Molecular Characterization of Hemagglutination Domains on the Fibers of Subgenus D Adenoviruses

The adenovirus fiber mediates the agglutination of erythrocytes. Based on differential hemagglutinating properties, subgenus D adenoviruses can be subdivided into clusters DI, DII, and DIII. While subgenus DI adenoviruses agglutinate rat and human erythrocytes, DII adenoviruses simply agglutinate rat erythrocytes and DIII adenoviruses display no or only weak rat erythrocyte agglutination. Amino acid sequence comparisons revealed distinct domains on the fiber knob which could be involved in hemagglutination. In order to localize and characterize the domains responsible for the interaction with rat and human erythrocytes, potential hemagglutination domains of the adenovirus type 9 (Ad9) (subgenus DI) fiber knob were introduced into Ad17 (subgenus DII) and Ad28 (subgenus DIII) fiber knobs by primer-directed mutagenesis. Furthermore, rat erythrocyte hemagglutination domains were also introduced into the Ad3 (subgenus B) fiber knob, which only agglutinated monkey erythrocytes. Altogether, 27 chimeric and mutated fiber proteins were expressed in Escherichia coli and subsequently tested for hemagglutination activity. The hemagglutination tests revealed that at least two domains can mediate the agglutination of rat erythrocytes. While one domain is located on the GH loop, the other domain extends from the C β strand to the CD loop. The domain on the GH loop was partially conserved in all adenoviruses showing an incomplete hemagglutination pattern with rat erythrocytes. The domains involved in the agglutination of human erythrocytes are located on the CD and HI loops of the subgenus DI fiber knob.Besides being associated with a variety of diseases, including respiratory, ophthalmic, and gastrointestinal infections, adenoviruses have recently received special attention as potential viral vectors for gene therapy. Since the fiber protein is responsible for the attachment of the virion to specific receptors on the cell surface (5, 30), thus also being of significant importance for tissue tropism, a detailed understanding of the molecular structure of this protein could be helpful in developing a new, tissue-specific generation of adenovirus vectors.The fiber protein, protruding outward from the 12 vertices of the capsid, comprises a short N-terminal tail, a shaft of variable length, and a globular C-terminal knob (12). The conserved N terminus contains the sequences responsible for association with the penton base as well as the nuclear localization signal (19, 29). The shaft consists of repeating motifs of a 15-amino-acid β structure, with the number of repeats varying among virus serotypes. A conserved amino acid sequence (TLWT) marks the boundary between the shaft and the knob domain, which is responsible for interaction with the host cell receptor. The published crystal structure of the adenovirus type 5 (Ad5) fiber knob domain allows the mapping of functional domains (40, 41). It was shown that the Ad5 knob can block virus infection (14) and that the receptor binding specificity of adenovirus fibers can be altered by exchanging the knob domains (11, 37). While subgenus C and B adenovirus serotypes recognize distinct receptors (6, 24, 38), subgenus C adenoviruses and Ad9 (subgenus D) share the same fiber receptor (33). It was recently demonstrated that a 46-kDa HeLa cell surface protein serves as a common receptor for subgenus C adenoviruses and coxsackie B viruses (3). Furthermore, it was reported that the class I major histocompatibility complex could also serve as an adenovirus receptor (21). The fiber knob also carries the type-specific γ antigen (9, 27), which determines, together with the ɛ antigen of the hexon, the serotype specificity of an adenovirus. The γ determinant is composed of at least 17 amino acids that are not restricted to a distinct region on the fiber knob (10).Since hemagglutination (HA) by human adenoviruses was first demonstrated by Rosén in 1958 (34), it has been shown that members of the six subgenera (A to F) display different HA properties (2, 26). While, e.g., subgenus B adenoviruses only agglutinate monkey erythrocytes, subgenus D adenoviruses can be classified into three clusters: cluster DI adenoviruses agglutinate rat and human erythrocytes, cluster DII adenoviruses agglutinate only rat erythrocytes, and cluster DIII adenoviruses show no or only weak agglutination of rat erythrocytes. The agglutination of erythrocytes is fiber mediated, and specific receptors seem to be present on the erythrocyte membrane. Since intact virions carry several fibers, they can establish a bridge between erythrocytes, leading to HA. In contrast, fibers alone cannot cause HA, as they are monovalent. However, it was shown that fibers obtained from tissue cultures (28) and recombinant fibers (25) can form polymers which are able to agglutinate erythrocytes.Amino acid sequence comparisons revealed distinct domains on the fiber knob which could be expected to mediate the agglutination of rat and human erythrocytes. To localize and characterize these domains, 27 chimeric and mutated Ad9 (subgenus DI), Ad17 (subgenus DII), Ad28 (subgenus DIII), and Ad3 (subgenus B) fiber proteins were expressed in Escherichia coli. The recombinant proteins were tested in HA tests.     

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.     

