Fibronectin Binds to and Induces Conformational Change in a Disordered Region of Leptospiral Immunoglobulin-like Protein B

Leptospira interrogans is a pathogenic spirochete that causes disease in both humans and animals. LigB (Leptospiral immunoglobulin-like protein B) contributes to the binding of Leptospira to extracellular matrix proteins such as fibronectin (Fn), fibrinogen, laminin, and collagen. A high affinity Fn-binding region of LigB has been recently localized to LigBCen2, which contains the partial eleventh and full twelfth immunoglobulin-like repeats (LigBCen2R) and 47 amino acids of the non-repeat region (LigBCen2NR) of LigB. In this study, LigBCen2NR was shown to bind to the N-terminal domain (NTD) of Fn (K_D = 379 nm) by an enzyme-linked immunosorbent assay and isothermal titration calorimetry. Interestingly, this sequence was not observed to adopt secondary structure by far UV circular dichroism or by differential scanning calorimetry, in agreement with computer-based secondary structure predictions. A low partition coefficient (K_av) measured with gel permeation chromatography, a high hydrodynamic radius (R_h) measured with dynamic light scattering, and the insensitivity of the intrinsic viscosity to guanidine hydrochloride treatment all suggest that LigBCen2NR possesses an extended and disordered structure. Two-dimensional ¹⁵N-¹H HSQC NMR spectra of intact LigBCen2 in the absence and presence of NTD are consistent with these observations, suggesting the presence of both a β-rich region and an unstructured region in LigBCen2 and that the latter of these selectively interacts with NTD. Upon binding to NTD, LigBCen2NR was observed by CD to adopt a β-strand-rich structure, suggestive of the known β-zipper mode of NTD binding.Leptospira interrogans is a pathogenic spirochete that causes leptospirosis throughout the world, especially in developing countries but also in regions of the United States where it has reemerged (1). Weil''s syndrome, a severe form of this disease, is an acute febrile illness associated with multiorgan damage, including liver failure (jaundice), renal failure (nephritis), pulmonary hemorrhage, and meningitis (1), and has a 15% mortality rate if not treated (2). The molecular pathogenesis of leptospirosis is poorly understood, and the bacterial virulence factors involved are largely unknown. Recently, several potential Leptospira virulence factors have been described, including sphingomyelinases, serine proteases, zinc-dependent proteases, and collagenase (3); LipL32 (4); lipopolysaccharide (5); a novel factor H, laminin, and Fn-binding protein (Lsa24 or Len) (6–8); Loa 22 (9); and Lig (Leptospiral immunoglobulin-like) proteins (10–12).Lig proteins, including LigA, LigB, and LigC, contain multiple immunoglobulin-like repeat domains (13 in LigA, 12 in LigB and LigC) (10–12). Interestingly, the first 630 residues, from the N terminus to the first half of the seventh immunoglobulin-like domain, are conserved between LigA and LigB, but the rest of the immunoglobulin-like domains are variable (10–12) between the two proteins. Also, a non-immunoglobulin-like repeat region found on the C-terminal tail of LigB is not found in LigA (10–12). Lig proteins are categorized as microbial surface components recognizing adhesive matrix molecules (MSCRAMMs)² due to their ability to bind to eukaryotic cells (13) through their interactions with extracellular matrix components, including fibronectin (Fn), laminin, collagens, elastin, and tropoelastin (13, 14, 45). Previously, a high affinity Fn-binding region was localized to LigBCen2, which includes the partial eleventh and complete twelfth immunoglobulin-like repeat region and the first 47 amino acids of the non-repeat regions of LigB (15). LigBCen2 was shown to bind to both the N-terminal domain (NTD) and the gelatin binding domain (GBD) of Fn. The addition of calcium induces a conformational change in LigBCen2 and enhances binding between LigBCen2 and the NTD of Fn (15).The first step in the process of bacterial infection is cellular adhesion, mediated by bacterial adhesins interacting with various components of the extracellular matrix (16). Known interaction modes between Fn and bacterial Fn-binding proteins include the β-zipper (17, 18) and the cationic cradle (19). It was recently discovered that the Fn-binding domains in certain Fn-binding proteins are disordered and extended but gain structure upon binding to the NTD of Fn (20–22).We have performed a fine-mapping study of the NTD-binding site on LigBCen2 and identified this site as LigBCen2NR, a portion of the non-repeat region (amino acids 1119–1165). The addition of NTD promotes the folding of LigBCen2NR from a disordered and extended structure to a folded structure. This finding is notable, since LigBCen2NR is located in the non-immunoglobulin-like region of LigB, as compared with other Fn-binding proteins, such as Staphylococcus aureus FnbpA and FnbpB (23), Streptococcus dysgalactiae FnBB (17), and Streptococcus pyogenes SfbI and SfbII (24). Thus, the binding mode appears to be similar to the known β-zipper mechanism but unique in sequence-specific interactions. This finding provides the fundamental groundwork for the development of a therapeutic agent to target this interaction in order to prevent or treat Leptospira infection.     

