Trauma is the number one killer of individuals 1–44 y of age in the United States. The prognosis and treatment of inflammatory complications in critically injured patients continue to be challenging, with a history of failed clinical trials and poorly understood biology. New approaches are therefore needed to improve our ability to diagnose and treat this clinical condition.
Methods and Findings
We conducted a large-scale study on 168 blunt-force trauma patients over 28 d, measuring ~400 clinical variables and longitudinally profiling leukocyte gene expression with ~800 microarrays. Marshall MOF (multiple organ failure) clinical score trajectories were first utilized to organize the patients into five categories of increasingly poor outcomes. We then developed an analysis framework modeling early within-patient expression changes to produce a robust characterization of the genomic response to trauma. A quarter of the genome shows early expression changes associated with longer-term post-injury complications, captured by at least five dynamic co-expression modules of functionally related genes. In particular, early down-regulation of MHC-class II genes and up-regulation of p38 MAPK signaling pathway were found to strongly associate with longer-term post-injury complications, providing discrimination among patient outcomes from expression changes during the 40–80 h window post-injury.
The genomic characterization provided here substantially expands the scope by which the molecular response to trauma may be characterized and understood. These results may be instrumental in furthering our understanding of the disease process and identifying potential targets for therapeutic intervention. Additionally, the quantitative approach we have introduced is potentially applicable to future genomics studies of rapidly progressing clinical conditions.
Please see later in the article for the Editors' Summary
Citation: Desai KH, Tan CS, Leek JT, Maier RV, Tompkins RG, et al. (2011) Dissecting Inflammatory Complications in Critically Injured Patients by Within-Patient Gene Expression Changes: A Longitudinal Clinical Genomics Study. PLoS Med 8(9): e1001093. doi:10.1371/journal.pmed.1001093
Academic Editor: Mervyn Singer, University College London, United Kingdom
Received: February 20, 2011; Accepted: August 3, 2011; Published: September 13, 2011
Copyright: © 2011 Desai et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research is supported in part by NIH grant U54 GM-62119 (PI: Ronald Tompkins). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: CARS, compensatory response syndrome; FDR, false discovery rate; ocMOF, ordered categorical MOF score; IHRI, Inflammation and the Host Response to Injury; IPA, Ingenuity Pathways Analysis; MOF, multiple organ failure; PCA, principal component analysis; SIRS, systemic inflammatory response syndrome; WPEC, within-patient expression changes
¶ Membership of the Inflammation and the Host Response to Injury Large-Scale Collaborative Research Program is provided in the Acknowledgments.
Trauma—a serious injury to the body caused by violence or by an accident—is a major global health problem. Every year, events that include traffic collisions, falls, blows, and fires cause injuries that kill more than 5 million people (9% of annual global deaths). Road traffic accidents alone are responsible for 1.3 million deaths a year and, if current trends continue, will be the fifth leading cause of death worldwide by 2030. Moreover, in many countries, including the US, trauma is the number one killer of individuals aged 1–44 y. Trauma can kill people rapidly through loss of blood or serious physical damage to internal organs, but it can also lead to localized infections and to sepsis, an infection of the bloodstream that is characterized by an amplified, body-wide (systemic) inflammatory response. Inflammation—redness, pain, and swelling—is an immune system response that normally provides protection against infections, but systemic inflammation can result in multiple organ failure (MOF) and death.
Why Was This Study Done?
Inflammatory complications of trauma are responsible for more than half of late trauma deaths, but at present it is impossible to predict which patients with major injuries will recover and which will spiral down into MOF and death, because the biological processes that underlie post-injury inflammatory complications are poorly understood. If the changes in gene expression (the process that converts the information encoded in genes into functional proteins) that accompany systemic inflammation could be elucidated, it might be possible to improve the diagnosis of MOF and to develop better treatments for post-trauma inflammatory complications. In this prospective, longitudinal clinical genomics study (part of the Inflammation and Host Response to Injury multi-disciplinary research program [IHRI]), the researchers developed an approach to associate early within-patient gene expression changes with later clinical outcomes. A prospective study is one in which patients with a specific condition are enrolled and then followed to see how various factors affect their outcomes; a longitudinal study analyzes multiple samples taken at different times from individual patients; a clinical genomics study investigates how genes and gene expression affect clinical outcomes.
What Did the Researchers Do and Find?
The researchers followed 168 patients for up to 28 d after they experienced blunt-force trauma (injuries caused when the human body hits or is hit by a large object such as a car). Using a molecular biology tool called a DNA microarray, they determined gene expression patterns in leukocytes (a type of immune system cell) isolated from multiple blood samples collected from each patient during the first few days after injury. Using clinical information collected by trained nurses, they organized the patients into five outcome categories based on a measure of MOF known as the Marshall score. Finally, they developed a statistical method (an analysis framework) to associate the early changes in gene expression with clinical outcomes.
A quarter of the patients' genes showed early expression changes that were associated with longer-term post-injury inflammatory complications. Among the associations revealed by this analysis, down-regulation (reduced expression) of MHC-class II genes (which encode proteins involved in antigen presentation, the process by which molecules from foreign invaders are presented to immune cells to initiate an immune response) and up-regulation of genes encoding components of the p38 MAPK signaling pathway (which helps to drive inflammatory responses) between 40 and 80 h post-injury were particularly strongly associated with longer-term post-injury complications and provided the strongest discrimination between patient outcomes.
What Do These Findings Mean?
The statistical approach used in this study to link the early changes in gene expression that occur after trauma to clinical outcomes provides a detailed picture of genome-wide gene expression responses to trauma. These findings could help scientists understand why some patients develop inflammatory complications of trauma while others do not, and they could help to identify those patients most at risk of developing complications. They could also help to identify targets for therapy, although further studies are needed to confirm and extend these findings. Importantly, the quantitative approach developed by the researchers for analyzing associations between within-patient gene changes over time and clinical outcomes should provide more robust predictions of outcomes than single measurements of gene expression and could be applicable to genomic studies of other rapidly progressing clinical conditions.
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001093.
- More details about the Inflammation and Host Response to Injury research program are available; the program's website includes a link to an article that explains how genomics can be used to understand the inflammatory complications of trauma
- The World Health Organization provides information on injuries and on violence and injury prevention (in several languages)
- The US National Institutes of Health has a factsheet on burns and traumatic injury in the USA
- The US Centers for Disease Control and Prevention has information on injury and violence prevention and control
- MedlinePlus provides links to further resources on injuries
Trauma is the number one killer of individuals aged 1–44 y , a source of some of the top health care costs in the United States , and a major global health priority ,. Trauma injuries frequently lead to infections, sepsis, and multiple organ failure (MOF) ,, which contribute to 51%–61% of late trauma mortality . A number of clinical trials for treating late trauma complications have failed, believed partly due to the inability to identify a proper patient population as well as the limited understanding of the interplay of biological processes underlying post-injury inflammatory complications ,. A more comprehensive characterization of the genomic response to trauma is therefore required in order to increase our understanding of the molecular basis of clinical outcomes, leading to improvements in diagnosis and treatment.
Despite the public health implications of improved trauma care, relatively few studies have been carried out to understand the molecular basis of trauma recovery, particularly from a genome-wide perspective . An endotoxin experiment on healthy volunteers  and a retrospective sepsis study  have shown a strong genomic response to trauma-related phenotypes. However, to date there has been no in-depth, prospective longitudinal characterization of the genome-wide expression response to blunt-force trauma that (a) identifies which pathways are fundamental determinants of the patient's recovery trajectory, and (b) elucidates the time period post-injury when these molecular signatures are most informative. Uncovering these factors can reveal new therapeutic strategies and the dynamic regimens for their administration.
To this end, the “Inflammation and the Host Response to Injury” (IHRI) research program conducted a large-scale, 28-d prospective clinical genomics study involving 168 patients, 797 microarrays, and 393 clinical variables. The key statistical challenge we faced was how to accurately associate early longitudinal gene expression measured at multiple time points with 28 d clinical trajectories captured by a constellation of clinical variables. We developed and applied a tractable and robust quantitative framework to analyze this complex clinical genomics study. Specifically, we sought to capitalize on the longitudinal structure within an individual, combining bioinformatics and statistical tools to elucidate pathway dynamics from the gene expression data.
We found that approximately one quarter of the genome changes during early stages of treatment in concordance with the observed variation in 28-d clinical outcomes. These expression changes are coordinated into five distinct modules, which together provide a fine-scale separation of patient outcomes. We pinpointed several pathways that appear to be key drivers of these modules and may be instrumental in furthering our understanding of the disease process and identifying potential targets for therapeutic intervention ,. We investigated the dynamics of these pathways and found that several discriminate among 28-d post-injury patients trajectories. Specifically, we identified p38 MAPK signaling pathway and MHC-class II genes as having the strongest discrimination in the first 40–80 h. Such information is potentially useful in determining the exact timing and effective dosage of drugs targeting these pathways in trauma patients.
A lack of reproducibility of clinical genomics results  has been shown to be largely due to patient heterogeneity, latent sources of confounding, and platform-dependent non-biological variation , all difficult to deal with when associating clinical outcome with a single snapshot of gene expression. Taking advantage of the longitudinal design of our study, we developed and applied an approach modeling “within-patient” gene expression dynamics for extracting robust signatures, thereby accounting for patient-specific effects and being more likely to reproduce in future patients. Our framework is likely applicable to other complex clinical genomics studies, especially in rapidly progressing clinical conditions.
Study Design and Patient Samples
In the IHRI prospective clinical genomics study, we studied a cohort of 168 patients (ages 16–55 y; 107 males) from a larger epidemiological study, involving 1977 severe blunt-force trauma patients, conducted from 2003 to 2011 through 7 U.S. Level I trauma centers across the United States. Figure 1 provides the flow chart leading to the 168 patients analyzed in this paper (epidemiological study: ClinicalTrials.gov identifier: NCT00257231). These 168 patients were followed for up to 28 hospital days post-injury, and their longitudinal genome-wide gene expression was measured. To ensure patients were at risk of developing MOF, infectious complications, and death (thereby satisfying the study requirements), the consortium employed a set of inclusion/exclusion enrollment criteria (Text S1). Patients with isolated traumatic brain injury were excluded. Samples were taken at fixed time points following injury according to study design and independent of physician influence. Thus there was no physician or severity of illness bias in the sample collection process.
