Discovering the cellular-localized functional modules and modular interactions in response to liver cancer

In this paper,we firstly identify the functional modules enriched with differentially expressed genes(DEGs) and characterized by biological processes in specific cellular locations,based on gene ontology(GO) and microarray data.Then,we further define and filter disease relevant signature modules accord-ing to the ranking of the disease discriminating abilities of the pre-seleeted functional modules.At last,we analyze the potential way by which they cooperate towards human disease.Application of the proposedmethod to the analysis of a liver cancer dataset shows that,using the same false discovery rate (FDR)threshold,we can find more biologically meaningful and detailed processes by using the cellular localiza-tion information.Some biological evidences support the relevancy of our biological modules to the diseasemechanism.

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.

Cloning and biological activity of an anti-tumor peptide of Tumstatin

To obtain an anti-tumor peptide of Tumstatin and detect its biological activity,the nucleotide sequence encoding 185-203 amino acids(19peptide)of Tumstatin was synthesized and inserted into the fusion protein vector pTYB2.After identification by sequencing and restriction endonucle-ases,the recombined vector was transformed into BL-21(DE3)E.coli competent cells.Transformed E.coli BL-21(DE3)were induced by isopropyl-β-thiogalactopyranoside(IPTG),and then expressed.By 1,4-dithiothreitol(DTT)reduction,the soluble 19peptide was obtained from a chitin affinity chromatograph.The biological activity of 19peptide was determined by 3-[4,5-dimethylthiazol-2-y1]-2,5-dipheny-tetrazolium bromide(MTT)assay,cell growth curve,the effect of the ascitic fluid transfevent H22 hepatoma on mice and via histopathological slices.The purified 19peptide directly inhibited proliferation and migration of murine B16 melanoma cells,SMMC-7721hepatoma carcinoma cells and human umbilical vein endothelial cells(HUVEC).The tumor inhibition rate of mice ascitic fluid transfevent H22 hepatoma was 48.46%.Histopathological slices showed that it could promote tumor tissue necrosis and decrease the density of blood vessels.With higher anti-tumor activity,19peptide has the potential to become a novel,potent anti-tumor agent.