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]     

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.     

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]     

The Cell Tropism of Human Immunodeficiency Virus Type 1 Determines the Kinetics of Plasma Viremia in SCID Mice Reconstituted with Human Peripheral Blood Leukocytes

Most individuals infected with human immunodeficiency virus type 1 (HIV-1) initially harbor macrophage-tropic, non-syncytium-inducing (M-tropic, NSI) viruses that may evolve into T-cell-tropic, syncytium-inducing viruses (T-tropic, SI) after several years. The reasons for the more efficient transmission of M-tropic, NSI viruses and the slow evolution of T-tropic, SI viruses remain unclear, although they may be linked to expression of appropriate chemokine coreceptors for virus entry. We have examined plasma viral RNA levels and the extent of CD4⁺ T-cell depletion in SCID mice reconstituted with human peripheral blood leukocytes following infection with M-tropic, dual-tropic, or T-tropic HIV-1 isolates. The cell tropism was found to determine the course of viremia, with M-tropic viruses producing sustained high viral RNA levels and sparing some CD4⁺ T cells, dual-tropic viruses producing a transient and lower viral RNA spike and extremely rapid depletion of CD4⁺ T cells, and T-tropic viruses causing similarly lower viral RNA levels and rapid-intermediate rates of CD4⁺ T-cell depletion. A single amino acid change in the V3 region of gp120 was sufficient to cause one isolate to switch from M-tropic to dual-tropic and acquire the ability to rapidly deplete all CD4⁺ T cells.The envelope gene of human immunodeficiency virus type 1 (HIV-1) determines the cell tropism of the virus (11, 32, 47, 62), the use of chemokine receptors as cofactors for viral entry (4, 17), and the ability of the virus to induce syncytia in infected cells (55, 60). Cell tropism is closely linked to but probably not exclusively determined by the ability of different HIV-1 envelopes to bind CD4 and the CC or the CXC chemokine receptors and initiate viral fusion with the target cell. Macrophage-tropic (M-tropic) viruses infect primary cultures of macrophages and CD4⁺ T cells and use CCR5 as the preferred coreceptor (2, 5, 15, 23, 26, 31). T-cell-tropic (T-tropic) viruses can infect primary cultures of CD4⁺ T cells and established T-cell lines, but not primary macrophages. T-tropic viruses use CXCR4 as a coreceptor for viral entry (27). Dual-tropic viruses have both of these properties and can use either CCR5 or CXCR4 (and infrequently other chemokine receptors [25]) for viral entry (24, 37, 57). M-tropic viruses are most frequently transmitted during primary infection of humans and persist throughout the duration of the infection (63). Many, but not all, infected individuals show an evolution of virus cell tropism from M-tropic to dual-tropic and finally to T-tropic with increasing time after infection (21, 38, 57). Increases in replicative capacity of viruses from patients with long-term infection have also been noted (22), and the switch to the syncytium-inducing (SI) phenotype in T-tropic or dual-tropic isolates is associated with more rapid disease progression (10, 20, 60). Primary infection with dual-tropic or T-tropic HIV, although infrequent, often leads to rapid disease progression (16, 51). The viral and host factors that determine the higher transmission rate of M-tropic HIV-1 and the slow evolution of dual- or T-tropic variants remain to be elucidated (4).These observations suggest that infection with T-tropic, SI virus isolates in animal model systems with SCID mice grafted with human lymphoid cells or tissue should lead to a rapid course of disease (1, 8, 44–46). While some studies in SCID mice grafted with fetal thymus and liver are in agreement with this concept (33, 34), our previous studies with the human peripheral blood leukocyte-SCID (hu-PBL-SCID) mouse model have shown that infection with M-tropic isolates (e.g., SF162) causes more rapid CD4⁺ T-cell depletion than infection with T-tropic, SI isolates (e.g., SF33), despite similar proviral copy numbers, and that this property mapped to envelope (28, 41, 43). However, the dual-tropic 89.6 isolate (19) caused extremely rapid CD4⁺ T-cell depletion in infected hu-PBL-SCID mice that was associated with an early and transient increase in HIV-1 plasma viral RNA (29). The relationship between cell tropism of the virus isolate and the pattern of disease in hu-PBL-SCID mice is thus uncertain. We have extended these studies by determining the kinetics of HIV-1 RNA levels in serial plasma samples of hu-PBL-SCID mice infected with primary patient isolates or laboratory stocks that differ in cell tropism and SI properties. The results showed significant differences in the kinetics of HIV-1 replication and CD4⁺ T-cell depletion that are determined by the cell tropism of the virus isolate.     

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]     

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]