1.
Comparative proteomic analysis of drug sodium iron chlorophyllin addition to Hep 3B cell line.
Zhang, J, Wang, W, Yang, F, Zhou, X, Jin, H, Yang, PY
The Analyst. 2012;(18):4287-94
Abstract
The human hepatoma 3B cell line was chosen as an experimental model for in vitro test of drug screening. The drugs included chlorophyllin and its derivatives such as fluo-chlorophyllin, sodium copper chlorophyllin, and sodium iron chlorophyllin. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) method was used in this study to obtain the primary screening results. The results showed that sodium iron chlorophyllin had the best LC(50) value. Proteomic analysis was then performed for further investigation of the effect of sodium iron chlorophyllin addition to the Hep 3B cell line. The proteins identified from a total protein extract of Hep 3B before and after the drug addition were compared by two-dimensional-gel-electrophoresis. Then 32 three-fold differentially expressed proteins were successfully identified by MALDI-TOF-TOF-MS. There are 29 unique proteins among those identified proteins. These proteins include proliferating cell nuclear antigen (PCNA), T-complex protein, heterogeneous nuclear protein, nucleophosmin, heat shock protein A5 (HspA5) and peroxiredoxin. HspA5 is one of the proteins which are involved in protecting cancer cells against stress-induced apoptosis in cultured cells, protecting them against apoptosis through various mechanisms. Peroxiredoxin has anti-oxidant function and is related to cell proliferation, and signal transduction. It can protect the oxidation of other proteins. Peroxiredoxin has a close relationship with cancer and can eventually become a disease biomarker. This might help to develop a novel treatment method for carcinoma cancer.
2.
Protein-centric data integration for functional analysis of comparative proteomics data.
McGarvey, PB, Zhang, J, Natale, DA, Wu, CH, Huang, H
Methods in molecular biology (Clifton, N.J.). 2011;:323-39
Abstract
High-throughput proteomic, microarray, protein interaction and other experimental methods all generate long lists of proteins and/or genes that have been identified or have varied in accumulation under the experimental conditions studied. These lists can be difficult to sort through for Biologists to make sense of. Here we describe a next step in data analysis--a bottom-up approach at data integration--starting with protein sequence identifications, mapping them to a common representation of the protein and then bringing in a wide variety of structural, functional, genetic, and disease information related to proteins derived from annotated knowledge bases and then using this information to categorize the lists using Gene Ontology (GO) terms and mappings to biological pathway databases. We illustrate with examples how this can aid in identifying important processes from large complex lists.
3.
[Separation of proteins on microchip electrophoresis and its comparison with DNA migration].
Liu, C, Xu, X, Zhang, J, Chen, J
Se pu = Chinese journal of chromatography. 2010;(3):296-300
Abstract
The efficient separation of six standard proteins on a home-made poly (dimethylsiloxane) microchip with an auto-deducting background diode laser induced fluorescence detector was accomplished within 6.4 min under the sieving matrix of 10 g/L hydroxyethyl cellulose (HEC), 1 g/L sodium dodecyl sulphonate (SDS), 40 mmol/L phosphate buffer at pH 7.0. The experimental results showed that the reproducibility of protein separation was satisfactory and the relative standard deviations (RSDs) of protein migration time were less than 10%. The migration times of the proteins are analyzed by a quantitative mathematical model of deoxyribonucleic acid (DNA) proposed by ourselves previously. The results showed that the migration character of SDS-protein complexes was similar with DNA. However, the linear relationships between the mobilities of SDS-protein complexes and their relative molecular mass as well as electric field strength became worse, which indicated the mathematical model for DNA separation should be revised before it is used for protein separation.