Incorporation of albumin fusion proteins into fibrin clots in vitro and in vivo: comparison of different fusion motifs recognized by factor XIIIa

<p>Abstract</p> <p>Background</p> <p>The transglutaminase activated factor XIII (FXIIIa) acts to strengthen pathological fibrin clots and to slow their dissolution, in part by crosslinking active α₂-antiplasmin (α₂AP) to fibrin. We previously reported that a yeast-derived recombinant fusion protein comprising α₂AP residues 13-42 linked to human serum albumin (HSA) weakened <it>in vitro </it>clots but failed to become specifically incorporated into <it>in vivo </it>clots. In this study, our aims were to improve both the stability and clot localization of the HSA fusion protein by replacing α₂AP residues 13-42 with shorter sequences recognized more effectively by FXIIIa.</p> <p>Results</p> <p>Expression plasmids were prepared encoding recombinant HSA with the following N-terminal 23 residue extensions: H₆NQEQVSPLTLLAG₄Y (designated XL1); H₆DQMMLPWAVTLG₄Y (XL2); H₆WQHKIDLPYNGAG₄Y (XL3); and their 17 residue non-His-tagged equivalents (XL4, XL5, and XL6). The HSA moiety of XL4- to XL6-HSA proteins was C-terminally His-tagged. All chimerae were efficiently secreted from transformed <it>Pichia pastoris </it>yeast except XL3-HSA, and following nickel chelate affinity purification were found to be intact by amino acid sequencing, as was an N-terminally His-tagged version of α₂AP(13-42)-HSA. Of the proteins tested, XL5-HSA was cross-linked to biotin pentylamine (BPA) most rapidly by FXIIIa, and was the most effective competitor of α₂AP crosslinking not only to BPA but also to plasma fibrin clots. In the mouse ferric chloride <it>vena cava </it>thrombosis model, radiolabeled XL5-HSA was retained in the clot to a greater extent than recombinant HSA. In the rabbit jugular vein stasis thrombosis model, XL5-HSA was also retained in the clot, in a urea-insensitive manner indicative of crosslinking to fibrin, to a greater extent than recombinant HSA.</p> <p>Conclusions</p> <p>Fusion protein XL5-HSA (DQMMLPWAVTLG₄Y-HSAH₆) was found to be more active as a substrate for FXIIIa-mediated transamidation than seven other candidate fusion proteins <it>in vitro</it>. The improved stability and reactivity of this chimeric protein was further evidenced by its incorporation into <it>in vivo </it>clots formed in thrombosis models in both mice and rabbits.</p

GLOBAL REGISTRATION OF MULTIPLE POINT CLOUDS USING SEMIDEFINITE PROGRAMMING

Consider N points in R-d and M local coordinate systems that are related through unknown rigid transforms. For each point, we are given (possibly noisy) measurements of its local coordinates in some of the coordinate systems. Alternatively, for each coordinate system, we observe the coordinates of a subset of the points. The problem of estimating the global coordinates of the N points (up to a rigid transform) from such measurements comes up in distributed approaches to molecular conformation and sensor network localization, and also in computer vision and graphics. The least-squares formulation of this problem, although nonconvex, has a well-known closed-form solution when M = 2 (based on the singular value decomposition (SVD)). However, no closed-form solution is known for M >= 3. In this paper, we demonstrate how the least-squares formulation can be relaxed into a convex program, namely, a semidefinite program (SDP). By setting up connections between the uniqueness of this SDP and results from rigidity theory, we prove conditions for exact and stable recovery for the SDP relaxation. In particular, we prove that the SDP relaxation can guarantee recovery under more adversarial conditions compared to earlier proposed spectral relaxations, and we derive error bounds for the registration error incurred by the SDP relaxation. We also present results of numerical experiments on simulated data to confirm the theoretical findings. We empirically demonstrate that (a) unlike the spectral relaxation, the relaxation gap is mostly zero for the SDP (i.e., we are able to solve the original nonconvex least-squares problem) up to a certain noise threshold, and (b) the SDP performs significantly better than spectral and manifold-optimization methods, particularly at large noise levels

Linearized ADMM and FAST nonlocal denoising for efficient plug-and-play restoration

In plug-and-play image restoration, the regularization is performed using powerful denoisers such as nonlocal means (NLM) or BM3D. This is done within the framework of alternating direction method of multipliers (ADMM), where the regularization step is formally replaced by an off-the-shelf denoiser. Each plug-and-play iteration involves the inversion of the forward model followed by a denoising step. In this paper, we present a couple of ideas for improving the efficiency of the inversion and denoising steps. First, we propose to use linearized ADMM, which generally allows us to perform the inversion at a lower cost than standard ADMM. Moreover, we can easily incorporate hard constraints into the optimization framework as a result. Second, we develop a fast algorithm for doubly stochastic NLM, originally proposed by Sreehari et al. (IEEE TCI, 2016), which is about 80� faster than brute-force computation. This particular denoiser can be expressed as the proximal map of a convex regularizer and, as a consequence, we can guarantee convergence for linearized plug-and-play ADMM. We demonstrate the effectiveness of our proposals for super-resolution and single-photon imaging. © 2018 IEEE