Previously Unrecognized Amino Acid Substitutions in the Hemagglutinin and Fusion Proteins of Measles Virus Modulate Cell-Cell Fusion,Hemadsorption, Virus Growth,and Penetration Rate

Wild-type measles virus (MV) isolated in B95a cells could be adapted to Vero cells after several blind passages. In this study, we have determined the complete nucleotide sequences of the genomes of the wild type (T11wild) and its Vero cell-adapted (T11Ve-23) MV strain and identified amino acid substitutions R516G, E271K, D439E and G464W (D439E/G464W), N481Y/H495R, and Y187H/L204F in the nucleocapsid, V, fusion (F), hemagglutinin (H), and large proteins, respectively. Expression of mutated H and F proteins from cDNA revealed that the H495R substitution, in addition to N481Y, in the H protein was necessary for the wild-type H protein to use CD46 efficiently as a receptor and that the G464W substitution in the F protein was important for enhanced cell-cell fusion. Recombinant wild-type MV strains harboring the F protein with the mutations D439E/G464W [F(D439E/G464W)] and/or H(N481Y/H495R) protein revealed that both mutated F and H proteins were required for efficient syncytium formation and virus growth in Vero cells. Interestingly, a recombinant wild-type MV strain harboring the H(N481Y/H495R) protein penetrated slowly into Vero cells, while a recombinant wild-type MV strain harboring both the F(D439E/G464W) and H(N481Y/H495R) proteins penetrated efficiently into Vero cells, indicating that the F(D439E/G464W) protein compensates for the inefficient penetration of a wild-type MV strain harboring the H(N481Y/H495R) protein. Thus, the F and H proteins synergistically function to ensure efficient wild-type MV growth in Vero cells.Measles virus (MV), which belongs to the genus Morbillivirus in the family Paramyxoviridae, is an enveloped virus with a nonsegmented negative-strand RNA genome. The MV genome encodes six structural proteins: the nucleocapsid (N), phosphoprotein (P), matrix (M), fusion (F), hemagglutinin (H), and large (L) proteins. The P gene also encodes two other accessory proteins, the C and V proteins. The C protein is translated from an alternative translational initiation site leading a different reading frame, and the V protein is synthesized from an edited mRNA. MV has two envelope glycoproteins, the F and H proteins. The former is responsible for envelope fusion, and the latter is responsible for receptor binding (12).Wild-type MV strains isolated in B95a cells and laboratory-adapted MV strains have distinct phenotypes (18). Wild-type MV strains can grow in B95a cells but not in Vero cells, while laboratory-adapted MV strains can grow in both B95a and Vero cells. Wild-type MV strains do not cause hemadsorption (HAd) in African green monkey red blood cells (AGM-RBC), while most of laboratory-adapted MV strains cause HAd. Importantly, wild-type MV strains are pathogenic and induce clinical signs that resemble human measles in experimentally infected monkeys while laboratory-adapted MV strains do not.One approach to identify amino acid substitutions responsible for these phenotypic differences is the comparison of a wild-type MV strain with a standard laboratory-adapted MV strain such as the Edmonston strain. With regard to the H protein, amino acid substitutions important for HAd activity and cell-cell fusion in tissue culture cells were identified by expressing the H proteins in mammalian cells (15, 21). Recently, Tahara et al. revealed that the M, H, and L proteins are responsible for efficient growth in Vero cells by constructing a series of recombinant viruses in which part of the genome of the wild-type MV was replaced with the corresponding sequences of the Edmonston strain (45, 46, 47).Another approach is the comparison of wild-type MV strains with their Vero cell-adapted MV strains. It was reported that Vero cell-adapted MV strains could be obtained by successive blind passages of wild-type MV strains in Vero cells (18, 24, 30, 43). Interestingly, in vivo and in vitro phenotypes of Vero cell-adapted MV strains were similar to those of laboratory-adapted standard MV strains (18, 19, 24, 30, 43). Comparison of the complete nucleotide sequences of the genomes of wild-type MV strains with those of Vero cell-adapted wild-type MV strains revealed amino acid substitutions in the P, C, V, M, H, and L proteins (27, 42, 48, 53).At present, these phenotypic differences are explained mainly by the receptor usage of MV. Wild-type MV strains can use signaling lymphocyte activation molecule (SLAM; also called CD150) but not CD46 as a cellular receptor, whereas laboratory-adapted MV strains can use both SLAM and CD46 as cellular receptors (7, 10, 16, 29, 56, 60).However, receptor usage per se cannot explain all of the phenotypic differences (20, 25, 48, 53). For example, recombinant Edmonston strains expressing wild-type H proteins can grow in Vero cells to some extent (17, 54). Several reports suggested the presence of the third MV receptor on Vero cells (14, 44, 54, 60). Other reports indicated the contribution of the M protein on cell-cell fusion and growth of MV in Vero cells (4, 27, 47). Recently, the unidentified epithelial cell receptor for MV was predicted in primary culture of human cells (1, 55) and several epithelial cell lines (23, 51). However, the identity of the third receptor on Vero cells and the unidentified epithelial cell receptor is not clear yet. Thus, the mechanism of Vero cell adaptation of wild-type MV is not completely understood.In order to understand the molecular mechanism of these phenotypic changes of wild-type MV strains during adaptation in Vero cells, we determined the complete nucleotide sequences of the genomes of the wild-type (T11wild) and its Vero cell-adapted (T11Ve-23) MV strains (43) and examined the effect of individual amino acid substitutions using a mammalian cell expression system and reverse genetics. We show here that previously unrecognized new amino acid substitutions in the H and F proteins are important for MV adaptation and HAd activity.     

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]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

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]     

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]