Structural and Mutational Characterization of the Blood Coagulation Factor VIII C Domain Lipid Binding Interface

Blood coagulation factor VIII (fVIII) functions as a cofactor in the blood coagulation cascade for proteolytic activation of factor X by factor IXa. During coagulation, fVIII is activated and subsequently binds to activated platelet surfaces by coordination of the fVIII C1 and C2 domains to the exposed phosphatidylserine of activated platelet membranes. Structural and mutational studies have suggested that both hydrophobic and electrostatic interactions occur between the two tandem C domains and activated lipid surfaces, but models of C domain phospholipid binding propose conflicting regions that directly interact with the membrane surface. This thesis reports the determination of the molecular structure of an isolated fVIII porcine C2 domain in the presence of the phosphatidylserine headgroup (OPLS) at 1.3 Å. The OPLS molecule makes direct contact with Q2213, N2217, S2289, and R2320. This structure represents the first deposited structure of fVIII C domains in the presence of a lipid headgroup moiety. Furthermore, phospholipid binding characteristics of basic residues within the proposed phospholipid binding regions were investigated by mutagenesis. Specifically, mutations at R2163, R2320, and a double mutant of R2163/R2320 caused almost complete abrogation of lipid binding to soluble lipid nanodiscs. Using these findings, an updated model of fVIII lipid binding is proposed using structural information from C2 domain inhibitors, previous literature, and newly defined interactions between C2 and OPLS. Together, this study proposes that R2163 and R2320 are the center of a conserved phospholipid binding motif that extends across homologous blood clotting proteins

Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation.

BACKGROUND: Although numerous investigations have compared gene expression microarray platforms, preprocessing methods and batch correction algorithms using constructed spike-in or dilution datasets, there remains a paucity of studies examining the properties of microarray data using diverse biological samples. Most microarray experiments seek to identify subtle differences between samples with variable background noise, a scenario poorly represented by constructed datasets. Thus, microarray users lack important information regarding the complexities introduced in real-world experimental settings. The recent development of a multiplexed, digital technology for nucleic acid measurement enables counting of individual RNA molecules without amplification and, for the first time, permits such a study. RESULTS: Using a set of human leukocyte subset RNA samples, we compared previously acquired microarray expression values with RNA molecule counts determined by the nCounter Analysis System (NanoString Technologies) in selected genes. We found that gene measurements across samples correlated well between the two platforms, particularly for high-variance genes, while genes deemed unexpressed by the nCounter generally had both low expression and low variance on the microarray. Confirming previous findings from spike-in and dilution datasets, this "gold-standard" comparison demonstrated signal compression that varied dramatically by expression level and, to a lesser extent, by dataset. Most importantly, examination of three different cell types revealed that noise levels differed across tissues. CONCLUSIONS: Microarray measurements generally correlate with relative RNA molecule counts within optimal ranges but suffer from expression-dependent accuracy bias and precision that varies across datasets. We urge microarray users to consider expression-level effects in signal interpretation and to evaluate noise properties in each dataset independently

Modeling and Evaluating Pilot Performance in NextGen: Review of and Recommendations Regarding Pilot Modeling Efforts, Architectures, and Validation Studies

NextGen operations are associated with a variety of changes to the national airspace system (NAS) including changes to the allocation of roles and responsibilities among operators and automation, the use of new technologies and automation, additional information presented on the flight deck, and the entire concept of operations (ConOps). In the transition to NextGen airspace, aviation and air operations designers need to consider the implications of design or system changes on human performance and the potential for error. To ensure continued safety of the NAS, it will be necessary for researchers to evaluate design concepts and potential NextGen scenarios well before implementation. One approach for such evaluations is through human performance modeling. Human performance models (HPMs) provide effective tools for predicting and evaluating operator performance in systems. HPMs offer significant advantages over empirical, human-in-the-loop testing in that (1) they allow detailed analyses of systems that have not yet been built, (2) they offer great flexibility for extensive data collection, (3) they do not require experimental participants, and thus can offer cost and time savings. HPMs differ in their ability to predict performance and safety with NextGen procedures, equipment and ConOps. Models also vary in terms of how they approach human performance (e.g., some focus on cognitive processing, others focus on discrete tasks performed by a human, while others consider perceptual processes), and in terms of their associated validation efforts. The objectives of this research effort were to support the Federal Aviation Administration (FAA) in identifying HPMs that are appropriate for predicting pilot performance in NextGen operations, to provide guidance on how to evaluate the quality of different models, and to identify gaps in pilot performance modeling research, that could guide future research opportunities. This research effort is intended to help the FAA evaluate pilot modeling efforts and select the appropriate tools for future modeling efforts to predict pilot performance in NextGen operations

