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]     

Phosphorylation Dependence of Hsp27 Multimeric Size and Molecular Chaperone Function

The molecular chaperone Hsp27 exists as a distribution of large oligomers that are disassembled by phosphorylation at Ser-15, -78, and -82. It is controversial whether the unphosphorylated Hsp27 or the widely used triple Ser-to-Asp phospho-mimic mutant is the more active molecular chaperone in vitro. This question was investigated here by correlating chaperone activity, as measured by the aggregation of reduced insulin or α-lactalbumin, with Hsp27 self-association as monitored by analytical ultracentrifugation. Furthermore, because the phospho-mimic is generally assumed to reproduce the phosphorylated molecule, the size and chaperone activity of phosphorylated Hsp27 were compared with that of the phospho-mimic. Hsp27 was triply phosphorylated by MAPKAP-2 kinase, and phosphorylation was tracked by urea-PAGE. An increasing degree of suppression of insulin or α-lactalbumin aggregation correlated with a decreasing Hsp27 self-association, which was the least for phosphorylated Hsp27 followed by the mimic followed by the unphosphorylated protein. It was also found that Hsp27 added to pre-aggregated insulin did not reverse aggregation but did inhibit these aggregates from assembling into even larger aggregates. This chaperone activity appears to be independent of Hsp27 phosphorylation. In conclusion, the most active chaperone of insulin and α-lactalbumin was the Hsp27 (elongated) dimer, the smallest Hsp27 subunit observed under physiological conditions. Next, the Hsp27 phospho-mimic is only a partial mimic of phosphorylated Hsp27, both in self-association and in chaperone function. Finally, the efficient inhibition of insulin aggregation by Hsp27 dimer led to the proposal of two models for this chaperone activity.Oligomeric heat shock protein 27 (Hsp27)² is a ubiquitous mammalian protein with a variety of functions in health and disease (1–8). These functions include ATP-independent chaperone activity in response to environmental stress, e.g. heat shock and oxidative stress, control of apoptosis, and regulation of actin cytoskeleton dynamics. Hsp27 is a member of the α-crystallin small heat shock protein family of which αB-crystallin is the archetype. These proteins are characterized by an α-crystallin domain of 80–90 residues consisting of roughly eight β-strands that form an intermolecular β-sheet interaction interface within a dimer, the basic building subunit of the oligomer (2, 4, 9–11).Hsp27 is in equilibrium between high molecular weight oligomers and much lower molecular weight multimers. It has been reported that unphosphorylated Hsp27 includes predominantly a distribution of high molecular species ranging in size from 12-mer to 35-mer (12–19). Phosphorylation of Hsp27 at serines 15, 78, and 82 by the p38-activated MAPKAP-2 kinase (20–22) or the use of the triple Ser-to-Asp phospho-mimic results in a major shift in the equilibrium toward much smaller multimers (23) and in an alteration of its function (1, 3, 6, 7, 24, 25). The size distribution of the smaller species has been reported to be between monomer and tetramer (12–16, 18, 19).Small heat shock proteins, including Hsp27, behave as ATP-independent molecular chaperones during cellular heat shock. They bind partially unfolded proteins and prevent their aggregation until the proteins can be refolded by larger ATP-dependent chaperones or are digested (7, 8, 26). This function includes the up-regulation and/or phosphorylation of Hsp27.It is not entirely clear what the role of Hsp27 size and phosphorylation state plays in its heat shock function because there are conflicting results in the literature. Some in vitro studies concluded that the unphosphorylated oligomeric Hsp27 (or the murine isoform Hsp25) protects proteins against aggregation better than does the phosphorylation mimic (13, 19, 27), whereas others found no difference (16, 28, 29), and still other studies found that the mimic protects better than does the unphosphorylated wild type (27, 30, 31). In-cell studies found that phosphorylation of Hsp27 was essential for thermo-protection of actin filaments (32), and the Hsp27 phosphorylation mimic decreased inclusion body formation better than did unphosphorylated Hsp27 (33). This study was undertaken to investigate the molecular chaperone function of Hsp27 by correlating chaperone activity with Hsp27 size and by comparing fully phosphorylated Hsp27 with its phospho-mimic.     

