Physiological Genomics

Comparison of longitudinal leukocyte gene expression after burn injury or trauma-hemorrhage in mice

James A. Lederer, Bernard H. Brownstein, M. Cecilia Lopez, Sandra MacMillan, Adam J. Delisle, Malcolm P. MacConmara, Mashkoor A. Choudhry, Wenzhong Xiao, Steven Lekousi, J. Perren Cobb, Henry V. Baker, John A. Mannick, Irshad H. Chaudry


A primary objective of the large collaborative project entitled “Inflammation and the Host Response to Injury” was to identify leukocyte genes that are differentially expressed after two different types of injury in mouse models and to test the hypothesis that both forms of injury would induce similar changes in gene expression. We report here the genes that are expressed in white blood cells (WBCs) and in splenocytes at 2 h, 1 day, 3 days, and 7 days after burn and sham injury or trauma-hemorrhage (T-H) and sham T-H. Affymetrix Mouse Genome 430 2.0 GeneChips were used to profile gene expression, and the results were analyzed by dCHIP, BRB Array Tools, and Ingenuity Pathway Analysis (IPA) software. We found that the highest number of genes differentially expressed following burn injury were at day 1 for both WBCs (4,989) and for splenocytes (4,715) and at day 1 for WBCs (1,167) and at day 3 for splenocytes (1,117) following T-H. The maximum overlap of genes that were expressed after both forms of injury were at day 1 in WBCs (136 genes) and at day 7 in splenocytes (433 genes). IPA revealed that the cell-to-cell signaling, cell death, immune response, antiapoptosis, and cell cycle control pathways were affected most significantly. In summary, this report provides a database of genes that are modulated in WBCs and splenocytes at sequential time points after burn or T-H in mice and reveals that relatively few leukocyte genes are expressed in common after these two forms of injury.

  • inflammation
  • gene microarray
  • immune response
  • gene microarray analysis
  • pathway analysis

over the past thirty years many clinicians have concluded that there is a substantial degree of similarity in the initial response to serious injury regardless of its cause (8, 10, 11, 19, 22, 36). This assumption has been supported by investigations of inflammatory and counter inflammatory reactions commonly seen early after traumatic, thermal, or major surgical procedure (2, 17, 21, 23, 27, 30, 32). However, surgeons and critical care specialists have also recognized that patients who have suffered major burns often have a clinical course quite different from that of patients who have sustained significant traumatic injury (12, 29, 30, 33). Unfortunately, up to the present day, similarities and differences in the molecular mechanisms involved in the response to these two forms of injury have been difficult to quantify.

The availability of gene microarray technology has made it possible to study the effects of both thermal and traumatic injury on the transcriptome in cell populations of interest. Since both types of injury are known to produce profound changes in the behavior of the immune system, sequential analysis of gene expression profiles of leukocytes offers an opportunity to characterize one important aspect of the response to injury. The National Institute of General Medical Sciences (NIGMS)-funded large-scale collaborative research program “Inflammation and the Host Response to Injury” was designed to perform sequential microarray analysis of messenger RNA (mRNA) abundance in leukocytes from burn and trauma patients and in animal models of thermal and traumatic injury. A major objective of this research project was to delineate differences and similarities in alterations of leukocyte gene expression following the two types of injury and to compare patient responses with those of the mouse models. We reasoned that the findings from these studies would allow us to test the hypotheses that these two types of injury might induce similar gene expression profiles and that mouse injury models resemble injury responses in humans. In addition, we wished to compare the gene expression profiles of circulating leukocytes with those from the spleen in the same animal models to begin to address the phenomenon of compartmentalization of the immune response to injury.

We have previously reported a comparison of the leukocyte gene expression patterns at an early time point following burn, trauma-hemorrhage (T-H), or endotoxin infusion in mice (6). One goal of the present study was to compare the dynamics of leukocyte gene expression in circulating white blood cells (WBCs) and splenocytes at multiple time points in the same models of burn injury or T-H. Unlike the early time point study, a model of endotoxin infusion was not included in the present experiments because it was subsequently observed that changes in leukocyte gene expression returned to or toward control levels within 1 day after endotoxin treatment in mice as was the case in human (7). Another goal of these studies was to use contemporary pathway analysis to identify functional modules influenced by genes whose expression was shown to be altered by either or both forms of injury in time sequential analysis. We believe that identification of such molecular modules should help provide a framework for future functional studies designed to address the influence of injury on the immune system.



