Zinc deficiency-induced defensin-like proteins are involved in the inhibition of root growth in Arabidopsis.

The depletion of cellular zinc (Zn) adversely affects plant growth. Plants have adaptation mechanisms for Zn-deficient conditions, inhibiting growth through the action of transcription factors and metal transporters. We previously identified three defensin-like (DEFL) proteins (DEFL203, DEFL206 and DEFL208) that were induced in Arabidopsis thaliana roots under Zn-depleted conditions. DEFLs are small cysteine-rich peptides involved in defense responses, development and excess metal stress in plants. However, the functions of DEFLs in the Zn-deficiency response are largely unknown. Here, phylogenetic tree analysis revealed that seven DEFLs (DEFL202-DEFL208) were categorized into one subgroup. Among the seven DEFLs, the transcripts of five (not DEFL204 and DEFL205) were upregulated by Zn deficiency, consistent with the presence of cis-elements for basic-region leucine-zipper 19 (bZIP19) or bZIP23 in their promoter regions. Microscopic observation of GFP-tagged DEFL203 showed that DEFL203-sGFP was localized to the apoplast and plasma membrane. Whereas a single mutation of the DEFL202 or DEFL203 genes only slightly affected root growth, defl202 defl203 double mutants showed enhanced root growth under all growth conditions. We also showed that the size of the root meristem was increased in the double mutants compared with the wild type. Our results suggest that DEFL202 and DEFL203 are redundantly involved in the inhibition of root growth under Zn-deficient conditions through a reduction in root meristem length and cell number.

Novel computational methodologies for structural modeling of spacious ligand binding sites of G-protein-coupled receptors: development and application to human leukotriene B4 receptor.

This paper describes a novel method to predict the activated structures of G-protein-coupled receptors (GPCRs) with high accuracy, while aiming for the use of the predicted 3D structures in in silico virtual screening in the future. We propose a new method for modeling GPCR thermal fluctuations, where conformation changes of the proteins are modeled by combining fluctuations on multiple time scales. The core idea of the method is that a molecular dynamics simulation is used to calculate average 3D coordinates of all atoms of a GPCR protein against heat fluctuation on the picosecond or nanosecond time scale, and then evolutionary computation including receptor-ligand docking simulations functions to determine the rotation angle of each helix of a GPCR protein as a movement on a longer time scale. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the proposed method was able to derive the appropriate 3D structure of the active-state GPCR which docks with its agonists.

Mass spectrometry-based prokaryote gene annotation.

MS combined with database searching has become the preferred method for identifying proteins present in cell or tissue samples. The technique enables us to execute large-scale proteome analyses of species whose genomes have already been sequenced. Searching mass spectrometric data against protein databases composed of annotated genes has been widely conducted. However, there are some issues with this technique; wrong annotations in protein databases cause deterioration in the accuracy of protein identification, and only proteins that have already been annotated can be identified. We propose a new framework that can detect correct ORFs by integrating an MS/MS proteomic data mapping and a knowledge-based system regarding the translation initiation sites. This technique can provide correction of predicted coding sequences, together with the possibility of identifying novel genes. We have developed a computational system; it should first conduct the probabilistic peptide-matching against all possible translational frames using MS/MS data, then search for discriminative DNA patterns around the detected peptides, and lastly integrate the facts using empirical knowledge stored in knowledge bases to obtain correct ORFs. We used photosynthetic bacteria Synechocystis sp. PCC6803 as a sample prokaryote, resulting in the finding of 14 N-terminus annotation errors and several new candidate genes.

The pattern of polymorphism in Arabidopsis thaliana.

We resequenced 876 short fragments in a sample of 96 individuals of Arabidopsis thaliana that included stock center accessions as well as a hierarchical sample from natural populations. Although A. thaliana is a selfing weed, the pattern of polymorphism in general agrees with what is expected for a widely distributed, sexually reproducing species. Linkage disequilibrium decays rapidly, within 50 kb. Variation is shared worldwide, although population structure and isolation by distance are evident. The data fail to fit standard neutral models in several ways. There is a genome-wide excess of rare alleles, at least partially due to selection. There is too much variation between genomic regions in the level of polymorphism. The local level of polymorphism is negatively correlated with gene density and positively correlated with segmental duplications. Because the data do not fit theoretical null distributions, attempts to infer natural selection from polymorphism data will require genome-wide surveys of polymorphism in order to identify anomalous regions. Despite this, our data support the utility of A. thaliana as a model for evolutionary functional genomics.