Meta- and Orthogonal Integration of Influenza "OMICs" Data Defines a Role for UBR4 in Virus Budding.

Several systems-level datasets designed to dissect host-pathogen interactions during influenza A infection have been reported. However, apparent discordance among these data has hampered their full utility toward advancing mechanistic and therapeutic knowledge. To collectively reconcile these datasets, we performed a meta-analysis of data from eight published RNAi screens and integrated these data with three protein interaction datasets, including one generated within the context of this study. Further integration of these data with global virus-host interaction analyses revealed a functionally validated biochemical landscape of the influenza-host interface, which can be queried through a simplified and customizable web portal (http://www.metascape.org/IAV). Follow-up studies revealed that the putative ubiquitin ligase UBR4 associates with the viral M2 protein and promotes apical transport of viral proteins. Taken together, the integrative analysis of influenza OMICs datasets illuminates a viral-host network of high-confidence human proteins that are essential for influenza A virus replication.

Whole genome functional analysis identifies novel components required for mitotic spindle integrity in human cells.

BACKGROUND: The mitotic spindle is a complex mechanical apparatus required for accurate segregation of sister chromosomes during mitosis. We designed a genetic screen using automated microscopy to discover factors essential for mitotic progression. Using a RNA interference library of 49,164 double-stranded RNAs targeting 23,835 human genes, we performed a loss of function screen to look for small interfering RNAs that arrest cells in metaphase. RESULTS: Here we report the identification of genes that, when suppressed, result in structural defects in the mitotic spindle leading to bent, twisted, monopolar, or multipolar spindles, and cause cell cycle arrest. We further describe a novel analysis methodology for large-scale RNA interference datasets that relies on supervised clustering of these genes based on Gene Ontology, protein families, tissue expression, and protein-protein interactions. CONCLUSION: This approach was utilized to classify functionally the identified genes in discrete mitotic processes. We confirmed the identity for a subset of these genes and examined more closely their mechanical role in spindle architecture.

A role for IkappaB kinase 2 in bipolar spindle assembly.

IkappaB kinase 2 (IKK2 or IKKbeta) is a component of the IKK complex that coordinates the cellular response to a diverse set of extracellular stimuli, including cytokines, microbial infection, and stress. In response to an external stimulus, the complex is activated, resulting in the phosphorylation and subsequent proteasome-mediated degradation of IkappaB proteins. This event triggers the nuclear import of the NF-kappaB transcription factor, which activates the transcription of genes that regulate a variety of fundamental biological processes, including immune response, cell survival, and development. Here, we define an essential role for IKK2 in normal mitotic progression and the maintenance of spindle bipolarity. Chemical and genetic perturbation of IKK2 promotes the formation of multipolar spindles and chromosome missegregation. Depletion of IKK2 results in the deregulation of Aurora A protein stability and coincident hyperactivation of a putative Aurora A substrate, the mitotic motor KIF11. These data support a function for IKK2 as an antagonist of Aurora A signaling during mitosis. Additionally, our results indicate a direct role for IKK2 in the maintenance of genome stability and underscore the potential for oncogenic consequences in targeting this kinase for therapeutic intervention.

A probability-based approach for the analysis of large-scale RNAi screens.

We describe a statistical analysis methodology designed to minimize the impact of off-target activities upon large-scale RNA interference (RNAi) screens in mammalian cells. Application of this approach enhances reconfirmation rates and facilitates the experimental validation of new gene activities through the probability-based identification of multiple distinct and active small interfering RNAs (siRNAs) targeting the same gene. We further extend this approach to establish that the optimal redundancy for efficacious RNAi collections is between 4-6 siRNAs per gene.

A functional genomics approach to the mode of action of apratoxin A.

The cyanobacterial metabolite apratoxin A (1) demonstrates potent cytotoxicity against tumor cell lines by a hitherto unknown mechanism. We have used functional genomics to elucidate the molecular basis for this activity. Gene expression profiling and DNA content analysis showed that apratoxin A induces G1-phase cell cycle arrest and apoptosis. Cell-based functional assays with a genome-wide collection of expression cDNAs showed that ectopic induction of fibroblast growth factor receptor (FGFR) signaling attenuates the apoptotic activity of apratoxin A. This natural product inhibited phosphorylation and activation of STAT3, a downstream effector of FGFR signaling. It also caused defects in FGF-dependent processes during zebrafish development, with concomitant reductions in expression levels of the FGF target gene mkp3. We conclude that apratoxin A mediates its antiproliferative activity through the induction of G1 cell cycle arrest and an apoptotic cascade, which is at least partially initiated through antagonism of FGF signaling via STAT3.

Identification of p53 regulators by genome-wide functional analysis.

The p53 tumor-suppressor protein is a critical mediator of cellular growth arrest and the induction of apoptosis. To identify proteins involved in the modulation of p53 transcriptional activity, a gain-of-function cellular screen was carried out with an arrayed matrix of approximately 20,000 cDNAs. Nine genes previously unknown to be involved in regulating p53 activity were identified. Overexpression of seven of these genes (Hey1, Hes1, TFAP4, Osr1, NR2F2, SFRS10, and FLJ11339) resulted in up-regulation of p53 activity; overexpression of two genes (M17S2 and cathepsin B) resulted in down-regulation of p53 activity in mammalian cells. HES1, HEY1, and TFAP4, which are members of the basic helix-loop-helix transcription family, and OSR1 were shown to activate p53 through repression of HDM2 transcription. Ectopic expression of these basic helix-loop-helix transcription factors in both zebrafish and avian developmental systems activated p53 and induced apoptosis in vivo, resulting in a phenotype similar to that of p53 overexpression. Furthermore, ras- and myc-mediated transformation of mouse embryonic fibroblasts was abrogated by expression of HEY1 in a p53-dependent manner. These results suggest that these transcription factors are members of an evolutionarily conserved network that governs p53 function.

Genome-scale functional profiling of the mammalian AP-1 signaling pathway.

Large-scale functional genomics approaches are fundamental to the characterization of mammalian transcriptomes annotated by genome sequencing projects. Although current high-throughput strategies systematically survey either transcriptional or biochemical networks, analogous genome-scale investigations that analyze gene function in mammalian cells have yet to be fully realized. Through transient overexpression analysis, we describe the parallel interrogation of approximately 20,000 sequence annotated genes in cancer-related signaling pathways. For experimental validation of these genome data, we apply an integrative strategy to characterize previously unreported effectors of activator protein-1 (AP-1) mediated growth and mitogenic response pathways. These studies identify the ADP-ribosylation factor GTPase-activating protein Centaurin alpha1 and a Tudor domain-containing hypothetical protein as putative AP-1 regulatory oncogenes. These results provide insight into the composition of the AP-1 signaling machinery and validate this approach as a tractable platform for genome-wide functional analysis.