Michelle Holko

Activation of the interferon system by short-interfering RNAs

RNA interference (RNAi) is a powerful tool used to manipulate gene expression or determine gene function. One technique of expressing the short double-stranded (ds) RNA intermediates required for interference in mammalian systems is the introduction of short-interfering (si) RNAs. Although RNAi strategies are reliant on a high degree of specificity, little attention has been given to the potential non-specific effects that might be induced. Here, we found that transfection of siRNAs results in interferon (IFN)-mediated activation of the Jak–Stat pathway and global upregulation of IFN-stimulated genes. This effect is mediated by the dsRNA-dependent protein kinase, PKR, which is activated by 21-base-pair (bp) siRNAs and required for upregulation of IFN-β in response to siRNAs. In addition, we show by using cell lines deficient in specific components mediating IFN action that the RNAi mechanism itself is independent of the interferon system. Thus, siRNAs have broad and complicating effects beyond the selective silencing of target genes when introduced into cells. This is of critical importance, as siRNAs are currently being explored for their potential therapeutic use.

Functional classification of interferon-stimulated genes identified using microarrays

Interferons (IFNs) are a family of multifunctional cytokines thatactivate transcription of subsets of genes. The gene products inducedby IFNs are responsible for IFN antiviral, antiproliferative, andimmunomodulatory properties. To obtain a more comprehensive list and abetter understanding of the genes regulated by IFNs, we compiled datafrom many experiments, using two different microarray formats. Thecombined data sets identified >300 IFN‐stimulated genes (ISGs). Toprovide new insight into IFN‐induced cellular phenotypes, we assignedthese ISGs to functional categories. The data are accessible on the World Wide Web at http://www.lerner.ccf.org/labs/williams, including functional categories and individual genes listed in asearchable database. The entries are linked to GenBank and Unigenesequence information and other resources. The goal is to eventuallycompile a comprehensive list of all ISGs. Recognition of the functionsof the ISGs and their specific roles in the biological effects of IFNsis leading to a greater appreciation of the many facets of theseintriguing and essential cytokines. This review focuses on thefunctions of the ISGs identified by analyzing the microarray data andfocuses particularly on new insights into the protein kinaseRNA‐regulated (PRKR) protein, which have been made possible with theavailability of PRKR‐null mice.

Annotating the human genome with Disease Ontology.

BackgroundThe human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.ResultsWe 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.ConclusionThe 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.

From disease ontology to disease-ontology lite

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. Contact: s-lin2@northwestern.edu

Promyelocytic Leukemia Zinc Finger Protein Regulates Interferon-Mediated Innate Immunity

Summary Interferons (IFNs) direct innate and acquired immune responses and, accordingly, are used therapeutically to treat a number of diseases, yet the diverse effects they elicit are not fully understood. Here, we identified the promyelocytic leukemia zinc finger (PLZF) protein as a previously unrecognized component of the IFN response. IFN stimulated an association of PLZF with promyelocytic leukemia protein (PML) and histone deacetylase 1 (HDAC1) to induce a decisive subset of IFN-stimulated genes (ISGs). Consequently, PLZF-deficient mice had a specific ISG expression defect and as a result were more susceptible to viral infection. This susceptibility correlated with a marked decrease in the expression of the key antiviral mediators and an impaired IFN-mediated induction of natural killer cell function. These results provide new insights into the regulatory mechanisms of IFN signaling and the induction of innate antiviral immunity.

