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Henry V. Baker, Ph.D., Ulysses Balis, M.D., Paul Bankey, M.D., Timothy R. Billiar, M.D., Bernard H. Brownstein, Ph.D., Steven E. Calvano, Ph.D., Irshad H. Chaudry, Ph.D., J. Perren Cobb, M.D., Joseph Cuschieri, M.D., Asit K. De, Ph.D., , Bradley Freeman, M.D., Richard L. Gamelli, M.D., Nicole S. Gibran, M.D., Brian G. Harbrecht, M.D., Douglas L. Hayden, M.A., Laura Hennessy, R.N,. Jureta W. Horton, Ph.D., Jeffrey Johnson, M.D., Matthew B. Klein, M.D., , Stephen F. Lowry, M.D., Ronald V. Maier, M.D., John A. Mannick, M.D., Philip H. Mason, Ph.D., Grace P. McDonald-Smith, M.Ed., Carol L. Miller-Graziano, Ph.D., Michael N. Mindrinos, Ph.D., Joseph P. Minei, M.D., Ernest E. Moore, M.D., Avery B. Nathens, M.D., Ph.D., M.P.H., Grant E. O'Keefe, M.D., M.P.H., Laurence G. Rahme, Ph.D., Daniel G. Remick, Jr. M.D., David A. Schoenfeld, Ph.D., Michael B. Shapiro, M.D., Geoffrey M. Silver, M.D., John Storey, Ph.D., Robert Tibshirani, Ph.D., Mehmet Toner, Ph.D., H. Shaw Warren, M.D., Michael A. West, M.D.
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 ~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.
Second only to motor vehicle accidents as the leading cause of accidental deaths in the United States, burn injuries result in nearly half a million patients requiring medical treatments and nearly 4000 deaths annually in the United States. Severe burn injury is one of the most devastating forms of trauma that affects the functions of nearly every organ system in the body by causing serious tissue damage, fluid loss, and overwhelming systemic metabolic and inflammatory responses 1–3. The pathophysiological response induced by severe burn injury has a marked inflammatory component stemming from the release of a wide range of inflammatory mediators that subsequently contribute to the development of a systemic inflammatory response syndrome (SIRS), immune dysfunction, and multiple organ failure (MOF) 4, 5. Despite recent advances in burn treatment and management, the complex interactions that occur during SIRS, as well as the mechanisms that lead to MOF have not been fully characterized 5–7. Moreover, current methods for predicting the likelihood of mortality are unreliable and non-individualized 6–8.
Recently, there has been an increasing interest in applying high throughput genomics and proteomics approaches in large-scale studies of complex human diseases with the aims of elucidating the underlying signaling pathways of the diseases and discovering novel genes or proteins as predictors of disease outcomes and as new therapeutic targets 9–14. For example, several recent studies reported the application of genome-wide expression analyses to circulating blood leukocytes and tissue samples derived from trauma patients to gain some insight into the pathways that underlie systemic inflammation in humans 9, 15, 16. Proteomics technologies offer the advantages of directly measuring protein abundances, including cell-depleted biofluids such as blood plasma. In clinical research, plasma proteomics has become one of the most rapidly emerging fields because blood plasma is an easily accessible non-invasive source and a reservoir for circulating proteins throughout the body17, 18.
In this study, we report the first quantitative plasma proteome profiling in severe burn patients with the aim of identifying plasma proteins associated with the response to burn injury. Plasma samples were collected from 15 severely burned patients that had burns covering at least 20% of their body and 10 healthy subjects matched by ages and body weights. The application of a recently reported methodology that utilizes a stable isotope 18O-labeled “universal” reference19 in conjunction with immunoaffinity depletion, cysteinyl-peptide enrichment, and high resolution liquid chromatography-mass spectrometry (LC-MS ) analyses enabled identification and relative quantification of 313 plasma proteins. The results reveal significant inflammatory and hypermetabolic responses to burn injury as many of the proteins that exhibited significant changes in abundance are associated with acute phase response signaling, complement, and coagulation system pathways. Moreover, nearly 50 proteins were revealed as novel burn-associated proteins after evaluating statistically significant protein abundance changes following injury. Importantly, this study provides a preliminary baseline of burn-responding plasma proteins for discovering novel biomarkers predictive of injury outcomes and targets for therapeutic interventions.
