A novel mutant with triple the dietary fiber content in white rice.

Dietary fiber has high functional value in relation to gut flora. We searched for a high-lysine mutant of the most popular rice cultivar in Japan, 'Koshihikari', as a route to a higher dietary fiber content like a success case in new barley cultivar, 'Beau-fiber'. We found several promising high-lysine mutants with high dietary fiber content. One of these, 'WFE5', has three times the dietary fiber content in white rice. Two rounds of backcrossing to Koshihikari produced a near-isogenic line with a high fiber content. The line's agronomic traits were close to those of Koshihikari except for yield and eating quality. As these two traits are critical, we discuss how to improve them.

Bordetella bronchiseptica Bcr4 antagonizes the negative regulatory function of BspR via its role in type III secretion.

Bordetella species, including B. pertussis, have a type III secretion system that is highly conserved among gram-negative pathogenic bacteria. Genes encoding the component proteins of the type III secretion system are localized at the bsc locus in the Bordetella genome. Here, the function of a hypothetical protein Bcr4 encoded at the bsc locus in the B. bronchiseptica genome was investigated. A Bcr4-deficient mutant was created and the amounts of type III secreted proteins (e.g., BopB, BopN and Bsp22) in both the supernatant fraction and whole-cell lysates of the Bcr4-deficient mutant were determined. It was found that the amounts of these proteins were significantly lower than in the wild-type strain. The amounts of type III secreted proteins in the supernatant fraction and whole-cell lysates were much greater in a Bcr4-overproducing strain than in the wild-type strain. The type III secreted protein BspR reportedly negatively regulates the type III secretion system. Here, it was observed that a Bcr4 + BspR double-knockout mutant did not secrete type III secreted proteins, whereas the amounts of these proteins in whole-cell lysates of this mutant were nearly equal to those in whole-cell lysates of the BspR-deficient mutant. Bcr4 thus appears to play an essential role in the extracellular secretion of type III secreted proteins. Our data also suggest that Bcr4 antagonizes the negative regulatory function of BspR.

Solvatochromic fluorescence properties of pyrazine-boron complex bearing a ß-iminoenolate ligand.

Pyrazine-based monoboron complexes bearing two fluorine atoms (BF2 complex) or two phenyl groups (BPh2 complex) on the boron atom were synthesized, and the fluorescence properties were investigated. The BPh2 complexes exhibited red-shifted maximum absorption and maximum fluorescence wavelengths and lower molar absorption coefficients than the corresponding BF2 complexes in n-hexane. The fluorescence quantum yields of the BPh2 complexes were higher than or comparable to the corresponding BF2 complexes owing to the relatively low nonradiative rate constants. Although the nonsubstituted and trifluoromethyl-substituted derivatives did not show solvatochromism, the dimethylamino-substituted BF2 and BPh2 complexes exhibited pronounced solvatochromism in the fluorescence spectra. Dual fluorescence was observed for the dimethylamino-substituted BF2 complex in toluene, 1,4-dioxane, and chloroform, corresponding to locally excited (LE) and twisted intramolecular charge-transfer (TICT) states.

Prediction of protein-protein interaction strength using domain features with supervised regression.

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.