Quantitative proteomic analysis of a paired human liver healthy versus carcinoma cell lines with the same genetic background to identify potential hepatocellular carcinoma markers

To comprehensively measure global changes in protein expression associated with human hepatocellular carcinoma (HCC), comparative proteomic analysis of two cell lines derived from the healthy and carcinoma tissue of a same donor respectively was conducted using quantitative amino acid-coded mass tagging /stable isotope labeling with amino acids in cell culture-based LC-MS/MS approach. Among a total of 501 proteins precisely quantified, the expressions of 128 proteins were significantly altered including 70 proteins up-regulated and 58 down-regulated in HCC cells. According to their previously characterized functions, the differentially expressed proteins were found associated with nine functional categories including glycolysis, stress response, cell communication, cell cycle, apoptosis/death, etc. For example, multiple enzymes involving glycolysis pathway were found differentially regulated in HCC cells, illustrating the critical participation of glycolysis in the HCC transformation. The accuracy of certain differentially expressed proteins identified through the amino acid-coded mass tagging-based quantification was validated in the paired cell lines, and later their pathological correlations were examined in multiple clinical pairs of normal versus tumor tissues from HCC specimen by using a variety of biological approaches including Western blotting and in situ immunoassays. These consistencies suggested that multiple proteins such as HSP27, annexin V, glyceraldehyde-3-phosphate dehydrogenase, nucleolin and elongation factor Tu could be the biomarkers candidates for diagnosis of HCC.     

A proteomic approach combining MS and bioinformatic analysis for the detection and identification of biomarkers of administration of exogenous human growth hormone in humans

An integrated MS-based proteomic approach is described that combines MALDI-MS and LC-MS with artificial neural networks for the identification of protein and peptide biomarkers associated with recombinant human growth hormone (rhGH) administration. Serum from exercised males administered with rhGH or placebo was analysed using ELISA to determine insulin-like growth factor-I concentrations. Diluted serum from rhGH- and placebo-treated subjects was analysed for protein biomarkers by MALDI-MS, whereas LC-MS was used to analyse tryptically digested ACN-depleted serum extracts for peptide biomarkers. Ion intensities and m/z values were used as inputs to artificial neural networks to classify samples into rhGH- and placebo-treated groups. Six protein ions (MALDI-MS) correctly classified 96% of samples into their respective groups, with a sensitivity of 91% (20 of 22 rhGH treated) and specificity of 100% (24 of 24 controls). Six peptide ions (LC-MS) were also identified and correctly classified 93% of samples with a sensitivity of 90% (19 of 21 rhGH treated) and a specificity of 95% (20 of 21 controls). The peptide biomarker ion with the highest significance was sequenced using LC-MS/MS and database searching and found to be associated with leucine-rich α-2-glycoprotein.     

多环芳烃(PAHs)对虹鳟(Oncorhynchus mykiss)鱼腮细胞系细胞毒性的Fisher判别分析

采用量子化学PM3算法计算得到的多环芳烃(PAHs)的量子化学参数,应用逐步判别分析法分析PAHs的细胞毒性,建立了能成功预测PAHs细胞毒性的Fisher线性判别函数,函数预测结果的正确识别率达到100％。研究认为影响PAHs细胞毒性的主要因素是PAHs的分子量(Mw)、偶极矩(μ)、分子最高占据轨道能(EHDMO)、分子最低未占据轨道能(ELUMO)和(ELUMO-EHOMO)^2。