Male C57BL/6J mice were purchased from the Jackson Laboratory (Bar Harbor, ME). As in prior animal experiments in this collaborative study, the mice were maintained in accredited animal facilities at the University of Alabama at Birmingham and at the Brigham and Women's Hospital, Harvard Medical School, Boston, MA, in accordance with the guidelines of the National Institutes of Health and the respective universities. The study was approved by the animal use and care committees of each institution. The mice were acclimated for at least 1 wk prior to use in these experiments at 8 wk of age.

Injury models.

The burn injury model was studied at the Brigham and Women's Hospital and the T-H model at the University of Alabama. To initiate each experiment, groups of six mice undergoing the injury protocols were given inhalation anesthesia with isoflurane and were then subjected to 25% total body surface area scald burn, or T-H as previously described (6, 7). Following burn injury, mice were resuscitated by intraperitoneal injection with 1 ml Ringer's lactate solution. T-H consisted of laparotomy followed by withdrawal of sufficient blood from an arterial line to decrease and maintain mean arterial blood pressure at 35 mmHg for 90 min after which these mice were resuscitated with Ringer's lactate (four times the shed blood volume). At the same time, groups of six mice also underwent sham burn or sham T-H procedures under anesthesia.

Sample collection.

In separate experiments, groups of six injured or sham mice were anesthetized with isoflurane and exsanguinated by cardiac puncture at 2 h, 1 day, 3 days, or 7 days after injury. Blood was collected in syringes containing 0.1 ml EDTA anticoagulant solution (169 mM solution in sterile distilled water). The blood samples were added to 5 ml of a proprietary red blood cell (RBC) lysis buffer previously shown to prevent loss of neutrophils (PMN), which occurred when conventional RBC lysis buffers were used (6). After 5 min, the RBC lysis was neutralized by addition of 9 ml of a neutralization buffer. The sample tubes were then centrifuged at 100 g for 15 min to pellet cells and reduce platelet contamination. The supernatant was carefully decanted, and the cell pellet suspended by light agitation in 1 ml of RLT buffer (Qiagen, Valencia, CA) supplemented with the recommended amount of 2-mercaptoethanol. The pellet was then homogenized in the RLT buffer by aspirating and injecting the sample through a 1 ml syringe fitted with a 20-gauge needle. The homogenized cell suspension in RLT buffer was then shipped in dry ice to Washington University in St. Louis for RNA isolation, labeling, and microarray hybridization. This protocol for blood leukocyte (WBC) RNA extraction was selected to conform with the protocol used in studies of human trauma and burn patients supported by the same “Inflammation and the Host Response to Injury” collaborative project (9).

Following exsanguination, spleens were removed and cell suspensions were prepared by mincing the tissue on sterile stainless-steel screens. Cells were prepared in Dulbecco's phosphate-buffered saline. The same RBC lysis procedure described above was then carried out with the spleen cell suspensions. Following RBC lysis, 20% of the total spleen cells were homogenized in RLT buffer, and samples were shipped in dry ice to Washington University St. Louis for RNA preparation.

Microarray protocol.

The Washington University St. Louis site extracted cellular RNA and performed microarray analyses. Total RNA was isolated using the manufacturer's recommended protocol for RNeasy (Qiagen, Valencia, CA). Isolated RNA was quantified, and quality was assessed using an Agilent A2100 BioAnalyzer with the RNA NanoChip (Agilent, Andover, MA). RNA quality was judged on an arbitrary scale of 1–4 with 4 being the highest (7). Four of the six RNA samples showing the highest quality and greatest yield were chosen for labeling and gene microarray hybridizations. We used 1–2 μg of total RNA to make single-stranded antisense cDNA with the NuGEN Technologies (San Carlo, CA) Ovation Biotin System in accordance with the manufacturer's directions. Labeled targets were hybridized to Affymetrix (Santa Clara, CA) MOE430 2.0 GeneChip microarrays for 16 h at 45°C. The arrays were washed and scanned according to Affymetrix standard protocols (6). The National Center of Biotechnology Information (NCBI) Gene Expression Omnibus series number for these data is GSE7404.

Gene expression analysis.

