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1.  Structural Basis of Multisite Single-Stranded DNA Recognition and ACTA2 Repression by Purine-Rich Element Binding Protein B (Purβ)† 
Biochemistry  2013;52(26):4439-4450.
A hallmark of dysfunctional fibroblast to myofibroblast differentiation associated with fibrotic disorders is persistent expression of ACTA2, the gene encoding the cyto-contractile protein smooth muscle α-actin. In this study, a PURB-specific gene knockdown approach was used in conjunction with biochemical analyses of protein subdomain structure and function to reveal the mechanism by which purine-rich element binding protein B (Purβ) restricts ACTA2 expression in mouse embryo fibroblasts (MEFs). Consistent with the hypothesized role of Purβ as a suppressor of myofibroblast differentiation, stable short hairpin RNA-mediated knockdown of Purβ in cultured MEFs promoted changes in cell morphology, actin isoform expression, and cell migration indicative of conversion to a myofibroblast-like phenotype. Promoter-reporter assays in transfected Purβ knockdown MEFs confirmed that these changes were attributable, in part, to de-repression of ACTA2 transcription. To map the domains in Purβ responsible for ACTA2 repression, several recombinant truncation mutants were generated and analyzed based on hypothetical, computationally-derived models of the tertiary and quaternary structure of Purβ. Discrete subdomains mediating sequence- and strand-specific cis-element binding, protein-protein interaction, and inhibition of a composite ACTA2 enhancer were identified using a combination of biochemical, biophysical, and cell-based assays. Our results indicate that the Purβ homodimer possesses three separate but unequal single-stranded DNA-binding modules formed by subdomain-specific inter- and intramolecular interactions. This structural arrangement suggests that the cooperative assembly of the dimeric Purβ repressor on the sense strand of the ACTA2 enhancer is dictated by the association of each subdomain with distinct purine-rich binding sites within the enhancer.
PMCID: PMC3750979  PMID: 23724822
2.  Structural and Biochemical Studies Reveal Differences in the Catalytic Mechanisms of Mammalian and Drosophila melanogaster Thioredoxin Reductases† 
Biochemistry  2007;46(16):4694-4705.
Thioredoxin reductase (TR) from Drosophila melanogaster(DmTR) is a member of the glutathione reductase (GR) family of pyridine nucleotide disulfide oxidoreductases and catalyzes the reduction of the redox-active disulfide bond of thioredoxin. DmTR is notable for having high catalytic activity without the presence of a selenocysteine (Sec) residue (which is essential for the mammalian thioredoxin reductases). We report here the X-ray crystal structure of DmTR at 2.4 Å resolution (Rwork = 19.8 %, Rfree = 24.7%) in which the enzyme was truncated to remove the C-terminal tripeptide sequence Cys-Cys-Ser. We also demonstrate that tetrapeptides equivalent to the oxidized C-terminal active sites of both mouse mitochondrial TR (mTR3) and DmTR, are substrates for the truncated forms of both enzymes. This truncated enzyme/peptide substrate system examines the kinetics of the ring opening step that occurs during the enzymatic cycle of TR. The ring opening step is 300-500 fold slower when Sec is replaced with Cys in mTR3 when using this system. Conversely, when Cys is replaced with Sec in DmTR, the rate of ring opening is only moderately increased (5-36 fold). Structures of these tetrapeptides were oriented in the active site of both enzymes using oxidized glutathione bound to GR as a template. DmTR has a more open subunit interface than the mouse enzyme and accommodates peptide Ser-Cys-Cys-Ser(ox) in a cis conformation that allows for protonation of the leaving group Cys by His464′, which helps to explain why this TR can function without the need for Sec. In contrast, mTR3 shows a narrower subunit interface. One possible result of this narrower interface is that the mammalian redox-active tetrapeptide Gly-Cys-Sec-Gly may adopt a trans conformation for a better fit. This places the Sec residue farther away from the protonating histidine residue, but the lower pKa of Sec in comparison to Cys eliminates the need for Sec to be protonated.
PMCID: PMC3687216  PMID: 17385893
3.  From Principle to Practice: Bridging the Gap in Patient Profiling 
PLoS ONE  2013;8(1):e54728.