Human blood plasma samples from 10 healthy individuals and 15 burn patients were used in this study, which was approved by the Institutional Review Boards of the University of Texas Medical Branch (Galveston, TX), Loyola University Medical College (Chicago, IL), University of Texas Southwestern (Dallas, TX), University of Washington (Seattle, WA), Pacific Northwest National Laboratory (Richland, WA), Massachusetts General Hospital (Boston, MA), and University of Florida College of Medicine (Gainesville, FL) in accordance with federal regulations. All burn patients were admitted within 96 h after injury to the participating hospitals and had burns that covered more than 20% of their total body surface area that required at least one surgical intervention. Blood plasma samples collected at two different time-points after hospital admission consisted of early time-point plasma (T1), which was collected shortly after admission and late time-point plasma (T2), which was collected at the peak of the first MOF episode. A brief summary of patient demographics is provided in Table 1.
All plasma samples were supplied by the Department of Surgery at the University of Florida, College of Medicine, which served as the sample collection and coordination site for this multi-centered clinical study. A total of 300 μL plasma per healthy subject or per time-point for burn subjects was applied towards proteomics analysis. Initial protein concentrations in the plasma samples were determined by a BCA Protein Assay (Pierce, Rockford, IL). A reference sample was generated by pooling 100 μL aliquots of plasma obtained from each of the 10 healthy controls and from each time for the 15 burn patients. Unless otherwise noted, protein sample processing was performed at 4 °C.
Each patient plasma sample and the pooled reference sample were depleted individually of 12 abundant blood plasma proteins—albumin, IgG, α1-antitrypsin, IgA, IgM, transferrin, haptoglobin, α1-acid glycoprotein, α2-macroglobulin, apolipoprotein A-I, apolipoprotein A-II, and fibrinogen—in a single step. Depletion was accomplished using a pre-packed Seppro IgY12 LC-5 affinity column (GenWay Biotech, San Diego, CA) with loading capacity of 65 μL human plasma and an Agilent 1200 series HPLC system (Agilent, Palo Alto, CA) per the manufacturer’s instructions 20. For each patient, ~130 μL of plasma was depleted, and only the flow-through fractions were collected and saved as samples. For the universal reference sample, ~3.0 mL of reference plasma sample was depleted (using the same procedure), but in this case, the flow-through fractions were pooled. Each flow-through portion was individually concentrated in iCON concentrators with 9 kDa molecular weight cutoffs (Pierce), followed by buffer exchange with 50 mM NH4HCO3 per the manufacturer’s instructions. Protein concentration was measured using a BCA protein assay (Pierce).
Protein samples were denatured and reduced in 50 mM NH4HCO3 buffer (pH 8.2), 8 M urea, and 10 mM dithiothreitol (DTT) for 1 h at 37 °C. The resulting protein mixture was diluted 10-fold with 50 mM NH4HCO3 before sequencing grade modified porcine trypsin (Promega, Madison, WI) was added at a trypsin:protein ratio of 1:50 (w/w). The sample was incubated for 5 h at 37 °C. Each digested sample was loaded onto a 1-mL SPE C18 column (Supelco, Bellefonte, PA) and washed with 4 mL of 0.1% trifluoroacetic acid (TFA)/5% acetonitrile (ACN). Peptides were eluted from the SPE column with 1 mL of 0.1% TFA/80% ACN and then lyophilized. Resulting peptide samples were reconstituted in 25 mM NH4HCO3 and the residual trypsin activity was quenched by boiling the samples for 10 min and immediately placing the samples on ice for 30 min. The resulting peptide concentration in each sample was measured using a BCA protein assay (Pierce).
18O-labeling for preparing the reference sample was carried out using a previously described procedure 21. Briefly, the peptide sample was lyophilized to dryness and initially reconstituted in 60 μL of acetonitrile, followed by the addition of 600 μL of 50 mM NH4HCO3 in 18O-enriched water (95%; ISOTEC, Miamisburg, OH). Then, 6 μL of 1 M CaCl2 and 30 μL of immobilized trypsin (Applied Biosystems, Foster City, CA) were added to the digest and the sample was mixed continuously for 5 h at 30 °C. After labeling, the sample was acidified by adding 6 μL of formic acid, and the supernatant was collected after centrifuging the samples for 5 min at 15,000 × g. The labeled sample was lyophilized and reconstituted in 25 mM NH4HCO3; the peptide concentration was measured using a BCA Protein assay (Pierce). The labeled reference sample was divided into 40 identical aliquots and placed in 40 individual tubes, after which an equal amount of peptides from each patient sample was added to each tube to form 40 patient/reference mixed samples (i.e., 10 samples for healthy subjects, and 30 samples for the 15 burn patients with two time-points per patient).
