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]     

CD4 Glycoprotein Degradation Induced by Human Immunodeficiency Virus Type 1 Vpu Protein Requires the Function of Proteasomes and the Ubiquitin-Conjugating Pathway

The human immunodeficiency virus type 1 (HIV-1) vpu gene encodes a type I anchored integral membrane phosphoprotein with two independent functions. First, it regulates virus release from a post-endoplasmic reticulum (ER) compartment by an ion channel activity mediated by its transmembrane anchor. Second, it induces the selective down regulation of host cell receptor proteins (CD4 and major histocompatibility complex class I molecules) in a process involving its phosphorylated cytoplasmic tail. In the present work, we show that the Vpu-induced proteolysis of nascent CD4 can be completely blocked by peptide aldehydes that act as competitive inhibitors of proteasome function and also by lactacystin, which blocks proteasome activity by covalently binding to the catalytic β subunits of proteasomes. The sensitivity of Vpu-induced CD4 degradation to proteasome inhibitors paralleled the inhibition of proteasome degradation of a model ubiquitinated substrate. Characterization of CD4-associated oligosaccharides indicated that CD4 rescued from Vpu-induced degradation by proteasome inhibitors is exported from the ER to the Golgi complex. This finding suggests that retranslocation of CD4 from the ER to the cytosol may be coupled to its proteasomal degradation. CD4 degradation mediated by Vpu does not require the ER chaperone calnexin and is dependent on an intact ubiquitin-conjugating system. This was demonstrated by inhibition of CD4 degradation (i) in cells expressing a thermally inactivated form of the ubiquitin-activating enzyme E₁ or (ii) following expression of a mutant form of ubiquitin (Lys₄₈ mutated to Arg₄₈) known to compromise ubiquitin targeting by interfering with the formation of polyubiquitin complexes. CD4 degradation was also prevented by altering the four Lys residues in its cytosolic domain to Arg, suggesting a role for ubiquitination of one or more of these residues in the process of degradation. The results clearly demonstrate a role for the cytosolic ubiquitin-proteasome pathway in the process of Vpu-induced CD4 degradation. In contrast to other viral proteins (human cytomegalovirus US2 and US11), however, whose translocation of host ER molecules into the cytosol occurs in the presence of proteasome inhibitors, Vpu-targeted CD4 remains in the ER in a transport-competent form when proteasome activity is blocked.

The human immunodeficiency virus type 1 (HIV-1)-specific accessory protein Vpu performs two distinct functions in the viral life cycle (11, 12, 29, 34, 46, 47, 50–52; reviewed in references 31 and 55): enhancement of virus particle release from the cell surface, and the selective induction of proteolysis of newly synthesized membrane proteins. Known targets for Vpu include the primary virus receptor CD4 (63, 64) and major histocompatibility complex (MHC) class I molecules (28). Vpu is an oligomeric class I integral membrane phosphoprotein (35, 48, 49) with a structurally and functionally defined domain architecture: an N-terminal transmembrane anchor and C-terminal cytoplasmic tail (20, 34, 45, 47, 50, 65). Vpu-induced degradation of endoplasmic reticulum (ER) membrane proteins involves the phosphorylated cytoplasmic tail of the protein (50), whereas the virion release function is mediated by a cation-selective ion channel activity associated with the membrane anchor (19, 31, 45, 47).CD4 is a 55-kDa class I integral membrane glycoprotein that serves as the primary coreceptor for HIV entry into cells. CD4 consists of a large lumenal domain, a transmembrane peptide, and a 38-residue cytoplasmic tail. It is expressed on the surface of a subset of T lymphocytes that recognize MHC class II-associated peptides, and it plays a pivotal role in the development and maintenance of the immune system (reviewed in reference 30). Down regulation of CD4 in HIV-1-infected cells is mediated through several independent mechanisms (reviewed in references 5 and 55): intracellular complex formation of CD4 with the HIV envelope protein gp160 (8, 14), endocytosis of cell surface CD4 induced by the HIV-1 nef gene product (1, 2), and ER degradation induced by the HIV-1 vpu gene product (63, 64).Vpu-induced degradation of CD4 is an example of ER-associated protein degradation (ERAD). ERAD is a common outcome when proteins in the secretory pathway are unable to acquire their native structure (4). Although it was thought that ERAD occurs exclusively inside membrane vesicles of the ER or other related secretory compartments, this has gained little direct experimental support. Indeed, there are several recent reports that ERAD may actually represent export of the target protein to the cytosol, where it is degraded by cytosolic proteases. It was found that in yeast, a secreted protein, prepro-α-factor (pαF), is exported from microsomes and degraded in the cytosol in a proteasome-dependent manner (36). This process was dependent on the presence of calnexin, an ER-resident molecular chaperone that interacts with N-linked oligosaccharides containing terminal glucose residues (3). In mammalian cells, two human cytomegalovirus (HCMV) proteins, US2 and US11, were found to cause the retranslocation of MHC class I molecules from the ER to the cytosol, where they are destroyed by proteasomes (61, 62). In the case of US2, class I molecules were found to associate with a protein (Sec61) present in the channel normally used to translocate newly synthesized proteins into the ER (termed the translocon), leading to the suggestion that the ERAD substrates are delivered to the cytosol by retrograde transport through the Sec61-containing pore (61). Fujita et al. (24) reported that, similar to these findings, the proteasome-specific inhibitor lactacystin (LC) partially blocked CD4 degradation in transfected HeLa cells coexpressing CD4, Vpu, and HIV-1 Env glycoproteins. In the present study, we show that Vpu-induced CD4 degradation can be completely blocked by proteasome inhibitors, does not require the ER chaperone calnexin, but requires the function of the cytosolic polyubiquitination machinery which apparently targets potential ubiquitination sites within the CD4 cytoplasmic tail. Our findings point to differences between the mechanism of Vpu-mediated CD4 degradation and ERAD processes induced by the HCMV proteins US2 and US11 (61, 62).     