The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is the likely cause of thrombosis, thrombin generation assays (TGA) have gained acceptance as “global assays” of haemostasis. These assays generate an enormous amount of data including four key thrombin parameters (lag time, maximum rate, peak and total thrombin) that may change to varying degrees over time in longitudinal studies. Currently, each thrombin parameter is averaged and presented individually in a table, bar graph or box plot; no method exists to visualize comprehensive thrombin generation data over time. To address this need, we have created a method that visualizes all four thrombin parameters simultaneously and can be animated to evaluate how thrombin generation changes over time. This method uses all thrombin parameters to intrinsically rank individuals based on their haemostatic status. The thrombin generation parameters can be derived empirically using TGA or simulated using computational models (CM). To establish the utility and diverse applicability of our method we demonstrate how warfarin therapy (CM), factor VIII prophylaxis for haemophilia A (CM), and pregnancy (TGA) affects thrombin generation over time. The method is especially suited to evaluate an individual's thrombotic and bleeding risk during “normal” processes (e.g pregnancy or aging) or during therapeutic challenges to the haemostatic system. Ultimately, our method is designed to visualize individualized patient profiles which are becoming evermore important as personalized medicine strategies become routine clinical practice.
PMCID: PMC3556038  PMID: 23372761
4.  Modeling of human factor Va inactivation by activated protein C 
BMC Systems Biology  2012;6:45.
Because understanding of the inventory, connectivity and dynamics of the components characterizing the process of coagulation is relatively mature, it has become an attractive target for physiochemical modeling. Such models can potentially improve the design of therapeutics. The prothrombinase complex (composed of the protease factor (F)Xa and its cofactor FVa) plays a central role in this network as the main producer of thrombin, which catalyses both the activation of platelets and the conversion of fibrinogen to fibrin, the main substances of a clot. A key negative feedback loop that prevents clot propagation beyond the site of injury is the thrombin-dependent generation of activated protein C (APC), an enzyme that inactivates FVa, thus neutralizing the prothrombinase complex. APC inactivation of FVa is complex, involving the production of partially active intermediates and “protection” of FVa from APC by both FXa and prothrombin. An empirically validated mathematical model of this process would be useful in advancing the predictive capacity of comprehensive models of coagulation.
A model of human APC inactivation of prothrombinase was constructed in a stepwise fashion by analyzing time courses of FVa inactivation in empirical reaction systems with increasing number of interacting components and generating corresponding model constructs of each reaction system. Reaction mechanisms, rate constants and equilibrium constants informing these model constructs were initially derived from various research groups reporting on APC inactivation of FVa in isolation, or in the presence of FXa or prothrombin. Model predictions were assessed against empirical data measuring the appearance and disappearance of multiple FVa degradation intermediates as well as prothrombinase activity changes, with plasma proteins derived from multiple preparations. Our work integrates previously published findings and through the cooperative analysis of in vitro experiments and mathematical constructs we are able to produce a final validated model that includes 24 chemical reactions and interactions with 14 unique rate constants which describe the flux in concentrations of 24 species.
This study highlights the complexity of the inactivation process and provides a module of equations describing the Protein C pathway that can be integrated into existing comprehensive mathematical models describing tissue factor initiated coagulation.
PMCID: PMC3403913  PMID: 22607732
Coagulation; Factor Va; Activated protein C; Prothrombinase; Prothrombin; Factor Xa; Mathematical modeling
5.  Defining the Boundaries of Normal Thrombin Generation: Investigations into Hemostasis 
PLoS ONE  2012;7(2):e30385.
In terms of its soluble precursors, the coagulation proteome varies quantitatively among apparently healthy individuals. The significance of this variability remains obscure, in part because it is the backdrop against which the hemostatic consequences of more dramatic composition differences are studied. In this study we have defined the consequences of normal range variation of components of the coagulation proteome by using a mechanism-based computational approach that translates coagulation factor concentration data into a representation of an individual's thrombin generation potential. A novel graphical method is used to integrate standard measures that characterize thrombin generation in both empirical and computational models (e.g max rate, max level, total thrombin, time to 2 nM thrombin (“clot time”)) to visualize how normal range variation in coagulation factors results in unique thrombin generation phenotypes. Unique ensembles of the 8 coagulation factors encompassing the limits of normal range variation were used as initial conditions for the computational modeling, each ensemble representing “an individual” in a theoretical healthy population. These “individuals” with unremarkable proteome composition was then compared to actual normal and “abnormal” individuals, i.e. factor ensembles measured in apparently healthy individuals, actual coagulopathic individuals or artificially constructed factor ensembles representing individuals with specific factor deficiencies. A sensitivity analysis was performed to rank either individual factors or all possible pairs of factors in terms of their contribution to the overall distribution of thrombin generation phenotypes. Key findings of these analyses include: normal range variation of coagulation factors yields thrombin generation phenotypes indistinguishable from individuals with some, but not all, coagulopathies examined; coordinate variation of certain pairs of factors within their normal ranges disproportionately results in extreme thrombin generation phenotypes, implying that measurement of a smaller set of factors may be sufficient to identify individuals with aberrant thrombin generation potential despite normal coagulation proteome composition.