The 40 patient/reference samples were fractionated into cysteinyl (Cys)- and non-Cys-peptide fractions by following a previously described procedure 21, 22. Briefly, the tryptic digest was reduced with 5 mM DTT in Tris buffer, and Cys-peptides were captured by Thiopropyl Sepharose 6B thiol-affinity resin (4 × 100 μL; Amersham Biosciences) following incubation of the reduced peptides with the resin. The non-Cys-peptide supernatant portion was collected. The resin was washed to remove any non-specifically bound peptides. The captured Cys-peptides were released by incubation with 20 mM DTT for 30 min at room temperature, and the released peptides were then alkylated with 80 mM iodoacetamide. Both the eluted Cys-peptide and the unbound, non-Cys-peptide samples were desalted using a SPE C18 column and lyophilized. Both fractions were reconstituted in 25 mM NH4HCO3 and peptide concentrations were measured using a BCA Protein assay (Pierce).
Both Cys- and non-Cys-peptide samples from individual burn patients were analyzed using a fully automated custom-built two-column capillary LC system 23 that was coupled online via an in-house-manufactured electrospray ionization interface to an 11.5 Tesla Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. The capillary columns were prepared by slurry packing 5 μm Jupiter C18 bonded particles (Phenomenex, Torrence, CA) into a 65-cm long, 150-μm i.d. fused-silica capillary (Polymicro Technologies, Phoenix, AZ). Mobile phases consisted of 0.2% acetic acid and 0.05% trifluoroacetic acid (TFA) in water (A) and 0.1% TFA in 90% acetonitrile/10% water (B). 10 μL aliquots of each peptide sample at concentrations of 0.1 μg/μL were injected onto the reversed-phase column for LC-FTICR analysis. The mobile phase was maintained at 100% A for 20 min and then switched to an exponential gradient elution generated by increasing the mobile-phase composition to ~70% B over 150 min. The LC-FTICR was configured and operated as described elsewhere 24. Samples were analyzed in randomized order on the two columns. To assess analytical reproducibility, all samples were analyzed on the same instrument three months later.
LC-MS data analysis was similar to that previously described19. Initially, LC-FTICR datasets were automatically analyzed using an in-house-developed software package that included Decon2LS and VIPER informatics software tools25. Initial analysis of the raw LC-MS data involved the use of Decon2LS to perform a mass transformation or de-isotoping step, which generated a text file report for each LC-MS dataset that for each mass spectrum included both the monoisotopic masses and the corresponding intensities for all detected species. Each dataset was then processed by using the feature-matching tool VIPER to identify and quantify peptides, and then display the data in a two-dimensional mass and LC-elution time format. The feature matching process included “distinct feature” (i.e., a peak with unique mass and elution time) finding, searching for 16O/18O feature pairs, computing abundance ratios for pairs of features, an intensity report for all detected features, normalizing LC elution times via alignment to a database, and feature identification.
Feature identification was performed by matching the accurately measured masses and normalized LC elution time (NET) values of each detected feature to a pre-established human plasma proteome accurate mass and elution time (AMT) tag database. The plasma AMT tag databases were generated from the combined results of several comprehensive LC-MS/MS profiling investigations 26, including a recent profiling of the human trauma patient plasma proteome 22. Note that the human IPI protein database (February 7, 2008, V3.39) was used for MS/MS peptide identification in these earlier investigations. The process of generating a AMT tag database and the criteria for peptide inclusion in the database have been described previously in detail 26 and the AMT tag database essentially serves as a “look-up” table for LC-MS feature identifications. The overall false discovery rate of the peptide identifications included in the AMT tag database is <5% based on a reversed database approach 27. A dynamic modification of 4.0085 Da was applied during the peak (or feature) matching process to enable both unlabeled peptides and 18O-labeled peptides to be identified simultaneously. The peptide sequences of a given feature or pair of features were assigned when the measured mass and NET for each given feature matched the calculated mass and NET of a peptide in the AMT tag database within a 4 ppm mass error and 2% NET error (the mass error tolerance was later refined to 2.5 ppm as the final cutoff). For each identified peptide, the relative 16O/18O abundance ratio was reported if the feature was paired. Details for computing the 16O/18O abundance ratios using VIPER are described elsewhere26.
