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.     

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]     

Analysis of Non-canonical Fibroblast Growth Factor Receptor 1 (FGFR1) Interaction Reveals Regulatory and Activating Domains of Neurofascin

Fibroblast growth factor receptors (FGFRs) are important for many different mechanisms, including cell migration, proliferation, differentiation, and survival. Here, we show a new link between FGFR1 and the cell adhesion molecule neurofascin, which is important for neurite outgrowth. After overexpression in HEK293 cells, embryonal neurofascin isoform NF166 was able to associate with FGFR1, whereas the adult isoform NF186, differing from NF166 in additional extracellular sequences, was deficient. Pharmacological inhibitors and overexpression of dominant negative components of the FGFR signaling pathway pointed to the activation of FGFR1 after association with neurofascin in neurite outgrowth assays in chick tectal neurons and rat PC12-E2 cells. Both extra- and intracellular domains of embryonal neurofascin isoform NF166 were able to form complexes with FGFR1 independently. However, the cytosolic domain was both necessary and sufficient for the activation of FGFR1. Cytosolic serine residues 56 and 100 were shown to be essential for the neurite outgrowth-promoting activity of neurofascin, whereas both amino acid residues were dispensable for FGFR1 association. In conclusion, the data suggest a neurofascin intracellular domain, which activates FGFR1 for neurite outgrowth, whereas the extracellular domain functions as an additional, regulatory FGFR1 interaction domain in the course of development.The four known fibroblast growth factor receptors (FGFRs),² which are targeted by a large family of 22 fibroblast growth factor ligands, represent a highly diverse signaling system important for migration, proliferation, differentiation, and survival of many different cell types (1, 2). fibroblast growth factor activation of FGFR leads to the activation of mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K), and phospholipase Cγ (PLCγ), depending on the cellular system under study. Non-canonical FGFR interactions with NCAM, cadherins, and syndecan via extracellular domains were also described (1). However, the contribution of intracellular interactions of FGFR1 with further membrane co-receptors is poorly understood. Only cytosolic interaction between FGFRs and EphA4 have been described that are involved in mutual transphosphorylation (3).The cell adhesion molecule neurofascin is important for cell-cell communication in the nervous system (4, 5). Neurofascin regulates many different functions in the brain, suggesting that it functions as a key regulator for both developing and differentiated neural cells. Different alternatively spliced neurofascin isoforms are expressed in different cells and at different times of development (6). Embryonal neurofascin NF166 is important for neurite outgrowth and guidance (7, 8). Recently, a role for neurofascin NF166 for early processes of inhibitory synaptogenesis at the axon hillock and for the positioning of inhibitory synapses at the axon initial segment has been proven (9, 10).In the more developed nervous system, NF166 is replaced by NF186, which is inhibitory for neurite outgrowth (11). NF186 is linked to the cortical actin cytoskeleton via ankyrin_G (12). Clustering of voltage-gated sodium channels both at axon initial segments and at the nodes of Ranvier is conferred by neurofascin NF186 (13, 14). A further cytosolic interaction partner is the PDZ molecule syntenin-1 (15).Despite the well known functional importance of neurofascin in the nervous system, corresponding signaling pathways have not been investigated. In contrast, signaling by the related molecules NCAM and L1 have been studied with regard to the induction of neurite outgrowth in greater detail (for a review, see Refs. 16–18). Both NCAM and L1 induce neurite outgrowth through activation of FGFR1 (19–23). NCAM may further undergo lateral interactions with PrP (prion precursor protein) or GFRα, which is part of the glia-derived neurotrophic factor receptor (24, 25). In addition to FGFR1 interaction, both L1 and NCAM are connected to non-receptor tyrosine kinases. However, whereas NCAM employs the non-receptor kinase c-Fyn as an upstream component, L1 is linked to c-Src (26, 27). L1 converges with NCAM signaling upstream of the MAPK pathway at the level of Raf (18, 21, 28, 29). NCAM may induce alternative signaling pathways, including protein kinase A-dependent signaling or G-proteins (18, 30). NCAM signaling to the nucleus may include activation of CREB and c-Fos or NF-κB (29, 31, 32).Here, we elucidate the molecular mechanisms of neurofascin-FGFR1 interaction for neurite outgrowth. We show that both cytosolic and the extracellular domains are important for the association of FGFR1 with neurofascin. Although the cytosolic domain represents a critical determinant for FGFR1 activation, the extracellular sequences of neurofascin act as a regulator for FGFR1-dependent signal transduction in the course of development.