Computational Prediction of MicroRNAs Encoded in Viral and Other Genomes

We present an overview of selected computational methods for microRNA prediction. It is especially aimed at viral miRNA detection. As the number of microRNAs increases and the range of genomes encoding miRNAs expands, it seems that these small regulators have a more important role than has been previously thought. Most microRNAs have been detected by cloning and Northern blotting, but experimental methods are biased towards abundant microRNAs as well as being time-consuming. Computational detection methods must therefore be refined to serve as a faster, better, and more affordable method for microRNA detection. We also present data from a small study investigating the problems of computational miRNA prediction. Our findings suggest that the prediction of microRNA precursor candidates is fairly easy, while excluding false positives as well as exact prediction of the mature microRNA is hard. Finally, we discuss possible improvements to computational microRNA detection

Custom Design and Analysis of High-Density Oligonucleotide Bacterial Tiling Microarrays

Not until recently have custom made high-density oligonucleotide microarrays been available at an affordable price. The aim of this thesis was to design microarrays and analysis algorithms for DNA repair and DNA damage detection, and to apply the methods in real experiments. Thomassen et al. have used their custom designed whole genome-tiling microarrays for detection of transcriptional changes in Escherichia coli after exposure to DNA damageing reagents. The transcriptional changes in E. coli treated with UV light or the methylating reagent MNNG were shown to be larger and to include far more genes than previously reported. To optimize the data analysis for the custom made arrays, Thomassen and coworkers designed their own normalization and analysis algorithms, and showed these more suitable than established methods that are currently applied on custom tiling arrays. Among other findings several novel stress-induced transcripts were detected, of which one is predicted to be a UV-induced short transmembrane protein. Additionally, no upregulation of the previously described UV-inducible aidB is shown. In the MNNG study several genes are shown as downregulated in response to DNA damage although having upstream regulatory sequences similar to the established LexA box A and B. This indicates that the LexA regulon also might control gene repression and that the box A and B sequence can not alone answer for the LexA controlled gene regulation. Thomassen et al. have also custom designed a microarray for oncogenic fusion gene detection. Cancer specific fusion genes are often used to subgroup cancers and to define the optimal treatment, but currently the laboratory detection procedure is both laborious and tedious. In a blinded study on six cancer cell lines proof of principle was shown by detection of six out of six positive controls. The design and analysis methods for this microarray are now being refined to make a diagnostic fusion gene detection tool

Assessment of fusion gene status in sarcomas using a custom made fusion gene microarray.

Sarcomas are relatively rare malignancies and include a large number of histological subgroups. Based on morphology alone, the differential diagnoses of sarcoma subtypes can be challenging, but the identification of specific fusion genes aids correct diagnostication. The presence of individual fusion products are routinely investigated in Pathology labs. However, the methods used are time-consuming and based on prior knowledge about the expected fusion gene and often the most likely break-point. In this study, 16 sarcoma samples, representing seven different sarcoma subtypes with known fusion gene status from a diagnostic setting, were investigated using a fusion gene microarray. The microarray was designed to detect all possible exon-exon breakpoints between all known fusion genes in a single analysis. An automated scoring of the microarray data from the 38 known sarcoma-related fusion genes identified the correct fusion gene among the top-three hits in 11 of the samples. The analytical sensitivity may be further optimised, but we conclude that a sarcoma-fusion gene microarray is suitable as a time-saving screening tool to identify the majority of the correct fusion genes

