Bayesian Modeling of Multiple Structural Connectivity Networks During the Progression of Alzheimer's Disease

Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity which may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches.Comment: Accepted to Biometrics January 202

Characterization of a site on PAI-1 that binds to vitronectin outside of the somatomedin B domain

Vitronectin and plasminogen activator inhibitor-1 (PAI-1) are proteins that interact in the circulatory system and pericellular region to regulate fibrinolysis, cell adhesion, and migration. The interactions between the two proteins have been attributed primarily to binding of the somatomedin B (SMB) domain, which comprises the N-terminal 44 residues of vitronectin, to the flexible joint region of PAI-1, including residues Arg-103, Met-112, and Gln-125 of PAI-1. A strategy for deletion mutagenesis that removes the SMB domain demonstrates that this mutant form of vitronectin retains PAI-1 binding (Schar, C. R., Blouse, G. E., Minor, K. M., and Peterson, C. B. (2008) J. Biol. Chem. 283, 10297-10309). In the current study, the complementary binding site on PAI-1 was mapped by testing for the ability of a battery of PAI-1 mutants to bind to the engineered vitronectin lacking the SMB domain. This approach identified a second, separate site for interaction between vitronectin and PAI-1. The binding of PAI-1 to this site was defined by a set of mutations in PAI-1 distinct from the mutations that disrupt binding to the SMB domain. Using the mutations in PAI-1 to map the second site suggested interactions between α-helices D and E in PAI-1 and a site in vitronectin outside of the SMB domain. The affinity of this second interaction exhibited a KD value ∼100-fold higher than that of the PAI-1-somatomedin B interaction. In contrast to the PAI-1-somatomedin B binding, the second interaction had almost the same affinity for active and latent PAI-1. We hypothesize that, together, the two sites form an extended binding area that may promote assembly of higher order vitronectin-PAI-1 complexes. © 2008 by The American Society for Biochemistry and Molecular Biology, Inc

H\u3csub\u3e2\u3c/sub\u3e Oxidation Over Supported Au Nanoparticle Catalysts: Evidence for Heterolytic H\u3csub\u3e2\u3c/sub\u3e Activation at the Metal-Support Interface

Water adsorbed at the metal-support interface (MSI) plays an important role in multiple reactions. Due to its importance in CO preferential oxidation (PrOx), we examined H2 oxidation kinetics in the presence of water over Au/TiO2 and Au/Al2O3 catalysts, reaching the following mechanistic conclusions: (i) O2 activation follows a similar mechanism to that proposed in CO oxidation catalysis; (ii) weakly adsorbed H2O is a strong reaction inhibitor; (iii) fast H2 activation occurs at the MSI, and (iv) H2 activation kinetics are inconsistent with traditional dissociative H2 chemisorption on metals. Density function theory (DFT) calculations using a supported Au nanorod model suggest H2 activation proceeds through a heterolytic dissociation mechanism, resulting in a formal hydride residing on the Au and a proton bound to a surface TiOH group. This potential mechanism was supported by infrared spectroscopy experiments during H2 adsorption on a deuterated Au/TiO2 surface, which showed rapid H-D scrambling with surface hydroxyl groups. DFT calculations suggest that the reaction proceeds largely through proton-mediated pathways and that typical Brønstednsted-Evans Polanyi behavior is broken by introducing weak acid/base sites at the MSI. THe kinetics data were successfully reinterpreted in the context of the heterolytic H2 activation mechanism, tying together the experimental and computational evidence and rationalizing the observed inhibition by physiorbed water on the support as blocking the MSI sites required for heterolytic H2 activation. In addition to providing evidence for the unusual H2 activation mechanism, these results offer additional insight into why water dramatically improves CO PrOx catalysis over Au

Bayesian Inference of Networks Across Multiple Sample Groups and Data Types

In this paper, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of Chronic Obstructive Pulmonary Disease (COPD)

A mechanism for assembly of complexes of vitronectin and plasminogen activator inhibitor-1 from sedimentation velocity analysis

Plasminogen activator inhibitor-1 (PAI-1) and vitronectin are cofactors involved in pathological conditions such as injury, inflammation, and cancer, during which local levels of PAI-1 are increased and the active serpin forms complexes with vitronectin. These complexes become deposited into surrounding tissue matrices, where they regulate cell adhesion and pericellular proteolysis. The mechanism for their co-localization has not been elucidated. We hypothesize that PAI-1-vitronectin complexes form in a stepwise and concentration-dependent fashion via 1:1 and 2:1 intermediates, with the 2:1 complex serving a key role in assembly of higher order complexes. To test this hypothesis, sedimentation velocity experiments in the analytical ultracentrifuge were performed to identify different PAI-1-vitronectin complexes. Analysis of sedimentation data invoked a novel multisignal method to discern the stoichiometry of the two proteins in the higher-order complexes formed (Balbo, A., Minor, K. H., Velikovsky, C. A., Mariuzza, R. A., Peterson, C. B., and Schuck, P. (2005) Proc. Natl. Acad. Sci. U. S. A. 102, 81-86). Our results demonstrate that PAI-1 and vitronectin assemble into higher order forms via a pathway that is triggered upon saturation of the two PAI-1-binding sites of vitronectin to form the 2:1 complex. This 2:1 PAI-1-vitronectin complex, with a sedimentation coefficient of 6.5 S, is the key intermediate for the assembly of higher order complexes