Figure 1. Patient selection for the IHRI study.doi:10.1371/journal.pmed.1001093.g001
The institutional review board of each center approved the study, and written informed consent was obtained from all patients or their legal next of kin. The same standardized patient care protocol was used to minimize the impact of variability in clinical care across centers. Patient clinical information, typically consisting of >300 variables (some longitudinal), was collected by trained nurses and entered into a central database to maintain conformity and consistency across all participating centers. For each patient, genome-wide longitudinal gene expression was measured for total blood leukocytes isolated from peripheral blood samples (collection, processing, and normalization described in Text S1 and Figure S1). The data (de-identified as defined by the Health Insurance Portability and Accountability Act of 1996; see http://www.gluegrant.org/trdb.htm) are freely available at http://www.gluegrant.org; see http://www.gluegrant.org/glueadmin/register_consortium.jsp for details.
Figure 2. Schematic of the analysis framework.
There are three fundamental steps in the analysis framework. Step 1: characterizing phenotypes from longitudinal clinical data; Step 2: quantifying within-patient expression changes from the genomic data; and Step 3: statistical modeling and hypothesis testing to relate the two.doi:10.1371/journal.pmed.1001093.g002
We used a time-dependent modified Marshall score  (excluding the neurological component) as the measure of developing MOF, providing an up-to-28-d Marshall score time course trajectory for each patient. We applied hierarchical clustering to the 168 patient Marshall score trajectories, yielding five clusters (Figure 2, Step 1; Text S2). We used relevant patient information, such as the 28-d mortality and morbidity rates, to order these five subgroups (Figure 3; Table 1). This yielded a clinically interpretable, ordered categorical MOF score (ocMOF) ranging from i (good outcome) to v (bad outcome).
Figure 3. Order of the ocMOF subgroups.
The ordering can be determined with the following clinical variables: days from injury to discharge/death, proportion of ICU-free days, and proportion of ICU ventilation–free days. Note that all patients in ocMOF v died.doi:10.1371/journal.pmed.1001093.g003
Table 1. Summary of the demographic and outcome variables of the 168 patients.doi:10.1371/journal.pmed.1001093.t001
For the gene expression analysis, we considered 126 patients with three or more arrays meeting the RNA quality requirements among hours 12–250 (Text S2 and Figure S2). We sought to characterize within-patient expression changes (WPEC) by quantifying per hour log-fold change. To compute WPEC for each probeset, we regressed log gene expression on time (in hours) and extracted the linear slope (Figure 2, Step 2).
We tested each probeset's WPEC for an association with ocMOF using an adjusted Spearman rank-based correlation test and obtained a p-value for each probeset (Figure 2, Step 3; Text S2). Associating WPEC with ocMOF through a rank-based test enhances robustness, as WPEC values are used to establish an ordering of directional changes across patients, but do not rely on the actual magnitudes. Statistical significance of the 54,675 resulting p-values (Figure 4a) was assessed using the false discovery rate (FDR)  yielding an estimate of the total percentage of probesets associated with ocMOF as well as specific probesets identified as significant at various FDR thresholds (Figure 4b).
Figure 4. Statistical significance and reproducibility for the IHRIP data.
(a) The histogram of 54,675 p-values from our framework. (b) The number of significant probesets at various FDR cut-offs. These results indicate strong statistical significance. (c) Our strategy to assess reproducibility. (d) Reproducibility assessment of our framework: 20 quantile-quantile plots for 20 cross-validations. Consistently large downward deviations from the diagonal (dashed line) indicate reproducibility.doi:10.1371/journal.pmed.1001093.g004
All the methodological, algorithmic, and data-filtering decisions in Step 1 (clinical data) and in Step 2 (genomic data) were made completely independently by two different analysts to avoid any potential over-fitting.
Additional mathematical and algorithmic details about the statistical methods can be found in Text S2. The R statistical software environment (http://www.r-project.org/) was utilized to perform all data analyses. Computer code that reproduces all results is available as Dataset S1 with detailed documentation provided in Text S2.
PCA-Based Analysis of WPEC Matrix
To assess the overall effectiveness of WPEC in explaining trauma response, we performed principal component analysis (PCA)  on the WPEC matrix and determined the number of principal components (PCs) via the scree plot. We used these PCs as explanatory variables to model each of the 393 measured clinical variables and identified the top ten most significant clinical variables (Text S3).
Assessment of Reproducibility
We took a principled strategy to assess reproducibility (Figure 4c and 4d). Our strategy (a) obtains the most significant probesets using a “discovery set,” (b) computes their corresponding p-values in a “validation set,” and (c) assesses whether the validation set p-values show systematic, reproducible significance. We complete the last step by comparing the validation set p-values to the Uniform (0,1) distribution , which corresponds to the distribution of p-values when there is no significance. We performed 20 cross-validations by randomly splitting the study into discovery and validation datasets (Text S3).
Dominant Expression Trajectories and Dynamic Co-expression Modules
We utilized the DAVID software package to classify probesets with FDR <10% into 54 functionally related gene sets. We further aggregated these gene sets into five modules according to the similarity of their dominant trajectories across the ocMOF subgroups (Text S4). We used the average Pearson correlation of dominant trajectories across ocMOF subgroups as the similarity metric. If the signs of these Pearson correlations were inconsistent across ocMOF subgroups, then the similarity metric was set to zero (no similarity). Next, we used Ward as the agglomeration method and performed hierarchical clustering to obtain five modules.
Specifically, to obtain the five dominant expression trajectories for a gene set, we first removed patient–probeset-specific effects by standardizing the gene expression values (scaling each patient using the sample standard deviation of all of its probesets and separately mean-centering each scaled probeset), and subsequently fitted a loess curve to each ocMOF subgroup. These five dominant trajectories were aligned to a common initial reference.
For each module, the five dominant trajectories were obtained by averaging the corresponding gene sets trajectories (Text S4). We then applied Ingenuity Pathways Analysis (IPA) to identify significant (p-value<0.002, after Bonferroni correction) canonical pathways among the probesets making up each module. The exact settings employed for DAVID and IPA are discussed in Text S4.
We conducted a large-scale 28-d prospective clinical genomics study involving 168 patients, 797 microarrays, and 393 clinical variables (Methods) in order to understand the molecular basis of clinical responses to trauma from a genome-wide perspective. To this end, the central data-analytic challenge we faced was to associate longitudinal gene expression and 28-d Marshall score time course trajectories without over-fitting the data and while maintaining clinical interpretability. To address this challenge, we developed a within-patient longitudinal gene expression framework (Figure 2), derived from the following a priori statistical hypotheses: (a) there are several distinct trauma recovery trajectories (or physiological responses to trauma), reflected in the time varying clinical measures of interest, and (b) by using each patient as her/his own internal control while modeling the gene expression, inter-patient heterogeneity and confounding are reduced. The framework collapses Marshall score trajectories into clinically interpretable, ocMOF scores and longitudinal gene expression into within-patient expression changes (WPEC). We associated ocMOF and WPEC for each probeset using a rank-based correlation test (Methods).
Composite Longitudinal ocMOF Score Captures Relevant Clinical Variation
The ocMOF score is designed to capture the clinical variation among patients across the 28-d treatment window (Table 1; Figure 3; Text S5). Note that ocMOF is not introduced here to replace standard clinical measures, but instead serves as a 28-d longitudinal composite of overall patient variation and outcomes in which the higher the score, the worse the patient experience.
The ocMOF i subgroup captures uncomplicated recovery with minor or no inflammatory and infectious complications, whereas ocMOF v group captures complications leading to MOF and death. Figure 2, Step 1 shows the average Marshall score trajectories for the five ordinal patient subgroups. Note, for example, that the patients with ocMOF iii (0% mortality rate) and v (100% mortality rate) have very similar Marshall score trajectories during the first 7-d (Figures S3 and S4), and hence are difficult to separate using just Marshall scores (e.g., for the first 7-d mean Marshall scores, the two-sample t-test p-value is 0.506).
WPEC Measure Robustly Captures Relevant Clinical Variation
Instead of associating absolute expression values with ocMOF, which is the de facto analysis strategy, we instead sought to associate the within-patient change in gene expression with clinical outcome. For each probeset and patient, a within-patient expression change (WPEC) was formed by quantifying per-hour log-fold change over hours 12–250 post injury (i.e., regressing log gene expression on time and estimating the linear slope), which was adequate (Text S6 and Figures S5 and S6).
We first performed a PCA-based analysis to assess the overall effectiveness of WPEC in explaining trauma response (Text S6). Eight PCs were obtained from the WPEC matrix (54,675 probesets by 126 patients), capturing 31% of total variation (Figure S7a). Among the 393 clinical variables, those related with Marshall and Denver scores are among the top ten most significant clinical variables associated with these eight PCs collectively, with ocMOF being the most significant (Table S1). We repeated the same analysis on the mean expression matrix (taking the mean across the time course, which effectively combines baseline expression and WPEC) for hours 12–250 (eight PCs, 62% variation, Figure S7b) and found sampling phase and trauma center among the ten most significant variables (Table S2), implying patient-specific baseline expression is susceptible to confounders.
Significance Analysis of WPEC Associations with 28-d Trauma Outcome
We then performed a test of association between each probeset's WPEC measure and ocMOF to identify probesets that show a statistically significant association with clinical outcome (Methods). Both the resulting p-values (Figure 4a) and the FDR calculations (Figure 4b) indicate strong statistical significance. This information and mean WPEC for each ocMOF subgroup for all 54,675 probesets are provided in Table S3. The estimated percentage of probesets associated  with ocMOF was ≥24%, indicating that at least one-quarter of the genome undergoes early within-patient expression changes associated with 28-d trauma outcome.
A lack of reproducible results has been a major hurdle for translational research in clinical genomics ,. With thousands of genes tested for association and many being involved in the disease, it is important to assess the reproducibility of our association analysis. Therefore, we developed and applied a principled cross-validation strategy to assess reproducibility of significance (Figure 4c). We consistently observed small p-values for the top 100 probesets (Figure 4d), suggesting strong reproducibility of significant associations.
Dynamic Co-expression Modules Discriminating Trauma Outcome
We took a functional genomics approach to characterize the dynamic expression variation driving the WPEC and trauma outcome associations (Methods). We first applied DAVID ,, a state-of-the-art classification algorithm that groups genes based on their co-occurrences in annotation terms, to functionally cluster 1,256 of the 3,663 probesets with FDR <10% (Table S4). We further aggregated these gene sets into five modules according to the similarity of their dominant trajectories across the ocMOF subgroups (Figure S8). These modules were then organized in descending order of their average pairwise similarity metric, Module A (largest) to Module E (smallest); their dominant trajectories are provided in Figures S9, S10, S11, S12, S13. The average dominant trajectory of each ocMOF subgroup in each module shows highly coordinated dynamic co-expression patterns that discriminate the ocMOF groups in an ordered manner (Figure 5), especially in the early time window post-injury.