Microarray data were normalized using the PM-only algorithms of dChip, version 1.3 software package (15, 16). For unsupervised analysis, probe sets whose hybridization signal intensity varied across the data set with a coefficient of variation >0.5 were identified and visualized by average linkage hierarchal clustering using the clustering algorithms implemented in dChip. For supervised analysis, probe sets whose hybridization signal intensities showed significant (P < 0.001) variation between the injured and sham groups of mice were identified by f-test using the class comparison tools and the time series algorithms implemented in BRB Array Tools developed by Richard Simon and Amy Peng Lam ( Next, burn and T-H mice were compared with one another at the four time points to determine genes differentially expressed in common in WBCs and in splenocytes. Finally, burn and T-H animals were compared with their respective shams at all time points with regard to genes up- or downregulated in common in WBCs and splenocytes. The gene identification for each probe set reported in this manuscript uses the NCBI gene symbol names derived from the NetAffx database for the MOE430 2.0 GeneChip.

Ingenuity pathway network analysis.

To explore gene-gene interactions and functional modules of interest from the experimental data sets we used the Ingenuity Pathway Analysis software (IPA Ingenuity Systems, Mountain View, CA). The IPA uses the Ingenuity Pathways Knowledge Base, which is a curated database of biological networks consisting of millions of individually modeled, peer-reviewed pathway relationships. The following steps were performed to generate the resulting data: 1) Genes identified as informational from the BRB analysis were overlaid on the Genomic Network from the database and labeled as “focus” genes, a subset having direct interactions with other genes in the data base. 2) Connections for each “focus” gene were calculated by the percentage of its connections to other significant genes. The initiation and growth of the pathways proceeded from genes with the highest specificity of connections and pathways of highly interconnected genes were identified by statistical likelihood using the equation: Math Given the number of genes in the genomic network (N) of which (G) are focus genes for a pathway of s genes, f of which are focus genes, C (nk) is the binomial coefficient. Pathways with a score >4, corresponding to P < 0.0001, were combined to form a composite network representing the underlying biology of the process. Gene symbols were colored according to the direction of their expression (red indicating increased and blue indicating reduced expression). A more detailed description of this process can be found at the website (

Real-time PCR.

In some instances, splenocyte RNA preparations used for the microarrays were also subjected to real-time RT-PCR validation assays using primers specific for a randomly selected cohort of those genes. The primer sets for these genes were designed to amplify 100–200 bp segments and primers specific for glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were used as a housekeeping gene control. All primers were custom-designed from GenBank mRNA sequences and prepared by InVitrogen (Carlsbad, CA). Their sequences can be found as supplemental information online.1 cDNA was synthesized from 1 μg RNA samples using oligo d(T)18 to prime the reverse transcriptase reaction (Superscript III RT, InVitrogen). RT-PCR was carried out on the Gene Amp 5700 instrument (Applied Biosystems, Foster City, CA) with the QPCR SYBR Green detection reagent (AB.Gene, Rochester, NY) as previously reported (6). Cell threshold values for each gene were determined and fold induction compared with GAPDH was calculated using the ΔΔCt method (26). RT-PCR results were recorded for each gene as fold change vs. sham. PCR products were evaluated by dissociation curves to confirm single amplicons and the absence of significant primer-dimer contamination.


Gene microarray hybridization and analysis.

WBC and spleen leukocyte RNA samples from four individual mice of the six injured or sham animals killed at each time point at each of the two institutions were selected for hybridization to Affymetrix MOE430 2.0 microarrays containing 45,101 probe sets. Selection was based on RNA yield and quality as judged by Agilent analysis and not on cell yield. Thus, a total of 128 microarray hybridizations were available for analysis. Initial evaluation by the dChip software program revealed that no microarrays were deemed outliers according to default criteria (>15% probe sets as outliers). To explore differences in mRNA abundance over time across both models, an unsupervised cluster analysis of probe sets differentially expressed with a coefficient of variation >0.5 in injured and sham animals from both models at all time points is presented in supplemental materials online (unsupervised cluster diagram). The sequential gene expression in each of the injury models differed considerably from those of their respective shams at all time points. The sham groups from each of the two experimental sites also differed substantially from one another as shown in supplemental information online, thus confirming the value of comparing each injury model to its own sham.

