Thermal injury triggers both local and systemic responses including cellular protection mechanisms, inflammation, hypermetabolism, prolonged catabolism, organ dysfunction and immuno-suppression (1). Burn injury has a significant impact on the liver since it is the main organ controlling inflammatory response, metabolites levels and acute phase proteins production (2). It is known that inflammatory mediators triggered by cutaneous burn injury in the circulation results in persistent alterations in gene expression levels in the liver (1, 3). Therefore, liver is one of the target organs to understand the underlying molecular mechanisms of the disease state and to propose therapeutic approaches. The aim of this study is to gain a better understanding of how burn injury affects the hepatic gene expression patterns during the first 24 h following the burn injury. Gene expression patterns in both burn and shamburn (control) rat liver 24hr post-treatment, while bioinformatics tools reveal dynamic changes of gene expression profiles of burn group compared to those of control group.
A systemic hypermetabolic response was induced by applying a full-thickness burn on an area of the dorsal skin corresponding to 20% of the total body surface area (TBSA). Rats were first randomized into two groups: burn and sham burn (control group). Then, they were anesthetized (80 to 100 mg/kg ketamine + 12 to 10 mg/kg xylazine). The animal's back after removing all hair was immersed in water at 100°C for 10 s to produce a full-thickness scald injury. Immediately after burns, the animals were resuscitated with 50 mL/kg of saline injected intraperitoneally. Negative controls (sham burn) consisted of animals treated identically but immersed in warm water (37°C). Rats were single caged after burn or sham burn and given standard rat chow and water ad libitum until sacrifice. No post-burn analgesics were administered, consistent with other studies with this full thickness burn model since the nerve endings in the skin are destroyed and the skin becomes insensate (5). Animal body weights were monitored daily and found to increase at the same rate in both groups. Animals are sacrificed (starting at 9am) at different time points (0, 2, 4, 8, 16 and 24hr post-treatment, i.e., sham burn and burn) and liver tissues were collected and frozen for microarray analysis (n=3 per time point per group). The tissues were lysed and homogenized using Trizol, and the RNAs were further purified and treated with DNase using RNeasy columns (Qiagen). Then cRNAs prepared from the RNAs of liver tissues using protocols provided by Affymetrix were utilized to hybridize Rat Genome 230 2.0 Array (GeneChip, Affymetrix) comprised of more than 31,000 probe sets.
In order to analyze concurrently sham and burn expression profiles, gene expression data analysis includes normalization, filtering for “between class temporal differential expression”, combining the datasets and clustering. First, DNA chip analyzer (dChip) software was used with invariant-set normalization and perfect match (PM) model to generate expression values. Then normalized two data sets corresponding to burn and sham groups were investigated to identify the differentially expressed probesets using EDGE. This step determined a set of probesets whose expression patterns were significantly altered after the treatment. Finally concatenated data sets corresponding to differentially expressed probesets in burn and sham groups were combined to form one single matrix, which was then clustered using the approach of “consensus clustering” (6). The goal was to identify subsets of transcripts with coherent expression pattern in sham and burn. In total 621 burn-responsive probesets in 4 clusters were identified. We characterized the biological relevance of the intrinsic responses by evaluating the enrichment of the corresponding subsets by using KEGG database through ARRAYTRACK (7) as well as analyzed the functions of each individual gene (8).
Consensus clustering identifies 4 statistically dominant transcriptional clusters composed of 62, 82, 404, 73 probe sets respectively. Functional characterization elucidated that these responses are mainly associated with pro-inflammation (cluster 1), fatty acid biosynthesis (cluster 2), anti-inflammation (cluster 3), and insulin signaling pathway and amino acid metabolism (cluster 4). More specifically, Cluster 1 exhibits an early up-regulation during the first two hours following thermal injury, which is related to a pro-inflammatory response. A group of inflammatory genes are involved in this cluster including cytokine, chemokines and chemokine receptors (1l1a, Cxcl16, Ccl11 and Ccl9). Cluster 2 is considered to be suppressed following burn injury which is associated mainly with fatty acid biosynthesis. The genes in this cluster are involved in biosynthesis of unsaturated fatty acid pathway (Acot1, Acot2, and Acot3) and synthesis of ketone bodies (Hmgcs2). Besides, many other genes in this specific motif are relevant to fatty acid biosynthesis or transport such as Eci1, Pigo, cyp4b1, Adfp, Pnpla8, Pank4, Crot, Etfdh. Cluster 3 is activated around 8h post-burn which is associated with the late anti-inflammatory response. Genes in this major temporal class are important in complement and coagulation (C2, C4bpa, C8a, Cfh, Masp1, and Serping1), N-Glycan biosynthesis pathway (B4galt1, Dad1, Ddost, Dpagt1, Ganab, and Man1b1), and ribosome (Rps25, Rps2). All these functions can be interpreted as the synthesis of acute phase proteins and important anti-inflammatory mediators. Cluster 4, is mainly related to insulin regulated metabolism, is downregulated around 24h post-burn. The genes in this cluster are involved in the insulin signaling pathway (Gck, Irs1, Mknk2, and Trip10), phenylalanine metabolism (Ddc), glycine, serine and threonine metabolism (Bhmt), and galactose metabolism (Gck).
Our results indicate that the pro-inflammatory response is rapidly up-regulated within the first 2 hours following burn injury, while the anti-inflammatory response is activated later on, around 8h post burn, which would otherwise be considered as a biphasic response starting from beginning with an early downregulation and a late upregulation if control group was assumed to be a constant value (0 time point). Thus, once the pro-inflammatory response has mounted it serves as a subsequent signal for stimulating the anti-inflammatory response to inhibit the pro-inflammatory response and drive the system back to homeostasis.
We have previously determined that the dynamic profiles representing fatty acid biosynthesis and insulin regulated metabolism as well as amino acid metabolism in sham group exhibited daily oscillation, consistent with previous observations elucidating the circadian rhythmicity in the gene expression patterns of rat liver (9). Following burn injury, it was observed that the genes in the corresponding clusters lost rhythmicity and were suppressed starting from soon after (~2h) and much later (~16h) postburn respectively compared to dynamic gene expression profiles of control group. Lipid biosynthesis associated enzymes are observed to be downregulated following the burn, implying an enhanced energy demand. Downregulation of the genes involved in insulin signaling pathway further suggests a potential mechanism to explain the onset of hypermetabolism, a well-known characteristic of burn injury with potential adverse outcome.
In conclusion, our results reveal critical gene expression pattern changes triggered by burn injury which reflect host physiological and biological alterations and provides a more comprehensive understanding of the pathophysiology of the disease state. Simultaneous analysis of both burn and sham-burn groups’ expression profiles is advantageous since it enables to characterize the dynamic patterns of both groups. Moreover, it also provides a more accurate estimation of the activation time and expression pattern of a certain response.
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