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Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation.

ABSTRACT: PURPOSE:In mass spectrometry (MS)-based studies to discover urinary protein biomarkers, an important question is how to analyze the data to find the most promising potential biomarkers to be advanced to large-scale validation studies. Here, we describe a "systems biology-based" approach to address this question. EXPERIMENTAL DESIGN:We analyzed large-scale liquid chromatography-tandem mass spectrometry (LC-MS/MS) data of urinary exosomes from renal allograft recipients with biopsy-proven evidence of immunological rejection or tubular injury (TI). We asked whether bioinformatic analysis of urinary exosomal proteins can identify biological-process based protein groups that correlate with biopsy findings and whether the protein groups fit with general knowledge of the pathophysiological mechanisms involved. RESULTS:LC-MS/MS analysis of urinary exosomal proteomes identified more than 1000 proteins in each pathologic group. These protein lists were analyzed computationally to identify the Biological Process and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway terms that are significantly associated with each pathological group. Among the most informative terms for each group were: "sodium ion transport" for TI; "immune response" for all rejection; "epithelial cell differentiation" for cell-mediated rejection; and "acute inflammatory response" for antibody-mediated rejection. Based on these terms, candidate biomarkers were identified using a novel strategy to allow a dichotomous classification between different pathologic categories. CONCLUSIONS AND CLINICAL RELEVANCE:The terms and candidate biomarkers identified make rational connections to pathophysiological mechanisms, suggesting that the described bioinformatic approach will be useful in advancing large-scale biomarker identification studies toward a validation phase.

SUBMITTER: Pisitkun T

PROVIDER: S-EPMC3809079 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature

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Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation.

Pisitkun Trairak T Gandolfo Maria T MT Das Samarjit S Knepper Mark A MA Bagnasco Serena M SM

Proteomics. Clinical applications 20120601 5-6

<h4>Purpose</h4>In mass spectrometry (MS)-based studies to discover urinary protein biomarkers, an important question is how to analyze the data to find the most promising potential biomarkers to be advanced to large-scale validation studies. Here, we describe a "systems biology-based" approach to address this question.<h4>Experimental design</h4>We analyzed large-scale liquid chromatography-tandem mass spectrometry (LC-MS/MS) data of urinary exosomes from renal allograft recipients with biopsy- ...[more]

PMID: 22641613

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Project description:BackgroundThe success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology.ResultsHere, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/).ConclusionsTranslating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.

Dataset Information

Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation.

Publications

Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation.

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