Meiosis-induced alterations in transcript architecture and noncoding RNA expression in S. cerevisiae.

Changes in transcript architecture can have powerful effects on protein expression. Regulation of the transcriptome is often dramatically revealed during dynamic conditions such as development. To examine changes in transcript architecture we analyzed the expression and transcript boundaries of protein-coding and noncoding RNAs over the developmental process of meiosis in Saccharomyces cerevisiae. Custom-designed, high-resolution tiling arrays were used to define the time-resolved transcriptome of cells undergoing meiosis and sporulation. These arrays were specifically designed for the S. cerevisiae strain SK1 that sporulates with high efficiency and synchrony. In addition, new methods were created to define transcript boundaries and to identify dynamic changes in transcript expression and architecture over time. Of 8407 total segments, 699 (8.3%) were identified by our algorithm as regions containing potential transcript architecture changes. Our analyses reveal extensive changes to both the coding and noncoding transcriptome, including altered 5' ends, 3' ends, and splice sites. Additionally, 3910 (46.5%) unannotated expressed segments were identified. Interestingly, subsets of unannotated RNAs are located across from introns (anti-introns) or across from the junction between two genes (anti-intergenic junctions). Many of these unannotated RNAs are abundant and exhibit sporulation-specific changes in expression patterns. All work, including heat maps of the tiling array, annotation for the SK1 strain, and phastCONS conservation analysis, is available at http://groups.molbiosci.northwestern.edu/sontheimer/sk1meiosis.php. Our high-resolution transcriptome analyses reveal that coding and noncoding transcript architectures are exceptionally dynamic in S. cerevisiae and suggest a vast array of novel transcriptional and post-transcriptional control mechanisms that are activated upon meiosis and sporulation.

An integrated genome screen identifies the Wnt signaling pathway as a major target of WT1.

WT1, a critical regulator of kidney development, is a tumor suppressor for nephroblastoma but in some contexts functions as an oncogene. A limited number of direct transcriptional targets of WT1 have been identified to explain its complex roles in tumorigenesis and organogenesis. In this study we performed genome-wide screening for direct WT1 targets, using a combination of ChIP-ChIP and expression arrays. Promoter regions bound by WT1 were highly G-rich and resembled the sites for a number of other widely expressed transcription factors such as SP1, MAZ, and ZNF219. Genes directly regulated by WT1 were implicated in MAPK signaling, axon guidance, and Wnt pathways. Among directly bound and regulated genes by WT1, nine were identified in the Wnt signaling pathway, suggesting that WT1 modulates a subset of Wnt components and responsive genes by direct binding. To prove the biological importance of the interplay between WT1 and Wnt signaling, we showed that WT1 blocked the ability of Wnt8 to induce a secondary body axis during Xenopus embryonic development. WT1 inhibited TCF-mediated transcription activated by Wnt ligand, wild type and mutant, stabilized beta-catenin by preventing TCF4 loading onto a promoter. This was neither due to direct binding of WT1 to the TCF binding site nor to interaction between WT1 and TCF4, but by competition of WT1 and TCF4 for CBP. WT1 interference with Wnt signaling represents an important mode of its action relevant to the suppression of tumor growth and guidance of development.

Ultrastructural, immunofluorescence, and RNA evidence support the hypothesis of a "new" virus associated with Kawasaki disease.

BACKGROUND: Intracytoplasmic inclusion bodies (ICI) have been identified in ciliated bronchial epithelium of Kawasaki disease (KD) patients using a synthetic antibody derived from acute KD arterial IgA plasma cells; ICI may derive from the KD etiologic agent. METHODS: Acute KD bronchial epithelium was subjected to immunofluorescence for ICI and cytokeratin, high-throughput sequencing, and transmission electron microscopy (TEM). Interferon pathway gene expression profiling was performed on KD lung. RESULTS: An intermediate filament cytokeratin "cage" was not observed around KD ICI, making it unlikely that ICI are overproduced or misfolded human protein aggregates. Many interferon-stimulated genes were detected in the bronchial epithelium, and significant modulation of the interferon response pathway was observed in the lung tissue of KD patients. No known virus was identified by sequencing. Aggregates of virus-like particles (VLP) were detected by TEM in all 3 acute KD patients from whom nonembedded formalin-fixed lung tissue was available. CONCLUSIONS: KD ICI are most likely virus induced; bronchial cells with ICI contain VLP that share morphologic features among several different RNA viral families. Expedited autopsies and tissue fixation from acute KD fatalities are urgently needed to more clearly ascertain the VLP. These findings are compatible with the hypothesis that the infectious etiologic agent of KD may be a "new" RNA virus.