Figure 5. Dynamic co-expression modules and their dominant trajectories across the five ocMOF subgroups.
We used DAVID to obtain 54 functionally related gene sets from the 3,663 most significant probesets (10% FDR), which were then clustered into five modules according to the similarity of their dominant trajectories across the ocMOF subgroups. Modules A, B, C, D, and E contain 47, 37, 577, 231, and 364 probesets, respectively. We applied IPA to identify enriched pathways within each module. This IPA analysis shortlisted the following pathways as statistically significant (p-value<0.002, after Bonferroni correction; see Table S5): Oxidative Phosphorylation (Module A); RAN, IL-10 and IL-6 signaling, and the Glycosphingolipid Biosynthesis-Lactoseries Pathway (Module C); Allograft Rejection Signaling, Antigen Presentation Pathway, Cytotoxic T Lymphocyte-mediated Apoptosis of Target Cells, OX40 Signaling Pathway, Nur77 Signaling in T Lymphocytes (Module D); and Protein Ubiquitination Pathway, Hypoxia Signaling, and Cleavage and Polyadenylation of Pre-mRNA in the Cardiovascular System (Module E). Note that Module A contains 47 probesets and one statistically significant pathway, and Module B contains 37 probesets and no statistically significant pathway.doi:10.1371/journal.pmed.1001093.g005
We then applied IPA, a hand-curated database of biological interactions and functional annotations, to identify statistically significant canonical pathways among the probesets making up each module (Methods). In general, the significant canonical pathways within each module were relevant and related to inflammation and immunity (see Figure 5 caption and Table S5). Module A's dominant trajectory for ocMOF v is notably different from the other ocMOF subgroups, and its significant canonical pathway is oxidative phosphorylation. Metabolic dysfunction from trauma and infections has adverse effects on organ systems, and it generally originates from the mitochondrion, which is involved in the metabolism processes through oxidative phosphorylation ,. For Module E, the most significant canonical pathway is protein ubiquitination; targeting ubiquitin-mediated signaling is thought to regulate nuclear factor-κB (NF-κB), currently of interest as a therapeutic target in inflammatory diseases .
Key Pathways Associated with Trauma Outcome
To identify the key drivers of the genomic response to trauma, we performed ontological analyses on the top 500 most significant probesets using DAVID and IPA. For these 500 probesets, the heatmap of all the gene expression data collected throughout the study (stratified by day and ocMOF outcome) is provided in Figure S14. We sought to identify gene sets that are enriched for biological processes leading to poor trauma outcomes, show tightly coordinated dynamic expression trajectories, and have strong discriminatory power for post-injury MOF.
The top six canonical pathways (p-values<2.2×10−5) identified by IPA are dendritic cell maturation, Toll-like receptor (TLR) signaling, p38 mitogen-activated protein kinase (p38 MAPK) signaling, interleukin(IL)-6 signaling, production of nitric oxide and reactive oxygen species in macrophages, and antigen presentation (see Table S6 for the top 20). All six are involved in cellular immune responses, implying a common theme. Two of the pathways, TLR and p38 MAPK signaling, were recently identified in a genome-wide expression study on early sepsis . We applied DAVID to functionally cluster the top 500 probesets (Table S7), yielding five gene groups that strongly support the IPA results. Both analyses identified several genes that have been individually targeted in previous model system studies ,. We discuss below two gene sets identified from the analyses, with analogous results being shown for three others.
Antigen presentation pathway.
The top gene group from DAVID is enriched with the major histocompatibility complex class II (MHC-II) genes, in which 16 of the 17 probesets in that group are MHC-II, with four being in top 50. One of the top six canonical pathways from IPA is antigen presentation (p-value = 2.2×10−5), which also consists of MHC-II genes (Text S7 and Figure S15). The MHC-II molecules are relevant to MOF because they present foreign antigens on the cell surface, which is essential for adaptive or innate immunity ,. We used the 16 MHC-II probesets (representing HLA-DMB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DRA, HLA-DRB1, HLA-DRB3, HLA-DRB4, LOC100294318, and LOC100133678 genes) identified by DAVID to comprise the MHC-II gene set for subsequent analysis (Figure 6a–6c).
Figure 6. ocMOF and gene expression dynamics of MHC-II and p38 MAPK.
For each ocMOF group, the dominant trajectory (thick colored line) was obtained by averaging all the standardized MHC-II (a) and p38 MAPK (d) probesets trajectories (gray lines) of patients within the ocMOF subgroup. Generally, the dominant trajectory for MHC-II increases with time for ocMOF i and ii, initially decreases and then increases for ocMOF iii, and decreases for ocMOF iv and v. For p38 MAPK, the early dominant trajectory decreases with time for ocMOF i and ii, initially increases and then decreases for ocMOF iii, and increases for ocMOF iv and v. The dominant trajectories within the first 100 h suggest that early expression changes (gray region) of MHC-II (b) and p38 MAPK (e) correlate with patient outcome. The number of up-regulated MHC-II (c) and p38 MAPK (f) probesets (computed using the two sampling time points closest to the 40–80 h post-injury interval) separates patients with ocMOF i, ii, and iii from patients with ocMOF iv and v (p-value of the Kruskal-Wallis test is 0.00004 for MHC-II and 0.00668 for p38 MAPK).doi:10.1371/journal.pmed.1001093.g006
The boxplots of WPEC suggest that, in moving from ocMOF i to v, the WPEC decreases for all 16 MHC-II probesets, exhibiting a dosage effect (Figure S16). The dominant expression trajectories also exhibited a similar trend (Figure 6a). The difference among dominant trajectories is particularly pronounced during the 40–80 h window, suggesting that early expression changes can be used to discriminate among the patient ocMOF scores (Figure 6b). Using the two time points closest to 40–80 h post-trauma, we counted the number of up-regulated MHC-II probesets within each patient, and observed consistently high counts for ocMOF i, ii, and iii and low counts for ocMOF iv and v (Figure 6c).
p38 MAPK signaling pathway.
All of the top five canonical pathways from IPA contain the mitogen-activated protein kinase 14 (MAPK14) gene, and all four probesets representing it are among the top 500 with three being in top 50. The MAPK14 gene is an isoform of the p38 MAPK gene, the signaling pathway of which (one of the top five canonical pathways, Figure S17) is known to play an important role in driving the inflammatory response to either microbial products (via PAMPs), endogenous danger signals (via DAMPs or alarmins), and pro-inflammatory cytokines by phosphorylating transcription factors, resulting in the further expression of inflammatory mediators ,,. This suggests that the p38 MAPK signaling pathway is an integral signaling mechanism for the other top five canonical pathways and hence the presence of MAPK14 in those pathways. Using IPA, we obtained an additional eight (out of the top 500) probesets representing the genes involved in the p38 MAPK signaling pathway (Text S7), giving us the p38 MAPK gene set (12 probesets, representing MAPK14, CREB5, IL1R1, IL1RN, IRAK2, IRAK3, MAP2K6, and TIFA) for further analysis (Figure 6d–6f).
For the p38 MAPK probesets, we observed a trend opposite that of the MHC-II probesets. Generally, WPEC increased in moving from ocMOF i to v (Figure S18), and the early dominant trajectories also discriminated the ocMOF groups (Figure 6e–6f). A similar analysis was performed on three other gene sets, representative of the remaining top six canonical pathways, suggesting trends similar to those of p38 MAPK (Text S7 and Figures S19, S20, S21, S22, S23, S24).
Using a controlled endotoxin experiment dataset , which served as a corroborative experiment, we obtained dominant trajectories for MHC-II and p38 MAPK among healthy individuals administered with endotoxin (Text S7). The trends of these trajectories from ≥5 h were similar to those seen with ocMOF i and ii (Figure S25).
In this paper we have provided a comprehensive analysis of the longitudinal IHRIP study, taking into account all major sources of collected data. Despite inherent complexities in clinical genomic data, we showed that robust and relevant genomic signatures can be obtained with our framework, aimed at facilitating straightforward translation into a clinical setting. Our results have implications for the design and analysis of future large-scale clinical genomics studies.
We showed that clinical association using WPEC is straightforward to calculate and appears to be robust to confounders. Our framework collapses Marshall score trajectories and longitudinal gene expression into clinically interpretable quantities, permitting reliable statistical modeling without over-fitting the data. Using our framework we identified genes whose WPEC discriminate among the ocMOF outcomes. One of the main advantages of utilizing WPEC is that it leads more directly than do other measures to a clinical translation of the results, because it captures the change in expression within a patient, regardless of the patient's baseline value, which is susceptible to patient heterogeneity, confounders, and technical effects. On the other hand, any snapshot, baseline, or average expression profile will be susceptible to these effects. We repeated the above analyses using both an estimate of the hour 12 expression value and the average over the entire time course. Both measures showed evidence of being influenced by confounders (particularly batch and trauma center effects), and neither produced biological significance greater than WPEC.
We performed a global functional genomics analysis of the top 3,663 statistically significant WPEC associations with trauma outcome (FDR = 10%), identifying five dynamic co-expression modules highly enriched for immune pathways. We also performed a more focused pathway analysis of the top 500 associations (FDR = 0.6%) and identified a number of relevant gene sets. We pinpointed the MHC-II and p38 MAPK gene sets, showing that their expression dynamics suggest their potential as biomarkers. Moreover, our analysis suggests that the strongest discrimination occurs in the first 40–80 h post-injury.
From the dynamic co-expression modules results, one can consider the configuration of ocMOF-specific trajectories within and among these modules along with the biological significance of the modules to construct a spectrum of biologically relevant gene expression variation discriminating the clinical outcomes (ocMOF i to v). For example, our module-based analysis pointed to the NF-κB pathway. Previous studies have indicated that this pathway, which is downstream of the TLR, is critical in the context of post-traumatic immune dysfunction-induced poor outcomes ,. Taken as a whole, this systems analysis revealed a large and coordinated gene expression response to trauma, characterized by the modules and gene sets, indicating that something is to be gained from a systems-level understanding  of the molecular biology of post-injury MOF in forming therapeutic targets and prognostic procedures.
Our findings on the down-regulation of MHC-II genes among patients from ocMOF iv to v are consistent with persistently low HLA-DR expression that has been associated with septic complications ,, because a marked depression of cell-mediated immune function (i.e., immunosuppression) is believed to play a role in sepsis after severe trauma. HLA-DR is a promising molecular surrogate marker for treating post-injury inflammatory complications , and monitoring HLA-DR expression to treat trauma patients with immunomodulatory drugs such as interferon-γ has been studied. Importantly, our genome-wide approach suggests the association of the entire MHC-II gene set (represented by 16 probesets, Kruskal-Wallis p-value = 0.00004, Figure 6c), besides HLA-DR (represented by five of these 16 probesets, Kruskal-Wallis p-value = 0.00029), may be more informative. Persistent systemic inflammatory response syndrome (SIRS) is shown to be predictive of nosocomial infection in trauma patients ,, which is consistent with the up-regulation of genes in the p38 MAPK signaling pathway among patients from ocMOF iii to v.
