Nonstructural Protein σ1s Mediates Reovirus-Induced Cell Cycle Arrest and Apoptosis

Reovirus nonstructural protein σ1s is implicated in cell cycle arrest at the G₂/M boundary and induction of apoptosis. However, the contribution of σ1s to these effects in an otherwise isogenic viral background has not been defined. To evaluate the role of σ1s in cell cycle arrest and apoptosis, we used reverse genetics to generate a σ1s-null reovirus. Following infection with wild-type virus, we observed an increase in the percentage of cells in G₂/M, whereas the proportion of cells in G₂/M following infection with the σ1s-null mutant was unaffected. Similarly, we found that the wild-type virus induced substantially greater levels of apoptosis than the σ1s-null mutant. These data indicate that σ1s is required for both reovirus-induced cell cycle arrest and apoptosis. To define sequences in σ1s that mediate these effects, we engineered viruses encoding C-terminal σ1s truncations by introducing stop codons in the σ1s open reading frame. We also generated viruses in which charged residues near the σ1s amino terminus were replaced individually or as a cluster with nonpolar residues. Analysis of these mutants revealed that amino acids 1 to 59 and the amino-terminal basic cluster are required for induction of both cell cycle arrest and apoptosis. Remarkably, viruses that fail to induce cell cycle arrest and apoptosis also are attenuated in vivo. Thus, identical sequences in σ1s are required for reovirus-induced cell cycle arrest, apoptosis, and pathogenesis. Collectively, these findings provide evidence that the σ1s-mediated properties are genetically linked and suggest that these effects are mechanistically related.     

Antigenic Analysis of Monoclonal Antibodies against Different Epitopes of σB Protein of Avian Reovirus

Background

Avian reovirus (ARV) causes arthritis, tenosynovitis, runting-stunting syndrome (RSS), malabsorption syndrome (MAS) and immunosuppression in chickens. σB is one of the major structural proteins of ARV, which is able to induce group-specific antibodies against the virus.

Methods and Results

The present study described the identification of two linear B-cell epitopes in ARV σB through expressing a set of partially overlapping and consecutive truncated peptides spanning σB screened with two monoclonal antibodies (mAbs) 1F4 and 1H3-1.The data indicated that ²¹KTPACW²⁶ (epitope A) and ³²WDTVTFH³⁸ (epitope B) were minimal determinants of the linear B cell epitopes. Antibodies present in the serum of ARV-positive chickens recognized the minimal linear epitopes in Western blot analyses. By sequence alignment analysis, we determined that the epitopes A and B were not conserved among ARV, duck reovirus (DRV) and turkey reovirus (TRV) strains. Western blot assays, confirmed that epitopes A and B were ARV-specific epitopes, and they could not react with the corresponding peptides of DRV and TRV.

Conclusions and Significance

We identified ²¹KTPACW²⁶ and ³²WDTVTFH³⁸ as σB -specific epitopes recognized by mAbs 1F4 and 1H3-1, respectively. The results in this study may have potential applications in development of diagnostic techniques and epitope-based marker vaccines against ARV groups.     

Optimum Length and Flexibility of Reovirus Attachment Protein σ1 Are Required for Efficient Viral Infection

Reovirus attachment protein σ1 is an elongated trimer with head-and-tail morphology that engages cell-surface carbohydrate and junctional adhesion molecule A (JAM-A). The σ1 protein is comprised of three domains partitioned by two flexible linkers termed interdomain regions (IDRs). To determine the importance of σ1 length and flexibility at different stages of reovirus infection, we generated viruses with mutant σ1 molecules of altered length and flexibility and tested these viruses for the capacity to bind the cell surface, internalize, uncoat, induce protein synthesis, assemble, and replicate. We reduced the length of the α-helical σ1 tail to engineer mutants L1 and L2 and deleted midpoint and head-proximal σ1 IDRs to generate ΔIDR1 and ΔIDR2 mutant viruses, respectively. Decreasing length or flexibility of σ1 resulted in delayed reovirus infection and reduced viral titers. L1, L2, and ΔIDR1 viruses but not ΔIDR2 virus displayed reduced cell attachment, but altering σ1 length or flexibility did not diminish the efficiency of virion internalization. Replication of ΔIDR2 virus was hindered at a postdisassembly step. Differences between wild-type and σ1 mutant viruses were not attributable to alterations in σ1 folding, as determined by experiments assessing engagement of cell-surface carbohydrate and JAM-A by the length and IDR mutant viruses. However, ΔIDR1 virus harbored substantially less σ1 on the outer capsid. Taken together, these data suggest that σ1 length is required for reovirus binding to cells. In contrast, IDR1 is required for stable σ1 encapsidation, and IDR2 is required for a postuncoating replication step. Thus, the structural architecture of σ1 is required for efficient reovirus infection of host cells.     