Semaphorin3A Signaling Mediated by Fyn-dependent Tyrosine Phosphorylation of Collapsin Response Mediator Protein 2 at Tyrosine 32

Collapsin response mediator protein 2 (CRMP2) is an intracellular protein that mediates signaling of Semaphorin3A (Sema3A), a repulsive axon guidance molecule. Fyn, a Src-type tyrosine kinase, is involved in the Sema3A signaling. However, the relationship between CRMP2 and Fyn in this signaling pathway is still unknown. In our research, we demonstrated that Fyn phosphorylated CRMP2 at Tyr³² residues in HEK293T cells. Immunohistochemical analysis using a phospho-specific antibody at Tyr³² of CRMP showed that Tyr³²-phosphorylated CRMP was abundant in the nervous system, including dorsal root ganglion neurons, the molecular and Purkinje cell layer of adult cerebellum, and hippocampal fimbria. Overexpression of a nonphosphorylated mutant (Tyr³² to Phe³²) of CRMP2 in dorsal root ganglion neurons interfered with Sema3A-induced growth cone collapse response. These results suggest that Fyn-dependent phosphorylation of CRMP2 at Tyr³² is involved in Sema3A signaling.Collapsin response mediator proteins (CRMPs)⁴ have been identified as intracellular proteins that mediate Semaphorin3A (Sema3A) signaling in the nervous system (1). CRMP2 is one of the five members of the CRMP family. CRMPs also mediate signal transduction of NT3, Ephrin, and Reelin (2–4). CRMPs interact with several intracellular molecules, including tubulin, Numb, kinesin1, and Sra1 (5–8). CRMPs are involved in axon guidance, axonal elongation, cell migration, synapse maturation, and the generation of neuronal polarity (1, 2, 4, 5).CRMP family proteins are known to be the major phosphoproteins in the developing brain (1, 9). CRMP2 is phosphorylated by several Ser/Thr kinases, such as Rho kinase, cyclin-dependent kinase 5 (Cdk5), and glycogen synthase kinase 3β (GSK3β) (2, 10–13). The phosphorylation sites of CRMP2 by these kinases are clustered in the C terminus and have already been identified. Rho kinase phosphorylates CRMP2 at Thr⁵⁵⁵ (10). Cdk5 phosphorylates CRMP2 at Ser⁵²², and this phosphorylation is essential for sequential phosphorylations by GSK3β at Ser⁵¹⁸, Thr⁵¹⁴, and Thr⁵⁰⁹ (2, 11–13). These phosphorylations disrupt the interaction of CRMP2 with tubulin or Numb (2, 3, 13). The sequential phosphorylation of CRMP2 by Cdk5 and GSK3β is an essential step in Sema3A signaling (11, 13). Furthermore, the neurofibrillary tangles in the brains of people with Alzheimer disease contain hyperphosphorylated CRMP2 at Thr⁵⁰⁹, Ser⁵¹⁸, and Ser⁵²² (14, 15).CRMPs are also substrates of several tyrosine kinases. The phosphorylation of CRMP2 by Fes/Fps and Fer has been shown to be involved in Sema3A signaling (16, 17). Phosphorylation of CRMP2 at Tyr⁴⁷⁹ by a Src family tyrosine kinase Yes regulates CXCL12-induced T lymphocyte migration (18). We reported previously that Fyn is involved in Sema3A signaling (19). Fyn associates with PlexinA2, one of the components of the Sema3A receptor complex. Fyn also activates Cdk5 through the phosphorylation at Tyr¹⁵ of Cdk5 (19). In dorsal root ganglion (DRG) neurons from fyn-deficient mice, Sema3A-induced growth cone collapse response is attenuated compared with control mice (19). Furthermore, we recently found that Fyn phosphorylates CRMP1 and that this phosphorylation is involved in Reelin signaling (4). Although it has been shown that CRMP2 is involved in Sema3A signaling (1, 11, 13), the relationship between Fyn and CRMP2 in Sema3A signaling and the tyrosine phosphorylation site(s) of CRMPs remain unknown.Here, we show that Fyn phosphorylates CRMP2 at Tyr³². Using a phospho-specific antibody against Tyr³², we determined that the residue is phosphorylated in vivo. A nonphosphorylated mutant CRMP2Y32F inhibits Sema3A-induced growth cone collapse. These results indicate that tyrosine phosphorylation by Fyn at Tyr³² is involved in Sema3A signaling.     

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]