ABSTRACT
Glioblastoma multiforme (GBM) or grade IV astrocytoma is the most common and lethal adult malignant brain tumor. The present study was conducted to investigate the alterations in the serum proteome in GBM patients compared to healthy controls. Comparative proteomic analysis was performed employing classical 2DE and 2D-DIGE combined with MALDI TOF/TOF MS and results were further validated through Western blotting and immunoturbidimetric assay. Comparison of the serum proteome of GBM and healthy subjects revealed 55 differentially expressed and statistically significant (p <0.05) protein spots. Among the identified proteins, haptoglobin, plasminogen precursor, apolipoprotein A-1 and M, and transthyretin are very significant due to their functional consequences in glioma tumor growth and migration, and could further be studied as glioma biomarkers and grade-specific protein signatures. Analysis of the lipoprotein pattern indicated elevated serum levels of cholesterol, triacylglycerol, and low-density lipoproteins in GBM patients. Functional pathway analysis was performed using multiple software including ingenuity pathway analysis (IPA), protein analysis through evolutionary relationships (PANTHER), database for annotation, visualization and integrated discovery (DAVID), and GeneSpring to investigate the biological context of the identified proteins, which revealed the association of candidate proteins in a few essential physiological pathways such as intrinsic prothrombin activation pathway, plasminogen activating cascade, coagulation system, glioma invasiveness signaling, and PI3K signaling in B lymphocytes. A subset of the differentially expressed proteins was applied to build statistical sample class prediction models for discrimination of GBM patients and healthy controls employing partial least squares discriminant analysis (PLS-DA) and other machine learning methods such as support vector machine (SVM), Decision Tree and Naïve Bayes, and excellent discrimination between GBM and control groups was accomplished.
Subject(s)
Biomarkers, Tumor/blood , Blood Proteins/analysis , Glioblastoma/blood , Proteome/analysis , Bayes Theorem , Blood Proteins/metabolism , Case-Control Studies , Decision Trees , Down-Regulation , Electrophoresis, Gel, Two-Dimensional , Glioblastoma/metabolism , Humans , Multivariate Analysis , Peptide Fragments/analysis , Protein Interaction Maps , Proteome/metabolism , Proteomics/methods , Reproducibility of Results , Signal Transduction , Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization , Up-RegulationABSTRACT
BACKGROUND: Liver injury is the most common cause of postmarketing withdrawal of drugs. Traditional animal toxicity testing methods have proved to be imperfect tools for predicting toxicity observed in the clinic. OBJECTIVE: Predictive methods that integrate data and insights from several in vitro methods to provide a deeper understanding of the impact of a drug on the liver are the need of the hour. METHOD: A systems approach based on mathematical modelling using the kinetics of biochemical pathways involved in liver homeostasis coupled with in vitro measurements to quantify drug-induced perturbations is described here. CONCLUSIONS: Integrating in silico and in vitro methods provides a powerful platform that allows reasonably accurate and mechanistic-level prediction of drug-induced liver injury. The method demonstrates that several physiological situations can be accurately modelled as can the effect of perturbations induced by drugs. It can also be used along with high-throughput 'omic' data to generate testable hypotheses leading to informed decision-making.
Subject(s)
Chemical and Drug Induced Liver Injury/etiology , Liver/drug effects , Systems Biology/methods , Adverse Drug Reaction Reporting Systems , Animal Testing Alternatives , Animals , Drug Evaluation, Preclinical/methods , Homeostasis/drug effects , Humans , Liver/pathology , Models, BiologicalABSTRACT
A comprehensive model was developed to simulate Lactobacillus rhamnosus growth on a medium containing multiple limiting carbon sources. The strategy of optimizing specific growth rate to predict growth on multiple substrates was demonstrated. The model predictions were based on parameters obtained from L. rhamnosus growth on individual substrates. The model was able to simulate the growth, substrate consumption, product formation and specific growth rate profiles of L. rhamnosus accurately. The model prediction that co-metabolism of glucose and pyruvate enhances growth rate of and flavor production by the bacterium was experimentally verified.
