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]     

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.     

Immunoglobulin G (IgG) Fab Glycosylation Analysis Using a New Mass Spectrometric High-throughput Profiling Method Reveals Pregnancy-associated Changes

The N-linked glycosylation of the constant fragment (Fc) of immunoglobulin G has been shown to change during pathological and physiological events and to strongly influence antibody inflammatory properties. In contrast, little is known about Fab-linked N-glycosylation, carried by ∼20% of IgG. Here we present a high-throughput workflow to analyze Fab and Fc glycosylation of polyclonal IgG purified from 5 μl of serum. We were able to detect and quantify 37 different N-glycans by means of MALDI-TOF-MS analysis in reflectron positive mode using a novel linkage-specific derivatization of sialic acid. This method was applied to 174 samples of a pregnancy cohort to reveal Fab glycosylation features and their change with pregnancy. Data analysis revealed marked differences between Fab and Fc glycosylation, especially in the levels of galactosylation and sialylation, incidence of bisecting GlcNAc, and presence of high mannose structures, which were all higher in the Fab portion than the Fc, whereas Fc showed higher levels of fucosylation. Additionally, we observed several changes during pregnancy and after delivery. Fab N-glycan sialylation was increased and bisection was decreased relative to postpartum time points, and nearly complete galactosylation of Fab glycans was observed throughout. Fc glycosylation changes were similar to results described before, with increased galactosylation and sialylation and decreased bisection during pregnancy. We expect that the parallel analysis of IgG Fab and Fc, as set up in this paper, will be important for unraveling roles of these glycans in (auto)immunity, which may be mediated via recognition by human lectins or modulation of antigen binding.Immunoglobulins are key players of the human immune system. Immunoglobulin G (IgG)¹ is the most abundant representative of this group, with serum concentrations of ∼10 mg/ml (1). It consists of two heavy chains (γ-chains) made up of three constant regions (C_H1, C_H2, and C_H3) and one variable region (V_H). Attached to each heavy chain is a light chain (λ or κ). Based on chemical and biological properties, different regions can be distinguished in the IgG molecule: two antigen binding fragments (obtained as F(ab′)₂ by IdeS treatment; herein referred to as Fab) and a crystallizable fragment (Fc). The structure of IgG is schematically presented in Fig. 1.Open in a separate windowFig. 1.Schematic representation of IgG with the heavy γ chains (dark blue), light chains (lighter blue), and N-glycans. In the top right-hand corner of the Fc and Fab areas, the percentages of galactosylation, sialylation, bisection, and fucosylation are depicted. The inset represents the stable heptasaccharide core with possible extensions.IgGs are glycoproteins, and N-glycans are present at Asn297 of the C_H2 domain. These glycans consist of a constant heptasaccharide core that is often modified by a core fucose and is in part decorated with bisecting N-acetylglucosamine (GlcNAc), galactose(s), and sialic acid(s) (Fig. 1) (1). The Fc glycans have been extensively studied, and glycosylation changes have been found to be associated with disease (e.g. rheumatoid arthritis) (2, 3) and aging (4–6). Several immune regulatory properties have been demonstrated for IgG Fc glycans (7–13). For example, Fc-linked glycans influence the IgG effector function by altering the three-dimensional structure of the protein, and thereby the binding to Fcγ-receptors (12, 13). Additionally, glycan–glycan interactions occur between IgG and Fcγ-receptor-IIIa (8), with the presence of a core fucose decreasing this affinity by ∼2 orders of magnitude (7).The Fab portion consists of the heavy chain C_H1 and V_H regions combined with a light chain and exhibits the antigen binding sites formed by the variable and hypervariable regions of those two chains. N-glycans are known to occur on 15% to 25% of the IgG Fab portions (1, 14, 15). The Fab N-glycans can be involved in immunomodulation, because they influence the affinity and avidity of antibodies for antigens (16–19), as well as antibody half-life (17, 20). The glycans of the Fab have been described as biantennary complex-type structures that are, in contrast to Fc glycans, highly sialylated (21–23). Additionally, high-mannose-type structures have been said to be located on the Fab portion (23).Pregnancy is known to be associated with overall changes in IgG glycosylation. Indeed, a marked increase of galactosylation and sialylation has been observed in IgG Fc glycosylation during pregnancy (3, 24, 25). In addition, lectin binding studies suggest changes in Fab glycosylation of IgG during pregnancy (26), which may be caused by increased levels of progesterone (27). Changes in glycosylation during pregnancy could be one of the mechanisms that contribute to acceptance of the fetal allograft by the maternal immune system (26).Our knowledge on the Fab glycosylation of IgGs from peripheral blood is scarce, which is in part due to difficulty detecting the glycans in a Fab-region-specific manner. Because of the polyclonal nature of serum IgG, one may expect Fab glycans to be attached to a large variety of sequence motifs arising from somatic rearrangements and mutations (28), making the analysis of Fab glycopeptides from polyclonal serum IgG very demanding, if feasible at all. Therefore, study of the Fab glycosylation of polyclonal serum IgG has mainly been pursued at the level of released glycans (14, 23). Difficulties lie in the purification of IgG and the separation of Fc and Fab glycosylation, which is essential for the assignment of the glycans to either part of the IgG molecule.Here we present a high-throughput method for studying Fab glycosylation at the level of released glycans obtained from serum-derived polyclonal IgG. Using state-of-the-art affinity capturing beads and enzymes, we were able to obtain Fab and Fc separately, which, after glycan release, resulted in Fc- and Fab-specific glycan pools. The released glycans were subjected to a novel derivatization protocol resulting in linkage-specific modification of sialic acids, followed by HILIC sample purification and MALDI-TOF-MS. Finally, because marked changes in glycosylation during pregnancy have been described, the technique was applied to consecutive serum samples from a cohort of pregnant women. This approach was chosen to determine the usefulness of this technique in a clinical setting. The method proved to be able to demonstrate pregnancy-related changes in glycosylation of the Fab portion, in addition to the already known changes in Fc glycosylation (3, 24, 25).     