Comprehensive genomic screens identify a role for PLZF-RARalpha as a positive regulator of cell proliferation via direct regulation of c-MYC.

The t(11;17)(q23;q21) translocation is associated with a retinoic acid (RA)-insensitive form of acute promyelocytic leukemia (APL), involving the production of reciprocal fusion proteins, promyelocytic leukemia zinc finger-retinoic acid receptor alpha (PLZF-RARalpha) and RARalpha-PLZF. Using a combination of chromatin immunoprecipitation promotor arrays (ChIP-chip) and gene expression profiling, we identify novel, direct target genes of PLZF-RARalpha that tend to be repressed in APL compared with other myeloid leukemias, supporting the role of PLZF-RARalpha as an aberrant repressor in APL. In primary murine hematopoietic progenitors, PLZF-RARalpha promotes cell growth, and represses Dusp6 and Cdkn2d, while inducing c-Myc expression, consistent with its role in leukemogenesis. PLZF-RARalpha binds to a region of the c-MYC promoter overlapping a functional PLZF site and antagonizes PLZF-mediated repression, suggesting that PLZF-RARalpha may act as a dominant-negative version of PLZF by affecting the regulation of shared targets. RA induced the differentiation of PLZF-RARalpha-transformed murine hematopoietic cells and reduced the frequency of clonogenic progenitors, concomitant with c-Myc down-regulation. Surviving RA-treated cells retained the ability to be replated and this was associated with sustained c-Myc expression and repression of Dusp6, suggesting a role for these genes in maintaining a self-renewal pathway triggered by PLZF-RARalpha.

Predicting nucleosome positioning using a duration Hidden Markov Model.

BACKGROUND: The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function. RESULTS: We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download. CONCLUSIONS: Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.

From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations.

Subjective methods have been reported to adapt a general-purpose ontology for a specific application. For example, Gene Ontology (GO) Slim was created from GO to generate a highly aggregated report of the human-genome annotation. We propose statistical methods to adapt the general purpose, OBO Foundry Disease Ontology (DO) for the identification of gene-disease associations. Thus, we need a simplified definition of disease categories derived from implicated genes. On the basis of the assumption that the DO terms having similar associated genes are closely related, we group the DO terms based on the similarity of gene-to-DO mapping profiles. Two types of binary distance metrics are defined to measure the overall and subset similarity between DO terms. A compactness-scalable fuzzy clustering method is then applied to group similar DO terms. To reduce false clustering, the semantic similarities between DO terms are also used to constrain clustering results. As such, the DO terms are aggregated and the redundant DO terms are largely removed. Using these methods, we constructed a simplified vocabulary list from the DO called Disease Ontology Lite (DOLite). We demonstrated that DOLite results in more interpretable results than DO for gene-disease association tests. The resultant DOLite has been used in the Functional Disease Ontology (FunDO) Web application at http://www.projects.bioinformatics.northwestern.edu/fundo.

Visual presentation as a welcome alternative to textual presentation of gene annotation information.

The functions of a gene are traditionally annotated textually using either free text (Gene Reference Into Function or GeneRIF) or controlled vocabularies (e.g., Gene Ontology or Disease Ontology). Inspired by the latest word cloud tools developed by the Information Visualization Group at IBM Research, we have prototyped a visual system for capturing gene annotations, which we named Gene Graph Into Function or GeneGIF. Fully developing the GeneGIF system would be a significant effort. To justify the necessity and to specify the design requirements of GeneGIF, we first surveyed the end-user preferences. From 53 responses, we found that a majority (64%, p < 0.05) of the users were either positive or neutral toward using GeneGIF in their daily work (acceptance); in terms of preference, a slight majority (51%, p > 0.05) of the users favored visual presentation of information (GeneGIF) compared to textual (GeneRIF) information. The results of this study indicate that a visual presentation tool, such as GeneGIF, can complement standard textual presentation of gene annotations. Moreover, the survey participants provided many constructive comments that will specify the development of a phase-two project (http://128.248.174.241/) to visually annotate each gene in the human genome.

Annotating the human genome with Disease Ontology.

BACKGROUND: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. RESULTS: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. CONCLUSION: The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.