BACKGROUND: Severe trauma, including burns, triggers a systemic response that significantly impacts on the liver, which plays a key role in the metabolic and immune responses aimed at restoring homeostasis. While many of these changes are likely regulated at the gene expression level, there is a need to better understand the dynamics and expression patterns of burn injury-induced genes in order to identify potential regulatory targets in the liver. Herein we characterized the response within the first 24 h in a standard animal model of burn injury using a time series of microarray gene expression data. METHODS: Rats were subjected to a full thickness dorsal scald burn injury covering 20% of their total body surface area while under general anesthesia. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Sham controls consisted of animals handled similarly but not burned. After identifying differentially expressed probe sets between sham and burn conditions over time, the concatenated data sets corresponding to these differentially expressed probe sets in burn and sham groups were combined and analyzed using a "consensus clustering" approach. RESULTS: The clustering method of expression data identified 621 burn-responsive probe sets in four different co-expressed clusters. Functional characterization revealed that these four clusters are mainly associated with pro-inflammatory response, anti-inflammatory response, lipid biosynthesis, and insulin-regulated metabolism. Cluster 1 pro-inflammatory response is rapidly up-regulated (within the first 2 h) following burn injury, while Cluster 2 anti-inflammatory response is activated later on (around 8 h post-burn). Cluster 3 lipid biosynthesis is down-regulated rapidly following burn, possibly indicating a shift in the utilization of energy sources to produce acute phase proteins, which serve the anti-inflammatory response. Cluster 4 insulin-regulated metabolism was down-regulated late in the observation window (around 16 h post-burn), which suggests a potential mechanism to explain the onset of hypermetabolism, a delayed but well-known response that is characteristic of severe burns and trauma with potential adverse outcome. CONCLUSIONS: Simultaneous analysis and comparison of gene expression profiles for both burn and sham control groups provided a more accurate estimation of the activation time, expression patterns, and characteristics of a certain burn-induced response based on which the cause-effect relationships among responses were revealed.
BACKGROUND: Severe trauma, including burns, triggers a systemic response that significantly impacts on the liver, which plays a key role in the metabolic and immune responses aimed at restoring homeostasis. While many of these changes are likely regulated at the gene expression level, there is a need to better understand the dynamics and expression patterns of burn injury-induced genes in order to identify potential regulatory targets in the liver. Herein we characterized the response within the first 24 h in a standard animal model of burn injury using a time series of microarray gene expression data. METHODS:Rats were subjected to a full thickness dorsal scald burn injury covering 20% of their total body surface area while under general anesthesia. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Sham controls consisted of animals handled similarly but not burned. After identifying differentially expressed probe sets between sham and burn conditions over time, the concatenated data sets corresponding to these differentially expressed probe sets in burn and sham groups were combined and analyzed using a "consensus clustering" approach. RESULTS: The clustering method of expression data identified 621 burn-responsive probe sets in four different co-expressed clusters. Functional characterization revealed that these four clusters are mainly associated with pro-inflammatory response, anti-inflammatory response, lipid biosynthesis, and insulin-regulated metabolism. Cluster 1 pro-inflammatory response is rapidly up-regulated (within the first 2 h) following burn injury, while Cluster 2 anti-inflammatory response is activated later on (around 8 h post-burn). Cluster 3 lipid biosynthesis is down-regulated rapidly following burn, possibly indicating a shift in the utilization of energy sources to produce acute phase proteins, which serve the anti-inflammatory response. Cluster 4 insulin-regulated metabolism was down-regulated late in the observation window (around 16 h post-burn), which suggests a potential mechanism to explain the onset of hypermetabolism, a delayed but well-known response that is characteristic of severe burns and trauma with potential adverse outcome. CONCLUSIONS: Simultaneous analysis and comparison of gene expression profiles for both burn and sham control groups provided a more accurate estimation of the activation time, expression patterns, and characteristics of a certain burn-induced response based on which the cause-effect relationships among responses were revealed.
Authors: M Baes; S Huyghe; P Carmeliet; P E Declercq; D Collen; G P Mannaerts; P P Van Veldhoven Journal: J Biol Chem Date: 2000-05-26 Impact factor: 5.157
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