High level of conservation and diversity among the endophytic seed bacteriome in eight alpine grassland species growing at the Qinghai Tibetan Plateau.

Seed borne microorganisms play an important role in plant biology. Concerns have recently been raised about loss of seed microbial diversity by seed treatments, crop domestication and plant breeding. Information on the seed microbiomes of native plants growing in natural ecosystems is beneficial as they provide the best settings to detect indigenous plant microbe interactions. Here, we characterized the seed bacterial community of 8 native alpine grassland plants. First, seed bacterial diversity was examined using Illumina DNA sequencing, then 28 cultivable bacteria were isolated and potential functions were explored. Across 8 plant species, 343 different bacterial genera were identified as seed endophytes, 31 of those were found in all plant species, indicating a high level of conservation. Proteobacteria, Actinobacteria, Firmicutes, Bacteroidetes and Chloroflexi were the top five dominant phyla. Plant species identity was a key determinant shaping the seed endophytic bacteriome. ACC deaminase activity, siderophores production and secretion of lytic enzymes were common functions shown by isolated bacteria. Our results demonstrate that highly diverse and beneficial bacterial populations are hosted by seeds of alpine grassland species to ensure the establishment of best bacterial symbionts for the next generation. This information is useful for crop improvement by reinstating beneficial seed microbial diversities for high-quality forage and crop seeds.

Genetic association of rs1344706 in ZNF804A with bipolar disorder and schizophrenia susceptibility in Chinese populations.

Rs1344706 in the the zinc finger protein 804A (ZNF804A) gene has been identified to be associated with schizophrenia and bipolar disorder (BD) in Europeans. However, whether rs1344706 is associated with schizophrenia in Chinese populations remains inconclusive; furthermore, the association between rs1344706 and BD in Chinese populations has been rarely explored. To explore the association between rs1344706 and schizophrenia/BD in Chinese populations, we genotyped rs1344706 among 1128 Chinese subjects (537 patients with BD and 591 controls) and found that rs1344706 showed marginal allelic association with BD (P = 0.028) with T-allele being more prevalent in cases than that in controls (OR = 1.19, 95% CI 1.03-1.37). Meta-analysis of rs1344706 by pooling all available data showed that rs1344706 was significantly associated with BD (P = 0.001). Besides, positive association of rs1344706 with schizophrenia was observed in Northern Chinese (P = 0.005). Furthermore, ZNF804A is highly expressed in human and mouse brains, especially in prenatal stage.

Predicting protein folding rates using the concept of Chou's pseudo amino acid composition.

One of the most important challenges in computational and molecular biology is to understand the relationship between amino acid sequences and the folding rates of proteins. Recent works suggest that topological parameters, amino acid properties, chain length and the composition index relate well with protein folding rates, however, sequence order information has seldom been considered as a property for predicting protein folding rates. In this study, amino acid sequence order was used to derive an effective method, based on an extended version of the pseudo-amino acid composition, for predicting protein folding rates without any explicit structural information. Using the jackknife cross validation test, the method was demonstrated on the largest dataset (99 proteins) reported. The method was found to provide a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.81 (with a highly significant level) and the standard error is 2.46. The reported algorithm was found to perform better than several representative sequence-based approaches using the same dataset. The results indicate that sequence order information is an important determinant of protein folding rates.

Predicting protein folding rate from amino acid sequence.

Predicting protein folding rate from amino acid sequence is an important challenge in computational and molecular biology. Over the past few years, many methods have been developed to reflect the correlation between the folding rates and protein structures and sequences. In this paper, we present an effective method, a combined neural network--genetic algorithm approach, to predict protein folding rates only from amino acid sequences, without any explicit structural information. The originality of this paper is that, for the first time, it tackles the effect of sequence order. The proposed method provides a good correlation between the predicted and experimental folding rates. The correlation coefficient is 0.80 and the standard error is 2.65 for 93 proteins, the largest such databases of proteins yet studied, when evaluated with leave-one-out jackknife test. The comparative results demonstrate that this correlation is better than most of other methods, and suggest the important contribution of sequence order information to the determination of protein folding rates.