Characterization of Protein–Protein Interfaces

We analyze the characteristics of protein–protein interfaces using the largest datasets available from the Protein Data Bank (PDB). We start with a comparison of interfaces with protein cores and non-interface surfaces. The results show that interfaces differ from protein cores and non-interface surfaces in residue composition, sequence entropy, and secondary structure. Since interfaces, protein cores, and non-interface surfaces have different solvent accessibilities, it is important to investigate whether the observed differences are due to the differences in solvent accessibility or differences in functionality. We separate out the effect of solvent accessibility by comparing interfaces with a set of residues having the same solvent accessibility as the interfaces. This strategy reveals residue distribution propensities that are not observable by comparing interfaces with protein cores and non-interface surfaces. Our conclusions are that there are larger numbers of hydrophobic residues, particularly aromatic residues, in interfaces, and the interactions apparently favored in interfaces include the opposite charge pairs and hydrophobic pairs. Surprisingly, Pro-Trp pairs are over represented in interfaces, presumably because of favorable geometries. The analysis is repeated using three datasets having different constraints on sequence similarity and structure quality. Consistent results are obtained across these datasets. We have also investigated separately the characteristics of heteromeric interfaces and homomeric interfaces.     

Predicting Protein–Protein Interfaces that Bind Intrinsically Disordered Protein Regions

A long-standing goal in biology is the complete annotation of function and structure on all protein–protein interactions, a large fraction of which is mediated by intrinsically disordered protein regions (IDRs). However, knowledge derived from experimental structures of such protein complexes is disproportionately small due, in part, to challenges in studying interactions of IDRs. Here, we introduce IDRBind, a computational method that by combining gradient boosted trees and conditional random field models predicts binding sites of IDRs with performance approaching state-of-the-art globular interface predictions, making it suitable for proteome-wide applications. Although designed and trained with a focus on molecular recognition features, which are long interaction-mediating-elements in IDRs, IDRBind also predicts the binding sites of short peptides more accurately than existing specialized predictors. Consistent with IDRBind's specificity, a comparison of protein interface categories uncovered uniform trends in multiple physicochemical properties, positioning molecular recognition feature interfaces between peptide and globular interfaces.     

Protein–Protein and Protein–Membrane Associations in the Lignin Pathway

Supramolecular organization of enzymes is proposed to orchestrate metabolic complexity and help channel intermediates in different pathways. Phenylpropanoid metabolism has to direct up to 30% of the carbon fixed by plants to the biosynthesis of lignin precursors. Effective coupling of the enzymes in the pathway thus seems to be required. Subcellular localization, mobility, protein–protein, and protein–membrane interactions of four consecutive enzymes around the main branch point leading to lignin precursors was investigated in leaf tissues of Nicotiana benthamiana and cells of Arabidopsis thaliana. CYP73A5 and CYP98A3, the two Arabidopsis cytochrome P450s (P450s) catalyzing para- and meta-hydroxylations of the phenolic ring of monolignols were found to colocalize in the endoplasmic reticulum (ER) and to form homo- and heteromers. They moved along with the fast remodeling plant ER, but their lateral diffusion on the ER surface was restricted, likely due to association with other ER proteins. The connecting soluble enzyme hydroxycinnamoyltransferase (HCT), was found partially associated with the ER. Both HCT and the 4-coumaroyl-CoA ligase relocalized closer to the membrane upon P450 expression. Fluorescence lifetime imaging microscopy supports P450 colocalization and interaction with the soluble proteins, enhanced by the expression of the partner proteins. Protein relocalization was further enhanced in tissues undergoing wound repair. CYP98A3 was the most effective in driving protein association.     

