A mechanism for hereditary angioedema caused by a methionine-379-to-lysine substitution in kininogens.

ABSTRACT: Hereditary angioedema (HAE) is associated with episodic kinin-induced swelling of the skin and mucosal membranes. Most patients with HAE have low plasma C1-inhibitor activity, leading to increased generation of the protease plasma kallikrein (PKa) and excessive release of the nanopeptide bradykinin from high-molecular-weight kininogen (HK). However, disease-causing mutations in at least 10% of patients with HAE appear to involve genes for proteins other than C1-inhibitor. A point mutation in the Kng1 gene encoding HK and low-molecular weight kininogen (LK) was identified recently in a family with HAE. The mutation changes a methionine (Met379) to lysine (Lys379) in both proteins. Met379 is adjacent to the Lys380-Arg381 cleavage site at the N-terminus of the bradykinin peptide. Recombinant wild-type (Met379) and variant (Lys379) versions of HK and LK were expressed in HEK293 cells. PKa-catalyzed kinin release from HK and LK was not affected by the Lys379 substitutions. However, kinin release from HK-Lys379 and LK-Lys379 catalyzed by the fibrinolytic protease plasmin was substantially greater than from wild-type HK-Met379 and LK-Met379. Increased kinin release was evident when fibrinolysis was induced in plasma containing HK-Lys379 or LK-Lys379 compared with plasma containing wild-type HK or LK. Mass spectrometry revealed that the kinin released from wild-type and variant kininogens by PKa is bradykinin. Plasmin also released bradykinin from wild-type kininogens but cleaved HK-Lys379 and LK-Lys379 after Lys379 rather than Lys380, releasing the decapeptide Lys-bradykinin (kallidin). The Met379Lys substitutions make HK and LK better plasmin substrates, reinforcing the relationship between fibrinolysis and kinin generation.

Uniform [13C,15N]-labeled and glycosylated IgG1 Fc expressed in Saccharomyces cerevisiae.

Despite the prevalence and importance of glycoproteins in human biology, methods for isotope labeling suffer significant limitations. Common prokaryotic platforms do not produce mammalian post-translation modifications that are essential to the function of many human glycoproteins, including immunoglobulin G1 (IgG1). Mammalian expression systems require complex media and thus introduce significant costs to achieve uniform labeling. Expression with Pichia is available, though expertise and equipment requirements surpass E. coli culture. We developed a system utilizing Saccharomyces cerevisiae, [13C]-glucose, and [15N]-ammonium chloride with complexity comparable to E. coli. Here we report two vectors for expressing the crystallizable fragment (Fc) of IgG1 for secretion into the culture medium, utilizing the ADH2 or DDI2 promoters. We also report a strategy to optimize the expression yield using orthogonal Taguchi arrays. Lastly, we developed two different media formulations, a standard medium which provides 86-92% 15N and 30% 13C incorporation into the polypeptide, or a rich medium which provides 98% 15N and 95% 13C incorporation as determined by mass spectrometry. This advance represents an expression and optimization strategy accessible to experimenters with the capability to grow and produce proteins for NMR-based experiments using E. coli.

Simulation of reaction diffusion processes over biologically relevant size and time scales using multi-GPU workstations.

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time simulations of modest-sized cells such as yeast are impractical on a single GPU. We present a new multi-GPU parallel implementation of the MPD-RDME method based on a spatial decomposition approach that supports dynamic load balancing for workstations containing GPUs of varying performance and memory capacity. We take advantage of high-performance features of CUDA for peer-to-peer GPU memory transfers and evaluate the performance of our algorithms on state-of-the-art GPU devices. We present parallel e ciency and performance results for simulations using multiple GPUs as system size, particle counts, and number of reactions grow. We also demonstrate multi-GPU performance in simulations of the Min protein system in E. coli. Moreover, our multi-GPU decomposition and load balancing approach can be generalized to other lattice-based problems.