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.     

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]     

PR55��, a Regulatory Subunit of PP2A, Specifically Regulates PP2A-mediated ��-Catenin Dephosphorylation

A central question in Wnt signaling is the regulation of β-catenin phosphorylation and degradation. Multiple kinases, including CKIα and GSK3, are involved in β-catenin phosphorylation. Protein phosphatases such as PP2A and PP1 have been implicated in the regulation of β-catenin. However, which phosphatase dephosphorylates β-catenin in vivo and how the specificity of β-catenin dephosphorylation is regulated are not clear. In this study, we show that PP2A regulates β-catenin phosphorylation and degradation in vivo. We demonstrate that PP2A is required for Wnt/β-catenin signaling in Drosophila. Moreover, we have identified PR55α as the regulatory subunit of PP2A that controls β-catenin phosphorylation and degradation. PR55α, but not the catalytic subunit, PP2Ac, directly interacts with β-catenin. RNA interference knockdown of PR55α elevates β-catenin phosphorylation and decreases Wnt signaling, whereas overexpressing PR55α enhances Wnt signaling. Taken together, our results suggest that PR55α specifically regulates PP2A-mediated β-catenin dephosphorylation and plays an essential role in Wnt signaling.Wnt/β-catenin signaling plays essential roles in development and tumorigenesis (1–3). Our previous work found that β-catenin is sequentially phosphorylated by CKIα⁴ and GSK3 (4), which creates a binding site for β-Trcp (5), leading to degradation via the ubiquitination/proteasome machinery (3). Mutations in β-catenin or APC genes that prevent β-catenin phosphorylation or ubiquitination/degradation lead ultimately to cancer (1, 2).In addition to the involvement of kinases, protein phosphatases, such as PP1, PP2A, and PP2C, are also implicated in Wnt/β-catenin regulation. PP2C and PP1 may regulate dephosphorylation of Axin and play positive roles in Wnt signaling (6, 7). PP2A is a multisubunit enzyme (8–10); it has been reported to play either positive or negative roles in Wnt signaling likely by targeting different components (11–21). Toward the goal of understanding the mechanism of β-catenin phosphorylation, we carried out siRNA screening targeting several major phosphatases, in which we found that PP2A dephosphorylates β-catenin. This is consistent with a recent study where PP2A is shown to dephosphorylate β-catenin in a cell-free system (18).PP2A consists of a catalytic subunit (PP2Ac), a structure subunit (PR65/A), and variable regulatory B subunits (PR/B, PR/B′, PR/B″, or PR/B‴). The substrate specificity of PP2A is thought to be determined by its B subunit (9). By siRNA screening, we further identified that PR55α, a regulatory subunit of PP2A, specifically regulates β-catenin phosphorylation and degradation. Mechanistically, we found that PR55α directly interacts with β-catenin and regulates PP2A-mediated β-catenin dephosphorylation in Wnt signaling.     

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]