Computational Prediction of Protein–Protein Interactions

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.     

IL-28 Supplants Requirement for Treg Cells in Protein σ1-Mediated Protection against Murine Experimental Autoimmune Encephalomyelitis (EAE)

Conventional methods to induce tolerance in humans have met with limited success. Hence, efforts to redirect tolerogen uptake using reovirus adhesin, protein sigma 1 (pσ1), may circumvent these shortcomings based upon the recent finding that when reovirus pσ1 is engineered to deliver chicken ovalbumin (OVA) mucosally, tolerance is obtained, even with a single dose. To test whether single-dose tolerance can be induced to treat EAE, proteolipid protein (PLP_130–151) was genetically fused to OVA to pσ1 (PLP:OVA-pσ1) and shown to significantly ameliorate EAE, suppressing proinflammatory cytokines by IL-10⁺ forkhead box P3 (FoxP3)⁺ CD25⁺CD4⁺ T_reg and IL-4⁺CD25⁻CD4⁺ Th2 cells. IL-10R or IL-4 neutralization reversed protection to EAE conferred by PLP:OVA-pσ1, and adoptive transfer of Ag-specific T_reg or Th2 cells restored protection against EAE in recipients. Upon assessment of each relative participant, functional inactivation of CD25 impaired PLP:OVA-pσ1''s protective capacity, triggering TGF-β-mediated inflammation; however, concomitant inactivation of TGF-β and CD25 reestablished PLP:OVA-pσ1-mediated protection by IL-28-producing FoxP3⁺CD25⁻CD4⁺ T cells. Thus, pσ1-based therapy can resolve EAE independently of or dependently upon CD25 and assigns IL-28 as an alternative therapy for autoimmunity.     

Protein–Protein Interactions in DNA Base Excision Repair

The system of base excision repair (BER) ensures correction of the most abundant DNA damages in mammalian cells and plays an important role in maintaining genome stability. Enzymes and protein factors participate in the multistage BER in a coordinated fashion, which ensures repair efficiency. The suggested coordination mechanisms are based on formation of protein complexes stabilized via either direct or indirect DNA-mediated interactions. The results of investigation of direct interactions of the proteins participating in BER with each other and with other proteins are outlined in this review. The known protein partners and sites responsible for their interaction are presented for the main participants as well as quantitative characteristics of their affinity. Information on the mechanisms of regulation of protein–protein interactions mediated by DNA intermediates and posttranslational modification is presented. It can be suggested based on all available data that the multiprotein complexes are formed on chromatin independent of the DNA damage with the help of key regulators of the BER process – scaffold protein XRCC1 and poly(ADP-ribose) polymerase 1. The composition of multiprotein complexes changes dynamically depending on the DNA damage and the stage of BER process.     

Structure of Serotype 1 Reovirus Attachment Protein σ1 in Complex with Junctional Adhesion Molecule A Reveals a Conserved Serotype-Independent Binding Epitope

Mammalian orthoreoviruses use glycans and junctional adhesion molecule A (JAM-A) as attachment receptors. We determined the structure of serotype 1 reovirus attachment protein σ1 alone and in complex with JAM-A. Comparison with the structure of serotype 3 reovirus σ1 bound to JAM-A reveals that both σ1 proteins engage JAM-A with similar affinities and via conserved binding epitopes. Thus, σ1–JAM-A interactions are unlikely to explain the differences in pathogenesis displayed by these reovirus serotypes.     