Formal time sequential analysis of 4,627 probe sets differentially expressed in injured vs. sham animals at the P < 0.001 level is shown in Fig. 1. It is apparent that large numbers of probe sets were differentially expressed in injured vs. sham animals in both blood and spleen at all time points. Lists of these differentially expressed genes can be found as supplemental information online. The numbers of genes up- and downregulated at each time point in blood and spleen for each model are shown in Table 1. The results summarized in Table 1 reveal that the highest numbers of differentially expressed genes following burn injury were at day 1, for both WBCs (4,850) and spleen cells (4,715), while the highest numbers of genes modulated following T-H were at day 1 for WBCs (1,167) and at day 3 for spleen cells (1,117). This finding suggests that burn injury and T-H differ in the kinetics and overall magnitude of gene expression responses with burn injury showing threefold higher number of genes differentially expressed at day 1 postinjury.

Fig. 1.

Time series analysis of differentially expressed genes following burn injury or trauma-hemorrhage (T-H). We identified 4,627 probe sets whose hybridization signal intensities showed significant (P < 0.001) variation between the injured and sham groups of mice by f-test using the class comparison tools and time series analysis of BRB Array Tools. The 1st column of color-coded letters, S, T, and B, identify treatments Sham, T-H, or Burn, respectively. The 2nd column of color-coded numbers, 2, 1, 3, 7, indicate time points; 2 h, 1 day, 3 days, or 7 days. The 3rd column of letters, B or S, identifies the tissue compartments: blood or spleen.

View this table:
Table 1.

Numbers of genes significantly up and downregulated over time in each animal model

We also identified genes up- or downregulated in common between blood and spleen in each model at most time points (the exception being day 1 for T-H) (Table 2). However, there were a larger number of genes whose expression was not altered in common between WBCs and splenocytes (Table 1). This observation suggests that mice display a marked compartmentalization of the leukocyte response to injury within each model. Since gene expression in leukocyte subsets was not measured in this study, compartmentalization may be due, at least in part, to differences in the proportion and abundance of cell subsets between blood and spleen.

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Table 2.

Numbers of genes significantly up- and downregulated in common between WBCs and spleen cells over time in each animal model

Ingenuity pathway network analysis.

To provide a functional context to study these probe sets shown to be differentially expressed over time, IPA was employed to aid in identifying pathway networks that are significantly altered by injury. The five functional pathways most significantly associated with the differentially expressed genes in WBCs and splenocytes from both models as revealed by this analysis are summarized in Fig. 2. Figure 2A illustrates the five pathways most significantly (P < 0.001) associated with altered gene expression in WBCs at all four time points for both the burn and T-H mouse models. Figure 2B displays the same information for splenocytes. The numbers of genes significantly up- or downregulated in each pathway are also indicated and the gene names are listed as supplemental information online.

Fig. 2.

Plots illustrating biological pathways identified by Ingenuity Pathway Analysis as being the most significantly influenced by burn injury or T-H in white blood cells (WBCs) or splenocytes. Ingenuity analysis was performed using significant probe set data from WBCs (A) and splenocytes (B). The 5 most highly significant pathways at each time point are plotted, and the numbers of significant up- or downregulated genes associated with each pathway are indicated in the bars. The gene symbols for each of the genes represented in this figure and the fold change after injury are available as supplemental information online.

WBC and splenocyte samples from the two models were compared with one another with respect to genes up- or downregulated vs. their respective shams across the four experimental time points. As summarized in Table 3, there is overlap in some of the genes differentially expressed in the two models at all time points; however, most of the affected genes (Table 1) are unique to each injury model. There is most concordance in WBC gene expression in the two models at the 1-day time point when 136 genes are identified as being up- or downregulated in common. Figure 3 illustrates an IPA of the molecular networks impacted by these 136 genes. Major functional pathways are highlighted in labeled boxes in this network diagram. Red-labeled symbols indicate the genes that were found to be upregulated by gene microarray analysis (P < 0.001) compared with shams, while blue labeling indicates significant downregulation. The functional pathways and the genes included in these pathways are listed in Table 4.

Fig. 3.

Ingenuity network plot illustrating pathways that are altered in common at day 1 after burn or T-H injury in WBCs. The boxed regions, A–C, in this network plot highlight the major pathways affected. A: cell-to-cell signaling, B: cell death, C: cellular development. The red symbols indicate upregulated, the blue symbols indicate downregulation, and the white symbols denote not significantly different.