It has been posited that MOF is an outcome of an inappropriate generalized inflammatory response, involving the interplay between mediators (e.g., cytokines and chemokines) and effector cells (e.g., neutrophils and macrophages) . The SIRS and compensatory response syndrome (CARS) are proposed to be involved in the etiology of MOF . It may well be that the increase in p38 MAPK expression seen here after severe trauma reflects the development of SIRS while a persistent decrease in MHC-II expression reflects the development of CARS (Text S8), which is supported from our agnostic, genome-wide analysis. Beyond the definitions of CARS and SIRS, the modern concept of post-traumatic immune suppression is also believed to be a major cause of secondary infections or organ dysfunction ,.
Although it was to date the largest clinical genomic study on trauma response of which we are aware, the current study has certain limitations. Since gene expression was measured for total blood leukocytes isolated from peripheral blood samples, some of the differential expression changes identified could be confounded by changes in individual leukocyte subpopulations. Arguably, the predictive utility of the identified biomarkers still exists.
In this study, we identified relevant pathways and gene sets showing a coordinated pattern of expression variation associated with response to trauma at a genome-wide scale. These findings potentially provide the most comprehensive picture of the gene expression response to trauma to date, thereby demonstrating the power of moving beyond candidate gene studies  of this clinical condition. The expression variation at the genomic level that we have characterized among patients may help to provide a more comprehensive set of drug targets and a means to identify relevant subsets of patients for which these may be effective.
Annotated scripts that reproduce the results in the paper. The scripts run the entire analysis in R statistical software (cran.r-project.org). See Text S2 for the details and http://genomine.org/trauma/ for instructions on obtaining the full dataset.
Microarray collection time points by patient. X-axis is the time from injury and Y-axis patient IDs. Each circle represents a microarray collected. Intended sampling was on days 0, 1, 4, 7, 14, 21, and 28 since injury, but depending on the total days from injury to discharge/death, the number of microarrays per patient ranged between 2 to 7.
Heatmap of patient–patient correlations. Using the WPEC matrix we computed patient–patient correlations for 129 patients. The heatmap of dichotomized correlations (black = negative; gray = positive) identified two patients as outliers with completely opposite correlations from the rest. We removed these two patients due to potential array quality issues.
Heatmap of the modified Marshall scores on day 0, 2, 3, …, 20 and the dendrogram of the hierarchical clustering. Hierarchical clustering was performed on the modified Marshall score trajectories, where missing scores were imputed using k-nearest neighbor. The left plot is the dendrogram of the hierarchical clustering from which we obtained five subgroups: ocMOF i to v. Patients from ocMOF i to iii tend to have low modified Marshall scores, with patients with ocMOF i recovering to 0 first, followed by ocMOF ii and iii, while patients from ocMOF iv and v tend to have high modified Marshall scores throughout the first 20 d.
Marshall score trajectories and ocMOF. Thin dashed lines in gray correspond to patient-specific Marshall score trajectories, and thick solid lines to the mean trajectories of the ocMOF subgroup. Only the observed modified Marshall scores are used to make these plots, but the actual clustering was performed on imputed data. Note that four out of five patients with ocMOF v died on or before day 10 post-injury, and that the red dashed line is for the remaining patient who died on day 24 post-injury. Mean ocMOF trajectories, together with other relevant patient clinical information, allowed us to order the ocMOF clusters in terms of overall patient severity. In particular, ocMOF i = good outcome (fast and uncomplicated recovery) and ocMOF v = very bad outcome (death).
Probesets with different dynamics. (a–d) Expression trajectory of probesets in the time window 0–250 h (shown in a and c) and 12–250 hours (shown in b and d). Patient-specific trajectories are represented by gray lines, and population average trajectories and population average linear trajectories are represented by the black and red lines respectively. (a) and (b) correspond to the most non-significant probeset from the DWPEC analysis, where the differences between the average trajectory and average linear trajectory are minimal. (c) and (d) correspond to the most significant probeset from the DWPEC analysis, where the differences between the average trajectory and average linear trajectory are large in time window 0–250 h but are reduced in time window 12–250 h.
Reasoning to exclude hours <12. Boxplots of mean-square difference (MSD) between the population average trajectory and average linear trajectory for the 5,000 most non-significant (ns) and significant (sig) probesets from the DWPEC analysis. We investigated the MSD for four different time windows: hour 0–250, hour 4–250, hour 8–250 and hour 12–250. The MSDs for the non-significant probesets are very similar across all four time windows, but the MSDs for the significant probesets are generally high for hour 0–250 and tend to decrease as we progressively exclude the early hours.
The scree plots to determine the number of principal components. (a) corresponds to the WPEC matrix and (b) to the mean expression matrix.
The dendrogram for grouping the 54 functional related gene sets into five modules according to the similarity of their dominant trajectories across the ocMOF subgroups.
The dominant trajectories for Module A. For each ocMOF subgroup, where (a)–(e) correspond to ocMOF i to v, the dominant trajectories of the module (thick colored lines) are obtained by averaging all dominant trajectories of gene sets belonging to the module (gray lines). (f) plots all five ocMOF subgroup dominant trajectories for Module A in one plot by aligning them to a common initial reference.
The dominant trajectories for Module B. See the caption for Figure S9 for details.
The dominant trajectories for Module C. See the caption for Figure S9 for details.
The dominant trajectories for Module D. See the caption for Figure S9 for details.
The dominant trajectories for Module E. See the caption for Figure S9 for details.
The heatmap of ranked gene expressions for all 168 patients over 28 d for the 500 most significant probesets from our analysis. For each probeset, we ranked the expression values across all 168 patients over 28 d, i.e. 797 microarrays (green = low rank, black = average rank, red = high rank). The columns are microarrays ordered by days, and within each day by ocMOF values. The intended sampling was on days 0, 1, 4, 7, 14, 21, and 28 since injury.
The antigen presentation pathway. The MHC-II genes have negative spearman correlation coefficients between WPEC and ocMOF (colored blue).
ocMOF and gene expression dynamics of MHC-II. (a) Boxplots of WPEC versus ocMOF indicate a negative dosage effect as we go from ocMOF i to v (p-value of the Spearman's test <10−15). (b) For each ocMOF group, the dominant trajectory (thick colored line) was obtained by averaging all the standardized MHC-II probeset trajectories (gray lines) of patients within the ocMOF subgroup. Generally, the dominant trajectory increases with time for ocMOF i and ii, initially decreases and then increases for ocMOF iii, and decreases for ocMOF iv and v. Both WPEC and the dominant trajectories exhibit similar trend. (c) The dominant trajectories within the first 100 h suggest that early expression changes (gray region) correlate with patient outcome. (d) The number of up-regulated MHC-II probesets (computed using two time points near hour 40–80) separates patients with ocMOF i, ii, and iii from patients with ocMOF iv and v (p-value of the Kruskal-Wallis test is 0.00004).
The p38 MAPK signaling pathway. Among the top 500 probesets, 15 are in this canonical pathway (representing 11 genes). Those genes in blue and red have negative and positive Spearman correlation coefficients between WPEC and ocMOF, respectively. TRADD, MEF2, and Max were removed from further analysis because their correlations were inconsistent with those identified by IPA.
Gene expression profiles of the 12 probesets involved in the p38 MAPK signaling pathway. (a) Boxplots of WPEC versus ocMOF indicate a positive dosage effect as we go from ocMOF i to v (p-value of the Spearman's test <10−15). (b) For each ocMOF group, the dominant trajectory (thick colored line) was obtained by averaging all the standardized p38 MAPK probeset trajectories (gray dotted lines) of patients within the ocMOF subgroup. Generally, the early dominant trajectory decreases with time for ocMOF i and ii, initially increases and then decreases for ocMOF iii, and increases for ocMOF iv and v. Both WPEC and the dominant trajectories exhibit similar trends. (c) The dominant trajectories within the first 100 h suggest that early expression changes (gray region) correlate with patient outcome. (d) The number of up-regulated p38 MAPK probesets (computed using two time points near hour 40–80) separates patients with ocMOF i, ii, and iii from patients with ocMOF iv and v (p-value of the Kruskal-Wallis test is 0.00668).
The Toll-like receptor (TLR) pathway. Among the top 500 probesets, 12 are in this canonical pathway (representing nine genes). Those genes in blue and red have negative and positive Spearman correlation coefficients between WPEC and ocMOF, respectively. JNK1 was removed from further analysis because its correlation was inconsistent with that identified by IPA.
Gene expression profiles of probesets involved in the TLR pathway. Similar to p38 MAPK signaling pathway. See Figure S18 for details. Altogether 11 probesets (representing eight genes) were used for this pathway. For (a) the p-value of the Spearman's test <10−15 and for (d) the p-value of the Kruskal-Wallis test is 0.02092.
The Interleukin (IL)-6 signaling pathway. Among the top 500 probesets, 14 are in this canonical pathway (representing ten genes). Genes in blue and red have negative and positive Spearman correlation coefficients between WPEC and ocMOF, respectively. JNK was removed from further analysis because its correlation was inconsistent with that identified by IPA.
Gene expression profiles of probesets involved in the IL-6 signaling pathway. Similar to p38 MAPK signaling pathway. See Figure S18 for details. Altogether 13 probesets (representing nine genes) were used for this pathway. For (a) the p-value of the Spearman's test <10−15 and for (d) the p-value of the Kruskal-Wallis test is 0.00898.
The production of nitric oxide and reactive oxygen species in macrophages pathway. Among the top 500 probesets, 18 are in this canonical pathway (representing 13 genes). Genes in blue and red have negative and positive Spearman correlation coefficients between WPEC and ocMOF, respectively. JNK and PP1/PP2a were removed from further analysis because their correlations were inconsistent with those identified by IPA. IkB and p38 MAPK were removed because of their complexity in this pathway.
Gene expression profiles of probesets involved in the production of nitric oxide and reactive oxygen species in macrophages pathway. Similar to p38 MAPK signaling pathway. See Figure S18 for details. Altogether 11 probesets (representing nine genes) were used for this pathway. For (a) the p-value of the Spearman's test <10−15 and for (d) the p-value of the Kruskal-Wallis test is 0.01370.