Halogen bonding: the σ-hole

Halogen bonding refers to the non-covalent interactions of halogen atoms X in some molecules, RX, with negative sites on others. It can be explained by the presence of a region of positive electrostatic potential, the σ-hole, on the outermost portion of the halogen’s surface, centered on the R–X axis. We have carried out a natural bond order B3LYP analysis of the molecules CF₃X, with X = F, Cl, Br and I. It shows that the Cl, Br and I atoms in these molecules closely approximate the configuration, where the z-axis is along the R–X bond. The three unshared pairs of electrons produce a belt of negative electrostatic potential around the central part of X, leaving the outermost region positive, the σ-hole. This is not found in the case of fluorine, for which the combination of its high electronegativity plus significant sp-hybridization causes an influx of electronic charge that neutralizes the σ-hole. These factors become progressively less important in proceeding to Cl, Br and I, and their effects are also counteracted by the presence of electron-withdrawing substituents in the remainder of the molecule. Thus a σ-hole is observed for the Cl in CF₃Cl, but not in CH₃Cl. Figure Schematic representation of the atomic charge generation. The molecular electrostatic potential (MEP) is calculated using the AM1* Hamiltonian. The semiempirical MEP is then scaled to DFT or ab initio level and atomic charges are generated from it by the restrained electrostatic potential (RESP) fit method.     

Neighboring group stabilization by σ-holes

We have used density-functional theory to investigate the neighboring-group stabilization of iodine, arsenic, and phosphorus-centered oxyanion moieties in species such as deprotonated 2-iodoxybenzoic acid (IBX) and its analogs. The magnitudes of different stabilizing effects and further candidates for analogous stabilization are analyzed.      

Polarization-induced σ-holes and hydrogen bonding

The strong collinear polarizability of the A-H bond in A-H···B hydrogen bonds is shown to lead to an enhanced σ-hole on the donor hydrogen atom and hence to stronger hydrogen bonding. This effect helps to explain the directionality of hydrogen bonds, the well known cooperative effect in hydrogen bonding, and the occurrence of blue-shifting. The latter results when significant additional electron density is shifted into the A-H bonding region by the polarization effect. The shift in the A-H stretching frequency is shown to depend essentially linearly on the calculated atomic charge on the donor hydrogen for all donors in which A belongs to the same row of the periodic table. A further result of the polarization effect, which is also expected for other σ-hole bonds, is that the strength of the non-covalent interaction depends strongly on external electric fields.     

ETS-NOCV description of σ-hole bonding

The ETS-NOCV analysis was applied to describe the σ-hole in a systematic way in a series of halogen compounds, CF₃-X (X?=?I, Br, Cl, F), CH₃I, and C(CH3)_nH_3-n-I (n?=?1,2,3), as well as for the example germanium-based systems. GeXH₃, X?=?F, Cl, H. Further, the ETS-NOCV analysis was used to characterize bonding with ammonia for these systems. The results show that the dominating contribution to the deformation density, Δρ ₁ , exhibits the negative-value area with a minimum, corresponding to σ-hole. The “size” (spatial extension of negative value) and “depth” (minium value) of the σ-hole varies for different X in CF₃-X, and is influenced by the carbon substituents (fluorine atoms, hydrogen atoms, methyl groups). The size and depth of σ-hole decreases in the order: I, Br, Cl, F in CF₃-X. In CH₃-I and C(CH3)_nH_3-n-I, compared to CF₃-I, introduction of hydrogen atoms and their subsequent replacements by methyl groups lead to the systematic decrease in the σ-hole size and depth. The ETS-NOCV σ-hole picture is consistent with the existence the positive MEP area at the extension of σ-hole generating bond. Finally, the NOCV deformation density contours as well as by the ETS orbital-interaction energy indicate that the σ-hole-based bond with ammonia contains a degree of covalent contribution. In all analyzed systems, it was found that the electrostatic energy is approximately two times larger than the orbital-interaction term, confirming the indisputable role of the electrostatic stabilization in halogen bonding and σ-hole bonding.

