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]     

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]     

Towards the elucidation of the true impact of adipocytokines on cardiovascular risk in rheumatoid arthritis

Adipo(cyto)kines are mostly produced by adipose tissue and orchestrate the adverse impact of excess adiposity on cardiovascular risk. Adipokines also contribute importantly to the pathophysiology of rheumatoid arthritis. Congruent with data reported in previous investigations, Kang and colleagues report in this issue of Arthritis Research & Therapy that adipokine concentrations are further associated with metabolic risk and inflammation and that the leptin–adiponectin ratio associates with the carotid artery resistive index in rheumatoid arthritis. Guided by evidence reported thus far on cardiovascular risk, we discuss six reasons why careful elucidation of adipokine–cardiovascular risk relations is needed in rheumatoid arthritis.In this issue of Arthritis Research & Therapy, Kang and colleagues investigate whether adipokines could link inflammation, metabolic risk factors and cardiovascular disease in rheumatoid arthritis (RA) [1]. Evidence in support of this paradigm was reported previously [2-6]. Patients with RA experience a markedly increased cardiovascular risk that is driven by metabolic risk factors and by high-grade inflammation [7]. Kang and colleagues measured adiponectin, leptin, resistin, tumor necrosis factor alpha and interleukin-6 concentrations and assessed the common carotid artery intima-media thickness, resistive index (RI) and plaque presence by high-resolution ultrasonography [1]. Concentrations of some of the adipokines related to inflammatory markers including C-reactive protein levels and the erythrocyte sedimentation rate, and to metabolic syndrome features.In a previous study by our group, leptin and adiponectin concentrations were not associated with carotid intima-media thickness and plaque [3]. In addition, the leptin–adiponectin ratio and carotid RI as markers of cardiovascular risk have not been reported in RA. For these reasons, besides the abovementioned analyses, Kang and colleagues assessed (only) the relationship of the leptin–adiponectin ratio with carotid RI. In univariate analysis, the leptin–adiponectin ratio as well as age, homeostasis model assessment for insulin resistance, waist circumference and body mass index were associated with the carotid RI. Importantly, in multivariate analysis, only age and the leptin–adiponectin ratio remained significantly related to the carotid RI. The leptin–adiponectin ratio may thus provide information about the presence of subclinical cardiovascular disease beyond that on insulin resistance as assessed by the homeostasis model of insulin resistance, as well as adiposity extent as represented by body mass index and waist circumference in RA.Adipo(cyto)kines comprise a vast range of disparate soluble bioactive proteins that are mostly secreted by adipose tissue [8]. These molecules participate in biological processes that include inflammatory responses and thereby orchestrate the adverse impact of excess adiposity on cardiovascular risk and incident type 2 diabetes [8]. Adipokines represent both adiposity extent and biological activity. RA is a prototypic inflammatory disease. In this context, ~200 recently reported investigations substantiate an important involvement of adipokines in RA activity and severity [9]. By contrast, despite the contribution of adipokines to altered cardiovascular risk in non-RA subjects and the enhanced cardiovascular risk in RA, there is a striking paucity of reported studies on the potential role of adipokines in atherogenesis in RA.A myriad of pertinent reasons exist why the role of adipokines in cardiovascular risk amongst patients with RA requires thorough elucidation. First, RA can modify adipokine production [3,9].Second, and presumably more important, the presence of autoimmunity can alter the effects of adipokines on cardiovascular risk [3,4]. In non-RA subjects, adiponectin production decreases with increasing adiposity and this adipokine has anti-inflammatory properties [8]. However, in RA adiponectin has marked proinflammatory properties [9]. In fact, in Kang and colleagues’ study the adiponectin concentrations were paradoxically positively associated with the erythrocyte sedimentation rate [1]. Whereas in non-RA subjects adiponectin improves metabolic risk and also directly inhibits atherogenesis, we reported recently that in RA, upon using comprehensive potential confounder-adjusted analysis, adiponectin concentrations associated paradoxically with high blood pressure [3,4] and in white but not black Africans with enhanced endothelial activation [4]. Endothelial activation mediates the very initial stages of atherosclerosis [3-6]. Whether such paradoxical relations represent altered effects mediated by RA or a compensatory increase in adiponectin production in the presence of heightened cardiovascular risk and in an attempt to reduce this risk needs further investigation [4].Third, conventional risk factors and disease characteristics can impact on adipokine–atherogenesis relationships in RA [5]. Resistin concentrations thus associate independently with endothelial activation in RA, but this relation is present only in those with, and not in those without, traditional risk factors, abdominal obesity, joint damage as reflected by the presence of deformed joints or prolonged disease duration [5]. This observation further supports the need for sensitivity analysis in the present context. By contrast, interleukin-6 concentrations are more consistently associated with endothelial activation in RA [6].Fourth, the effects of adipokines on cardiovascular risk require examination prior to targeting the respective molecules in an attempt to reduce disease activity and severity in RA [3]. Indeed, should the protective effect of adiponectin on cardiovascular risk be preserved amongst patients with RA, then its blockade would be expected to further enhance cardiovascular risk [3].Fifth, RA influences adiposity and its distribution, which also associates with atherosclerosis in this disease [7,10].Finally, as illustrated by the disparity in adiponectin–endothelial activation relations amongst Africans previously alluded to, population origin impacts on adipokine–cardiovascular risk relations in RA [4].A caveat of Kang and colleagues’ study is that potential confounders were not systematically identified. For example, gender, cardiovascular drug use, antirheumatic agent use and the glomerular filtration rate can all influence both the concentrations and effects of adipokines [3-6]. Nevertheless, this investigation reinforces previously reported evidence that strongly suggests an intriguing and important involvement of adipokines in RA atherogenesis.