An efficient sliding window strategy for accurate location of eukaryotic protein coding regions.

The sliding window is one of important factors that seriously affect the accuracy of coding region prediction and location for the methods based on power spectrum technique. It is very difficult to select the appropriate sliding step and the window length for different organisms. In this study, a novel sliding window strategy is proposed on the basis of power spectrum analysis for the accurate location of eukaryotic protein coding regions. The proposed sliding window strategy is very simple and the sliding step of window is changeable. Our tests show that the average location error for the novel method is 12 bases. Compared with the previous location error of 54 bases using the fixed sliding step, the novel sliding window strategy increased the location accuracy greatly. Further, the consumed CPU time to run the novel strategy is much shorter than the strategy of the fixed length sliding step. So, the computational complexity for the novel method is decreased greatly.

Direct correlation between proteins' folding rates and their amino acid compositions: an ab initio folding rate prediction.

Discovering the mechanism of protein folding, in molecular biology, is a great challenge. A key step to this end is to find factors that correlate with protein folding rates. Over the past few years, many empirical parameters, such as contact order, long-range order, total contact distance, secondary structure contents, have been developed to reflect the correlation between folding rates and protein tertiary or secondary structures. However, the correlation between proteins' folding rates and their amino acid compositions has not been explored. In the present work, we examined systematically the correlation between proteins' folding rates and their amino acid compositions for two-state and multistate folders and found that different amino acids contributed differently to the folding progress. The relation between the amino acids' molecular weight and degeneracy and the folding rates was examined, and the role of hydrophobicity in the protein folding process was also inspected. As a consequence, a new indicator called composition index was derived, which takes no structure factors into account and is merely determined by the amino acid composition of a protein. Such an indicator is found to be highly correlated with the protein's folding rate (r > 0.7). From the results of this work, three points of concluding remarks are evident. (1) Two-state folders and multistate folders have different rate-determining amino acids. (2) The main determining information of a protein's folding rate is largely reflected in its amino acid composition. (3) Composition index may be the best predictor for an ab initio protein folding rate prediction directly from protein sequence from the standpoint of practical application.

Identification of HLA-A*0201-restricted cytotoxic T lymphocyte epitope from TRAG-3 antigen.

PURPOSE: Identification of tumor antigen and subsequent identification of T-cell epitope from these antigens make specific immunotherapy for malignant tumor applicable. Because TRAG-3 antigen is expressed in most melanomas and 54% of non-small cell lung carcinomas and HLA-A2.1-expressing individuals cover >50% in the population of China, we aim at identifying TRAG-3-encoded peptide presented by HLA-A2.1. EXPERIMENTAL DESIGN: In our study, a HLA-A2.1-restricted CTL epitope was identified by using the following four-step procedure: (a) computer-based epitope prediction from the amino acid sequence of TRAG-3 antigen; (b) peptide-binding assay to determine the affinity of the predicted peptide with HLA-A2.1 molecule; (c) stimulation of primary T-cell response against the predicted peptides in vitro; and (d) testing of the induced CTLs toward LB373-MEL cells expressing TRAG-3 antigen and HLA-A2.1. RESULTS: Of the four tested peptides, effectors induced by a peptide of TRAG-3 at residue position 58-66 lysed LB373-MEL cells expressing both TRAG-3 and HLA-A2.1. Our results indicate that peptide TRAG-3(58 approximately 66) (ILLRDAGLV) is a new HLA-A2.1-restricted CTL epitope capable of inducing TRAG-3 specific CTLs in vitro. CONCLUSIONS: Because TRAG-3 is a cancer/testis antigen expressed in most melanomas and half of non-small cell lung carcinomas, identification of the TRAG-3/HLA-A2.1 peptide ILLRDAGLV may facilitate peptide-based specific immunotherapy for various histological tumors.