Gene expression profiles of MHC-II and p38 MAPK in a controlled endotoxin experiment. (a,b) The mean of the log-expression of MHC-II (a) and p38 MAPK (b). The black and red lines correspond to the healthy patients administered with placebo and endotoxin, respectively. After hour 5 (the region to the right of the dotted vertical line), the mean trajectories corresponding to healthy patients administered with endotoxin are similar to the dominant trajectories of ocMOF i and ii.
The ten most significant clinical variables (out of 393) associated with the eight principal components from the WPEC matrix. Legend: *Clinical variables are treated as categorical variables, and R2 corresponds to McFadden's pseudo R2.
The ten most significant clinical variables (out of 393) associated with the eight principal components from mean expression matrix. Legend: *Clinical variables are treated as categorical variables, and R2 corresponds to McFadden's pseudo R2.
Ranking of the 54,675 probesets according to the significance analysis of WPEC associations with 28-d trauma outcome.
Fifty-four functional related gene sets from DAVID for the 3,663 significant (FDR = 10%) probesets.
The top five canonical statistically significant (p-value<0.002, after Bonferroni correction) pathways for the five dynamic co-expression modules. For each canonical pathway we report the p-value of Fisher's exact test that ascertains enrichment and the proportion of genes in the pathway that were actually in the module within the brackets, and the gene names (in italics). Note that N.A. denotes no significant pathways with three or more genes were identified.
The top 20 canonical pathways for the top 500 probesets from WPEC and ocMOF association analysis (IPA, obtained May 2010). Legend: p, p-value of Fisher's exact test for ascertaining enrichment; and ratio, the proportion of genes in the pathway that are in the top 500.
Detailed results of DAVID analysis on the top 500 probesets from WPEC and ocMOF association analysis.
Inclusion/exclusion criteria and gene expression information.
Additional details on statistical framework.
Details on assessing WPEC robustness and reproducibility.
Details on module, pathway, and gene set analysis.
Marshall MOF-derived clinical outcomes.
Results on assessing WPEC robustness.
Key pathways associated with trauma outcomes.
p38 MAPK and MHC-II as biomarkers for SIRS and CARS.
All of the data are available at www.gluegrant.org.
The participants in the Inflammation and the Host Response to Injury Large-Scale Collaborative Research Program are: Lily Altstein, Ph.D., Henry V. Baker, Ph.D., Ulysses G.J. Balis, M.D., Paul E. Bankey, M.D., Ph.D., Timothy R. Billiar, M.D., Bernard H. Brownstein, Ph.D., Steven E. Calvano, Ph.D., David G. Camp II, Ph.D., J. Perren Cobb, M.D., Joseph Cuschieri, M.D., Ronald W. Davis, Ph.D., Asit K. De, Ph.D., Celeste C. Finnerty, Ph.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., David N. Herndon, M.D., Shari E. Honari, R.N., Marc G. Jeschke, M.D., Ph.D., Jeffrey L. Johnson, M.D., Matthew B. Klein, M.D., Stephen F. Lowry, M.D., Ronald V. Maier, M.D., Philip H. Mason, Ph.D., Grace P. McDonald-Smith, M.Ed., Bruce A. McKinley, Ph.D., Carol L. Miller-Graziano, Ph.D., Michael N. Mindrinos, Ph.D., Joseph P. Minei, M.D., Lyle L. Moldawer, Ph.D., Ernest E. Moore, M.D., Frederick A. 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, M.D., David A. Schoenfeld, Ph.D., Michael B. Shapiro, M.D., Richard D. Smith, Ph.D., Jason Sperry, M.D., John D. Storey, Ph.D., Robert Tibshirani, Ph.D., Ronald G. Tompkins, M.D., Mehmet Toner, Ph.D., Wenzhong Xiao, Ph.D., H. Shaw Warren, M.D., Michael A. West, M.D., PhD., Bram Wispelwey, M.S., and Wing H. Wong, Ph.D.
Conceived and designed the experiments: RVM RGT JDS. Analyzed the data: CST KHD JTL JDS. Contributed reagents/materials/analysis tools: CST KHD JTL JDS. Wrote the first draft of the manuscript: CST KHD JDS. Contributed to the writing of the manuscript: CST KHD JDS. ICMJE criteria for authorship read and met: CST KHD JTL RVM RGT JDS. Agree with manuscript results and conclusions: CST KHD JTL RVM RGT JDS. Enrolled patients: RVM RGT. The patients were collected and the expression profiling performed by investigators from the Inflammation and the Host Response to Injury Large-Scale Collaborative Research Program.
- 1. Sasser SM, Hunt RC, Sullivent EE, Wald MM, Mitchko J, et al. (2009) Guidelines for field triage of injured patients. Recommendations of the National Expert Panel on Field Triage. MMWR Recomm Rep 58: 1–35.
- 2. Soni A (2009) The five most costly conditions, 1996 and 2006: Estimates for the U.S. civilian noninstitutionalized population. Statistical Brief. Rockville: Agency for Healthcare Research and Quality. pp. 1–5.
- 3. Peden M, McGee K, Krug E, editors. (2002) Injury: A leading cause of the global burden of disease, 2000. Geneva: World Health Organization.
- 4. Hofman K, Primack A, Keusch G, Hrynkow S (2005) Addressing the growing burden of trauma and injury in low- and middle-income countries. Am J Public Health 95: 13–17.
- 5. DeCamp MM, Demling RH (1988) Posttraumatic multisystem organ failure. J Am Med Assoc 260: 530–534.
- 6. Marshall JC, Vincent JL, Sibbald WJ (1995) Clinical Trials for the Treatment of Sepsis. In: Vincent JL, Sibbald WJ, editors. Berlin: Springer-Verlag. pp. 122–138.
- 7. Dewar D, Moore FA, Moore EE, Balogh Z (2009) Postinjury multiple organ failure. Injury 40: 912–918.
- 8. Opal SM (2003) Clinical trial design and outcomes in patients with severe sepsis. Shock 20: 295–302.
- 9. Baue AE (1997) Multiple organ failure, multiple organ dysfunction syndrome, and systemic inflammatory response syndrome. Why no magic bullets? Arch Surg 132: 703–707.
- 10. Cobb JP, Mindrinos MN, Miller-Graziano C, Calvano SE, Baker HV, et al. (2005) Application of genome-wide expression analysis to human health and disease. Proc Natl Acad Sci U S A 102: 4801–4806.
- 11. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, et al. (2005) A network-based analysis of systemic inflammation in humans. Nature 437: 1032–1037.
- 12. Johnson SB, Lissauer M, Bochicchio GV, Moore R, Cross AS, et al. (2007) Gene expression profiles differentiate between sterile SIRS and early sepsis. Ann Surg 245: 611–621.
- 13. Collins F (2010) Has the revolution arrived? Nature 464: 674–675.
- 14. Feero WG, Guttmacher AE, Collins FS (2010) Genomic Medicine: Genomic Medicine – An Updated Primer. N Engl J Med 362: 2001–2011.
- 15. Frantz S (2005) An array of problems. Nat Rev Drug Discov 4: 362–363.
- 16. Potter JD (2001) At the interfaces of epidemiology, genetics and genomics. Nat Rev Genet 2: 142–147.
- 17. Marshall JC, Cook DJ, Christou NV, Bernard GR, Sprung CL, et al. (1995) Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit Care Med 23: 1638–1652.
- 18. Storey JD, Tibshirani R (2003) Statistical significance for genomewide studies. Proc Natl Acad Sci U S A 100: 9440–9445.
- 19. Jolliffe IT (2002) Principal Component Analysis. NY: Springer.
- 20. Ioannidis JPA (2005) Microarrays and molecular research: Noise discovery? Lancet 365: 454–455.
- 21. Michiels S, Koscielny S, Hill C (2005) Prediction of cancer outcome with microarrays: A multiple random validation strategy. Lancet 365: 488–492.
- 22. Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, et al. (2003) DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol 4: P3.
- 23. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, et al. (2007) The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol 8: R183.
- 24. Hasselgren PO, Hubbard WJ, Chaudry IH (2007) Metabolic and inflammatory responses to trauma and infection. In: Fischer JE, editor. Mastery of Surgery. Fifth ed. Philadelphia: Lippincott Williams & Wilkins. pp. 2–23.
- 25. Dare AJ, Phillips AR, Hickey AJ, Mittal A, Loveday B, et al. (2009) A systematic review of experimental treatments for mitochondrial dysfunction in sepsis and multiple organ dysfunction syndrome. Free Radic Biol Med 47: 1517–1525.
- 26. Wullaert A, Heyninck K, Janssens S, Beyaert R (2006) Ubiquitin: tool and target for intracellular NF-kappa B inhibitors. Trends Immunol 27: 533–540.
- 27. Han J, Lee JD, Bibbs L, Ulevitch RJ (1994) A MAP kinase targeted by endotoxin and hyperosmolarity in mammalian cells. Science 265: 808–811.
- 28. Rosengart MR, Nathens AB, Arbabi S, Neff MJ, Garcia I, et al. (2003) Mitogen-activated protein kinases in the intensive care unit: Prognostic potential. Ann Surg 237: 94–100.
- 29. Chain BM, Kaye PM, Shaw MA (1988) The biochemistry and cell biology of antigen processing. Immunol Rev 106: 33–58.
- 30. Cresswell P (1987) Antigen recognition by T lymphocytes. Immunol Today 8: 67–69.
- 31. Kyriakis JM, Avruch J (2001) Mammalian mitogen-activated protein kinase signal transduction pathways activated by stress and inflammation. Physiol Rev 81: 807–869.
- 32. Dong C, Davis RJ, Flavell RA (2002) MAP kinases in the immune response. Annu Rev Immunol 20: 55–72.
- 33. Zhang Q, Raoof M, Chen Y, Sumi Y, Sursal T, et al. (2010) Circulating mitochondrial DAMPs cause inflammatory responses to injury. Nature 464: 104-U115.
- 34. Liu SF, Malik AB (2006) NF-kappa B activation as a pathological mechanism of septic shock and inflammation. Am J Physiol Lung Cell Mol Physiol 290: L622–L645.
- 35. Bohrer H, Qiu F, Zimmerman T, Zhang YM, Jllmer T, et al. (1997) Role of NF kappa B in the mortality of sepsis. J Clin Invest 100: 972–985.
- 36. Ahn AC, Tewari M, Poon CS, Phillips RS (2006) The limits of reductionism in medicine: could systems biology offer an alternative? PLoS Med 3: e208. doi:10.1371/journal.pmed.0030208.