The final identified peptide list was then used to generate a non-redundant protein group list using ProteinProphet 28 and all of the peptides were annotated based on protein group identification for downstream data normalization and for rolling-up peptide level abundance information to the protein level. An identified peptide was excluded for the purpose of calculating relative abundance ratios if the peptide was not observed in at least five datasets. Additionally, peptides were removed from downstream data analysis if they were shared by more than three protein groups.
Relative abundance 16O/18O data in each LC-MS dataset were first transformed in Log2 format and then normalized by centering the median Log2Ratio to zero. Following data normalization, a re-scaling procedure for peptide profiles across datasets was performed for each protein. In this procedure, all peptide profiles that originate from the same protein are scaled to the same level by using a simple scaling factor for each peptide obtained relative to a chosen reference peptide for the given protein. The peptide with the most observations serves as the reference peptide for a given protein and the scaling factor for every other peptide is calculated as the median ratio of the common data points between the peptide to be scaled and the reference peptide19. Relative protein abundance ratios were calculated by averaging re-scaled relative peptide abundance ratios for each dataset. A statistical analysis of variance (ANOVA) test was applied to identify proteins with significant abundance differences across the control (healthy) subjects and the burn subjects at two different time-points post-burn injury. Proteins with significant abundance changes across the three biological groups were identified by requiring an ANOVA p-value of <0.05 and a corresponding q-value of <0.1. The q-value is an analog of the p-value and incorporates both a multiple testing correction and a posterior error probability, which corresponds to the local false discovery rate (FDR), i.e., the probability that a given observation is drawn from the null distribution 29. All data normalization, re-scaling, and statistical analysis procedures were performed using DAnTE, a software tool developed for the quantitative analysis of “omics” data 30.
Figure 1 depicts the overall analytical strategy for quantitative analysis of individual plasma samples. A recently reported 18O-labeled universal reference-based quantitative methodology that employs inclusion of the same stable isotope labeled internal standards for all samples was applied to achieve robust quantitative measurements. As a pooled sample, the 18O-labeled universal reference sample contained all proteins that when spiked into individual samples, served as internal standards for quantification. We integrated immunoaffinity depletion with Cys-peptide enrichment-based fractionation to enhance single dimensional LC-MS detection22 of low abundance plasma proteins. The initial 40 plasma samples were fractionated into 80 final Cys- and non-Cys-peptide fractions that were individually analyzed using the AMT tag strategy 21. Peptides were identified by matching peak information to information in a pre-established human plasma peptide database22. Relative peptide and protein abundances were quantified based on the 16O/18O ratios for all detected 16O/18O peptide pairs. Note that identifications and abundance data for the Cys- and non-Cys-fractions for each original patient plasma sample were subsequently combined to improve proteome coverage.
To assess the reproducibly or reliability of this quantification strategy, we analyzed the final 40 non-Cys-peptide samples two times each and at least three months apart. The two datasets allowed us to compare 16O/18O peptide abundance ratios for the same samples, as well as the relative abundance profiles across the 40 individual samples. Figure 2A shows the correlation of 16O/18O peptide abundance ratios between replicate analyses of the same biological sample. Although the analyses were performed more than three months apart and used different LC columns, the peptide abundance ratios appear reproducible based on the Pearson correlation coefficient of 0.92. The histogram in Figure 2B, which plotted the coefficient of variance (CV) for all peptides commonly quantified between the two replicated analyses of the 40 samples, provided another example of the reproducibility of peptide abundance ratio measurements. As shown, the majority of peptides are quantified with < 20% CV, which is based on peptides detected in technical replicates in each of the two analyses. Figures 2C and 2D further illustrated the reproducibility of protein abundance profiles across the 40 different samples; in this case, using two proteins as examples. Collectively, these data illustrated that the use of an 18O-labeled reference for relative quantification can effectively cancel out LC-MS platform performance variation in a large-scale study.
Following peak matching of each LC-MS dataset against a pre-established plasma AMT tag database and data filtering, 5245 unique peptides were identified that had quantitative 16O/18O peptide abundance ratios measured across different datasets. The FDR at the unique peptide level was estimated to be 4.5%, which is comparable to those recently obtained using similar approaches19, 31. In total, 313 proteins were confidently identified with two or more unique peptides. The relative abundances for these quantified peptides and proteins in Log2Ratio format against the reference across the 40 samples are listed in Supplemental Tables I and II, respectively. The observed coverage of 313 proteins with two or more peptide identifications is comparable to our previous results of 380 protein identifications obtained using SCX fractionation coupled to LC-MS/MS to analyze a single plasma sample32. Although only single-dimensional LC-MS was applied in this study, the analyses of 40 different plasma samples improved the overall proteome coverage. Table 2 exemplifies low abundance proteins that were quantified with at least two unique peptides from different control and burn patient samples and that had concentrations in sub-μg/mL level based on previous literature reports under normal conditions32–35. Most of these low abundance proteins were detected in a portion of the original 40 samples, that suggested that their concentrations were below the limit of detection in some of the samples.
