MicroRNAs Form Triplexes with Double Stranded DNA at Sequence-Specific Binding Sites; a Eukaryotic Mechanism via which microRNAs Could Directly Alter Gene Expression.

MicroRNAs are important regulators of gene expression, acting primarily by binding to sequence-specific locations on already transcribed messenger RNAs (mRNA) and typically down-regulating their stability or translation. Recent studies indicate that microRNAs may also play a role in up-regulating mRNA transcription levels, although a definitive mechanism has not been established. Double-helical DNA is capable of forming triple-helical structures through Hoogsteen and reverse Hoogsteen interactions in the major groove of the duplex, and we show physical evidence (i.e., NMR, FRET, SPR) that purine or pyrimidine-rich microRNAs of appropriate length and sequence form triple-helical structures with purine-rich sequences of duplex DNA, and identify microRNA sequences that favor triplex formation. We developed an algorithm (Trident) to search genome-wide for potential triplex-forming sites and show that several mammalian and non-mammalian genomes are enriched for strong microRNA triplex binding sites. We show that those genes containing sequences favoring microRNA triplex formation are markedly enriched (3.3 fold, p<2.2 × 10(-16)) for genes whose expression is positively correlated with expression of microRNAs targeting triplex binding sequences. This work has thus revealed a new mechanism by which microRNAs could interact with gene promoter regions to modify gene transcription.

A novel retinoblastoma therapy from genomic and epigenetic analyses.

Retinoblastoma is an aggressive childhood cancer of the developing retina that is initiated by the biallelic loss of RB1. Tumours progress very quickly following RB1 inactivation but the underlying mechanism is not known. Here we show that the retinoblastoma genome is stable, but that multiple cancer pathways can be epigenetically deregulated. To identify the mutations that cooperate with RB1 loss, we performed whole-genome sequencing of retinoblastomas. The overall mutational rate was very low; RB1 was the only known cancer gene mutated. We then evaluated the role of RB1 in genome stability and considered non-genetic mechanisms of cancer pathway deregulation. For example, the proto-oncogene SYK is upregulated in retinoblastoma and is required for tumour cell survival. Targeting SYK with a small-molecule inhibitor induced retinoblastoma tumour cell death in vitro and in vivo. Thus, retinoblastomas may develop quickly as a result of the epigenetic deregulation of key cancer pathways as a direct or indirect result of RB1 loss.

CREST maps somatic structural variation in cancer genomes with base-pair resolution.

We developed 'clipping reveals structure' (CREST), an algorithm that uses next-generation sequencing reads with partial alignments to a reference genome to directly map structural variations at the nucleotide level of resolution. Application of CREST to whole-genome sequencing data from five pediatric T-lineage acute lymphoblastic leukemias (T-ALLs) and a human melanoma cell line, COLO-829, identified 160 somatic structural variations. Experimental validation exceeded 80%, demonstrating that CREST had a high predictive accuracy.

Influenza in migratory birds and evidence of limited intercontinental virus exchange.

Migratory waterfowl of the world are the natural reservoirs of influenza viruses of all known subtypes. However, it is unknown whether these waterfowl perpetuate highly pathogenic (HP) H5 and H7 avian influenza viruses. Here we report influenza virus surveillance from 2001 to 2006 in wild ducks in Alberta, Canada, and in shorebirds and gulls at Delaware Bay (New Jersey), United States, and examine the frequency of exchange of influenza viruses between the Eurasian and American virus clades, or superfamilies. Influenza viruses belonging to each of the subtypes H1 through H13 and N1 through N9 were detected in these waterfowl, but H14 and H15 were not found. Viruses of the HP Asian H5N1 subtypes were not detected, and serologic studies in adult mallard ducks provided no evidence of their circulation. The recently described H16 subtype of influenza viruses was detected in American shorebirds and gulls but not in ducks. We also found an unusual cluster of H7N3 influenza viruses in shorebirds and gulls that was able to replicate well in chickens and kill chicken embryos. Genetic analysis of 6,767 avian influenza gene segments and 248 complete avian influenza viruses supported the notion that the exchange of entire influenza viruses between the Eurasian and American clades does not occur frequently. Overall, the available evidence does not support the perpetuation of HP H5N1 influenza in migratory birds and suggests that the introduction of HP Asian H5N1 to the Americas by migratory birds is likely to be a rare event.