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Table 3.

Numbers of genes significantly up or downregulated in common in WBCs or splenocytes over time following burn injury or T-H

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Table 4.

List of genes upregulated or downregulated in common in WBCs at day 1 in both models in pathways identified as most significant by IPA

A more robust example of concordant gene expression is evident in the splenocyte samples at the 7-day interval where 428 of the 460 genes upregulated in the T-H model are also upregulated in the burn model. However, even at this time point there are still a substantial number of genes significantly up- or downregulated in the burn splenocytes that are unique to burn injury since they are not altered in the splenocytes from T-H mice. Nevertheless, the large number of genes upregulated in common in a primary lymphoid organ 7 days after both forms of injury argues strongly for the idea that there is substantial commonality in the immune response to burn and traumatic injury at this time point. The 7- to 10-day postinjury period has often been cited as the time most often associated with infection and subsequent postinjury complications in man and relevant animal models (5, 13, 14, 18, 21, 28). For these reasons, the list of the genes whose expression was altered in common in splenocytes from both models at day 7 was also subjected to IPA (Fig. 4 and online). The functional pathways and the genes included in these pathways are listed in Table 5. It is apparent that these genes are most significantly associated with pathways related to the immune response, antiapoptosis, cell-cycle control, DNA replication, chromosome condensation and segregation, and pyrimidine metabolism. These genes in both models may be directing a “recovery” program for immune reactivity, cell division and resistance to apoptosis.

Fig. 4.

Ingenuity network plot illustrating pathways that are altered in common at day 7 after burn or T-H injury in splenocytes. The boxed regions, A–G, in this network plot highlight the major affected pathways. A: immune response, B: antiapoptosis, C: cell cycle, D: chromosome segregation, E: DNA replication, F: chromosome condensation, G: pyrimidine metabolism. The red symbols indicate upregulated, the blue symbols indicate downregulation, and the white symbols denote not significantly different.

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Table 5.

List of genes upregulated in common in splenocytes at day 7 in both models in the pathways identified as most significant by IPA

Validation of gene microarray experiments and analysis.

A randomly selected subset of 26 of the commonly expressed splenocyte genes found in the pathways by IPA (Table 5) was used to validate our microarray results. We used real-time PCR (RT-PCR) to evaluate the relative abundance of these 26 genes in splenocyte RNA samples from sham and injured mice. The fold change gene expression of injured vs. sham measured by RT-PCR was then compared directly to the fold change obtained by Affymetrix hybridization (Fig. 5). The results displayed in Fig. 5 show a high level of agreement between the fold change measured by RT-PCR and by Affymetrix. The burn injury RT-PCR results indicated a 92.3% agreement for upregulated genes and T-H RT-PCR findings indicated 84.6% agreement for gene upregulation. Moreover, we found that those genes showing the highest fold change by RT-PCR were also within the set of genes showing the highest fold change by Affymetrix hybridization. Interestingly, we observed that burn injury induced the highest fold induction by real-time PCR and the highest concordance between Affymetrix and real-time PCR fold change values. This observation is consistent with the overall finding that burn injury induces threefold higher number of differentially expressed genes at 7 days postinjury compared with T-H. The lower percentage agreement and higher variability in fold induction found in splenocyte RNA from T-H mice might be related to this biological difference between burn and T-H injuries. Taken together, the results of these RT-PCR studies validate the microarray data and support the use of gene chip technology to identify gene expression changes following these two types of injuries.

Fig. 5.

Real-time PCR validation of gene microarray results. Primers specific for a randomly selected subset of 26 splenocyte genes found significantly modulated in both models (Table 5) were used in RT-PCR studies to validate our microarray results. Splenocyte RNA was converted to cDNA by a reverse transcriptase reaction and relative changes in the indicated gene expression levels were measured by RT-PCR. Primers specific for GAPDH were used as an internal control gene and the ΔΔCt method was used to determine fold change values between sham and injured groups. The RT-PCR results are plotted alongside the Affymetrix-measured fold change. The results are plotted as means ± SE fold induction from 4 independent splenocyte RNA preparations.


