Plasma proteome response to severe burn injury revealed by 18O-labeled "universal" reference-based quantitative proteomics.

Biological Sciences Division and Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, USA.
Journal of Proteome Research (Impact Factor: 5.06). 09/2010; 9(9):4779-89. DOI: 10.1021/pr1005026
Source: PubMed

ABSTRACT A burn injury represents one of the most severe forms of human trauma and is responsible for significant mortality worldwide. Here, we present the first quantitative proteomics investigation of the blood plasma proteome response to severe burn injury by comparing the plasma protein concentrations of 10 healthy control subjects with those of 15 severe burn patients at two time-points following the injury. The overall analytical strategy for this work integrated immunoaffinity depletion of the 12 most abundant plasma proteins with cysteinyl-peptide enrichment-based fractionation prior to LC-MS analyses of individual patient samples. Incorporation of an 18O-labeled "universal" reference among the sample sets enabled precise relative quantification across samples. In total, 313 plasma proteins confidently identified with two or more unique peptides were quantified. Following statistical analysis, 110 proteins exhibited significant abundance changes in response to the burn injury. The observed changes in protein concentrations suggest significant inflammatory and hypermetabolic response to the injury, which is supported by the fact that many of the identified proteins are associated with acute phase response signaling, the complement system, and coagulation system pathways. The regulation of approximately 35 proteins observed in this study is in agreement with previous results reported for inflammatory or burn response, but approximately 50 potentially novel proteins previously not known to be associated with burn response or inflammation are also found. Elucidating proteins involved in the response to severe burn injury may reveal novel targets for therapeutic interventions as well as potential predictive biomarkers for patient outcomes such as multiple organ failure.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The complexity of the physiologic and inflammatory response in acute critical illness has stymied the accurate diagnosis and development of therapies. The Society for Complex Acute Illness was formed a decade ago with the goal of leveraging multiple complex systems approaches in order to address this unmet need. Two main paths of development have characterized the Society’s approach: i) data pattern analysis, either defining the diagnostic/prognostic utility of complexity metrics of physiological signals or multivariate analyses of molecular and genetic data, and ii) mechanistic mathematical and computational modeling, all being performed with an explicit translational goal. Here, we summarize the progress to date on each of these approaches, along with pitfalls inherent in the use of each approach alone. We suggest that the next decade holds the potential to merge these approaches, connecting patient diagnosis to treatment via mechanism-based dynamical system modeling and feedback control, and allowing extrapolation from physiologic signals to biomarkers to novel drug candidates. As a predicate example, we focus on the role of data-driven and mechanistic models in neuroscience, and the impact that merging these modeling approaches can have on general anesthesia.
    Journal of critical care 08/2014; · 2.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Mass spectrometry-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides. Another issue is that different mass spectrometry experiments of the same study have significantly varying numbers of peptides quantified, which can result in more missing peptide abundances in an experiment that has a smaller total number of quantified peptides. In order to detect as many biomarker proteins as possible, it is necessary to develop bioinformatics methods that appropriately handle these challenges.
    Bioinformatics 06/2014; · 5.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The proteomics work reported by Smith et al. represents a giant step forward in characterizing the cerebrospinal fluid (CSF) proteome in mouse models of human diseases. Whereas prior studies were limited to analysis of CSF pools, Smith et al. base their conclusions on data derived from individual mice, thereby capturing a fuller range of the biological diversity present. These results underscore how far proteomics has come in the past few years, developing into a modern tool with the capacity to remove bottlenecks in the study of neuropsychiatric diseases. Past efforts with mass spectrometry have been hampered by limitations in access to CSF samples, and small volumes when available. These barriers have been overcome with newer mass spectrometry platforms and advances in sample preparation. We are far closer than before to producing the production of clinically useful proteomic data for biomarker discovery and for deriving insights into pathogenesis that can lead to more effective treatments for many diseases. This article is protected by copyright. All rights reserved.
    Proteomics 03/2014; · 3.97 Impact Factor

Full-text (2 Sources)

Available from
Jul 4, 2014

Similar Publications