Expansion of the σ-hole concept

The term “σ-hole” originally referred to the electron-deficient outer lobe of a half-filled p (or nearly p) orbital involved in forming a covalent bond. If the electron deficiency is sufficient, there can result a region of positive electrostatic potential which can interact attractively (noncovalently) with negative sites on other molecules (σ-hole bonding). The interaction is highly directional, along the extension of the covalent bond giving rise to the σ-hole. σ-Hole bonding has been observed, experimentally and computationally, for many covalently-bonded atoms of Groups V–VII. The positive character of the σ-hole increases in going from the lighter to the heavier (more polarizable) atoms within a Group, and as the remainder of the molecule becomes more electron-withdrawing. In this paper, we show computationally that significantly positive σ-holes, and subsequent noncovalent interactions, can also occur for atoms of Group IV. This observation, together with analogous ones for the molecules (H₃C)₂SO, (H₃C)₂SO₂ and Cl₃PO, demonstrates a need to expand the interpretation of the origins of σ-holes: (1) While the bonding orbital does require considerable p character, in view of the well-established highly directional nature of σ-hole bonding, a sizeable s contribution is not precluded. (2) It is possible for the bonding orbital to be doubly-occupied and forming a coordinate covalent bond. Figure Two views of the calculated electrostatic potential on the 0.001 au molecular surface of SiCl₄. Color ranges, in kcal/mole, are: purple, negative; blue, between 0 and 8; green, between 8 and 11; yellow, between 11 and 18; red, more positive than 18. The top view shows three of the four chlorines. In the center is the σ-hole due to the fourth Cl−Si bond, its most positive portion (red) being on the extension of that bond. In the bottom view are visible two of the σ-holes on the silicon. In both views can be seen the σ-holes on the chlorines, on the extensions of the Si−Cl bonds; their most positive portions are green     

A Critical Assessment of Information-guided Protein–Protein Docking Predictions

The structures of protein complexes are increasingly predicted via protein–protein docking (PPD) using ambiguous interaction data to help guide the docking. These data often are incomplete and contain errors and therefore could lead to incorrect docking predictions. In this study, we performed a series of PPD simulations to examine the effects of incompletely and incorrectly assigned interface residues on the success rate of PPD predictions. The results for a widely used PPD benchmark dataset obtained using a new interface information-driven PPD (IPPD) method developed in this work showed that the success rate for an acceptable top-ranked model varied, depending on the information content used, from as high as 95% when contact relationships (though not contact distances) were known for all residues to 78% when only the interface/non-interface state of the residues was known. However, the success rates decreased rapidly to ∼40% when the interface/non-interface state of 20% of the residues was assigned incorrectly, and to less than 5% for a 40% incorrect assignment. Comparisons with results obtained by re-ranking a global search and with those reported for other data-guided PPD methods showed that, in general, IPPD performed better than re-ranking when the information used was more complete and more accurate, but worse when it was not, and that when using bioinformatics-predicted information on interface residues, IPPD and other data-guided PPD methods performed poorly, at a level similar to simulations with a 40% incorrect assignment. These results provide guidelines for using information about interface residues to improve PPD predictions and reveal a bottleneck for such improvement imposed by the low accuracy of current bioinformatic interface residue predictions.Proteins work in close association with other proteins to mediate the intricate functions of a cell. The atomic resolution of the structure of a protein complex can therefore help one understand a protein''s function in detail. Protein–protein docking (PPD),¹ a computational approach that complements experimental structure determinations, has attracted increasing research interest (1, 2), in part because it remains challenging to determine most structures of protein complexes via experimental techniques (3).To improve the performance of PPD predictions, experimentally derived data (e.g. distances) and information (e.g. the identity of interface residues) have been used either as a filter allowing less plausible docking solutions to be disregarded (4–9) or as a constraint to guide the docking process (10, 11). Various types of data and information have been used to aid PPD (12); these range from distances between, or the relative orientation of, the two interacting proteins to simple identification of the amino acid residues directly involved in the binding of the two proteins (13). Despite considerable success, the caveat for all these data-guided PPD predictions is that the data or information used must be correct in order to avoid spurious results caused by misguiding (12). It is therefore pertinent and important to evaluate the effects of errors in the incorporated data or information on the quality of PPD solutions.We have recently shown that the use of just a few distance constraints can improve the success rates of PPD such that they rival, or are even better than, those of a global search ranked using a sophisticated energy function, and that errors in the distance data significantly decrease the success rates of prediction (11). However, because distance data for interacting proteins are usually hard to obtain, other types of data or information, even if “ambiguous” (10), are increasingly used in PPD predictions (12, 14). In this study, we investigated the effects of incompletely and incorrectly assigned interface/non-interface residues, a major source of the so-called ambiguous data, on information-guided PPD predictions.As illustrated in Fig. 1, the information content of interface/non-interface residues can be rich enough to reveal the identity of every pair of residues in contact, but not their contact distances, or so poor as to reveal the interface/non-interface state of these residues but not their pairing relationship, for one or both of the two interacting proteins. To determine how these different levels of residue information content can help PPD predictions and the extent to which the use of incorrectly assigned residues degrades prediction success rates, we have developed a new interface information-driven PPD method (IPPD) and carried out a series of PPD simulations on a well-tested benchmark dataset. The results showed that when the information content was rich, excellent predictions (success rates for producing an acceptable top-ranked model > 70%) could be made via IPPD or by re-ranking a global search''s solutions using the same interface information, and that, encouragingly, the success of predictions remained respectable (top-ranked success rates > 15%) when the content was poor. However, when enough of the interface residues were incorrectly assigned, as would be the case when using interface residues predicted by a state-of-the-art bioinformatics method such as CPORT (15), few models ranked first by IPPD or other PPD methods, including HADDOCK (10), a popular ambiguous data-driven PPD method, came close to being acceptable. These results suggest that we can greatly increase the power of PPD predictions for practical applications only if the accuracy of current bioinformatics methods for predicting the interface residues of protein complexes can be significantly improved.Open in a separate windowFig. 1.Contact matrix of two interacting proteins, A and B, and the contact vectors of their residues. In the contact matrix, M_ij = 1 or 0, respectively, denotes contact or a lack of contact between residue i in protein A and residue j in protein B. In the contact vectors, V_Ai = 1 or 0, respectively, when residue Ai has, or does not have, at least one contact with any residue of protein B.     