- 37. Ditschkowski M, Kreuzfelder E, Rebmann V, Ferencik S, Majetschak M, et al. (1999) HLA-DR expression and soluble HLA-DR levels in septic patients after trauma. Ann Surg 229: 246–254.
- 38. Monneret G, Lepape A, Voirin N, Bohé J, Venet F, et al. (2006) Persisting low monocyte human leukocyte antigen-DR expression predicts mortality in septic shock. Intensive Care Med 32: 1175–1183.
- 39. Spruijt NE, Visser T, Leenen LPH (2010) A systematic review of randomized controlled trials exploring the effect of immunomodulative interventions on infection, organ failure, and mortality in trauma patients. Crit Care 14: R150.
- 40. Bochicchio GV, Napolitano LM, Joshi M, Knorr K, Tracy JK, et al. (2002) Persistent systemic inflammatory response syndrome is predictive of nosocomial infection in trauma. J Trauma 53: 245–251.
- 41. Hoover L, Bochicchio GV, Napolitano LM, Joshi M, Bochicchio K, et al. (2006) Systemic inflammatory response syndrome and nosocomial infection in trauma. J Trauma 61: 310–316.
- 42. Tsukamoto T, Chanthaphavong RS, Pape H-C (2010) Current theories on the pathophysiology of multiple organ failure after trauma. Injury 41: 21–26.
- 43. Munford RS, Pugin J (2001) Normal responses to injury prevent systemic inflammation and can be immunosuppressive. Am J Resp Crit Care 163: 316–321.
- 44. Reddy RC, Chen GH, Tekchandani PK, Standiford TJ (2001) Sepsis-induced immunosuppression - From bad to worse. Immunol Res 24: 273–287.
- 45. Mock CN, Dries DJ, Jurkovich GJ, Maier RV (1996) Assessment of two clinical trials: interferon-gamma therapy in severe injury. Shock 5: 235–240.


- SPECIAL REPORTS
- THE MAGAZINE
RUGBY 100LR, the next in the family of construction tough lasers from Leica Geosystems is available worldwide, now.
The RUGBY 100LR is an automatic self-leveling laser for general construction. According to Dan Dykhuis, Program Director of Lasers, “The RUGBY 100LR is a result of listening to our customers. It is built to be rugged, yet easy to use, and can make quick work of concrete forming, pad placement and framework, setting foundation and footings, as well as elevation indication for heavy equipment.” The RUGBY 100LR has a working range of up to 2500’ (770 m), automatic elevation alert function, manual grade up to +/- 10% with cross-axis self levelling. It levels automatically and very quickly too, has a simple five-switch keypad that controls all the functions, plus a low-battery and an out of level indicator.
“Building along the same principles that guided the development of the earlier product, the RUGBY 100, customers can look forward to the same level of reliability and robustness. The Rugby 100 has received an overwhelmingly enthusiastic response from both Dealers and Customers worldwide” added Dan.
The co-molded housing of high-impact plastic and rubber is as rugged as it looks. RUGBY 100LR is the first Leica branded product combining the heritage and expertise of Laser Alignment and Leica Geosystems.


"The harder you work, the luckier you get."
Below is the poem entitled winter of our love which was written by poet
Puddifoot. Please feel free to comment on this poem. However, please remember, PoetrySoup is a place of encouragement and growth.
Read Poems by
feelings are freezing
frost fingers nip frigid flesh -


FILED: January 9, 2002
and SUSAN SWEENEY,
an Oregon corporation,
ROMANIA RV CENTER, INC.,
an Oregon corporation,
Appeal from Circuit Court, Deschutes County.
A. Michael Adler, Judge.
Argued and submitted October 5, 1999.
Jacob Tanzer argued the cause for appellant. With him on the briefs were Steven C. Berman and Ball Janik LLP.
Steven D. Bryant argued the cause for respondent. With him on the brief was Bryant, Emerson & Fitch.
Before Landau, Presiding Judge, and Deits, Chief Judge,* and Linder, Judge.
*Deits, C. J., vice De Muniz, J., resigned.
Defendant SMC Corporation appeals from a judgment under ORS 646.315 to ORS 646.375, Oregon's "Lemon Law," based on a jury's finding that defendant had breached the express warranties on a motor home that it sold to plaintiffs. The jury awarded a refund of the purchase price of the vehicle in an amount that totaled $202,000. Pursuant to ORS 646.359 (1997), the trial court tripled that amount before entering judgment and also awarded plaintiffs their attorney fees. On appeal, the issues are whether the Lemon Law applies to motor homes and whether plaintiffs are entitled to triple damages. We affirm.
In 1996, plaintiffs bought a motor home from defendant, paying $188,000. Soon after buying the motor home they discovered a number of alleged defects, both in the vehicle's mechanical operation and in the living facilities that it contained. Among other things, plaintiffs complained about the steering, transmission, air conditioning, appliances, and water pressure and about a "chemical odor" that caused nose and eye irritation, breathing difficulties, and headaches. Defendant attempted to repair those alleged defects but did not do so to plaintiffs' satisfaction. During the first five months that plaintiffs owned the motor home, it spent more than 120 days in repair facilities.
Plaintiffs filed this action, asserting a claim under the Lemon Law together with claims for breach of express and implied warranties under both state and federal law. Before trial, defendant moved for partial summary judgment on the ground that the Lemon Law does not apply to motor homes; the trial court denied that motion. At trial, the jury awarded plaintiffs damages under the Lemon Law that consisted of the purchase price of $188,000 and $14,000 for taxes, license, and registration fees. (1) The jury also awarded plaintiffs an identical amount--that is, $188,000--on their warranty claims. In accordance with ORS 646.359 (1997), (2) the court tripled the Lemon Law damages and entered judgment for $606,000, together with plaintiffs' attorney fees, costs, and disbursements. Because the verdict under the warranty claims duplicated the purchase price refund under the Lemon Law, the court did not separately enter judgment for that amount.
On appeal, defendant first renews its position that the Lemon Law does not apply to motor homes, arguing that a motor home does not meet the law's definition of "motor vehicle." (3) ORS 646.325 provides a remedy to a consumer if a "new motor vehicle does not conform to applicable manufacturer's express warranties" and if the consumer complies with certain procedural requirements. Thus, the purchase of a new motor vehicle is a predicate to a Lemon Law claim. ORS 646.315(2) defines a "[m]otor vehicle" as "a passenger motor vehicle as defined in ORS 801.360 that is sold in this state." (Emphasis added.) Significantly, ORS 801.360, to which ORS 646.315(2) refers, does not define "passenger motor vehicle." Rather, it defines only the term "[m]otor vehicle," describing it as "a vehicle that is self-propelled or designed for self-propulsion."
This issue turns on the significance of the legislature's choice to add the term "passenger" to the definition of motor vehicle in the Lemon Law. In determining what that adds to the term's meaning under ORS 646.315, we begin with the statute's text. PGE v. Bureau of Labor and Industries, 317 Or 606, 610-12, 859 P2d 1143 (1993). The relevant dictionary definitions of "passenger" are helpful in that respect. Those definitions include "a traveler in a public conveyance (as a train, bus, airplane, or ship)," "one who is carried in a private conveyance (as an automobile) for compensation or expected benefit to the owner," and "a rider in an automobile." Webster's Third New Int'l Dictionary, 1650 (unabridged ed 1993). As a federal court noted some years ago, "in common parlance [passenger] means an occupant of a motor vehicle other than the person operating it[.]" Vogrin v. Hedstrong, 220 F2d 863, 866 (8th Cir 1955). Thus, the common characteristic of vehicles that are "passenger motor vehicles" is that they are designed for and capable of carrying persons other than the driver. That design and capacity is, we think, what makes a motor vehicle a "passenger motor vehicle." (4) Nothing in the Lemon Law suggests that the legislature intended to limit the term to automobiles or some other specific type of motor vehicle. Rather, by expressly covering all motor vehicles as defined in ORS 801.360, modified only by the adjective "passenger," the statutory text plainly encompasses any and all motor vehicles that are designed for and capable of carrying passengers--that is, persons other than the driver.
Defendant nevertheless argues that, because the legislature defined "passenger motor vehicle" by reference to a statute that defines only "motor vehicle," it intended to severely restrict the kinds of motor vehicles that are subject to the Lemon Law. Defendant refers to a number of other definitions in ORS chapter 801, which is part of the motor vehicle code, that define other, more limited classes of motor vehicles, such as farm tractors, ORS 801.265, golf carts, ORS 801.295, and tank vehicles, ORS 801.522. Defendant points out that, in defining motor vehicles for purposes of the Lemon Law, the legislature chose to use only the general definition of motor vehicle in ORS 801.360 rather than those more specific ones. That fact, according to defendant, suggests that it is no more likely that the legislature intended to include motor homes in the Lemon Law than that it intended to include any of those other specific types of motor vehicles. Defendant also points out that ORS 801.350(1) separately defines "[m]otor home" as a motor vehicle that is "reconstructed, permanently altered or originally designed to provide facilities for human habitation[.]" Defendant thus seems to suggest that the legislature intended to exclude any separately defined type of motor vehicle, including "motor homes," from being a "passenger motor vehicle" under the Lemon Law. Finally, relying on the ordinary meaning of "passenger" as someone who rides in an automobile, (5) defendant contends that a "passenger motor vehicle" must be a motor vehicle that is primarily designed for transportation, not for habitation. Thus, because ORS 801.350(1) defines a motor home as a vehicle designed to provide human habitation, a motor home cannot be a "passenger motor vehicle" in defendant's view.
Defendant's argument fails in at least two respects. First, it misunderstands the relationship among the definitions in the vehicle code. "Motor vehicle" is defined in ORS 801.360 as a general and broad category that includes every vehicle that "is self-propelled or designed for self-propulsion." The more specific classes of vehicles to which defendant refers are all defined first as "motor vehicles." Those definitions then differentiate each class by describing the particular characteristics that set it apart from other classes of motor vehicles. See, e.g., ORS 801.265 (farm tractors), ORS 801.295 (golf carts), and ORS 801.522 (tank vehicles). In other words, the specific definitions in ORS chapter 801 to which defendant points are subsets of the general category of "motor vehicles"; they do not limit the scope of what that general category encompasses. In short, the legislature defined "motor vehicle" for the purpose of the Lemon Law by expressly referring to the general definition of "motor vehicle" in ORS 801.360 and by adding the modifier "passenger" to it. That structure demonstrates that the legislature intended for the term "motor vehicle" under the Lemon Law to mean any type of motor vehicle, provided that the vehicle is also a "passenger" motor vehicle.
