Purification and characterization of β‐methylaspartase from Fusobacterium varium

Methylaspartase (EC 4.3.1.2) was purified 20fold in 35% yield from Fusobacterium varium, an obligate anaerobe. The purification steps included heat treatment, fractional precipitation with ammonium sulfate and ethanol, gel filtration, and ion exchange chromatography on DEAESepharose. The enzyme is dimeric, consisting of two identical 46 kDa subunits, and requires Mg²⁺ (K_m = 0.27 ± 0.01 mM) and K⁺ (K_m = 3.3 ± 0.8 mM) for maximum activity. Methylaspartasecatalyzed addition of ammonia to mesaconate yielded two diastereomeric amino acids, identified by HPLC as (2S,3S)3methylaspartate (major product) and (2S,3R)3methylaspartate (minor product). Optimal activity for the deamination of (2S,3S)3methylaspartate (K_m = 0.51 ± 0.04 mM) was observed at pH 9.7. The Nterminal protein sequence (30 residues) of the F. varium enzyme is 83% identical to the corresponding sequence of the clostridial enzyme.     

Decreased catalytic function with altered sumoylation of DNA topoisomerase I in the nuclei of scleroderma fibroblasts

Introduction  

Sumoylation is involved in nucleolus-nucleoplasm transport of DNA topoisomerase I (topo I), which may associate with changes of cellular and topo I functions. Skin fibroblasts of patients with systemic sclerosis (SSc) exhibit profibrotic cellular changes. The aims of this study were to examine the catalytic function and sumoylation of topo I in the nuclei of SSc fibroblasts, a major cell type involved in the fibrotic process.     

Path formation in a robot swarm

We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths in complex obstacle environments and exhibit very good scalability, robustness, and fault tolerance characteristics. Additionally, we observe that chains perform better for small robot group sizes, while vectorfield performs better for large groups.