Factor IXa:factor VIIIa interaction. helix 330-338 of factor ixa interacts with residues 558-565 and spatially adjacent regions of the a2 subunit of factor VIIIa

The physiologic activator of factor X consists of a complex of factor IXa, factor VIIIa, Ca(2+) and a suitable phospholipid surface. In one study, helix 330 (162 in chymotrypsin) of the protease domain of factor IXa was implicated in binding to factor VIIIa. In another study, residues 558-565 of the A2 subunit of factor VIIIa were implicated in binding to factor IXa. We now provide data, which indicate that the helix 330 of factor IXa interacts with the 558-565 region of the A2 subunit. Thus, the ability of the isolated A2 subunit was severely impaired in potentiating factor X activation by IXa(R333Q) and by a helix replacement mutant (IXa(helixVII) in which helix 330-338 is replaced by that of factor VII) but it was normal for an epidermal growth factor 1 replacement mutant (IXa(PCEGF1) in which epidermal growth factor 1 domain is replaced by that of protein C). Further, affinity of each 5-dimethylaminonaphthalene-1-sulfonyl (dansyl)-Glu-Gly-Arg-IXa (dEGR-IXa) with the A2 subunit was determined from its ability to inhibit wild-type IXa in the tenase assay and from the changes in dansyl fluorescence emission signal upon its binding to the A2 subunit. Apparent K(d(A2)) values are: dEGR-IXa(WT) or dEGR-IXa(PCEGF1) approximately 100 nm, dEGR-IXa(R333Q) approximately 1.8 micrometer, and dEGR-IXa(helixVII) >10 micrometer. In additional experiments, we measured the affinities of these factor IXa molecules for a peptide comprising residues 558-565 of the A2 subunit. Apparent K(d(peptide)) values are: dEGR-IXa(WT) or dEGR-IXa(PCEGF1) approximately 4 micrometer, and dEGR-IXa(R333Q) approximately 62 micrometer. Thus as compared with the wild-type or PCEGF1 mutant, the affinity of the R333Q mutant for the A2 subunit or the A2 558-565 peptide is similarly reduced. These data support a conclusion that the helix 330 of factor IXa interacts with the A2 558-565 sequence. This information was used to model the interface between the IXa protease domain and the A2 subunit, which is also provided herein.     

Structure of tick anticoagulant peptide at 1.6 A resolution complexed with bovine pancreatic trypsin inhibitor

The structure of tick anticoagulant peptide (TAP) has been determined by X-ray crystallography at 1.6 A resolution complexed with bovine pancreatic trypsin inhibitor (BPTI). The TAP-BPTI crystals are tetragonal, a = b = 46.87, c = 50.35 A, space group P41, four complexes per unit cell. The TAP molecules are highly dipolar and form an intermolecular helical array along the c-axis with a diameter of about 45 A. Individual TAP units interact in a head-to-tail fashion, the positive end of one molecule associating with the distal negative end of another, and vice versa. The BPTI molecules have a uniformly distributed positively charged surface that interacts extensively through 14 hydrogen bonds and two hydrogen bonded salt bridges with the helical groove around the helical TAP chains. Comparing the structure of TAP in TAP-BPTI with TAP bound to factor Xa(Xa) suggests a massive reorganization in the N-terminal tetrapeptide and the first disulfide loop of TAP (Cys5T-Cys15T) upon binding to Xa. The Tyr1(T)OH atom of TAP moves 14.2 A to interact with Asp189 of the S1 specificity site, Arg3(T)CZ moves 5.0 A with the guanidinium group forming a cation-pi-electron complex in the S4 subsite of Xa, while Lys7(T)NZ differs in position by 10.6 A in TAP-BPTI and TAP-Xa, all of which indicates a different pre-Xa-bound conformation for the N-terminal of TAP in its native state. In contrast to TAP, the BPTI structure of TAP-BPTI is practically the same as all those of previously determined structures of BPTI, only arginine and lysine side-chain conformations showing significant differences.     

<Emphasis Type="Italic">In-silico</Emphasis> identification of phenotype-biased functional modules

Background

Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.

Results

In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.

Conclusion

Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).

     

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.     