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]     

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]     

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]     

The Vac14-interaction Network Is Linked to Regulators of the Endolysosomal and Autophagic Pathway

The scaffold protein Vac14 acts in a complex with the lipid kinase PIKfyve and its counteracting phosphatase FIG4, regulating the interconversion of phosphatidylinositol-3-phosphate to phosphatidylinositol-3,5-bisphosphate. Dysfunctional Vac14 mutants, a deficiency of one of the Vac14 complex components, or inhibition of PIKfyve enzymatic activity results in the formation of large vacuoles in cells. How these vacuoles are generated and which processes are involved are only poorly understood. Here we show that ectopic overexpression of wild-type Vac14 as well as of the PIKfyve-binding deficient Vac14 L156R mutant causes vacuoles. Vac14-dependent vacuoles and PIKfyve inhibitor-dependent vacuoles resulted in elevated levels of late endosomal, lysosomal, and autophagy-associated proteins. However, only late endosomal marker proteins were bound to the membranes of these enlarged vacuoles. In order to decipher the linkage between the Vac14 complex and regulators of the endolysosomal pathway, a protein affinity approach combined with multidimensional protein identification technology was conducted, and novel molecular links were unraveled. We found and verified the interaction of Rab9 and the Rab7 GAP TBC1D15 with Vac14. The identified Rab-related interaction partners support the theory that the regulation of vesicular transport processes and phosphatidylinositol-modifying enzymes are tightly interconnected.Lipid kinases and phosphatases tightly regulate the interconversion and abundance of different phosphoinositide lipid derivatives (PIPs),¹ which are crucial components for the identity of eukaryotic membranes (1, 2). PIPs and their modifying proteins control multiple cellular functions such as signal transduction, cytoskeletal dynamics, and membrane trafficking (1, 2). Synthesis and turnover of the low-abundant lipid phosphatidylinositol-3,5-bisphosphate is controlled by the Vac14 complex. This complex contains the scaffold protein Vac14 (ArPIKfyve), the lipid kinase PIKfyve (Fab1), and its counteracting lipid phosphatase FIG4 (SAC3) (3, 4).The overall protein structure of Vac14 is conserved from yeast to human and consists of multiple tandem HEAT (huntingtin, elongation Factor 3, PR65/A, TOR) repeats and a rod-like helical domain that controls protein–protein interactions (5, 6). Previous studies showed that the aminoterminal part of Vac14 mediates the binding to PIKfyve. The carboxyterminal region is crucial for the interaction with FIG4 and also contains a PDZ-binding motif, which binds to nNOS (5, 7–11). Moreover, Vac14-oligomerization, which is essential for the nucleation of an active Vac14-complex, overlaps with the FIG4 interaction binding site (5, 6, 12, 13).The lack of Vac14 or the ectopic overexpression of a PIKfyve binding-deficient Vac14 mutant in vivo results in a reduced level of phosphatidylinositol-3,5-bisphosphate and is accompanied by enhanced formation of enlarged intracellular vesicles, hereinafter called vacuoles (5, 14, 15). This was initially observed in yeast, where it leads to enlargement of the yeast vacuole, which is comparable to the mammalian lysosome (12, 16, 17). The vacuoles in mammalian cells are heterogeneous, positive for early or late endosomal structures, and involved in vesicular trafficking processes from the late endosome to the trans-Golgi network (14, 18, 19).We now report that in addition to overexpression of the PIKfyve binding deficient mutant, overexpression of the Vac14 wild type was sufficient to induce vacuolization in human cell cultures. The Vac14-based vacuolization leads to a significant accumulation of predominantly late endosomal and autophagosomal marker proteins with only late endosomal proteins decorating the vacuolar membranes. These results almost completely phenocopy previously described effects of the inhibitor YM201636, which specifically blocks PIKfyve lipid kinase activity (20–24).To identify proteins involved in Vac14-induced vacuolization, we used a protein affinity approach combined with multidimensional protein identification technology (MudPIT). The evaluation of the identified peptides elucidated numerous potential Vac14 interacting proteins involved in intracellular trafficking and membrane dynamics, with Rab9 and TBC1D15 being the most promising candidates. The specificity of the Vac14 interaction with Rab9 and the Rab7 regulator TBC1D15 was confirmed by co-immunoprecipitation assays. Moreover, we were able to demonstrate that Rab9 accumulates during vacuolization and localizes on the limiting membranes of vacuoles as a result of Vac14 overexpression. In summary, the identification of Rab9 and TBC1D15 as novel interaction partners provides new insights into the molecular functions of Vac14 in vesicular transport processes.