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]     

Heterogeneous Ribonucleoprotein K (hnRNP K) Binds miR-122, a Mature Liver-Specific MicroRNA Required for Hepatitis C Virus Replication

Heterogeneous ribonucleoprotein K (hnRNP K) binds to the 5′ untranslated region of the hepatitis C virus (HCV) and is required for HCV RNA replication. The hnRNP K binding site on HCV RNA overlaps with the sequence recognized by the liver-specific microRNA, miR-122. A proteome chip containing ∼17,000 unique human proteins probed with miR-122 identified hnRNP K as one of the strong binding proteins. In vitro kinetic study showed hnRNP K binds miR-122 with a nanomolar dissociation constant, in which the short pyrimidine-rich residues in the central and 3′ portion of the miR-122 were required for hnRNP K binding. In liver hepatocytes, miR-122 formed a coprecipitable complex with hnRNP K. High throughput Illumina DNA sequencing of the RNAs precipitated with hnRNP K was enriched for mature miR-122. SiRNA knockdown of hnRNP K in human hepatocytes reduced the levels of miR-122. These results show that hnRNP K is a cellular protein that binds and affects the accumulation of miR-122. Its ability to also bind HCV RNA near the miR-122 binding site suggests a role for miR-122 recognition of HCV RNA.MicroRNAs (miRNAs) are a class of noncoding RNA of ∼22-nucleotides in length that can regulate gene expression by either targeting RNA for degradation or suppressing their translation through base pairing to the RNAs (1). Since their discovery in 1993 in Caenorhabditis elegans, miRNAs have been found in many species and are involved in the regulation of proliferation, differentiation, apoptosis, and development (1, 2). Moreover, miRNAs are also critical factors in the development of cancers, neurodegenerative diseases, and infectious diseases (3).MiR-122 is a highly abundant RNA in hepatocytes that regulates lipid metabolism, regeneration, and neoplastic transformation (4–6). In addition, miR-122 is required for the replication of the hepatitis C virus (HCV), a positive-strand RNA virus that infects over 170 million people worldwide (7–9). MiR-122 binds to a conserved sequence in the 5′ untranslated region (UTR) of the HCV RNA to increase the stability of the HCV RNA (10). Silencing of miR-122 can abolish HCV RNA accumulation in non-human primates (11). The expression of human miR-122 in non-hepatic cells can confer the ability to replicate HCV RNA (12). MiR-122 is one of the most critical host factors for HCV replication.We previously reported that the HCV RNA sequence that anneals to miR-122 is recognized by the heterogeneous ribonucleoprotein K (hnRNP K), a multifunctional RNA-binding protein known to be involved in RNA processing, translation, and the replication of several RNA viruses (13–15). In an unbiased screen for proteins from human proteome chips containing over 17,000 proteins, we identified 40 proteins that bind mature miR-122, including hnRNP K. Recombinant hnRNP K recognizes short pyrimidine sequences in miR-122 in vitro and a similar sequence in the HCV 5′ UTR. In hepatocytes endogenous hnRNP K can form a coprecipitable complex with miR-122, whether or not the cells contain replicating HCV. HnRNP K is thus a protein that binds a mature microRNA.     

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]     

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]