Fusion Gene Microarray Reveals Cancer Type-Specificity Among Fusion Genes

Detection of fusion genes for diagnostic purposes and as a guide to treatment is well-established in hematological malignancies, and the prevalence of fusion genes in epithelial cancers is also increasingly appreciated. To study whether established fusion genes are present within additional cancer types, we have used an updated version of our fusion gene microarray in a systematic survey of reported fusion genes in multiple cancer types. We assembled a comprehensive database of published fusion genes, including those reported only in individual studies and samples, and fusion genes resulting from deep sequencing of cancer genomes and transcriptomes. From the total set of 548 fusion genes, we designed 599,839 oligonucleotides, targeting both chimeric transcript junctions as well as sequences internal to each of the fusion gene partners. We investigated the presence of fusion genes in a series of 67 cell lines representing 15 different cancer types. Data from ten leukemia cell lines with known fusion gene status were used to develop an automated scoring algorithm, and in five cell lines the correct fusion gene was the top scoring hit, and one came second. Two additional fusion genes, BCAS4-BCAS3 in the MCF-7 breast cancer cell line and CCDC6-RET in the TPC-1 thyroid cancer cell line were validated as true positive fusion transcripts. However, these fusion genes were not new to these cancer types, and none of 548 fusion genes were identified from a novel cancer type. We therefore find it unlikely that the assayed fusion genes are commonly present across multiple cancer types. (C) 2011 Wiley-Liss, Inc

Top ranked fusion genes in two sarcoma samples.

<p>A. <i>EWSR1</i>-<i>NR4A3</i> in the 168/97 sample. B. <i>SS18</i>-<i>SSX1</i> in the 9972 sample. I) Intensity heat map of chimeric oligos. Each square represents one possible exon-exon boundary between the two gene partners. One square is highly expressed (A: 13-3, B: 10-6) and reflects the presence of chimeric RNA covering the corresponding exon-exon boundary. This fusion breakpoint corresponds with a shift in relative expression measured by intragenic oligos covering both the upstream (II) and downstream (III) fusion gene partners. Blue and red colours represent the two possible chimeric transcripts generated from the fusion gene.</p

Samples investigated in the study.

1<p>All 38 investigated fusion genes are ranked based on the likelihood of being the correct fusion gene in the particular sample. Lower numbers indicate higher likelihood.</p>2<p>Information about RT-PCR protocols for fusion gene detection. In house: protocol not previously published. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070649#s2" target="_blank">Materials and Methods</a> for more details. NK: Normal karyotype.</p

Unscrambling the genomic chaos of osteosarcoma reveals extensive transcript fusion, recurrent rearrangements and frequent novel TP53 aberrations

In contrast to many other sarcoma subtypes, the chaotic karyotypes of osteosarcoma have precluded the identification of pathognomonic translocations. We here report hundreds of genomic rearrangements in osteosarcoma cell lines, showing clear characteristics of microhomology-mediated break-induced replication (MMBIR) and end-joining repair (MMEJ) mechanisms. However, at RNA level, the majority of the fused transcripts did not correspond to genomic rearrangements, suggesting the involvement of trans-splicing, which was further supported by typical trans-splicing characteristics. By combining genomic and transcriptomic analysis, certain recurrent rearrangements were identified and further validated in patient biopsies, including a PMP22-ELOVL5 gene fusion, genomic structural variations affecting RB1, MTAP/CDKN2A and MDM2, and, most frequently, rearrangements involving TP53. Most cell lines (7/11) and a large fraction of tumor samples (10/25) showed TP53 rearrangements, in addition to somatic point mutations (6 patient samples, 1 cell line) and MDM2 amplifications (2 patient samples, 2 cell lines). The resulting inactivation of p53 was demonstrated by a deficiency of the radiation-induced DNA damage response. Thus, TP53 rearrangements are the major mechanism of p53 inactivation in osteosarcoma. Together with active MMBIR and MMEJ, this inactivation probably contributes to the exceptional chromosomal instability in these tumors. Although rampant rearrangements appear to be a phenotype of osteosarcomas, we demonstrate that among the huge number of probable passenger rearrangements, specific recurrent, possibly oncogenic, events are present. For the first time the genomic chaos of osteosarcoma is characterized so thoroughly and delivered new insights in mechanisms involved in osteosarcoma development and may contribute to new diagnostic and therapeutic strategies