The purpose of the present study was to compare sequential changes in leukocyte gene expression patterns in animal models of two forms of major injury commonly encountered clinically: burns involving >20% of the body surface and trauma accompanied by significant blood loss. Since leukocytes are the chief effector cells of both innate and adaptive immunity, we hoped to document the changes in mRNA abundance in these cells at several time points within the first week following injury, a period when prior investigations had shown marked injury-induced immune perturbation in patients and relevant animal models (1, 5, 8, 10, 14, 19, 21, 23, 24, 27, 3032, 37).

We believed that it was important at each time point to compare injured animals with appropriate shams that had been subjected to all manipulations experienced by the injured animals, other than the injury itself, to eliminate from analysis those changes in mRNA abundance induced by such factors as anesthesia, hair clipping, vessel cannulation, and fluid resuscitation. Moreover, the sham groups used for the T-H and burn underwent quite different experimental procedures, and as illustrated by the unsupervised cluster diagram provided online, sham burn and sham T-H groups show different gene expression patterns. While microarrays from each injury model could have been compared with those from a group of unmanipulated control mice, we believed more injury-specific gene expression data could be obtained by comparing mRNA expression in injured groups with injury-matched sham controls at each time point. The major advantage of this approach is that it provided us with lists of what were clearly injury-induced genes for each model and for each time point, which were then used to assess those genes expressed in common between the two animal models.

A hypothesis tested by this study was that during the first week after injury both burn and T-H would induce similar leukocyte gene expression patterns. Our findings here are consistent with a prior report comparing these same two mouse models of injury with a mouse model of endotoxin infusion, selected as a surrogate for sepsis, at only the 2 h time point (6). In that report, similarities in leukocyte gene expression in these three groups of mice were identified, but the majority of altered leukocyte mRNA abundance appeared to be unique to each form of injury (6). Moreover, the majority of genes differentially expressed in injured vs. sham animals were not differentially expressed in common in both WBCs and splenocytes. In the present experiments, the two injury models again showed only a minority of genes whose expression was altered in common in both WBCs and splenocytes at the 2 h time point, thus corroborating the prior study. In refutation of the hypothesis that burn and T-H would induce similar gene expression profiles, time series analysis of WBC gene expression changes at the 1 day, 3 day, and 7 day time points in the present study were consistent with the 2 h data in that most alterations were injury specific. The two models most resembled one another at the one day time point with 136 genes up- or downregulated in common, and progressively fewer commonly altered genes at 3 and 7 days.

Because unseparated WBCs and splenocytes were used as a source for RNA in this study, a potential reason for the lack of commonality between these two injury models is that they induce wide differences in cell populations. However, the cellular response to injury between these models was similar. We observed increases in WBC counts only at 2 h following burn injury or trauma-hemorrhage. Moreover, we observed modest increases in the percentage and absolute number of circulating neutrophils at the 2 h and 1 day time points in both animal models. However, neutrophils and monocytes at all times represented minor subset of the total WBC population, which was consistently 71–91% lymphocytes. Splenocytes in the two models in past studies have also remained 85% lymphocytes with neutrophils increasing from 2–5 to 8–12% by 7 days postinjury. It is, thus, unlikely that the differences in leukocyte gene expression profiles between these two animal models were due to significant differences in the cell populations studied. Experiments to examine purified subpopulations of WBCs and splenocytes are underway. We anticipate that examining gene changes in purified cell subsets by microarray will uncover changes in gene expression in minority cell types such as neutrophils, monocytes, and macrophages that may have been overshadowed in the present study by the higher abundance of mRNA from lymphocytes.