Viral kinetics suggests a reconciliation of the disparate observations of the modulation of claudin-1 expression on cells exposed to hepatitis C virus

The tight junction protein claudin-1 (CLDN1) is necessary for hepatitis C virus (HCV) entry into target cells. Recent studies have made disparate observations of the modulation of the expression of CLDN1 on cells following infection by HCV. In one study, the mean CLDN1 expression on cells exposed to HCV declined, whereas in another study HCV infected cells showed increased CLDN1 expression compared to uninfected cells. Consequently, the role of HCV in modulating CLDN1 expression, and hence the frequency of cellular superinfection, remains unclear. Here, we present a possible reconciliation of these disparate observations. We hypothesized that viral kinetics and not necessarily HCV-induced receptor modulation underlies these disparate observations. To test this hypothesis, we constructed a mathematical model of viral kinetics in vitro that mimicked the above experiments. Model predictions provided good fits to the observed evolution of the distribution of CLDN1 expression on cells following exposure to HCV. Cells with higher CLDN1 expression were preferentially infected and outgrown by cells with lower CLDN1 expression, resulting in a decline of the mean CLDN1 expression with time. At the same time, because the susceptibility of cells to infection increased with CLDN1 expression, infected cells tended to have higher CLDN1 expression on average than uninfected cells. Our study thus presents an explanation of the disparate observations of CLDN1 expression following HCV infection and points to the importance of considering viral kinetics in future studies of receptor expression on cells exposed to HCV.     

The effects of extracellular matrix proteins on neutrophil-endothelial interaction--a roadway to multiple therapeutic opportunities

Polymorphoneuclear leukocytes or neutrophils, a major component of white blood cells, contribute to the innate immune response in humans. Upon sensing changes in the microenvironment, neutrophils adhere to the vascular wall, migrate through the endothelial cell (EC)-pericyte bilayer, and subsequently through the extracellular matrix to reach the site of inflammation. These cells are capable of destroying microbes, cell debris, and foreign proteins by oxidative and non-oxidative processes. While primarily mediators of tissue homeostasis, there are an increasing number of studies indicating that neutrophil recruitment and transmigration can also lead to host-tissue injury and subsequently inflammation-related diseases. Neutrophil-induced tissue injury is highly regulated by the microenvironment of the infiltrated tissue, which includes cytokines, chemokines, and the provisional extracellular matrix, remodeled through increased vascular permeability and other cellular infiltrates. Thus, investigation of the effects of matrix proteins on neutrophil-EC interaction and neutrophil transmigration may help identify the proteins that induce pro- or anti-inflammatory responses. This area of research presents an opportunity to identify therapeutic targets in inflammation-related diseases. This review will summarize recent literature on the role of neutrophils and the effects of matrix proteins on neutrophil-EC interactions, with focus on three different disease models: 1) atherosclerosis, 2) COPD, and 3) tumor growth and progression. For each disease model, inflammatory molecules released by neutrophils, important regulatory matrix proteins, current anti-inflammatory treatments, and the scope for further research will be summarized.     

The Transcription Factor Wilms Tumor 1 Confers Resistance in Myeloid Leukemia Cells against the Proapoptotic Therapeutic Agent TRAIL (Tumor Necrosis Factor α-related Apoptosis-inducing Ligand) by Regulating the Antiapoptotic Protein Bcl-xL

Tumor necrosis factor α-related apoptosis-inducing ligand (TRAIL) is considered a promising cancer therapeutic agent due to its ability to induce apoptosis in a variety of cancer cells, while sparing normal cells. However, many human tumors including acute myeloid leukemia (AML) are partially or completely resistant to monotherapy with TRAIL, limiting its therapeutic utility. Therefore, identification of factors that contribute to TRAIL resistance may facilitate future development of more effective TRAIL-based cancer therapies. Here, we report a previously unknown role for WT1 in mediating TRAIL resistance in leukemia. Knockdown of WT1 with shRNA rendered TRAIL-resistant myeloid leukemia cells sensitive to TRAIL-induced cell death, and re-expression of shRNA-resistant WT1 restored TRAIL resistance. Notably, TRAIL-mediated apoptosis in WT1-silenced cells was largely due to down-regulation of the antiapoptotic protein Bcl-xL. Moreover, WT1 expression strongly correlated with overexpression of Bcl-xL in AML cell lines and blasts from AML patients. Furthermore, we found that WT1 transactivates Bcl-xL by directly binding to its promoter. We previously showed that WT1 is a novel client protein of heat shock protein 90 (Hsp90). Consistent with this, pharmacological inhibition of Hsp90 resulted in reduced WT1 and Bcl-xL expression leading to increased sensitivity of leukemia cells to TRAIL-mediated apoptosis. Collectively, our results suggest that WT1-dependent Bcl-xL overexpression contributes to TRAIL resistance in myeloid leukemias.     

Inhibition of Dengue virus and West Nile virus proteases by click chemistry-derived benz[d]isothiazol-3(2H)-one derivatives

Two click chemistry-derived focused libraries based on the benz[d]isothiazol-3(2H)-one scaffold were synthesized and screened against Dengue virus and West Nile virus NS2B-NS3 proteases. Several compounds (4l, 7j-n) displayed noteworthy inhibitory activity toward Dengue virus NS2B-NS3 protease in the absence and presence of added detergent. These compounds could potentially serve as a launching pad for a hit-to-lead optimization campaign.