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]     

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]     

Differential Regulation of Elastic Fiber Formation by Fibulin-4 and -5

Fibulin-4 and -5 are extracellular glycoproteins with essential non-compensatory roles in elastic fiber assembly. We have determined how they interact with tropoelastin, lysyl oxidase, and fibrillin-1, thereby revealing how they differentially regulate assembly. Strong binding between fibulin-4 and lysyl oxidase enhanced the interaction of fibulin-4 with tropoelastin, forming ternary complexes that may direct elastin cross-linking. In contrast, fibulin-5 did not bind lysyl oxidase strongly but bound tropoelastin in terminal and central regions and could concurrently bind fibulin-4. Both fibulins differentially bound N-terminal fibrillin-1, which strongly inhibited their binding to lysyl oxidase and tropoelastin. Knockdown experiments revealed that fibulin-5 controlled elastin deposition on microfibrils, although fibulin-4 can also bind fibrillin-1. These experiments provide a molecular account of the distinct roles of fibulin-4 and -5 in elastic fiber assembly and how they act in concert to chaperone cross-linked elastin onto microfibrils.Fibulins are a family of extracellular glycoproteins containing contiguous calcium-binding epidermal growth factor-like domains (cbEGFs)³ and a characteristic C-terminal fibulin (FC) domain (1–3). Recent studies have revealed that fibulin-4 and -5 are both essential for elastic fiber formation (4–7). Fibulin-4 is widely expressed from early embryogenesis and is necessary for normal vascular, lung, and skin development, since mice that lack fibulin-4 do not form elastic fibers and die perinatally (5). Furthermore, mice with reduced fibulin-4 expression develop aneurysms (8). Fibulin-5 is abundant in the aorta and large arteries during embryogenesis and following vascular injury (9, 10). Lack of fibulin-5 causes a less severe phenotype, with viable homozygous mice, but the elastic fibers in skin, lungs, and aorta are irregular and fragmented (6, 7), and there is altered vascular remodeling (11). These mice models also highlight that fibulin-4 and -5 have non-compensatory roles in elastic fiber formation. Mutations in both molecules can cause cutis laxa, a heritable disorder associated with elastic fiber degeneration leading to sagging skin, vascular tortuosity, and emphysematous lungs (12–15). A third isoform, fibulin-3, may play a minor role in elastic fiber formation, since its deficiency disrupts elastic fibers in Bruch''s membrane of the eye (16) and vaginal tissues (17).Elastic fiber formation is a complex multistep process (18–20). Initial pericellular microassembly of tropoelastin, which may involve the 67-kDa elastin-binding protein receptor, generates elastin globules that are stabilized by desmosine cross-links catalyzed mainly by lysyl oxidase (LOX) but also by LOXL1 (LOX-like 1). These globules are deposited on a fibrillin microfibril template, where they coalesce and undergo further cross-linking to form the elastin core of mature fibers. The ability of fibulin-4 and -5 to bind tropoelastin and fibrillin-1, the major structural component of microfibrils, supports a model in which these fibulins direct elastin deposition on microfibrils (4–7, 21–25). This model does not delineate the unique molecular contributions of fibulin-4 and -5 to elastic fiber formation, but some molecular differences have emerged. Tropoelastin was bound more strongly by fibulin-5 than by fibulin-4, whereas fibulin-5 was at the microfibril-elastin interface, but perichondrial fibulin-4 localized mainly to microfibrils (4).Fibulin-4 null mice offer tantalizing clues to how fibulin-4 contributes to elastic fiber formation (5). They had dramatically reduced (94%) desmosine cross-links despite no change in elastin or LOX expression levels, and electron-dense rodlike structures were prominent within elastin aggregates. Morphologically similar structures seen after chemically inhibiting LOX were previously identified as glycosaminoglycans, which can bind charged free ϵ-amino groups on lysines in tropoelastin (26). However, fibulin-4^+/− mice showed ∼20% increase in desmosine (5). LOX-null mice have phenotypic features similar to those of fibulin-4 null mice, dying perinatally with 60% reduced desmosine cross-links and major abnormalities in vascular and other elastic tissues (27, 28). In contrast, LOXL1-null mice are viable but have reduced desmosine (29), whereas fibulin-5 null mice have a 16% reduction in desmosine cross-links and survive well into adulthood (7). Detection of the LOXL1 pro-domain in fibulin-5 null mice skin but not wild-type skin implicates fibulin-5 in activation of LOXL1 (30).We and others have shown that fibrillin-1 and the microfibrillar protein MAGP-1 can both directly bind tropoelastin (31–34). However, the fibulin-null mice show that the fibrillin-1 interaction with tropoelastin is insufficient to support elastic fiber formation in vivo. Fibulin-5 has been reported to facilitate tropoelastin binding to the N-terminal half of fibrillin-1 (21). A study of elastin polypeptide self-assembly through coacervation and maturation phases showed that, although the N-terminal half of fibrillin-1 increased maturation velocity and droplet clustering, fibulin-4 and -5 both slowed maturation and limited globule growth (35). These studies imply that fibulins and fibrillin-1 act together to regulate elastin accretion on microfibrils.To gain further insights into the contributions of fibulin-4 and -5 to elastic fiber formation, we have delineated how they interact with tropoelastin, LOX, and fibrillin-1. Novel findings are that fibulin-4 directly binds LOX, and this interaction enhances fibulin-4 binding to tropoelastin, thus forming a ternary complex that may be critical for elastin cross-linking. Fibulin-5 can concurrently bind fibulin-4 and tropoelastin, but the interaction of both fibulins with fibrillin-1 strongly inhibits their binding to tropoelastin. These interactions indicate the molecular basis of how fibulins act as chaperones for deposition of elastin onto microfibrils. Our study thus provides a molecular account of the differential roles of fibulins-4 and -5 in elastic fiber formation.     

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]     

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]