A primary aim of this study was to provide statistically significant lists of injury-induced genes to the scientific community. Because the project involved two different animal models and multiple sample time points, it was not feasible to include these lists within the text of this manuscript. As an alternative, we organized these data into files for online access showing the gene information and corresponding fold change compared with controls for each time point. The reader is encouraged to examine these lists, which are organized in the Microsoft Excel format and are easily searched for genes of interest. Based on prior work using these injury models, we postulated that burn or T-H would induce inflammatory mediators such as interleukin-1 (IL-1), tumor necrosis factor (TNF), and stress-induced factors such as heat shock proteins (HSPs). Searching these WBC and splenocyte gene lists, we found that burn injury and T-H did induce significant increases (2- to 44-fold) in the expression of IL-1 and IL-1-associated genes including type 1 and 2 IL-1 receptors, IL-1 receptor antagonist, and IL-1 receptor accessory protein at the 2 h and 1 day postinjury time points. The TNF-associated genes included TNF signaling molecules, Traf1, Traf4, Traf6, Tradd, and several TNF-α-induced proteins. Examples of upregulated HSPs (2- to 23-fold) were also observed at the 2 h and 1 day postinjury time points. These included HSP70, HSP1, HSP1b, HSP2, HSF2, and calcium-regulated HSP. Interestingly, these inflammation and stress-induced genes were not found to be upregulated at the 3 or 7 day time points, indicating that they represent early injury response factors. In contrast, genes with counterinflammatory activity such as the IL-13 receptor, CD33, and CTLA-4, along with many genes responsible for cell division or DNA repair were found to be upregulated at these later time points. These observations regarding upregulated inflammatory genes early after injury followed by increased counterinflammatory gene upregulation are consistent with the concept that injury induces a systemic inflammatory response followed by a compensatory anti-inflammatory response. To our knowledge, this report provides the first comprehensive list of genes that are differentially expressed in WBCs or splenocytes over time in these animal models.

One of the most dramatic findings of the present study was the large number of genes upregulated in common in the spleen at the 7 day time point in both injury models. In fact, nearly every gene whose expression was altered in the spleens of the T-H animals was also altered in the spleens of the burn animals. However, an equally large number of genes whose expression was altered in burn splenocytes at day 7 were not differentially expressed in the T-H animals. This major difference in gene expression between the two models was not reflected in injury-induced mortality, which remained near zero in both models. However, this discrepancy is perhaps a reflection of the fact that burn injury causes more prolonged metabolic and physiological perturbations than nonthermal traumatic injury in reported clinical studies (25, 29, 34, 35, 37). Nevertheless, the >400 genes upregulated in common in the spleen of both injury models clearly indicates a great deal of concordance in leukocyte gene expression in a major secondary lymphoid organ at 7 days postinjury. In clinical studies, this intermediate time period after injury has often been associated with the onset of nosocomial infections and the multiple organ dysfunction syndrome (3, 4, 12, 20, 23, 30, 36). Time series analysis illustrated in Fig. 1 documents the fact that the two animal models upregulated the expression of this large number of genes with somewhat different kinetics with the T-H model manifesting these changes earlier than the burn model. As is clear from Fig. 4, these genes are associated with multiple intracellular pathways, the most significant of which are involved in the immune response, cell division, and protection from apoptosis. This then appears to be a generic transcriptional response associated with recovery from the perturbations of injury in both models.

Real-time PCR studies were performed to confirm up- or downregulation of 26 differentially expressed genes significantly associated with these pathways in the spleen. These targets were selected randomly from the subset of the 433 genes modulated in common between injury models and identified by IPA as significant. These studies, performed with the same RNA samples used for microarray analysis yielded similar results with regard to up- or downregulation of gene expression (Fig. 4). This validation of microarray data is consistent with our previously reported findings demonstrating high concordance between Affymetrix and RT-PCR of a randomly selected subset of genes (6). Spleen, rather than blood, RNA was used for these validation experiments because nearly all the WBC RNA was used for Affymetrix gene chip hybridizations. The results of this validation study provide additional support for the microarray and data analysis approaches used to identify genes uniquely and commonly altered by these two types of injuries. Moreover, the observation that common “housekeeping” gene probe sets (GAPDH, β-actin, cyclophilin A, ribosomal proteins) were not identified as significantly different, P < 0.001, from shams or injured groups for any time point or cell population provides additional verification that our findings do not show significant systematic variability (Fig. 1 gene list, online supplement).

Compartmentalization of the immune response to injury, which was suggested in our previous report, was further substantiated in the present experiment where a majority of differentially expressed genes in the WBC and spleen compartments at all four time points showed few obvious similarities (Tables 2 and 3) (6). It is certainly possible, however, that apparent compartmentalization could have results in part from differences in abundance of immune cell subsets between blood and spleen. An ongoing study performed by this collaborative group will examine the changes in gene expression at days 1 and 7 after injury in highly-purified leukocyte subsets (monocytes/macrophages and T cells) from the blood and spleen of mice exposed to T-H or burn injury. We believe the results of this study will more accurately reveal the degree of tissue compartmentalization of the burn or T-H response.

