A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are nonexistent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g., R ² between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.

Human survival from injury requires an appropriate inflammatory and immune response. We describe the circulating leukocyte transcriptome after severe trauma and burn injury, as well as in healthy subjects receiving low-dose bacterial endotoxin, and show that these severe stresses produce a global reprioritization affecting >80% of the cellular functions and pathways, a truly unexpected “genomic storm.” In severe blunt trauma, the early leukocyte genomic response is consistent with simultaneously increased expression of genes involved in the systemic inflammatory, innate immune, and compensatory antiinflammatory responses, as well as in the suppression of genes involved in adaptive immunity. Furthermore, complications like nosocomial infections and organ failure are not associated with any genomic evidence of a second hit and differ only in the magnitude and duration of this genomic reprioritization. The similarities in gene expression patterns between different injuries reveal an apparently fundamental human response to severe inflammatory stress, with genomic signatures that are surprisingly far more common than different. Based on these transcriptional data, we propose a new paradigm for the human immunological response to severe injury.

Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response toin vivoendotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-sourceedgesoftware package.

The enumeration of rare circulating epithelial cells (CEpCs) in the peripheral blood of metastatic cancer patients has shown promise for improved cancer prognosis. Moving beyond enumeration, molecular analysis of CEpCs may provide candidate surrogate endpoints to diagnose, treat, and monitor malignancy directly from the blood samples. Thorough molecular analysis of CEpCs requires the development of new sample preparation methods that yield easily accessible and purified CEpCs for downstream biochemical assays. Here, we describe a new immunomagnetic cell separator, the MagSweeper, which gently enriches target cells and eliminates cells that are not bound to magnetic particles. The isolated cells are easily accessible and can be extracted individually based on their physical characteristics to deplete any cells nonspecifically bound to beads. We have shown that our device can process 9 mL of blood per hour and captures >50% of CEpCs as measured in spiking experiments. We have shown that the separation process does not perturb the gene expression of rare cells. To determine the efficiency of our platform in isolating CEpCs from patients, we have isolated CEpCs from all 47 tubes of 9-mL blood samples collected from 17 women with metastatic breast cancer. In contrast, we could not find any circulating epithelial cells in samples from 5 healthy donors. The isolated CEpCs are all stored individually for further molecular analysis.