(281b) Gene Expression Profiling of Short- and Long-Term Changes in Rat Liver Following Burn Injury and CLP Treatment

An important player in systemic hypermetabolism is the liver, which generally controls circulating levels of metabolites. It is well known that the hepatic response to severe injury and bacterial infections are characterized by a significant up-regulation of glucose, fatty acid, and amino acid turnover in the liver [1-4]. Furthermore, up-regulation in the expression levels of genes involved in the urea cycle, gluconeogenesis, and the metabolism of several amino acids were also reported [1, 2]. Prior studies suggested that changes in stress related inflammatory pathways leading to hypermetabolic state are due to the persistent changes in gene expression in the liver [1]. In order to understand the mechanism behind the hypermetabolic response to burn injury and sepsis, a comprehensive analysis is required, such as the gene expression basis underlying the liver's short term and long term response to the insult.

The aim of this study is to gain a better understanding of gene expression changes in the rat liver during the first 10 day after the burn and cecal ligation and puncture (CLP) treatments. 20 % total body surface area third degree scald burn is applied to the animals using boiling water. On 2nd day after burn trauma, infection is induced by CLP. Control groups are treated identically except that they are given a sham-burn using 37 C water or sham-CLP the same survival surgical procedure without any puncture in cecum. Therefore, for systematic DNA microarray analysis, four different rat groups which receive different treatments: 1) sham-burn followed 2 days later by CLP, 2) burn followed by CLP, 3) sham-burn followed by sham-CLP and 4) burn followed by sham-CLP, and the dynamics of liver-specific transcriptional responses are recorded.

In order to analyze such a large amount of data, clustering and classification approaches developed by our lab recently are explored as an essential exploratory tool for discovering biological patterns and class prediction. The gene expression of above stated each experimental group is analyzed individually by the consensus clustering [5] which intends to identify a subset of more ?clusterable' set of genes that are highly coexpressed within a set of differentially expressed genes in each group.

Our results indicate significant circadian variability of liver-specific responses, as evaluated through the sham measurements, whereas comparisons among the alternative injury models will further elucidate the dynamics of the progression of the inflammatory response and help delineate the implications of compromised host to subsequent bacterial infections.

References

1. S. Banta, M. Vemula, T. Yokoyama, A. Jayaraman, F. Berthiaume, and M.L. Yarmush, Contribution of gene expression to metabolic fluxes in hypermetabolic livers induced through burn injury and cecal ligation and puncture in rats, Biotechnology and Bioengineering, 97 (2007) 118-137.

2. M. Vemula, F. Berthiaume, A. Jayaraman, and M.L. Yarmush, Expression profiling analysis of the metabolic and inflammatory changes following burn injury in rats, Physiological Genomics, 18 (2004) 87-98.

3. K. Lee, F. Berthiaume, G.N. Stephanopoulos, D.M. Yarmush, and M.L. Yarmush, Metabolic Flux Analysis of Postburn Hepatic Hypermetabolism, Metabolic Engineering, 2 (2000) 312-327.

4. K. Lee, F. Berthiaume, G.N. Stephanopoulos, and M.L. Yarmush, Profiling of dynamic changes in hypermetabolic livers, Biotechnology and Bioengineering, 83 (2003) 400-415.

5. T.T. Nguyen, R.S. Nowakowski, and I.P. Androulakis, Unsupervised Selection of Highly Coexpressed and Noncoexpressed Genes Using a Consensus Clustering Approach, OMICS A Journal of Integrative Biology, 13 (2009) 219-237.