Future studies to determine the biologic significance of altered gene expression in several of the molecular pathways identified in the present study would seem highly appropriate to provide clinical relevance of the present findings. Such studies will almost certainly need to be carried out in appropriate animal models such as the two used in the current experiments. The modulation of the activity of selected genes through the use of knockout mice, antibodies directed against the cognate gene product, or techniques to alter gene expression such as RNA interference could be used to establish the biological influence of identified pathways. However, the clinical significance of such studies would be greatly enhanced if we identify orthologs of the injury-affected mouse leukocyte genes shown to be similarly altered in sequential studies of gene expression in WBCs obtained from human trauma and burn patients. Such analyses of concordance (or lack thereof) in microarray studies of WBC gene expression in burn and trauma patients and the two mouse models of injury have been performed with the support of the “Inflammation and the Host Response to Injury” collaborative research project ( We believe these analyses will help define genes of interest whose altered expression following injury is evident in both species and will aid in identifying biologic pathways for future functional genomic studies in mice.

The present experiments were designed to run sufficient replicates of independent samples per condition to reduce chance variations in gene expression. In addition, we used simultaneously studied sham animals at each time point to separate changes in gene expression caused by injury from those caused by other extraneous factors. For practical reasons, we opted to use all WBCs and splenocytes rather than purified cells populations in this first report. Nevertheless, significant changes in mRNA abundance defined in the present study are likely to reflect those induced in the major leukocyte cell type in both blood and spleen, i.e., lymphocytes. We recognize that profound changes in gene expression in lymphocyte subsets and other important immune cell types, e.g., PMNs and monocytes/macrophages, may not be evident in the total leukocyte populations surveyed in the present report. Therefore, studies of the effects of injury in the two models on gene expression in pure populations of PMNs, monocytes/macrophages, and T cells are currently under way. The results of these studies should help to further elucidate the molecular pathways affected by injury-induced gene expression changes in these three leukocyte subsets known to play important roles in the immune response to injury.


The work was funded by NIGMS Grant 1U54-GM-62119-03.


The Inflammation and the Host Response to Injury Collaborative Research Program Participants: Ulysses Balis, MD, Paul Bankey, MD, Timothy R. Billiar, MD, Steven E. Calvano, PhD, David G. Camp II, PhD, Joseph Cuschieri, MD, Ronald W. Davis, PhD, Asit K. De, PhD, Constance Elson, PhD, Celeste C Finnerty, PhD, Bradley Freeman, MD, Richard L. Gamelli, MD, Nicole S. Gibran, MD, Brian G. Harbrecht, MD, Douglas L. Hayden, MA, Laura Hennessy, RN, David N. Herndon, MD, Jureta W. Horton, PhD, Marc G. Jeschke, MD, PhD, Jeffrey Johnson, MD, Matthew B. Klein, MD, Stephen F. Lowry, MD, Ronald V. Maier, MD, Philip H. Mason, PhD, Grace P. McDonald-Smith, MEd, Carol L. Miller-Graziano, PhD, Michael N. Mindrinos, PhD, Joseph P. Minei, MD, Lyle L. Moldawer, PhD, Ernest E. Moore, MD, Avery B. Nathens, MD, PhD, MPH., Grant E. O'Keefe, MD., MPH, Laurence G. Rahme, PhD, Daniel G. Remick, Jr. MD, David A. Schoenfeld, PhD, Michael B. Shapiro, MD, Geoffrey M. Silver, MD, Richard D. Smith, PhD, John Storey, PhD, Robert Tibshirani, PhD, Ronald G. Tompkins, MD, ScD, Mehmet Toner, PhD, H. Shaw Warren, MD, Michael A. West, MD.

The authors thank Satoshi Fujimi, Nochiketa Mohanty, and Martin Schwacha for assistance. The authors also thank Linda Nevins for administrative assistance.


  • 1 The online version of this article contains supplemental material.

  • Address for reprint requests and other correspondence: J. A. Lederer, Dept. of Surgery, Brigham and Women's Hospital/Harvard Medical School, 75 Francis St., Boston, MA 02115 (e-mail: jlederer{at}

    Article published online before print. See web site for date of publication (


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