Dissecting inflammatory complications in critically injured patients by within-patient gene expression changes: a longitudinal clinical genomics study

Abstract

Background: 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.

Conclusions: 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.

Trial registration: ClinicalTrials.gov NCT00257231

Figure 1. Patient selection for the IHRI study.

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.

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.

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.

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.

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).