Profiling of rat urinary proteomic patterns associated with drug-induced nephrotoxicity using CE coupled with MS as a potential model for detection of drug-induced adverse effects

We have investigated urine obtained from Sprague Dawley rats before and after administration of cis-Platin, aiming at the definition of biomarkers for drug-induced cytotoxicity. Rats were treated with 3 or 6 mg/kg cis-Platin (i.p., single injection) and urine samples were collected before and after drug or saline treatment. Analysis of the low molecular weight proteome (<20 kDa) using capillary-electrophoresis coupled mass spectrometry allowed us to tentatively identify 34 urinary peptides that show significant differences between control and treated animals, and hence may serve as a potential biomarker for cis-Platin-induced nephrotoxicity. These biomarkers were confirmed in a blinded assessment of additional samples. The blinded data also revealed time-dependency of induced changes. Some of the potential biomarkers could be sequenced. This information revealed great similarity between cis-Platin-induced changes and significant changes in the urinary proteome of patients suffering from tubular injury (Fanconi syndrome). Our study strongly suggests that (drug-induced) nephrotoxicity can be detected with high accuracy in laboratory rodents using urinary proteome analysis. The effects observed are very similar to those seen in corresponding human diseases and similar approaches may be very helpful in evaluating drug-induced organ damage in preclinical animal models. This study aiming at the definition of biomarkers for drug-induced cytotoxicity may serve as a proof-of-principle for the use of urinary proteomics in assessment of drug-induced nephrotoxicity.     

Proteomic analysis of BRCA1-depleted cell line reveals a putative role for replication protein A2 up-regulation in BRCA1 breast tumor development

Purpose : Germline mutations in BRCA1 result in a strong predisposition to breast cancer, with frequent loss of heterozygosity of the remaining wild‐type allele. The development of BRCA1 tumors is likely to depend on additional genetic alterations and gene expression changes which follow growth and DNA repair defects associated with BRCA1 deficiency. The identification of these modifications offers an opportunity to find surrogate markers of BRCA1 tumors. Here, we sought to identify differentially expressed proteins related to BRCA1 depletion. Experimental design : We used isogenic HeLa cells either stably knocked‐down or not for BRCA1 (BRCA1^KD) and compared protein profiles of these cells by DIGE. Results : We detected increased levels of Replication protein A2 (RPA2) in BRCA1^KD cells as compared to control cells. RPA2 is an essential protein required for DNA replication and repair. We further demonstrated that depletion of RPA2 subunit delays growth of BRCA1^KD respect to isogenic control cells. Strikingly, elevated levels of RPA2 were more frequently observed in BRCA1 tumors when triple‐negative tumors from BRCA1 mutation carriers (n=13) and non‐carriers (n=36) were stained in situ for RPA2. Conclusions and clinical relevance : RPA2 up‐regulation may thus be involved in the growth and/or survival of BRCA1 tumor cells and useful in immunohistochemical discrimination of triple‐negative BRCA1 tumors.     

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。     

RECPAM: a computer program for recursive partition amalgamation for censored survival data and other situations frequently occurring in biostatistics. II. Applications to data on small cell carcinoma of the lung (SCCL)

The RECPAM methodology previously presented in part I (A. Ciampi et al., Comput. Methods Programs Biomed. 26 (1988) 239–256) is applied to the analysis of survival data on small cell carcinoma of the lung (SCCL). It is shown how RECPAM can help answer the following questions which occur frequently in the analysis of clinical data: Is it possible to find a classification of patients with a certain disease into distinct prognostic groups? Given a covariate of special interest, does it have an independent prognostic significance even after confounding is taken into account? Does the prognostic significance of a covariate of special interest vary across patient subgroups? For the SCCL data, a prognostic classification is obtained and the tumor marker is treated as a variable of special interest. Many features of RECPAM are illustrated, including, among others, Forward and Backward (Pruning) Stopping Rules, treatment of missing data, and use of several dissimilarity measures.