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]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]     

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]     

Distinct Pathways for Tumor Necrosis Factor Alpha and Ceramides in Human Cytomegalovirus Infection

Human cytomegalovirus (HCMV) infection can be fatal to immunocompromised individuals. We have previously reported that gamma interferon and tumor necrosis factor alpha (TNF-α) synergistically inhibit HCMV replication in vitro. Ceramides have been described as second messengers induced by TNF-α. To investigate the mechanisms involved in the inhibition of HCMV by TNF-α, in the present study we have analyzed ceramide production by U373 MG astrocytoma cells and the effects of TNF-α versus ceramides on HCMV replication. Our results show that U373 MG cells did not produce ceramides upon incubation with TNF-α. Moreover, long-chain ceramides induced by treatment with exogenous bacterial sphingomyelinase inhibited HCMV replication in synergy with TNF-α. Surprisingly, short-chain permeant C6-ceramide increased viral replication. Our results show that the anti-HCMV activity of TNF-α is independent of ceramides. In addition, our results suggest that TNF-α and endogenous long-chain ceramides use separate pathways of cell signalling to inhibit HCMV replication, while permeant C6-ceramide appears to activate a third pathway leading to an opposite effect.Human cytomegalovirus (HCMV) infections are well controlled in the immunocompetent host. Cellular immune responses (CD4⁺ and CD8⁺ T cells and NK cells) which accompany both acute and latent infections (for a review, see reference 4) are thought to be the main components of this control. HCMV infection during immunosuppression such as in cancer, transplantations, or AIDS results in severe pathology (4). We have previously shown that tumor necrosis factor alpha (TNF-α), in synergy with gamma interferon (IFN-γ), inhibits the replication of HCMV (7). In mice, TNF-α is involved in the clearance of CMV infection (25). TNF-α is a cytokine with multiple effects which is produced by many cell types, including macrophages and CD8⁺ and CD4⁺ T lymphocytes (for a review, see reference 40), and is known to possess antiviral effects (20, 47). The molecular mechanisms involved in the signalling by TNF-α depend on the type of receptor, p55 (TNF-R1) or p75 (TNF-R2) (5), to which it binds. Some cells express only one type of TNF-α receptor; however, expression of these receptors is not always mutually exclusive (5). The cytotoxicity of TNF-α has been reported to be mediated by TNF-R1 (38), whose intracellular region carries a death domain which signals for programmed cell death (39). Signalling through TNF-R1 with specific antibodies can also protect Hep-G2 cells from vesicular stomatitis virus-mediated cytopathic effects (48). Ceramide production after TNF-α treatment has been widely reported (16, 19, 31) and has also been shown to depend on signalling through the TNF-R1 receptor (45). In these experiments concerning myeloid cells, TNF-α induced the activation of a sphingomyelinase, which cleaved sphingomyelin to release ceramide and phosphocholine. The production of ceramides can lead to cell apoptosis (11, 14, 23) or cell cycle arrest (13). Induction of apoptosis by TNF-α has been mimicked by exogenous sphingomyelinase and by synthetic, short-chain, permeant ceramides, which suggests that ceramides, as second messengers, are sufficient to induce the cytotoxic effects of TNF-α (11, 23). Acidic and neutral sphingomyelinases activated in different cell compartments may be responsible for the diverse effects of TNF-α (46), with the former being involved in signalling through NF-κB (34) and the latter being involved in signalling through a ceramide-activated protein kinase and phospholipase A₂ (46).One of the characteristics of HCMV infection is the increase in the content of intracellular DNA, reported to be of viral (3, 8, 18) or cellular (12, 37) origin. Since TNF-α has been known to display antiproliferative properties and to block cells in the G₁ phase (29), we initially tested its effects on the cell cycle of infected cells. Then, based on studies reporting that TNF-α induces ceramides in cells (16, 19, 31) and on a study showing the role of ceramide in cell cycle blockade (13), we originally postulated that ceramide was responsible for the antiviral effect of TNF-α. In the present study, we used astrocytoma cells (U373 MG) as a model for brain cells, which are important targets of HCMV in vivo (22). In contrast to fibroblasts, infected U373 MG cells release smaller quantities of virus particles even though all the cells were infected in our experiments. We believe that the U373 MG model is closer to HCMV infection in vivo. We show that ceramides are not produced by U373 MG cells upon incubation with even high concentrations of TNF-α. In addition, we demonstrate that exogenously added sphingomyelinase induces anti-HCMV effects whereas permeant C6-ceramide increases HCMV proliferation in U373 MG cells. This suggests that lipid second messengers can modulate HCMV infection and that TNF-α and ceramides use distinct signalling pathways in the control of HCMV infection. This is supported by our observation that the protein kinase JNK1 is activated exclusively by TNF-α in U373 MG cells and that TNF-α and exogenous sphingomyelinase act in synergy on HCMV infection.     

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]