The quantitative protein abundance data for the 313 confidently identified proteins were subjected to ANOVA statistical analyses to reveal plasma proteins that exhibited significant abundance changes in response to severe burn injury. Figure 3 shows the p-value distributions for this set of proteins. Note that a significant proportion of the detected proteins were determined to have p-values of ≤0.05. By requiring a p-value of <0.05 and a corresponding q-value indicative of an FDR <0.1, 110 proteins were identified with statistically significant abundance changes across the three biological groups (control, burn time-point 1, and burn time-point 2). The quantitative data for these statistically significant proteins are listed in Supplemental Table III. Among the 110 proteins, the majority (~80%) of them are considered extracellular and plasma membrane proteins.
Figure 4 shows a heatmap of relative abundance profiles for the 110 proteins across the 40 plasma samples. All protein abundances are displayed in Log2Ratio format, i.e., protein abundances for each protein within a given sample are indicated as relative ratio values against the labeled reference. Note the large sample-to-sample variations. Also note that a good portion of proteins exhibit decreased concentrations in response to burn injury, which is apparent in the bottom set of proteins, while other proteins show increased abundances following injury. In Figure 5 the protein abundance changes in four example proteins further illustrate that abundance changes between burn and control samples are readily observed.
The 110 statistically different proteins were examined by applying the Ingenuity Pathway Analysis (IPA) tool to uncover biological pathways or functional processes associated with these burn-responding proteins. Figure 6A shows the top five canonical pathways mapped by these proteins. Activation of the acute phase response signaling pathway and the complement and coagulation systems suggest a significant post-burn inflammatory and hypermetabolic response36. Figure 6B shows a partial representation of the acute phase response signaling pathway in which all quantified proteins mapped to the pathway exhibit decreased concentrations following burn injury. This finding is in good agreement with existing knowledge about these negative acute phase proteins during the acute phase response. Table 3 lists 36 proteins that are known to be associated with either the inflammatory or immune response; the majority of these proteins are known as acute phase proteins. The induction of the acute phase response37 is supported by the increased concentrations for positive acute phase proteins and the decreased concentrations for negative acute phase proteins at both the early and later time-points following burn injury.
The quantitative changes for several proteins (i.e., C-reactive protein, plasma retinol binding protein, and transthyretin) are in good agreement with post-burn changes of these proteins obtained using orthogonal nephelometry and ELISA techniques38. The overall good agreement between our quantitative proteomics data and the existing knowledge of the acute phase response confirms the robustness of our quantitative strategy for large-scale clinical studies and the confidence of this dataset. Only complement C4-A shows an apparent disagreement of observed changes with reports that complement C4-A is a positive acute phase protein. The discrepancy could be due to the multiple isoforms that exist for complement proteins.
A number of proteins play a role in the immune response in addition to the acute phase response. For example, CD44, beta-2 microglobulin, HSPA5 protein, SPARC, and alpha-enolase have all been observed to be regulated by lipopolysaccharide (LPS) with directional changes in good agreement with the current dataset39–41. Additionally, vascular cell adhesion molecule 1 (VCAM1), protein S100-A9, TREML1, mannose-binding protein (MBL2), heat shock protein (HSPA5) have all been reported as pro-inflammatory mediators associated with sepsis and SIRS42. The observed down-regulation of plasminogen, plasma prekallikrein, and kininogen-1 also supports increased proteolytic production of plasmin, kallikrein, and bradykinin, all of which are critical enzymes during inflammatory response to burn injury 37, 43.
Among the 110 significant proteins are many proteins that have not been reported with clear implications in burn injury. These potentially novel proteins associated with burn injury are listed in Table 4. Some of these proteins have been implicated in immune response, inflammation, cell adhesion and movement, metabolic response, and antioxidant response as annotated in the table. Additionally, two proteins — leucine-rich alpha-2-glycoprotein (LRG1) and cadherin-5 (CDH5)—were observed in a previous plasma proteome survey21 as up-regulated in human volunteers following lipopolysaccharide stimulation.