Persistent host markers in pandemic and H5N1 influenza viruses.

Avian influenza viruses have adapted to human hosts, causing pandemics in humans. The key host-specific amino acid mutations required for an avian influenza virus to function in humans are unknown. Through multiple-sequence alignment and statistical testing of each aligned amino acid, we identified markers that discriminate human influenza viruses from avian influenza viruses. We applied strict thresholds to select only markers which are highly preserved in human influenza virus isolates over time. We found that a subset of these persistent host markers exist in all human pandemic influenza virus sequences from 1918, 1957, and 1968, while others are acquired as the virus becomes a seasonal influenza virus. We also show that human H5N1 influenza viruses are significantly more likely to contain the amino acid predominant in human strains for a few persistent host markers than avian H5N1 influenza viruses. This sporadic enrichment of amino acids present in human-hosted viruses may indicate that some H5N1 viruses have made modest adaptations to their new hosts in the recent past. The markers reported here should be useful in monitoring potential pandemic influenza viruses.

A novel approach for the analysis of T-cell reconstitution by using a T-cell receptor beta-based oligonucleotide microarray in hematopoietic stem cell transplantation.

OBJECTIVE: Analysis of T-cell population diversity is important to hematopoietic stem cell transplantation (HSCT). The millions of specificities in T-cell receptor (TCR) hypervariable complementarity- determining region 3 (CDR3) precludes detection of all T-cell populations by antibody-based flow cytometry. An alternative method, the TCR CDR3 spectratyping assay, involves multiple polymerase chain reaction (PCR) analyses and is interpreted only qualitatively. In this study, we designed the first TCRbeeta-based oligonucleotide microarray and investigated its specificity, clonality discrimination, sensitivity of detection, and feasibility for monitoring T-cell population diversity in HSCT. MATERIALS AND METHODS: The array contains 27 TCR Vbeta probes and 13 Jbeta probes. TCRbeta repertoire diversity was detected with single PCR, microarray hybridization system, and Spotfire analysis software. RESULTS: TCRO-based microarray provides specific sequence-based information and can distinguish T-cell monoclonal expansion within a polyclonal population. We successfully used this microarray to quantitatively and qualitatively analyze T-cell population diversity in recipients of hematopoietic stem cell transplants. CONCLUSION: This success suggests broad potential applications of the microarray for use in many other areas, including anti-tumor immunity, vaccination, autoimmunity, infectious diseases, and leukemia. By providing a single PCR-based assay to quantify multiple T-cell populations in parallel, this device will allow clinicians and researchers to rapidly perform high-throughput surveys.

Large-scale sequence analysis of avian influenza isolates.

The spread of H5N1 avian influenza viruses (AIVs) from China to Europe has raised global concern about their potential to infect humans and cause a pandemic. In spite of their substantial threat to human health, remarkably little AIV whole-genome information is available. We report here a preliminary analysis of the first large-scale sequencing of AIVs, including 2196 AIV genes and 169 complete genomes. We combine this new information with public AIV data to identify new gene alleles, persistent genotypes, compensatory mutations, and a potential virulence determinant.

An overview of Spotfire for gene-expression studies.

Spotfire DecisionSite for Functional Genomics (referred to here as Spotfire) is a powerful data mining and visualization application with use in many disciplines. This unit provides an overview of Spotfire's utility in analyzing gene expression data obtained from DNA microarray experiments. Analysis of microarray data requires software-based solutions able to handle and manipulate the enormous amount of data generated. Spotfire provides a solution for accessing, analyzing and visualizing data generated from microarray experiments. Spotfire is designed to allow biologists with little or no programming or statistical skills to transform, process, and analyze microarray data.

An overview of Spotfire for gene-expression studies.

Spotfire DecisionSite for Functional Genomics (referred to here as Spotfire) is a powerful data mining and visualization application with use in many disciplines. This unit provides an overview of Spotfire's utility in analyzing gene expression data obtained from DNA microarray experiments. Analysis of microarray data requires software-based solutions able to handle and manipulate the enormous amount of data generated. Spotfire provides a solution for accessing, analyzing and visualizing data generated from microarray experiments. Spotfire is designed to allow biologists with little or no programming or statistical skills to transform, process, and analyze microarray data.

Loading and preparing data for analysis in spotfire.