A Second-generation Protein–Protein Interaction Network of Helicobacter pylori

Helicobacter pylori infections cause gastric ulcers and play a major role in the development of gastric cancer. In 2001, the first protein interactome was published for this species, revealing over 1500 binary protein interactions resulting from 261 yeast two-hybrid screens. Here we roughly double the number of previously published interactions using an ORFeome-based, proteome-wide yeast two-hybrid screening strategy. We identified a total of 1515 protein–protein interactions, of which 1461 are new. The integration of all the interactions reported in H. pylori results in 3004 unique interactions that connect about 70% of its proteome. Excluding interactions of promiscuous proteins we derived from our new data a core network consisting of 908 interactions. We compared our data set to several other bacterial interactomes and experimentally benchmarked the conservation of interactions using 365 protein pairs (interologs) of E. coli of which one third turned out to be conserved in both species.Helicobacter pylori is a Gram-negative, microaerophilic bacterium that colonizes the stomach, an unusual highly acidic niche for microorganisms. In 1983, Warren and Marshall found it to be associated with gastric inflammation and duodenal ulcer disease (1, 2). A chronic infection with H. pylori can lead to development of stomach carcinoma and MALT lymphoma (reviewed in (3)). Hence, the World Health Organization has classified H. pylori as a class I carcinogen (4). It is estimated that half of the world′s population harbors H. pylori but with large variations in the geographical and socioeconomic distribution while causing annually 700,000 deaths worldwide (reviewed in (5)).The pathogenesis of H. pylori has been extensively studied, including the effector CagA, cytotoxin VacA, its adhesins and urease (reviewed in (3, 5–7)). The latter allows the bacterium to neutralize the stomach acid through ammonia production. However, H. pylori is not a classical model organism and thus many gaps in our knowledge still exist.The genome of H. pylori reference strain 26695 was completely sequenced in 1997 (8) and encodes 1587 proteins of which about 950 (61%) have been assigned functions (excluding “putatives”; Uniprot, CMR (9)). These numbers indicate that a large fraction of the proteins of H. pylori has not been functionally characterized.Protein–protein interactions (PPIs)¹ are required for nearly all biological processes. Unbiased interactomes are helpful to understand proteins or pathways and how they are linking poorly or uncharacterized proteins via their interactions. For instance, our study of the Treponema pallidum interactome (10) has led to the characterization of several previously “unknown” proteins such as YbeB, a ribosomal silencing factor (11), or TP0658, a regulator of flagellar translation and assembly (12, 13). However, only a few other comprehensive bacterial interactome studies have been published to date, including Campylobacter jejuni (14), Synechocystis sp. (15), Mycobacterium tuberculosis (16), Mesorhizobium loti (17), and recently Escherichia coli (18). In addition, partial interactomes are available for Bacillus subtilis (19) and H. pylori (20). Most of them used the yeast two-hybrid (Y2H) screening technology (21) which allows the pairwise detection of PPIs. Furthermore, a few other studies (22–25) systematically identified protein complexes and their compositions in bacteria.In 2001, Rain and colleagues have established a partial interactome of H. pylori, the first published protein interaction network of a bacterium (20). In this study, 261 bait constructs were screened against a random prey pool library resulting in the detection of over 1500 PPIs. Although this network likely represents a small fraction of all PPIs that occur in H. pylori, many downstream studies were motivated by these results (see below).Recent studies have disproved the notion that Y2H data sets are of poor quality (26, 27). Similarly, a high false-negative rate can be avoided by multiple Y2H expression vector systems (28–30) or protein fragments as opposed to full-length constructs (31). The aim of this study was to systematically screen the H. pylori proteome for binary protein interactions using a complementary approach to that of Rain et al. to produce an extended protein–protein interaction map of H. pylori. As a result, we have roughly doubled the number of known binary protein–protein interactions for H. pylori in this study.     

