene expression profiles of EMF-exposed human and yeast cells were evaluated with DNA microarrays containing 13,971 and 6,212 oligonucleotides, respectively. After exposure to the EMF, we did not observe an increase in the amount of strand breaks or oxidated DNA bases relative to controls or a variation in gene expression profiles. The results suggest that extremely low-frequency EMFs do not induce DNA damage or affect gene expression in these two different eukaryotic cell systems.     

Local correlation of expression profiles with gene annotations--proof of concept for a general conciliatory method

MOTIVATION: Integrated analysis of expression data and gene ontology annotations is a prime example of biological data that need co-explanatory interpretation. This particular application is used to validate a new method for integrated analysis of varied biological information. RESULTS: The proposed method consists of determining local correlation coefficients and the corresponding P-values calculated per biological entity. This measure considers the combined intensity and significance of the agreement or disagreement, between two data sources about the same biological entity. The method is applied to the integrated analysis of gene expression and annotation of two gene sets, one from yeast and other from mouse. The potential of the method to generate accurate mechanistic hypothesis is also demonstrated. Specially, negative correlation results pose a new kind of biological hypothesis. Method performance was compared with annotation enrichment methods, and optimal conditions for the superiority of local correlation results are discussed.     

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.     

Decreased expression of the DBC2 gene and its clinicopathological significance in breast cancer: correlation with aberrant DNA methylation

Loss of DBC2 (deleted in breast cancer 2) gene expression is frequent in breast cancer tissues. This can be explained by homozygous deletions or other mutations in a minority of cases but alternative mechanisms need to be investigated. Here, DBC2 expression was significantly suppressed compared with normal breast tissues in breast cancer tissues when analyzed by RT-PCR. Furthermore, DNA methylation on DBC2 was more prevalent in breast tumors than in normal tissues. DBC2 mRNA levels correlated with the degree of DBC2 methylation in breast cancer tissues and in a breast cancer cell line (T47D). Clinico-pathological correlation analysis showed that DBC2 promoter methylation was associated with tumor-node-metastasis stages II and III/IV, lymph node metastasis, p53 mutation, and HER2-positive status. Thus loss of DBC2 expression is caused by abnormal methylation of DBC2 and might have a role in breast cancer development.     

Mining gene expression profiles: expression signatures as cancer phenotypes

Many examples highlight the power of gene expression profiles, or signatures, to inform an understanding of biological phenotypes. This is perhaps best seen in the context of cancer, where expression signatures have tremendous power to identify new subtypes and to predict clinical outcomes. Although the ability to interpret the meaning of the individual genes in these signatures remains a challenge, this does not diminish the power of the signature to characterize biological states. The use of these signatures as surrogate phenotypes has been particularly important, linking diverse experimental systems that dissect the complexity of biological systems with the in vivo setting in a way that was not previously feasible.     

miR-143 expression profiles in urinary bladder cancer: correlation with clinical and epidemiological parameters

Molecular Biology Reports - Hsa-mir-143 and hsa-let-7c have been reported to be deregulated in multiple neoplasms. The main purpose of this study was to investigate the expression of these miRNAs...     

DNA methylation and gene expression profiles characterize epigenetic regulation of lncRNAs in colon adenocarcinoma

The long noncoding RNAs (lncRNAs) are associated with tumorigenesis and progression of cancer. While DNA methylation is a common epigenetic regulator of gene expression, the methylation of lncRNAs was rarely studied. To address this gap, we integrated DNA methylation and RNA-seq data to characterize the landscape of lncRNA methylation in colon adenocarcinoma (COAD). We collected and analyzed the lncRNA expression and methylation data from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia to identify the epigenetically regulated lncRNAs. We further investigated the biological and clinical relevance of the identified lncRNAs via bioinformatics analysis. We identified 20 epigenetically upregulated lncRNAs in COAD, including several well-studied lncRNAs whose methylation regulation were poorly investigated, such as PVT1 and UCA1. We also revealed several novel tumor-associated lncRNAs in COAD, including GATA2-As1 and CYTOR. Next, we explored their biology function using gene set enrichment analysis and competitive endogenous RNA analysis. We characterized the methylation landscape of lncRNA in COAD and identified 20 epigenetically upregulated lncRNAs. Our findings will shed new light on the epigenetic regulation of lncRNA expression by DNA methylation.