Lipid metabolism genes in contralateral unaffected breast and estrogen receptor status of breast cancer

Risk biomarkers that are specific to estrogen receptor (ER) subtypes of breast cancer would aid the development and implementation of distinct prevention strategies. The contralateral unaffected breast of women with unilateral breast cancer (cases) is a good model for defining subtype-specific risk because women with ER-negative (ER−) index primaries are at high risk for subsequent ER-negative primary cancers. We conducted random fine needle aspiration of the unaffected breasts of cases. Samples from 30 subjects [15 ER-positive (ER+) and 15 ER− cases matched for age, race and menopausal status] were used for Illumina expression array analysis. Findings were confirmed using quantitative real-time PCR (qRT-PCR) in the same samples. A validation set consisting of 36 subjects (12 ER+, 12 ER− and 12 standard-risk healthy controls) was used to compare gene expression across groups. ER− case samples displayed significantly higher expression of 18 genes/transcripts, 8 of which were associated with lipid metabolism on gene ontology analysis (GO: 0006629). This pattern was confirmed by qRT-PCR in the same samples, and in the 24 cases of the validation set. When compared to the healthy controls in the validation set, significant overexpression of 4 genes (DHRS2, HMGCS2, HPGD and ACSL3) was observed in ER− cases, with significantly lower expression of UGT2B11 and APOD in ER+ cases, and decreased expression of UGT2B7 in both subtypes. These data suggest that differential expression of lipid metabolism genes may be involved in the risk for subtypes of breast cancer, and are potential biomarkers of ER-specific breast cancer risk. Cancer Prev Res; 6(4); 321–30. ©2013 AACR.

Differential gene expression associated with estrogen receptor status of breast cancer identified by microarray meta-analysis.

Abstract #2041 Introduction: Estrogen receptor (ER) positive breast cancer can be prevented by Tamoxifen. Currently, there are no markers for breast cancer risk that discriminate between the risk of ER positive versus ER negative disease. To identify patients likely to benefit from endocrine chemoprevention, a robust marker predicting ER status is needed. Microarray technology has identified genomic classifiers for breast cancer prognosis and prediction. We therefore conducted a meta-analysis of microarray data to identify individual genes associated with ER status of breast cancer.
 Methods: Publicly available microarray gene expression data sets were mined using Gene Expression Omnibus (GEO), Oncomine, ArrayExpress, and Stanford Microarray Database (SMD). Study inclusion criteria are no prior drug treatment, known ER status, and raw data files available for at least 50 arrays. All analyses were conducted using Bioconductor Project9s (http://www.bioconductor.org) open-source software in R statistical programming language. Packages used include limma, ArrayQualityMetrics, MetaArray, and GOstats. Data sets were individually normalized using rma, and differential gene expression was based on ER status. Meta-analytic test statistic was computed, multiple test correction was performed, and genes above the threshold of |Zscore| = 19.5 were included in further analysis.
 Results: The search identified 23 publicly available gene expression data sets of primary breast tumors with known ER status, ten of which met inclusion criteria. Four of the ten contained cDNA array data files from SMD and were combined since they contained redundant files. To minimize platform and methodology discrepancies, we chose to focus our analysis on the six data sets which utilized the Affymetrix U133A array. The six data sets each contain 99-245 arrays, and represent a total of 644 ER+ and 268 ER- breast tumors. Bioinformatic analysis of the 86 differentially expressed genes associated with ER status using the criteria described above was then performed. To enrich for gene expression changes in pre-neoplastic lesions, genes known to be involved in progression and metastasis were eliminated from further study. The remaining genes were searched in Oncomine for differential expression according to ER status, in PubMed for previous association with breast cancer, and with our unpublished data for Gene Ontology annotations associated with ER status in breast cancer. We further narrowed the resulting list of 22 to 15 (ESR1, TFF1, AGR2, SCUBE2, CYP21A2, PIP, DACH1, BCL2, PROM1, RARES1, TMSL8, SRD5A1, SLC39A6, SCGB2A2, and FOXA1) based on functional redundancy.
 Conclusion: Over the next 4 months, this gene set (along with 5 house-keeping genes) will be tested for differential expression in samples that we have collected from the contralateral unaffected breasts of women with ER positive and ER negative breast cancer. These studies are likely to identify candidate genes that may serve as markers of ER-specific risk. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2041.