The pathophysiology of severe burn injury manifests the full spectrum of the complexity of inflammation and the host response to injury. For example, severe burn patients often have increased susceptibility to infection, SIRS, adult respiratory distress syndrome, and multiple organ dysfunction syndrome, which may develop further into MOF and result in death 37, 44. With no reliable biomarkers available at present for early assessment of patient outcomes following burn, trauma, and other critical illness, clinicians have been forced to rely on parameters such as heart rate, hypotension, and hyperglycemia to monitor patient trajectories. In the studies of the pathophysiologic response to severe burn injury, a number of acute phase proteins and cytokines have been monitored in blood serum using traditional assays36, 38.
The present high throughput proteomics application to a relatively large set of healthy (n=10) and severe burn subjects (n=15) provides for the first time a relative broad picture of the human plasma proteome response to severe burn injury. Robust quantitative measurements for this relatively large sample set were achieved by incorporating an 18O-labeled reference-based quantification strategy 19. Reproducibility is especially important for large-scale proteome studies where instrument performance variations typically challenge the ability to obtain reproducible measurements over extended time periods (e.g., months) when using label-free quantification approaches17. In this work, the universal reference afforded relatively good reproducibility over several months for 16O/18O labeled pair data (Figure 2), although the reproducibility for label-free 16O intensities was poor, i.e., a median CV >100% (data not shown).
Among the 313 proteins quantified with two or more peptides, 110 proteins exhibited statistically significant abundance changes in response to burn injury either at one or both time-points following the injury. Although we were unable to detect sub-ng/mL level proteins such as cytokines, a class of important inflammatory mediators, the results covered most of the relatively abundant and moderately abundant proteins with concentrations that range from μg/mL to ng/mL, as evidenced by the coverage of acute phase proteins and some of the low abundance proteins listed in Table 2. Overall, the observed post-burn quantitative changes appeared in good agreement with prior literature reports for the acute phase response, complement, coagulation, and the immune response (Table 3), including the post-burn response of several acute phase proteins previously monitored in serum38. These changes support a significant post-burn inflammatory and hypermetabolic response.
Even more interestingly, our results revealed ~50 potential novel burn-associated proteins (Table 4). Although most of these proteins are not known to be associated with burn injury, a number of these proteins have been implicated in immune response and inflammation. For example, CARD11, a protein containing a caspase recruitment domain (CARD) may play pivotal roles in signal transduction leading to apoptosis and NF-kappaB activation and inflammation 45. Pancreatic ribonuclease (RNASE1) is reported to induce dendritic cell maturation and activation 46, while sialic acid-binding immunoglobulin-like lectin (SIGLEC14) potentially regulates innate immune responses that modulate the life span of granulocytes 47. Moreover, carbonic anhydrase 1, hepatocyte growth factor activator, and apolipoprotein B-100 link to inflammation48–50.
Severe burn injury also causes significant metabolic disturbances and produces reactive oxygen species3, 51 that are implicated in inflammation and systemic inflammatory response syndrome. Several of the novel burn-associated proteins support both the disruption of metabolic response and a reduced antioxidant capacity. For example, insulin growth factor II (IGF2) and IGF acid labile chain, two proteins known to modulate metabolic response in critical illness52 were observed with significant decreases in concentration following burn injury. Observation of down-regulation of glutathione peroxidase 3 and selenoprotein P, two proteins known to play a role in the antioxidant defense system 53, 54 support a potential decrease in antioxidant capacity.
In summary, the importance of the acute phase response, complement, coagulation, and related inflammatory responses in severe burn injury is supported by the plasma proteome changes observed in this study. The temporal changes of these known inflammatory proteins, as well as the nearly 50 novel burn-associated plasma proteins significantly expand our knowledge regarding the basic mechanisms of severe burn-induced inflammatory and metabolic responses. As such, this dataset provides an initial basis for future studies of individual proteins as potential targets for therapeutic interventions, as well as for discovering novel protein biomarkers related to disease outcome.
Portions of this research were supported by the National Institute of General Medical Sciences (NIGMS; Large Scale Collaborative Research Grants U54 GM-62119-02 and T32 GM-008256), the NIH National Center for Research Resources (RR18522), and EMSL (Environmental Molecular Science Laboratory). EMSL is a national scientific user facility sponsored by the U.S. Department of Energy (DOE) Office of Biological and Environmental Research on the Pacific Northwest National Laboratory (PNNL) campus in Richland, Washington. PNNL is operated by Battelle for the DOE under contract DE-AC05-76RLO-1830.