Structure of Blood Coagulation Factor VIII in Complex With an Anti-C2 Domain Non-Classical, Pathogenic Antibody Inhibitor

Factor VIII (fVIII) is a procoagulant protein that binds to activated factor IX (fIXa) on platelet surfaces to form the intrinsic tenase complex. Due to the high immunogenicity of fVIII, generation of antibody inhibitors is a common occurrence in patients during hemophilia A treatment and spontaneously occurs in acquired hemophilia A patients. Non-classical antibody inhibitors, which block fVIII activation by thrombin and formation of the tenase complex, are the most common anti-C2 domain pathogenic inhibitors in hemophilia A murine models and have been identified in patient plasmas. In this study, we report on the X-ray crystal structure of a B domain-deleted bioengineered fVIII bound to the non-classical antibody inhibitor, G99. While binding to G99 does not disrupt the overall domain architecture of fVIII, the C2 domain undergoes an ~8 Å translocation that is concomitant with breaking multiple domain-domain interactions. Analysis of normalized B-factor values revealed several solvent-exposed loops in the C1 and C2 domains which experience a decrease in thermal motion in the presence of inhibitory antibodies. These results enhance our understanding on the structural nature of binding non-classical inhibitors and provide a structural dynamics-based rationale for cooperativity between anti-C1 and anti-C2 domain inhibitors

Structure of Blood Coagulation Factor VIII in Complex with Anti-C2 Domain Inhibitory Antibody

Factor VIII (fVIII) is a procoagulant protein that binds to activated factor IX (fIXa) on platelet surfaces to form the intrinsic tenase complex. Due to the high immunogenicity of fVIII, generation of antibody inhibitors is a common occurrence in patients during hemophilia A treatment and spontaneously occurs in acquired hemophilia A patients. Non-classical antibody inhibitors, which block fVIII activation by thrombin and formation of the tenase complex, are the most common anti-C2 domain pathogenic inhibitors in hemophilia A murine models and have been identified in patient plasmas. In this study, we report on the X-ray crystal structure of a B domain-deleted bioengineered fVIII bound to the non classical antibody inhibitor, G99. While binding to G99 does not disrupt the overall domain architecture of fVIII, the C2 domain undergoes an ~8 Å translocation that is concomitant with breaking multiple domain-domain interactions. Analysis of normalized B-factor values revealed several solvent-exposed loops in the C1 and C2 domains which experience a decrease in thermal motion in the presence of inhibitory antibodies. Our results enhance our understanding on the structural nature of binding non-classical inhibitors and provide a structural dynamics-based rationale for cooperativity between anti-C domain inhibitors

Neoliberalism is not a theory of everything: a Bourdieuian analysis of illusio in educational research

 Despite the frequency with which the concept of neoliberalism is employed within academic literature, its complex and multifaceted nature makes it difficult to define and describe. Indeed, data reported in this article suggest that there is a tendency in educational research to make extensive use of the word ‘neoliberalism’ (or its variants neoliberal, neo-liberal and neo-liberalism) as a catch-all for something negative but without offering a definition or explanation. The article highlights a number of key risks associated with this approach and draws on the Bourdieuian concept of illusio to suggest the possibility that when as educational researchers we use the word ‘neoliberalism’ in this way, rather than interrupting the implementation of neoliberal policies and practices, we may, in fact, be further entrenching the neoliberal doxa. That is to say, we are both playing the neoliberal game and inadvertently demonstrating our belief that it is a game worth being played. In so doing, this article seeks to extend understandings of what illusio means within the context of educational research

Hundreds of variants clustered in genomic loci and biological pathways affect human height

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

Patient-specific cancer genes contribute to recurrently perturbed pathways and establish therapeutic vulnerabilities in esophageal adenocarcinoma

Abstract: The identification of cancer-promoting genetic alterations is challenging particularly in highly unstable and heterogeneous cancers, such as esophageal adenocarcinoma (EAC). Here we describe a machine learning algorithm to identify cancer genes in individual patients considering all types of damaging alterations simultaneously. Analysing 261 EACs from the OCCAMS Consortium, we discover helper genes that, alongside well-known drivers, promote cancer. We confirm the robustness of our approach in 107 additional EACs. Unlike recurrent alterations of known drivers, these cancer helper genes are rare or patient-specific. However, they converge towards perturbations of well-known cancer processes. Recurrence of the same process perturbations, rather than individual genes, divides EACs into six clusters differing in their molecular and clinical features. Experimentally mimicking the alterations of predicted helper genes in cancer and pre-cancer cells validates their contribution to disease progression, while reverting their alterations reveals EAC acquired dependencies that can be exploited in therapy