Degradation of Amyloid �� Protein by Purified Myelin Basic Protein

The progressive accumulation of β-amyloid (Aβ) in senile plaques and in the cerebral vasculature is the hallmark of Alzheimer disease and related disorders. Impaired clearance of Aβ from the brain likely contributes to the prevalent sporadic form of Alzheimer disease. Several major pathways for Aβ clearance include receptor-mediated cellular uptake, blood-brain barrier transport, and direct proteolytic degradation. Myelin basic protein (MBP) is the major structural protein component of myelin and plays a functional role in the formation and maintenance of the myelin sheath. MBP possesses endogenous serine proteinase activity and can undergo autocatalytic cleavage liberating distinct fragments. Recently, we showed that MBP binds Aβ and inhibits Aβ fibril formation (Hoos, M. D., Ahmed, M., Smith, S. O., and Van Nostrand, W. E. (2007) J. Biol. Chem. 282, 9952–9961; Hoos, M. D., Ahmed, M., Smith, S. O., and Van Nostrand, W. E. (2009) Biochemistry 48, 4720–4727). Here we show that Aβ40 and Aβ42 peptides are degraded by purified human brain MBP and recombinant human MBP, but not an MBP fragment that lacks autolytic activity. MBP-mediated Aβ degradation is inhibited by serine proteinase inhibitors. Similarly, Cos-1 cells expressing MBP degrade exogenous Aβ40 and Aβ42. In addition, we demonstrate that purified MBP also degrades assembled fibrillar Aβ in vitro. Mass spectrometry analysis identified distinct degradation products generated from Aβ digestion by MBP. Lastly, we demonstrate in situ that purified MBP can degrade parenchymal amyloid plaques as well as cerebral vascular amyloid that form in brain tissue of Aβ precursor protein transgenic mice. Together, these findings indicate that purified MBP possesses Aβ degrading activity in vitro.The progressive accumulation of β-amyloid (Aβ)³ in senile/neuritic plaques and the cerebral vasculature is the hallmark of Alzheimer disease (AD) and is widely used in the pathological diagnosis of the disease. Aβ is generated by proteolytic cleavage of amyloid precursor protein (APP) by β-secretase and γ-secretase (1, 2). The main species of Aβ are 40 and 42 amino acids in length. Aβ42 is much more amyloidogenic than Aβ40 because of its two additional hydrophobic amino acids at the carboxyl-terminal end of the peptide (3). The Aβ42 peptide is the predominant form in senile plaques, forming a β-sheet structure, which is insoluble and resistant to proteolysis.Although increased production of Aβ has been implicated in the onset of familial forms of AD, it has been hypothesized that the more common sporadic forms of AD may be caused by the impaired clearance of Aβ peptides from the CNS. Several major pathways for Aβ clearance have been proposed including receptor-mediated cellular uptake, blood-brain barrier transport into the circulation, and direct proteolytic degradation (4–6). In the latter case, several proteinases or peptidases have been identified that are capable of degrading Aβ, including neprilysin (7, 8), insulin-degrading enzyme (9), the urokinase/tissue plasminogen activator-plasmin system (10), endothelin-converting enzyme (11), angiotensin-converting enzyme (12), gelatinase A (matrix metalloproteinase-2) (13, 14), gelatinase B (matrix metalloproteinase-9) (15), and acylpeptide hydrolase (16). Each of these enzymes has been shown to cleave Aβ peptides at multiple sites (5). However, only neprilysin, insulin-degrading enzyme, endothelin-converting enzyme, and matrix metalloproteinase-9 have been shown to have a significant role in regulating Aβ levels in the brains of experimental animal models (8, 17, 