Complementation of a Herpes Simplex Virus ICP0 Null Mutant by Varicella-Zoster Virus ORF61p

The herpes simplex virus (HSV) ICP0 protein acts to overcome intrinsic cellular defenses that repress viral α gene expression. In that vein, viruses that have mutations in ICP0''s RING finger or are deleted for the gene are sensitive to interferon, as they fail to direct degradation of promyelocytic leukemia protein (PML), a component of host nuclear domain 10s. While varicella-zoster virus is also insensitive to interferon, ORF61p, its ICP0 ortholog, failed to degrade PML. A recombinant virus with each coding region of the gene for ICP0 replaced with sequences encoding ORF61p was constructed. This virus was compared to an ICP0 deletion mutant and wild-type HSV. The recombinant degraded only Sp100 and not PML and grew to higher titers than its ICP0 null parental virus, but it was sensitive to interferon, like the virus from which it was derived. This analysis permitted us to compare the activities of ICP0 and ORF61p in identical backgrounds and revealed distinct biologic roles for these proteins.Alphaherpesviruses encode orthologs of the herpes simplex virus (HSV) α gene product ICP0. ICP0 is a nuclear phosphoprotein that behaves as a promiscuous activator of viral and cellular genes (7, 11, 28, 29). ICP0 also functions as an E3 ubiquitin ligase to target several host proteins for proteasomal degradation (4, 10, 11, 16, 26). Through this activity, ICP0 promotes degradation of components of nuclear domain 10 (ND10) bodies, including the promyelocytic leukemia protein (PML) and Sp100. These proteins are implicated in silencing of herpesvirus genomes (9, 10, 22, 34). Therefore, ICP0-mediated degradation of ND10 components may disrupt silencing of HSV genes to enable efficient gene expression. This hypothesis provides a plausible mechanistic explanation of how ICP0 induces gene activation.Introduction of DNA encoding the ICP0 orthologs from HSV, bovine herpesvirus, equine herpesvirus, and varicella-zoster virus (VZV) can also affect nuclear structures and proteins (27). In addition, and more specific to this report, ORF61p, the VZV ortholog, activates viral promoters and enhances infectivity of viral DNA like ICP0, the prototype for this gene family (24, 25). However, we have previously demonstrated two key biological differences between the HSV and VZV orthologs. We first showed that unlike ICP0, ORF61p is unable to complement depletion of BAG3, a host cochaperone protein. As a result, VZV is affected by silencing of BAG3 (15), whereas growth of HSV is altered only when ICP0 is not expressed (17). Furthermore, we have shown that while both proteins target components of ND10s, expression of ICP0 results in degradation of both PML and Sp100, whereas ORF61p specifically reduces Sp100 levels (16). These findings suggest that these proteins have evolved separately to provide different functions for virus replication.Virus mutants lacking the ICP0 gene have an increased particle-to-PFU ratio, a substantially lower yield, and decreased levels of α gene expression, in a multiplicity-of-infection (MOI)- and cell-type-dependent manner (2, 4, 8, 33). These mutants are also defective at degrading ND10 components (23). Depletion of PML and Sp100 accelerates virus gene expression and increases plaquing efficiency of HSV ICP0-defective viruses but has no effect on wild-type virus, suggesting that PML and Sp100 are components of an intrinsic anti-HSV defense mechanism that is counteracted by ICP0''s E3 ligase activity (9, 10). Interestingly, ICP0 null viruses are also hypersensitive to interferon (IFN) (26), a property that was suggested to be mediated via PML (3).To directly compare the activities of the two orthologs, we constructed an HSV mutant virus that expresses ORF61p in place of ICP0. The resulting chimeric virus only partially rescues the ICP0 null phenotype. Our studies emphasize the biological differences between ICP0 and ORF61p and shed light on the requirements for PML and Sp100 during infection.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]