An ABA-binding protein with nucleic acid-binding property

Maize cDNA expression library was screened using the antibodies against ABA-binding proteins (ABBP). From 200 000 separated phage plaques, only one cDNA clone coding for a polypeptide was obtained. It shows 60%-65% homology to the genes of nucleic acid-binding proteins in mammalia. However, about 120 cDNA clones coding maize 17s rRNA were unexpectedly obtained. In view of this, the ABBP purified by ABA affinity chromatography and the antibodies derived from the purified ABBP were examined. It is found that rRNA is indeed present in the ABBP. In addition, the obtained antibodies could recognize RNA as well as ABBP. It is suggested that the ABBP have nucleic acid-binding property and can form a complex with rRNA.

Colonic microbiota alters host susceptibility to infectious colitis by modulating inflammation, redox status, and ion transporter gene expression

Individuals vary in their resistance to enteric infections. The role of the intestinal microbiota in altering susceptibility to enteric infection is relatively unknown. Previous studies have identified that C3H/HeOuJ mice suffer 100% mortality during Citrobacter rodentium-induced colitis, whereas C57BL/6 mice recover from infection. The basis for their differences in susceptibility is unclear and has been mainly attributed to differences in host genetics. This study investigated the role of the intestinal microbiota in altering susceptibility to C. rodentium-induced colitis. When the feces of C57BL/6 mice were gavaged into antibiotic treated C3H/HeOuJ mice, the C57BL/6 microflora led to a complete reversal in mortality patterns where 100% of the C3H/HeOuJ mice survived infection. This protection corresponded with reduced colonic pathology and less systemic pathogen load and was associated with increased inflammatory and redox responses with reduced epithelial cell death. C3H/HeOuJ mice are normally susceptible to infection-induced dehydration due to defective expression of colonic ion transporters such as Dra, CA IV, and CA I; expression of these genes was normalized when C3H/HeOuJ mice were colonized with the C57BL/6 microflora. Together, these data reveal that the colonic microbiota play a critical role in protecting against intestinal infection by inducing proinflammatory and prooxidant responses that control pathogen load as well as ion transporter gene expression previously shown to prevent fatal dehydration. Protection of mice from lethal colitis was associated with higher levels of bacteria from Bacteroidetes. This study reveals that the microbiota is sufficient to overcome inherent genetic susceptibility patterns in C3H/HeOuJ mice that cause mortality during C. rodentium infection.

Fragment-similarity-based QSAR (FS-QSAR) algorithm for ligand biological activity predictions

Quantitative structure-activity relationship (QSAR) studies are useful computational tools often used in drug discovery research and in many scientific disciplines. In this study, a robust fragment-similarity-based QSAR (FS-QSAR) algorithm was developed to correlate structures with biological activities by integrating fragment-based drug design concept and a multiple linear regression method. Similarity between any pair of training and testing fragments was determined by calculating the difference of lowest or highest eigenvalues of the chemistry space BCUT matrices of corresponding fragments. In addition to the BCUT-similarity function, molecular fingerprint Tanimoto coefficient (Tc) similarity function was also used as an alternative for comparison. For validation studies, the FS-QSAR algorithm was applied to several case studies, including a dataset of COX2 inhibitors and a dataset of cannabinoid CB2 triaryl bis-sulfone antagonist analogues, to build predictive models achieving average coefficient of determination (r(2)) of 0.62 and 0.68, respectively. The developed FS-QSAR method is proved to give more accurate predictions than the traditional and one-nearest-neighbour QSAR methods and can be a useful tool in the fragment-based drug discovery for ligand activity prediction.