PMCID: PMC3271084  PMID: 22319567
6.  The impact of uncertainty in a blood coagulation model 
Mathematical Medicine and Biology  2009;26(4):323-336.
Deterministic mathematical models of biochemical processes operate as if the empirically derived rate constants governing the dynamics are known with certainty. Our objective in this study was to explore the sensitivity of a deterministic model of blood coagulation to variations in the values of its 44 rate constants. This was accomplished for each rate constant at a given time by defining a normalized ensemble standard deviation (wkif(t)) that accounted for the sensitivity of the predicted concentration of each protein species to variation in that rate constant (from 10 to 1000% of the accepted value). A mean coefficient of variation derived from (wkif(t)) values for all protein species was defined to quantify the overall variation introduced into the model's predictive capacity at that time by the assumed uncertainty in that rate constant. A time-average value of the coefficient of variation over the 20-min simulation for each rate constant was then used to rank rate constants. The model's predictive capacity is particularly sensitive (50% of the aggregate variation) to uncertainty in five rate constants involved in the regulation of the formation and function of the factor VIIa–tissue factor complex. Therefore, our analysis has identified specific rate constants to which the predictive capability of this model is most sensitive and thus where improvements in measurement accuracy will yield the greatest increase in predictive capability.
PMCID: PMC3499082  PMID: 19451209
blood coagulation; uncertainty; math modeling
7.  Structural and functional consequences of the substitution of glycine 65 by arginine in the N-lobe of human transferrin 
Biochemistry  2009;48(9):1945-1953.
The G65R mutation in the N-lobe of human transferrin was created to mimic a naturally occurring variant (G394R) found in the homologous C-lobe. Because Gly65 is hydrogen-bonded to the iron-binding ligand Asp63, it comprises part of the second shell hydrogen bond network surrounding the iron within the metal binding cleft of the protein. Substitution with an arginine residue at this position disrupts the network, resulting in much more facile removal of iron from the G65R mutant. As shown by UV-vis and EPR spectroscopy, and by kinetic assays measuring the release of iron, the G65R mutant can exist in three forms. Two of the forms (yellow and pink in color) are inter-convertible. The yellow form predominates in 1 M bicarbonate; the pink form is generated from the yellow form upon exchange into 1 M HEPES buffer, pH 7.4. The third form (also pink in color) is produced by the addition of Fe3+-(nitrilotriacetate)2 to apo-G65R. Hydrogen/deuterium exchange experiments are consistent with all forms of the G65R mutant assuming a more open conformation. Additionally, mass spectroscopic analysis reveals the presence of nitrilotriacetate in the third form. The inability to obtain crystals of the G65R mutant, led to development of a novel crystallization strategy in which the double mutation G65R/K206E stabilizes a single closed pink conformer and captures Arg65 in a single position. Collectively, these studies highlight the importance of the hydrogen bond network in the cleft, as well as the inherent flexibility of the N-lobe which although able to adapt to accommodate the large arginine substitution exists in multiple conformations.
PMCID: PMC2693239  PMID: 19219998
The Journal of biological chemistry  2006;281(34):24934-24944.
Serum transferrin reversibly binds iron in each of two lobes and delivers it to cells by a receptor-mediated, pH-dependant process. The binding and release of iron results in a large conformational change in which two subdomains in each lobe close or open with a rigid twisting motion around a hinge. We report the structure of human serum transferrin (hTF) lacking iron (apo-hTF) which was independently determined by two methods: (1) the crystal structure of recombinant non-glycosylated apo-hTF was solved at 2.7 Å resolution using a MAD phasing strategy, by substituting the nine methionines in hTF with selenomethionine and (2) the structure of glycosylated apo-hTF (isolated from serum) was determined to a resolution of 2.7 Å by molecular replacement using the human apo-N-lobe and the rabbit holo-C1-subdomain as search models. These two crystal structures are essentially identical. They represent the first published model for full-length human TF and reveal that, in contrast to family members (human lactoferrin and hen ovotransferrin), both lobes are almost equally open: 59.4° and 49.5° rotations are required to open the N- and C-lobe, respectively, (compared to closed pig TF). Availability of this structure is critical to a complete understanding of the metal binding properties of each lobe of hTF; the apo-hTF structure suggests that differences in the hinge regions of the N- and C-lobes may influence the rates of iron binding and release. In addition, we evaluate potential interactions between apo-hTF and the human transferrin receptor.
PMCID: PMC1895924  PMID: 16793765

Results 1-8 (8)