Dataset Information

Relative quantification of several plasma proteins during liver transplantation surgery.

ABSTRACT: Plasma proteome is widely used in studying changes occurring in human body during disease or other disturbances. Immunological methods are commonly used in such studies. In recent years, mass spectrometry has gained popularity in high-throughput analysis of plasma proteins. In this study, we tested whether mass spectrometry and iTRAQ-based protein quantification might be used in proteomic analysis of human plasma during liver transplantation surgery to characterize changes in protein abundances occurring during early graft reperfusion. We sampled blood from systemic circulation as well as blood entering and exiting the liver. After immunodepletion of six high-abundant plasma proteins, trypsin digestion, iTRAQ labeling, and cation-exchange fractionation, the peptides were analyzed by reverse phase nano-LC-MS/MS. In total, 72 proteins were identified of which 31 could be quantified in all patient specimens collected. Of these 31 proteins, ten, mostly medium-to-high abundance plasma proteins with a concentration range of 50-2000?mg/L, displayed relative abundance change of more than 10%. The changes in protein abundance observed in this study allow further research on the role of several proteins in ischemia-reperfusion injury during liver transplantation and possibly in other surgery.

SUBMITTER: Parviainen V

PROVIDER: S-EPMC3237013 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Relative quantification of several plasma proteins during liver transplantation surgery.

Parviainen Ville V Joenväärä Sakari S Tukiainen Eija E Ilmakunnas Minna M Isoniemi Helena H Renkonen Risto R

Journal of biomedicine & biotechnology 20111210

Plasma proteome is widely used in studying changes occurring in human body during disease or other disturbances. Immunological methods are commonly used in such studies. In recent years, mass spectrometry has gained popularity in high-throughput analysis of plasma proteins. In this study, we tested whether mass spectrometry and iTRAQ-based protein quantification might be used in proteomic analysis of human plasma during liver transplantation surgery to characterize changes in protein abundances ...[more]

PMID: 22187521

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Project description:MicroRNAs (miRNAs) have been shown to play an important role in many different cellular, developmental, and physiological processes. Accordingly, numerous methods have been established to identify and quantify miRNAs. The shortness of miRNA sequence results in a high dynamic range of melting temperatures and, moreover, impedes a proper selection of detection probes or optimized PCR primers. While miRNA microarrays allow for massive parallel and accurate relative measurement of all known miRNAs, they have so far been less useful as an assay for absolute quantification. Here, we present a microarray based approach for global and absolute quantification of miRNAs. The method relies on an equimolar pool of about 1000 synthetic miRNAs of known concentration which is used as an universal reference and labeled and hybridized in a dual colour approach on the same array as the sample of interest. Each single miRNA is quantified with respect to the universal reference outbalancing bias related to sequence, labeling, hybridization or signal detection method. We demonstrate the accuracy of the method by various spike in experiments. Further, we quantified miRNA copy numbers in liver samples and CD34(+)CD133(-) hematopoietic stem cells. We analyzed to which extend the universal reference can be used as a tool for the relative quantification of miRNAs across multiple experiments. We compared the results of direct hybridizations i.e. sample vs. sample to those of indirect hybridizations i.e. sample vs. UR. For the direct hybridizations, we hybridized 5Âµg liver total RNA vs 5 Âµg brain total RNA (n = 3) and for the indirect hybridization 5 Âµg liver or brain total RNA vs UR (5 fmol/miRNA) (n = 3). We calculated the so-called re-ratios for the UR experiments by dividing the signal ratios of the liver vs. UR array by the respective brain vs. UR array gaining a liver vs. brain re-ratio. Each RNA sample was mixed with 5 fmol of each of 18 RNA oligonucleotides reverse complement to miRControl 3 probes and subsequently fluorescently labelled. The RNA mix was hybridized in a dual colour approach to microarrays. The mean ratios of all probes were normalized to the median of the ratios detected for the spiked 18 synthetic RNA oligonucleotides reverse complement to miRControl 3 probes.

Project description:Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery. Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation. Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%). Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Dataset Information

Relative quantification of several plasma proteins during liver transplantation surgery.

Publications

Relative quantification of several plasma proteins during liver transplantation surgery.

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