Involvement of cis-acting repressive element(s) in the 3'-untranslated region of human connective tissue growth factor gene.

To analyze the regulatory mechanism of connective tissue growth factor expression, the 3'-untranslated region (3'-UTR) of CTGF cDNA was amplified from HeLa cell RNA. Direct nucleotide sequencing revealed a single major population in the amplicon, which was nearly identical to other sequences. Subsequently, the effect of the 3'-UTR on gene expression was evaluated. When it was fused downstream of a firefly luciferase gene, the 3'-UTR strongly repressed luciferase gene expression. Interestingly, the repressive effect of the antisense 3'-UTR appeared to be more prominent than that of the sense one. Together with the fact that several consensus sequences for regulatory elements are found in it, these results suggest the involvement of multiple sets of regulatory elements in the CTGF 3'-UTR.     

Prediction of Associations between microRNAs and Gene Expression in Glioma Biology

Despite progress in the determination of miR interactions, their regulatory role in cancer is only beginning to be unraveled. Utilizing gene expression data from 27 glioblastoma samples we found that the mere knowledge of physical interactions between specific mRNAs and miRs can be used to determine associated regulatory interactions, allowing us to identify 626 associated interactions, involving 128 miRs that putatively modulate the expression of 246 mRNAs. Experimentally determining the expression of miRs, we found an over-representation of over(under)-expressed miRs with various predicted mRNA target sequences. Such significantly associated miRs that putatively bind over-expressed genes strongly tend to have binding sites nearby the 3'UTR of the corresponding mRNAs, suggesting that the presence of the miRs near the translation stop site may be a factor in their regulatory ability. Our analysis predicted a significant association between miR-128 and the protein kinase WEE1, which we subsequently validated experimentally by showing that the over-expression of the naturally under-expressed miR-128 in glioma cells resulted in the inhibition of WEE1 in glioblastoma cells.     

Identification and comparative analysis of microRNAs from tomato varieties showing contrasting response to ToLCV infections

Increasing incidence of viral infections in crop plants adversely affects their growth and yield. Tomato (Solanum lycopersicum) is considered to be a favorite host for viruses with over 50 species of begomoviruses naturally infecting this crop. Tomato leaf curl virus (ToLCV) is among the most widespread and devastating begomoviruses affecting tomato production. microRNAs (miRs) have been established as key regulators of gene expression and plant development. The miR pathways are disturbed during infection by viruses. Thus, comprehension of regulatory miR networks is crucial in understanding the effect of viral pathogenicity. To identify key miRs involved in ToLCV infection, a high throughput approach involving next generation sequencing was employed. Healthy and infected leaf tissues of two tomato varieties, differing in their susceptibility to ToLCV infection were analyzed. NGS data analysis followed by computational predictions, led to identification of 91 known miRs, 15 novel homologs and 53 novel miRs covering two different varieties of tomato, susceptible (Pusa Ruby) and tolerant (LA1777) to ToLCV infection. The cleaved targets of these miRs were identified using online available degradome libraries from leaf, flower and fruit of tomato and showed their involvement in various biological pathways through KEGG Orthology. With detailed comparative profiling of expression pattern of these miRs, we could associate the specific miRs with the resistant and infected genotypes. This study depicted that in depth analysis of miR expression patterns and their functions will help in identification of molecules that can be used for manipulation of gene expression to increase crop production and developing resistance against diseases.     

Genomic strategies to identify mammalian regulatory sequences

With the continuing accomplishments of the human genome project, high-throughput strategies to identify DNA sequences that are important in mammalian gene regulation are becoming increasingly feasible. In contrast to the historic, labour-intensive, wet-laboratory methods for identifying regulatory sequences, many modern approaches are heavily focused on the computational analysis of large genomic data sets. Data from inter-species genomic sequence comparisons and genome-wide expression profiling, integrated with various computational tools, are poised to contribute to the decoding of genomic sequence and to the identification of those sequences that orchestrate gene regulation. In this review, we highlight several genomic approaches that are being used to identify regulatory sequences in mammalian genomes.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

Microarray data integration for genome-wide analysis of human tissue-selective gene expression

Background

Microarray gene expression data are accumulating in public databases. The expression profiles contain valuable information for understanding human gene expression patterns. However, the effective use of public microarray data requires integrating the expression profiles from heterogeneous sources.

Results

In this study, we have compiled a compendium of microarray expression profiles of various human tissue samples. The microarray raw data generated in different research laboratories have been obtained and combined into a single dataset after data normalization and transformation. To demonstrate the usefulness of the integrated microarray data for studying human gene expression patterns, we have analyzed the dataset to identify potential tissue-selective genes. A new method has been proposed for genome-wide identification of tissue-selective gene targets using both microarray intensity values and detection calls. The candidate genes for brain, liver and testis-selective expression have been examined, and the results suggest that our approach can select some interesting gene targets for further experimental studies.

Conclusion

A computational approach has been developed in this study for combining microarray expression profiles from heterogeneous sources. The integrated microarray data can be used to investigate tissue-selective expression patterns of human genes.