PDB-wide identification of physiological hetero-oligomeric assemblies based on conserved quaternary structure geometry

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A high‐confidence reference dataset of differentially expressed proteins in elongating cotton fiber cells

In our previous study, we used a comparative proteomic approach based on 2DE to profile dynamic proteomes of cotton fibers and found 235 protein spots differentially expressed during the elongation process ranging from 5 to 25 days post‐anthesis. Of them, only 106 differentially expressed proteins (DEPs) were identified by MS due to database limitations at the time. In the present work, we successfully identified the remaining 129 DEPs from the same experimental system using high‐resolution MS with an updated database. Bioinformatic analysis revealed that proteins involved in carbohydrate and protein metabolism, transport, and redox homeostasis are the most abundant, and glycolysis was found to be the most significantly regulated process during fiber elongation. Our high‐confidence reference dataset, composed of 235 DEPs, provides a valuable resource for future studies on the molecular mechanism of cotton fiber elongation.     

New insights into the dispersion history and adaptive evolution of taxon Aegilops tauschii in China

Aegilops tauschii, the wild progenitor of wheat D-genome and a valuable germplasm for wheat improvement, has a wide natural distribution from eastern Turkey to China. However, the phylogenetic relationship and dispersion history of Ae. tauschii in China has not been scientifically clarified. In this study, we genotyped 208 accessions (with 104 in China) using ddRAD sequencing and 55K SNP array, and classified the population into six sublineages. Three possible spreading routes or events were identified, resulting in specific distribution patterns, with four sublineages found in Xinjiang, one in Qinghai, two in Shaanxi and one in Henan. We also established the correlation of SNP-based, karyotype-based and spike-morphology-based techniques to demonstrate the internal classification of Ae. tauschii, and developed consensus dataset with 1245 putative accessions by merging data previously published. Our analysis suggested that eight inter-lineage accessions could be assigned to the putative Lineage 3 and these accessions would help to conserve the genetic diversity of the species. By developing the consensus phylogenetic relationships of Ae. tauschii, our work validated the hypothesis on the dispersal history of Ae. tauschii in China, and contributed to the efficient and comprehensive germplasm-mining of the species.     

Establishing a minimum dataset for soil quality assessment based on soil properties and land-use changes

To assess soil quality, a minimum dataset (MDS) of soil properties has to be proposed commonly through calculating the total load of each candidate soil parameter on all of the qualified principal components by use of principal component analysis (PCA) and Norm-value computation. Considering intensive land-use changes, the method introduced in this study on MDS establishment integrates the quantified contributions of land-use type and land-use duration on each soil parameter by using multivariate analysis and mean multiple comparison. In this way, a MDS maximally representing all candidates with minimal loss of the soil quality information contained by those non-MDS soil parameters is established. The MDS proposed can not only well integrate the quantified influence of land-use changes and land-use duration on soil parameters, but is also quite flexible and extendable with the potential to be extrapolated to assess soil quality in other regions. Based on two sets of soil database obtained separately in 1985 and 2004, two MDSs established are compared with each other. It is found that only quite a small change in MDS components occurs during a 20-year period. For a better assessment of soil quality, it seems necessary to examine on what kind of temporal scale and how much MDS will change for a site-specific area with intensive land-use changes.     

Protein family clustering for structural genomics

A major goal of structural genomics is the provision of a structural template for a large fraction of protein domains. The magnitude of this task depends on the number and nature of protein sequence families. With a large number of bacterial genomes now fully sequenced, it is possible to obtain improved estimates of the number and diversity of families in that kingdom. We have used an automated clustering procedure to group all sequences in a set of genomes into protein families. Bench-marking shows the clustering method is sensitive at detecting remote family members, and has a low level of false positives. This comprehensive protein family set has been used to address the following questions. (1) What is the structure coverage for currently known families? (2) How will the number of known apparent families grow as more genomes are sequenced? (3) What is a practical strategy for maximizing structure coverage in future? Our study indicates that approximately 20% of known families with three or more members currently have a representative structure. The study indicates also that the number of apparent protein families will be considerably larger than previously thought: We estimate that, by the criteria of this work, there will be about 250,000 protein families when 1000 microbial genomes have been sequenced. However, the vast majority of these families will be small, and it will be possible to obtain structural templates for 70-80% of protein domains with an achievable number of representative structures, by systematically sampling the larger families.     

Non-linear cancer classification using a modified radial basis function classification algorithm

Summary This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.     

Comparison of sequence and structure-based datasets for nonredundant structural data mining

Structural data mining studies attempt to deduce general principles of protein structure from solved structures deposited in the protein data bank (PDB). The entire database is unsuitable for such studies because it is not representative of the ensemble of protein folds. Given that novel folds continue to be unearthed, some folds are currently unrepresented in the PDB while other folds are overrepresented. Overrepresentation can easily be avoided by filtering the dataset. PDB_SELECT is a well-used representative subset of the PDB that has been deduced by sequence comparison. Specifically, structures with sequences that exhibit a pairwise sequence identity above a threshold value are weeded from the dataset. Although length criteria for pairwise alignments have a structural basis, this automated method of pruning is essentially sequence-based and runs into problems in the twilight zone, possibly resulting in some folds being overrepresented. The value-added structure databases SCOP and CATH are also a potential source of a nonredundant dataset. Here we compare the sequence-derived dataset PDB_SELECT with the structural databases SCOP (Structural Classification Of Proteins) and CATH (Class-Architecture-Topology-Homology). We show that some folds remain overrepresented in the PDB_SELECT dataset while other folds are not represented at all. However, SCOP and CATH also have their own problems such as the labor-intensiveness of the update process and the problem of determining whether all folds are equally or sufficiently distant. We discuss areas where further work is required.     

Nickel Carcinogenicity in Relation to the Health Risks from Residual Oil Fly Ash

Epidemiological studies of workers in the nickel industry, animal exposure studies, and reports on the potential mechanisms of nickel-induced toxicity and carcinogenicity indicate that only crystalline sulfidic nickel compounds have been clearly established as carcinogenic or potentially carcinogenic in humans. This observation indicates the need to modify and update regulatory approaches for nickel to reflect noncancer toxicity values for some individual nickel species. Analysis of nickel compounds in residual oil fly ash (ROFA) indicates that sulfidic nickel compounds (e.g., nickel subsulfide, nickel sulfide) are not present. Thus, the potential for emission of carcinogenic nickel compounds from residual oil fly ash appears to be low. Preliminary reference concentrations (RfCs) for a number of nickel compounds, based on non-carcinogenic endpoints, are proposed on the basis of the benchmark dose approach in conjunction with NTP data for nickel species.