This unit strictly focuses on data preparation within Spotfire. Microarray data exist in a variety of formats, which often depend on the particular array technology and detection instruments used. The first protocols in this unit describe loading Affymetrix and GenePix data into Spotfire. Once the data are loaded, it is necessary to filter and preprocess the data prior to analysis. Subsequently, the data transformation and normalization techniques presented here, are critical to correctly performing powerful microarray data mining expeditions. These steps extract or enhance meaningful data characteristics and prepare the data for the application of certain analysis methods such as statistical tests to compute significance and clustering methods-which mostly require data to be normally distributed. The unit outlines several methods for normalizing the data within an experiment and between multiple experiments.

Analyzing and visualizing expression data with Spotfire.

This unit assumes the reader is familiar with the Spotfire environment, has successfully installed Spotfire, and has uploaded and prepared data for analysis. It presents numerous methods for analyzing microarray data. Specifically, the first two protocols describe methods for identifying differentially expressed genes via the t-test/ANOVA and the distinction calculation respectively. Another protocol discusses how to conduct a profile search. Additional protocols illustrate various clustering methods, such as hierarchical clustering, K-means clustering, and principal components analysis. A protocol explaining coincidence testing allows the reader to compare the results from multiple clustering methods. Additional protocols demonstrate querying the Internet for information based on the microarray data, mathematically transforming data within Spotfire to generate new data columns, and exporting Spotfire visualizations.

Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells.

To elucidate the genomics of cellular responses to cancer treatment, we analyzed the expression of over 9,600 human genes in acute lymphoblastic leukemia cells before and after in vivo treatment with methotrexate and mercaptopurine given alone or in combination. Based on changes in gene expression, we identified 124 genes that accurately discriminated among the four treatments. Discriminating genes included those involved in apoptosis, mismatch repair, cell cycle control and stress response. Only 14% of genes that changed when these medications were given as single agents also changed when they were given together. These data indicate that lymphoid leukemia cells of different molecular subtypes share common pathways of genomic response to the same treatment, that changes in gene expression are treatment-specific and that gene expression can illuminate differences in cellular response to drug combinations versus single agents.

Characterization of the MLL partner gene AF15q14 involved in t(11;15)(q23;q14).

Translocations interrupting the mixed lineage leukemia gene (MLL) occur in 7-10% of acute lymphoblastic leukemia (ALL) and 5-6% of acute myeloid leukemia (AML) cases. One of these translocations, t(11;15)(q23;q14), occurs rarely in both ALL and AML. The gene on chromosome 15, AF15q14, was cloned recently in a patient with AML-M4. We have identified the same gene in a de novo T-ALL patient. However, both the MLL and AF15q14 breakpoints in these patients differed: in the previously reported AML-M4, both gene breaks were within exons, while in our ALL case the MLL break is intronic and the AF15q14 break is exonic. The MLL-AF15q14 fusion described previously shares no AF15q14 residues in common with the chimera reported here. The fusion proteins also differ with respect to MLL--the previously described fusion contains 55 extra amino acids as its MLL break is in exon 11, while the chimera we report breaks in intron 9. Contrary to the originally described normal AF15q14 (5925-bp cDNA encoding a 1833-aa protein), we identify a 7542-bp cDNA and a 2342-aa AF15q14 protein. AF15q14 appears identical to an mRNA previously found to be expressed in melanoma rendered nontumorigenic by microcell-mediated introduction of normal chromosome 6, suggesting the gene may function normally to suppress cell growth and/or enhance maturation.

Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling.

Treatment of pediatric acute lymphoblastic leukemia (ALL) is based on the concept of tailoring the intensity of therapy to a patient's risk of relapse. To determine whether gene expression profiling could enhance risk assignment, we used oligonucleotide microarrays to analyze the pattern of genes expressed in leukemic blasts from 360 pediatric ALL patients. Distinct expression profiles identified each of the prognostically important leukemia subtypes, including T-ALL, E2A-PBX1, BCR-ABL, TEL-AML1, MLL rearrangement, and hyperdiploid >50 chromosomes. In addition, another ALL subgroup was identified based on its unique expression profile. Examination of the genes comprising the expression signatures provided important insights into the biology of these leukemia subgroups. Further, within some genetic subgroups, expression profiles identified those patients that would eventually fail therapy. Thus, the single platform of expression profiling should enhance the accurate risk stratification of pediatric ALL patients.