The second problem with defendant's argument is its assumption that a motor vehicle that has certain characteristics, such as being adapted for human habitation, cannot also have other characteristics, such as being adapted for carrying passengers. (6) In particular, assuming that the definition of "motor home" in ORS 801.350(1) bears on the analysis, nothing in that definition prevents a motor home from also being a passenger motor vehicle. Again, that statute defines "motor home" as a motor vehicle that is "reconstructed, permanently altered or originally designed to provide facilities for human habitation[.]" Those characteristics do not, however, necessarily preclude a motor home from having other characteristics in common with other motor vehicles, such as the ability to carry passengers. In short, we are not persuaded that the provisions of ORS chapter 801 demonstrate that a motor home cannot be a motor vehicle.
The history of changes made to the Lemon Law reinforces that conclusion. See SAIF v. Walker, 330 Or 102, 108-10, 996 P2d 979 (2000) (history of changes to a statute is part of the statute's context). As originally adopted, the definition of "motor vehicle" in ORS 646.315(2) concluded with the phrase "other than a motorcycle." Or Laws 1983, ch 469, § 1. Thus, in order to exempt motorcycles from the law's coverage, the legislature expressly excluded them. Then, in 1989, the legislature repealed the express exclusion, which had the effect of bringing motorcycles back within the Lemon Law. Or Laws 1989, ch 202, § 1. The initial adoption and the later repeal of the exemption for motorcycles suggest that the legislature understood the reference in ORS 646.315(2) to "passenger motor vehicle" to encompass motor vehicles, other than automobiles, that are designed for and capable of transporting passengers unless such vehicles are expressly exempted. (7) That is true even though a motorcycle is a subclass of motor vehicles that, like a motor home, is separately defined in chapter 801 of the vehicle code. See ORS 801.365. Morever, in that definition, a motorcycle is described, among other characteristics, as having a "seat or saddle for use of the rider"; the definition says nothing about a motorcycle's capacity to carry passengers. See id.
As we have noted, many different kinds of motor vehicles qualify as being designed and having the capacity to carry passengers. A motor home is within that category. The fact that it also provides habitation is irrelevant to that conclusion. As discussed above, a vehicle that is capable of fulfilling one function is not necessarily incapable of fulfilling some other function. Defendants do not dispute in this case that the motor home in question was designed for carrying more than one person, only one of whom would be driving at any particular time. Any person who was not driving would necessarily be a passenger. Plaintiffs' motor home was thus a passenger motor vehicle as defined in ORS 646.315(2). (8)
Defendants argue that such an interpretation of "passenger motor vehicle" will result in the Lemon Law applying to transactions beyond what the legislature intended. But that argument overlooks the fact that the range of claims coming within the Lemon Law is not dictated solely by what qualifies as a new passenger motor vehicle. Only a "consumer" is entitled to a remedy under the Lemon Law, see ORS 646.325(1), and a "consumer" is defined as the "purchaser or lessee, other than for purposes of resale, of a new motor vehicle normally used for personal, family or household purposes." ORS 646.315(1). As a result, the normal use of the vehicle and the purchaser's purpose are significant limitations on the availability of a remedy under the Lemon Law. In this case, there is no dispute that the motor home was being used for personal, family, or household purposes or that plaintiffs purchased their motor home other than for the purpose of resale. For all of the above reasons, then, the trial court correctly denied defendant's motion for partial summary judgment.
In its second assignment of error, defendant argues that the trial court erred by trebling the amount of the jury's verdict pursuant to ORS 646.359 (1997) when it entered judgment. More specifically, defendant contends that, because plaintiffs pursued other judicial remedies in addition to a Lemon Law claim, ORS 646.359 (1997) limited plaintiffs' recovery to the purchase price of the vehicle and certain incidental expenses and precluded an award of triple damages based on that refunded purchase price. To frame the issue better, we begin with an overview of the Lemon Law's provisions as they relate to the substantive remedies in ORS 646.335 and the triple damages award in ORS 646.359 (1997).
Under ORS 646.325, a consumer is entitled to a remedy if a new motor vehicle does not conform to the manufacturer's warranties, the consumer reports the nonconformities to the manufacturer or dealer within a specified period, and the manufacturer is notified of the defects and has an opportunity to correct them. Under ORS 646.335(1), a consumer is entitled to one of two remedies if the failure to repair or correct defects under the warranty impairs the vehicle's use, market value, or safety. First, the manufacturer may be required to replace the motor vehicle with a new one. Alternatively, it may be required to accept return of the vehicle from the consumer and to refund the purchase price and certain associated costs, less a "reasonable allowance" for the consumer's use of the vehicle. Id. The remedy available under the Lemon Law is not exclusive. Rather, ORS 646.375 expressly declares that the Lemon Law is not intended "in any way to limit the rights or remedies that are otherwise available to a consumer under any other law." But neither can the Lemon Law be used in conjunction with other remedies to recover more than the Lemon Law alone would provide. As provided in ORS 646.375, if the consumer elects to pursue any judicial remedies, "the remedy available under ORS 646.315 to 646.375" may not exceed the recovery authorized by ORS 646.335 without proof that any excess damages were a result of fault.
Those basic provisions have been a part of the Lemon Law since its original enactment in 1983. See generally Or Laws 1983, ch 469. In addition, the original act encouraged manufacturers to establish informal dispute settlement procedures in accordance with applicable federal regulations. If a manufacturer did so, the consumer could not seek a remedy under the Lemon Law without first resorting to the procedure. Or Laws 1983, ch 469, § 5, codified as ORS 646.355. In its original form, the Lemon Law did not contain any other references to that procedure and did not impose any penalties on a manufacturer who failed to establish one.
In 1987, the legislature amended the Lemon Law to increase the incentives to manufacturers to provide informal dispute settlement procedures and to ensure that the procedures functioned fairly. See Or Laws 1987, ch 476, §§ 4-6. As pertinent here, the amended Lemon Law requires consumers to notify a manufacturer and give it an opportunity to correct defects before seeking a remedy under the Lemon Law; "notification" includes a request for an informal dispute resolution procedure. ORS 646.325(3). Of particular relevance to our analysis here, as a result of the 1987 amendments, the Lemon Law now provides for an award of triple damages to a successful plaintiff if the manufacturer either did not act in good faith in the informal dispute settlement procedure or has not established such a procedure. In both cases, a successful plaintiff may also be entitled to attorney fees and costs. ORS 646.359 (1997).
With that background, we turn to defendant's challenge to the award of triple damages in this case. Defendant's argument depends on ORS 646.375, the statute that limits the consumer's remedy if the consumer pursues other judicial remedies in addition to a Lemon Law claim. In relevant part, the statute provides:
"[I]f the consumer elects to pursue any other remedy in state or federal court, the remedy available under ORS 646.315 to 646.375 shall not be available insofar as it would result in recovery in excess of the recovery authorized by ORS 646.335 without proof of fault resulting in damages in excess of such recovery."
In this case, plaintiffs pleaded and prevailed on claims brought under state and federal law that were additional to their remedy under the Lemon Law. Defendant argues that, because plaintiffs pursued those other claims, their Lemon Law remedy is limited to the refund of the purchase price of the vehicle, together with reimbursement of certain other costs and fees, as authorized by ORS 646.335(1). According to defendant, any recovery for triple damages under ORS 646.359 (1997) is a recovery in excess of what ORS 646.335(1) authorizes and therefore requires proof of fault, which plaintiffs did not present.
The correctness of defendant's argument turns on what the legislature meant when it referred to "the remedy available under ORS 646.315 to 646.375." In defendant's view, that language plainly encompasses both the remedy that a consumer receives under ORS 646.335(1) for a dealer's failure to repair or correct defects in the vehicle and the further award of triple damages that a consumer may receive under ORS 646.359 (1997) for the dealer's failure to have in place a good-faith dispute settlement procedure. We disagree. As explained below, "the remedy" to which the statute refers is the consumer's remedy for the defective vehicle.
As a textual matter, the statute refers to "the remedy" using the definite article "the" and the singular form of the word "remedy." That language suggests that the legislature intended to refer to a specific remedy denoted elsewhere in the scheme, not to multiple remedies or remedies more generally. See Osborn v. PSRB, 325 Or 135, 142-43, 934 P2d 391 (1997) (statute's use of the definite article "the" indicates legislature's intent to refer to a previous part of the statute); Anderson v. Jensen Racing, Inc., 324 Or 570, 578-79, 931 P2d 763 (1997) (the definite article "the" functions as an adjective to denote a particular, specified thing, not a general, unspecified class of things).
Context reinforces that conclusion and confirms that the specific single remedy referred to in the limitations provision (ORS 646.375) is the consumer's remedy for the defective motor vehicle under ORS 646.335(1). Using basically identical language, ORS 646.325 provides that "[t]he remedy under the provisions of ORS 646.315 to 646.375 is available to a consumer" if particular circumstances are satisfied. (Emphasis added.) (9) The circumstances that ORS 646.325 then lists all pertain to the defective condition of the consumer's vehicle and the manufacturer's opportunity to correct the defects. See ORS 646.325(1)-(3). The ordinary presumption is that the same language, when used in related statutes, has the same meaning. See PGE, 317 Or at 611. Because both ORS 646.375 and ORS 646.325 refer to "the remedy under" ORS 646.315 to ORS 646.375, and because that language in ORS 646.325 clearly means only the remedy available to a consumer under ORS 646.335(1) based on the defective condition of the vehicle, the remedy referred to in ORS 646.375(1) also properly is interpreted to be that remedy. Thus, if a consumer pursues other judicial remedies in addition to a Lemon Law claim, ORS 646.375 limits the consumer's remedy for purchase of a defective vehicle. It does not preclude an award for the manufacturer's failure to have a good-faith informal dispute settlement procedure in place.
That understanding, moreover, is consistent with the Lemon Law's overall structure. ORS 646.375 permits a plaintiff to pursue other remedies at the same time as pursuing a Lemon Law claim. It also, however, limits the consumer's recovery under the Lemon Law--in the absence of proof of fault--insofar as the aggregate recovery would exceed the amount authorized by ORS 646.335(1). Thus, the purpose of the limitation provision is to prevent a windfall to the plaintiff in the form of a double recovery for the value of the defective vehicle. In contrast, the triple damages award under ORS 646.359 (1997) serves the different purpose of penalizing manufacturers who fail to have in place a good-faith informal dispute settlement procedure.
