In silico prediction and experimental validation of natural antisense transcripts in two cancer-associated regions of human chromosome 6

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.

Long non-coding RNAs and cancer: a new frontier of translational research?

Tiling array and novel sequencing technologies have made available the transcription profile of the entire human genome. However, the extent of transcription and the function of genetic elements that occur outside of protein-coding genes, particularly those involved in disease, are still a matter of debate. In this review, we focus on long non-coding RNAs (lncRNAs) that are involved in cancer. We define lncRNAs and present a cancer-oriented list of lncRNAs, list some tools (for example, public databases) that classify lncRNAs or that scan genome spans of interest to find whether known lncRNAs reside there, and describe some of the functions of lncRNAs and the possible genetic mechanisms that underlie lncRNA expression changes in cancer, as well as current and potential future applications of lncRNA research in the treatment of cancer.

Undetectable and Decreased Expression of KIAA1949 (Phostensin) Encoded on Chromosome 6p21.33 in Human Breast Cancers Revealed by Transcriptome Analysis

Cytogenetic aberration and loss of heterozygosity (LOH) are documented on chromosome 6 in many cancers and the introduction of a neo-tagged chromosome 6 into breast cancer cell lines mediates suppression of tumorigenicity. In this study, we described the identification of KIAA1949 (phostensin) as a putative tumor suppressor gene. Our microarray analysis screened 25,985 cDNAs between a tumorigenic and metastatic breast cancer cell line MDA-MB-231 and the chromosome 6-mediated suppressed, non-tumorigenic and non-metastatic derivative cell line MDA/H6, resulting in the identification of 651 differentially expressed genes. Using customized microarrays containing these 651 cDNAs and 117 controls, we identified 200 frequently dysregulated genes in 10 breast cancer cell lines and 5 tumor tissues using MDA/H6 as reference. Our bioinformatics analysis revealed that chromosome 6 encodes 25 of these 200 genes, with 4 downregulation and 21 upergulation. Northern analysis validated microarray results and was used to detect the size and number of RNA species of 2 downregulated (KIAA1949, PTK7) and 2 upregulated (SFRS3, HMGN3) genes in the cell lines. Particularly, the KIAA1949 gene at 6p21.33, a band region with the frequent cytogenetic aberration and LOH encodes 4 poly(A)-RNA species (4.6-, 4-, 3- and 1.5-kb) in multiple normal and breast cancer samples. Microarray analysis revealed KIAA1949 downregulation in 86.7% (n=15) of breast cancer cell lines and tumor tissues. Northern analysis demonstrated undetectable and decreased expression of KIAA1949 in 100% (n=10) of breast cancer cell lines. Taken together, these results strongly suggest KIAA1949 is a novel candidate breast cancer suppressor gene.