Abstract
Experimental models, mimicking physiology, and molecular dynamics of diseases in human, harbor the possibility to study the effect of interventions and transfer results from bench to bedside. Recent advances in high-throughput technologies, standardized protocols, and integration of knowledge from databases yielded rising consistency and usability of results for inter-species comparisons. Here, we explored similarities and dissimilarities in gene expression from blood samples of a murine sepsis model (peritoneal contamination and infection, PCI) and patients from the pediatric intensive care unit (PICU) measured by microarrays. Applying a consistent pre-processing and analysis workflow, differentially expressed genes (DEG) from PCI and PICU data significantly overlapped. A major fraction of DEG was commonly expressed and mapped to adaptive and innate immune response related pathways, whereas the minor fraction, including the chemokine (C–C motif) ligand 4, exhibited constant inter-species disparities. Reproducibility of transcriptomic observations was validated experimentally in PCI. These data underline, that inter-species comparison can obtain commonly expressed transcriptomic features despite missing homologs and different protocols. Our findings point toward a high suitability of an animal sepsis model and further experimental efforts in order to transfer results from animal experiments to the bedside.
Highlights
Application of high-throughput technologies like microarrays extended the understanding of the complex molecular interactions in infectious disease processes (Jenner and Young, 2005; Polpitiya et al, 2009)
These can be subject to data- and knowledge-driven analyses by bioinformatic and systems biology approaches in order to extract hypotheses about molecular interactions and predict interventional targets
Observed intersections of differentially expressed genes (DEG) from peritoneal contamination and infection (PCI) and pediatric intensive care unit (PICU) were significantly higher than would be expected by chance
Summary
Application of high-throughput technologies like microarrays extended the understanding of the complex molecular interactions in infectious disease processes (Jenner and Young, 2005; Polpitiya et al, 2009). Contrary to a single gene study, unbiased quantitative insights are collected by simultaneously monitored gene expression levels These can be subject to data- and knowledge-driven analyses by bioinformatic and systems biology approaches in order to extract hypotheses about molecular interactions and predict interventional targets. Comparison of expression differences in meta-studies, focusing on comparing lists of differentially expressed genes (DEG) derived from literature, e.g., studies about dietary restriction spanning six organisms for different species (Han and Hickey, 2005) or of sepsis markers in humans (Tang et al, 2010), yielded no agreement or only small intersections, respectively This may be related to biological variability as well as different protocols for experimental set-up and data analysis. Using a consistent workflow and filtering according to microarray quality control (MAQC, Shi et al, 2006) criteria, DEG with approximately 50% overlap in murine and human monocytes were obtained (Ingersoll et al, 2010), which is driving attempts to find conserved markers for diseases like sepsis across species
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