In the context of the Lemon Law as a whole, then, it makes particular sense to read ORS 646.375 as limiting only the consumer's remedy for refund or replacement of the defective vehicle and not as also precluding a triple award for the manufacturer's failure to provide a good-faith informal dispute settlement procedure. Only the remedy for the defective vehicle is potentially duplicated by the pursuit of other judicial claims. There is no other claim that a consumer can bring to vindicate the consumer's, or the general public's, interest in having available an informal dispute settlement procedure to avoid the costly litigation of Lemon Law claims in court. To deny a consumer a triple damages award because the consumer pursued multiple potential remedies for the defective vehicle would do nothing to further the purpose of avoiding a double recovery by a successful plaintiff. It would, however, frustrate the purpose of providing an independent incentive to manufacturers to provide a settlement procedure, and it would provide manufacturers with a windfall in that sense. For that reason, defendant's proposed understanding of the key statutory language simply is not plausible when considered in the context of the statutory scheme as a whole. See State v. Hval, 174 Or App 164, 168-69, 25 P3d 958 (2001) (for a statute to be ambiguous, it must be susceptible of two meanings that are plausible in the context of the statutory scheme as a whole).
Consequently, we hold that, when a consumer pursues other judicial remedies in addition to a Lemon Law claim, ORS 646.375 limits only a plaintiff's remedy under ORS 646.335 for the failure to conform the motor vehicle to its express warranties. The statute does not preclude an award of triple damages under ORS 646.359 (1997) based on the manufacturer's failure to have established a good-faith informal dispute settlement procedure. The trial court therefore did not err in this case in awarding triple damages to plaintiffs, because it is undisputed that the manufacturer had no such procedure in place.
In its third and fourth assignments of error, defendant argues that the jury's verdict awarding plaintiffs a refund of the purchase price of the vehicle does not constitute the "damages" to which ORS 646.359 (1997) refers. Specifically, defendant contends that such a remedy is the equivalent of rescission, which is legally distinct from an award of damages. In traditional terms, rescission places the parties in the positions that they occupied before the transaction occurred; damages, in contrast, make a plaintiff whole for harm that the plaintiff has suffered. See Sager v. McClenden, 296 Or 33, 37, 672 P2d 697 (1983) (when used in the plural, "damages" means compensation in money for a loss or damage; citing Black's Law Dictionary). Here, the award restored plaintiffs to their previous position. That is, they returned the motor home to the dealer and, in return, received a refund of the purchase price and related expenses. Therefore, according to defendant, the jury's award of the purchase price of the defective vehicle could not appropriately be the foundation for an award of triple damages.
Defendant's argument assumes that whenever the legislature uses the term "damages" it must intend the legal meaning given to the term for purposes of traditional common-law remedies. That is not the only meaning, however, that can be attributed to the term. In a broader everyday usage, the term "damages" can encompass "the estimated reparation in money for detriment or injury sustained : compensation or satisfaction imposed by law for wrong or injury caused by a violation of a legal right." Webster's at 571 (emphasis added). "Reparation," in turn, is "the act or process of mending or restoring : the act of making amends, offering explanation, or giving satisfaction for a wrong or injury." Id. at 1923. A refund of the purchase price of the vehicle qualifies as compensation or satisfaction imposed by law for a wrong--the failure of the vehicle to conform to the manufacturer's express warranties. The ordinary meaning of the term "damages" therefore is broad enough to encompass the relief awarded by the jury in this case. (10)
Again, we must construe the terms that the legislature used in the context of the Lemon Law as a whole. We consider, then, whether one or both meanings of the term "damages" is plausible given the overall purpose and structure of the statutory scheme. The immediate problem with attributing only the more narrow common law definition to damages is that such an interpretation would render the triple damages award authorized by ORS 646.335(1) largely inconsequential, because the only "damages" that would qualify for tripling would be the award for taxes, license fees, and the like. As compared to the refunded purchase price of the new motor vehicle, those amounts, even when tripled, are relatively trivial and would provide little or no incentive for manufacturers to establish informal dispute settlement procedures. In the context of the structure and purpose of the Lemon Law as a whole, interpreting the reference in ORS 646.375 to mean only damages to make a plaintiff "whole" for harm or injury suffered would so undermine the obvious objective of the statute as not to be a plausible reading in context.
But even if it were a plausible way to read the term, that conclusion would at most render the term ambiguous at the textual and contextual level. We would then have to consider the provision's legislative history. PGE, 317 Or at 611. That history eliminates any uncertainty as to the legislature's intent. In the hearings on the 1987 amendments, legislators repeatedly referred to the triple damages award as "potentially for the full value of the vehicle." Minutes, Senate Committee on Business, Housing & Finance, HB 2599, May 19, 1987, p 3 (testimony of Rep. Peg Jolin). One specific example hypothesized a car purchased for $20,000 and a triple damages award that would be "three times the $20,000 less usage of the vehicle." Id. at p 4 (comments of Sen. Bill Kennemer). At no point did any legislator express disagreement with the measure of damages being the purchase price of the defective vehicle. Rather, legislative debate focused on whether three was the appropriate multiplier and on the extent of a trial court's discretion in making the award. See generally id. at pp 2-5.
In short, to whatever extent the legislature's use of the term "damages" in ORS 646.375 is susceptible of more than one meaning, the legislative history eliminates that ambiguity. The legislature expressly contemplated that triple damages would be based on the purchase price of the defective motor vehicle, less the reasonable value of the consumer's use of the vehicle. (11) The trial court in this case properly calculated the award of triple damages based on the refund value of the motor home, together with the amounts awarded for associated costs and fees.
1. Under ORS 646.335(1), the manufacturer's obligation is either to replace the vehicle with a new vehicle or to accept return of the vehicle and to refund to the plaintiff "the full purchase or lease price paid, including taxes, license and registration fees * * *, less a reasonable allowance for the consumer's use of the vehicle."
Return to previous location.
2. Although it is not relevant to this case, we note that in 1999 the legislature placed a cap of $50,000 on awards of triple damages under ORS 646.359. Or Laws 1999, ch 346, § 1.
Return to previous location.
3. Because the issue presented by defendant's motion for summary judgment is purely one of law, we may review it on appeal even though the motion was denied and was followed by a full trial. Payless Drug Stores v. Brown, 300 Or 243, 245-48, 708 P2d 1143 (1985).
Return to previous location.
4. That same understanding is reflected in other statutes in Oregon. For example, ORS 830.005(10) defines "passenger" for the purposes of the regulation of small watercraft as "every person on board a boat who is not the master, operator, crew member or other person engaged in any capacity in the business of the boat." That definition is consistent with our understanding of the use of "passenger" in the Lemon Law.
Return to previous location.
5. See Webster's at 1650.
Return to previous location.
6. In another transportation mode, a "passenger train" may have facilities for human habitation, such as sleeping rooms, dining and lounge facilities, and restrooms. Those facilities do not mean that it is not designed for carrying passengers; rather, they are provided to support the train's ability to function in that role.
Return to previous location.
7. Defendant relies on statements in the legislative history referring to the Lemon Law as applying to automobiles and motorcycles as demonstrating that "passenger motor vehicle" was intended to have a limited meaning. Those statements, however, demonstrate only that legislators used common terminology in discussing the bill and discussed it in terms of the types of claims that they thought might be most common. The statements do not establish that the legislators intended "passenger motor vehicle" to have a particular technical meaning or to be restricted to those specific vehicles.
Return to previous location.
8. Given our conclusion that plaintiffs' motor home is a "passenger motor vehicle," it follows that the Lemon Law applies to all of plaintiffs' complaints, whether they relate to the aspects of the motor home that are vehicular (such as steering or transmission) or that involve habitation (such as water pressure or appliances). ORS 646.325 grants a remedy if the motor vehicle does not conform to the "applicable manufacturer's express warranties"; it is not limited to only some of those warranties.
Return to previous location.
9. ORS 646.325 refers to "[t]he remedy under the provisions of ORS 646.315 to ORS 646.375" (emphasis added) and ORS 646.375 refers to "the remedy available under ORS 646.315 to 646.375." Although the precise wording of the two statutes differs slightly, the difference is not substantive.
Return to previous location.
10. This case presents no issue regarding whether a triple damages award may be predicated on the value of a replacement vehicle awarded to a successful Lemon Law plaintiff, and we express no view on that issue.
Return to previous location.
11. Defendant does not claim an entitlement to such an offset on the record in this case. Such a reduction can, however, be significant; indeed, in at least one reported case under a similar statute in another jurisdiction, the offset was greater than the amount of the refund, as a result of which the plaintiff received no recovery and no triple damages. See Taylor v. Volvo North America Corp., 339 NC 238, 451 SE2d 618 (1994) (value of the plaintiff's use of vehicle was greater than payments the plaintiff had made on lease).
Return to previous location.


Re: [SI-LIST] : Trace Capacitance
Hans Mellberg (firstname.lastname@example.org)
Mon, 21 Jun 1999 17:38:10 -0700 (PDT)
two or more possible scenarios.
1) your trace widths are not exactly 5 or 20 mils. Over or under etch
could contribute to the error. Example, the 5 mil could have been 7
mils and the 20 mil could have been 17 mils.
2) Fringe field effects will make the 5 mil trace look a bit wider in
comparison to the 20 mil trace making the 4 to 1 ratio more like a 3 to
1 ratio. Fringe fields are more noticeable with thicker (1 or 2 oz)
traces than the thin (1/2 oz)traces.
Do You Yahoo!?
Get your free @yahoo.com address at http://mail.yahoo.com
**** To unsubscribe from si-list: send e-mail to email@example.com. In the BODY of message put: UNSUBSCRIBE si-list, for more help, put HELP. si-list archives are accessible at http://www.qsl.net/wb6tpu/si-list ****


Canada by Richard Ford
Canada tells the story of Dell Parson, a 15-year-old boy living in Great Falls Montana in 1960 with his parents. Father Bev was a bomber pilot in the war, and still works for the Air Force, which means the family has moved around America, never settling too long in one place. This causes resentment from Dell’s twin sister Berner, who Dell looks up to. She is smart and sassy and has a boyfriend, while Dell is more introverted, likes chess and collecting bees.
The twins’ mother Neeva comes from a Jewish family who don’t take kindly to their daughter marrying a Southern Air Force pilot and so the the twins do not see their grandparents. Dell, who narrates this story, wonders how a studious and intelligent woman wound up married to a man like his father who is light-hearted and easy-going.
Central to this family’s story are the events that lead to Dell’s father committing a bank robbery and his wife’s willingness to be his accomplice. Ford takes us deep into the mindset of Dell’s reaction to his parents’ big secret which raises questions about how we perceive our parents.
Ford has written a novel about what happens when we deal with our parents’ faults and the immediate impact of that upon our lives. He portrays his characters in their rawest and most emotional state and raises questions of responsibility for the choices we make and how we accept the consequences for them. A most exceptional novel.
Michael Awosoga-Samuel is from Readings Carlton