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1.  Construction of a novel vector expressing the fusion suicide gene yCDglyTK and hTERT-shRNA and its antitumor effects 
This study aimed to construct a novel recombinant expression vector, pcDNA3.1(-)hTERT-shRNA/yCDglyTK. Its bioactivity and antitumor effects were investigated in the SGC7901 human gastric cancer cell line. Interfering RNA (RNAi) targeting human telomerase reverse transcriptase (hTERT) was applied to construct the pYr1.1-hTERT-shRNA vector. The shRNA expression cassette (including U6 promoter) was subcloned into the pcDNA3.1(-) CV-yCDglyTK vector to build a new vector, pcDNA3.1(-) hTERT-shRNA/yCDglyTK, which was identified by restriction enzyme digestion and gene sequencing. All the plasmids were delivered into SGC7901 cells using calcium phosphate nanoparticles (CPNPs). Expression of yCDglyTK and hTERT was detected by immunofluorescence, real-time PCR and western blot analysis. MTT assays were applied to measure the cytotoxic effect of the plasmids with 5-fluorocytosine (5-FC). Cell apoptosis was detected by flow cytometry. Restriction enzyme digestion and gene sequencing confirmed that the recombinant vector pcDNA3.1(-)hTERT-shRNA/yCDglyTK had been successfully constructed. Immunofluorescence, real-time PCR and western blot analysis showed that yCDglyTK was expressed, and that hTERT expression was inhibited in cells transfected with the recombinant vector. The cells transfected with the recombinant vector were the most sensitive to 5-FC and the apoptosis rates of the cells were also increased. The pcDNA3.1(-)hTERTshRNA/yCDglyTK vector was constructed successfully; it was confirmed that targeting hTERT through RNAi could synergize with suicide gene therapy.
PMCID: PMC3503886  PMID: 23181115
RNA interference; suicide gene; gastric cancer; combined gene therapy; SGC7901
2.  Features-Based Deisotoping Method for Tandem Mass Spectra 
Advances in Bioinformatics  2012;2011:210805.
For high-resolution tandem mass spectra, the determination of monoisotopic masses of fragment ions plays a key role in the subsequent peptide and protein identification. In this paper, we present a new algorithm for deisotoping the bottom-up spectra. Isotopic-cluster graphs are constructed to describe the relationship between all possible isotopic clusters. Based on the relationship in isotopic-cluster graphs, each possible isotopic cluster is assessed with a score function, which is built by combining nonintensity and intensity features of fragment ions. The non-intensity features are used to prevent fragment ions with low intensity from being removed. Dynamic programming is adopted to find the highest score path with the most reliable isotopic clusters. The experimental results have shown that the average Mascot scores and F-scores of identified peptides from spectra processed by our deisotoping method are greater than those by YADA and MS-Deconv software.
PMCID: PMC3259476  PMID: 22262971

Results 1-2 (2)