18).The “classic” myelin basic proteins (MBP) are major structural components of myelin sheaths accounting for 30% of total myelin protein. There are four different major isoforms generated from alternative splicing with molecular masses of 17.3, 18.5, 20.2, and 21.5 kDa. The 18.5-kDa variant, composed of 180 amino acids including 19 Arg and 12 Lys basic residues, is most abundant in mature myelin (19). One of the major functions of MBP is to hold together the cytoplasmic leaflets of myelin membranes to maintain proper compaction of the myelin sheath through the electrostatic interaction between the positive Arg and Lys residues of MBP and the negatively charged phosphate groups of the membrane lipid (20). MBP plays an important role in the pathology of multiple sclerosis, which is an autoimmune disease characterized by demyelination within white matter (21). Recently, it was reported that purified MBP exhibits autocleavage activity, generating distinct peptide fragments (22). In this study, serine 151 was reported as the active site serine residue involved in autocatalysis.In the early stages of AD, appreciable and diffuse myelin breakdown in the white matter is observed (23). Also, in white matter regions there are much fewer fibrillar amyloid deposits than are commonly found in gray matter regions. Recently, our laboratory has shown that MBP strongly interacts with Aβ peptides and prevents their assembly into mature amyloid fibrils (24, 25). Through the course of these studies we observed that upon longer incubations the levels of Aβ peptides were reduced upon treatment with MBP. In light of this observation, coupled with the report that MBP possesses proteolytic activity, we hypothesized that MBP may degrade Aβ peptides. In the present study, we show that purified human brain MBP and recombinantly expressed human MBP can degrade soluble Aβ40 and Aβ42 peptides in vitro. Purified MBP also degraded fibrillar Aβ in vitro. Mass spectrometry analysis identified distinct degradation products generated from soluble and fibrillar Aβ digestion by MBP. Furthermore, purified MBP degraded parenchymal and vascular fibrillar amyloid deposits in situ in the brain tissue of APP transgenic mice. Together, these findings indicate that purified MBP possesses Aβ degrading activity in vitro.     

Multiple Modes of Protein–Protein Interactions Promote RNP Granule Assembly

Eukaryotic cells are known to contain a wide variety of RNA–protein assemblies, collectively referred to as RNP granules. RNP granules form from a combination of RNA–RNA, protein–RNA, and protein–protein interactions. In addition, RNP granules are enriched in proteins with intrinsically disordered regions (IDRs), which are frequently appended to a well-folded domain of the same protein. This structural organization of RNP granule components allows for a diverse set of protein–protein interactions including traditional structured interactions between well-folded domains, interactions of short linear motifs in IDRs with the surface of well-folded domains, interactions of short motifs within IDRs that weakly interact with related motifs, and weak interactions involving at most transient ordering of IDRs and folded domains with other components. In addition, both well-folded domains and IDRs in granule components frequently interact with RNA and thereby can contribute to RNP granule assembly. We discuss the contribution of these interactions to liquid–liquid phase separation and the possible role of phase separation in the assembly of RNP granules. We expect that these principles also apply to other non-membrane bound organelles and large assemblies in the cell.