Ubiquitin-Mediated Proteasome Degradation Regulates Optic Fissure Fusion

Optic fissure fusion is a critical event during retinal development. Failure of fusion leads to coloboma, a potentially blinding congenital disorder. Pax2a is an essential regulator of optic fissure fusion and the target of numerous morphogenetic pathways. In our current study, we examined the negative regulator of pax2a expression, Nz2, and the mechanism modulating Nlz2 activity during optic fissure fusion. Upregulation of Nlz2 in zebrafish embryos resulted in downregulation of pax2a expression and fissure fusion failure. Conversely, upregulation of pax2a expression also led to fissure fusion failure suggesting Pax2 levels require modulation to ensure proper fusion. Interestingly, we discovered Nlz2 is a target of the E3 ubiquitin ligase Siah. We show that zebrafish siah1 expression is regulated by Hedgehog signaling and that Siah1 can directly target Nlz2 for proteasomal degradation, in turn regulating the levels of pax2a mRNA. Finally, we show that both activation and inhibition of Siah activity leads to failure of optic fissure fusion dependent on ubiquitin-mediated proteasomal degradation of Nlz2. In conclusion, we outline a novel, proteasome-mediated degradation regulatory pathway involved in optic fissure fusion

Improving GSEA for Analysis of Biologic Pathways for Differential Gene Expression across a Binary Phenotype

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of single genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the single-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Specifically, we illustrate, in a simulation study, that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are highly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs perfectly in the simulation study: none of the null gene sets is identified with statistical significance, while all of the truly-associated gene sets are. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show the advantages of SAM-GS over GSEA, both statistically and biologically

Improving gene set analysis of microarray data by SAM-GS

<p>Abstract</p> <p>Background</p> <p><it>Gene-set </it>analysis evaluates the expression of biological pathways, or <it>a priori </it>defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).</p> <p>Results</p> <p>Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with <it>p53 </it>mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of <it>p53</it>. Of the 31 gene sets, 11 actually involve <it>p53 </it>directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of <it>p53 </it>signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with <it>p53</it>.</p> <p>Conclusion</p> <p>We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.</p