(549b) Bioinformatic Profiling of Short Term Liver Response to Thermal Injury

Thermal injury initiates an inflammatory response as part of the healing process that is associated with extensive transcriptional and subsequent metabolic adjustments. It has been previously shown (7) using DNA microarrays and biochemical measurements that a peak in inflammatory response is observed at 24 hours post burn, and that free fatty acids and triglycerides serve as the primary energy source in the liver. The focus of the current work is to demonstrate the elements of an integrated bioinformatics framework that allows the identification of the cellular processes which contribute to the inflammatory response.

Briefly, our methodology (8) allows the systematic analysis of temporal gene expression data. The basic elements of the approach are as follows:

1. fine-grained clustering for the identification of possible expression motifs 2. selection of a reduced set of maximally informative genes and complete function characterization 3. construction of networks of gene interactions and identification of key interacting genes (5) 4. identification of common transcription factor binding sites and assembly of possible regulators 5. quantification of regulatory interaction via Network Component Analysis (3) 6. identification of major regulatory controls

Through this work we have identified 4 major motifs (clusters) with a little over 200 genes which accurately define both the physiological and temporal components of the inflammatory response following injury. We have determined that following an initial increase in cellular energetics, a process which has been documented (7) as crucial in the progression of inflammatory response and which has been detected with our approach, there is a production of acute phase proteins (APP) utilized in processes such as the complement cascade as well as clotting and the kinin cascade (4). These processes are upregulated at 8 hours post burn, following the production of ATP which is needed to produce these proteins. We have also found that the final phase of the acute phase response to thermal injury is characterized by three major components: 1) extracellular matrix remodeling; 2) oxidative stress which occurs in response to the large energy turnover seen in the form of ATP production; 3) hypoxia which may result due to lowered oxygen transport. In addition to understanding the processes which arise during inflammation, we have also identified control factors which regulate these processes. To determine these regulatory factors, we have taken a two prong approach: 1) identifying transcription factors which regulate genes in each of the established motifs; 2) utilizing pathway creation algorithms to identify biological interactions between genes within each motif, and then identifying the major regulators within each of these interaction maps. Utilizing the transcription factor approach, we have found that the dominant physiological responses that are present during the inflammatory response are predominantly regulated through Pregnane X receptor (NR1i2), nuclear receptor heterodimer retinoid X receptor alpha : retionic acid receptor alpha (RXR:RAR), peroxisome proliferator-activated receptor alpha (PPAR), tumor protein 53 (p53), hepatic nuclear factor 1 (HNF1), and signal transducer and activator of transcription 1 and 3 (STAT1, STAT3) protein. To establish biological interaction networks of genes in each motif, we utilized a text-mining approach to search publicly available databases, such as PubMed, to identify biological relationships of the following types: 1) expression; 2) molecular transport; 3) protein modification; 4) binding; 5) molecular synthesis; 6) chemical reaction. We then utilized clique based clustering methods to reduce the pathway to a smaller map of highly connected hubs. Two major finding can be gleamed from this approach: 1) a majority of the genes which are identified as hubs within the connection maps are of the signaling ontology; 2) the hub genes provide points of connection not only within motifs but also between motifs. The former point is commonsensical being that signaling regulation is the segue way that exists between most biological process. With respect to the later point, the genes with the highest connectivity within a motif, such as mitogen-activated protein kinase 1 (MAP2K1), tumor necrosis factor (TNF), interferon gamma (IFNG), epidermal growth factor (EGF), and interleukin 6 (Il-6) also become integration points between motifs. In other words they are also hubs which exhibit the highest level of connectivity both within and between motifs. All of these genes have been quite extensively documented in the literature to be influential in the outcome of inflammation (1, 2, 6).

Taken together, this work has provided a comprehensive understanding of thermal-induced liver responses which may provide important insights into the pathogenesis of progressive liver failure which often follows prolonged inflammation in response to thermal injury. In addition, the transcription factors we have identified, as well as the hubs of the interaction maps, may be targets for potential therapies during the early onset of inflammation.

1. Corbacho AM, Valacchi G, Kubala L, Olano-Martin E, Schock BC, et al. 2004. Tissue-specific gene expression of prolactin receptor in the acute-phase response induced by lipopolysaccharides. Am J Physiol Endocrinol Metab 287: E750-7

2. Jayaraman A, Yarmush ML, Roth CM. 2005. Evaluation of an in vitro model of hepatic inflammatory response by gene expression profiling. Tissue Eng 11: 50-63

3. Kao KC, Yang YL, Liao JC, Boscolo R, Sabatti C, Roychowdhury V. 2004. Network component analysis of Escherichia coli transcriptional regulation. Abstracts of Papers of the American Chemical Society 227: U216-U7

4. Kaplan AP, Joseph K, Shibayama Y, Reddigari S, Ghebrehiwet B, Silverberg M. 1997. The intrinsic coagulation/kinin-forming cascade: assembly in plasma and cell surfaces in inflammation. Adv Immunol 66: 225-72

5. Nikitin A, Egorov S, Daraselia N, Mazo I. 2003. Pathway studio--the analysis and navigation of molecular networks. Bioinformatics 19: 2155-7

6. Schwabe RF, Brenner DA. 2006. Mechanisms of Liver Injury. I. TNF-alpha-induced liver injury: role of IKK, JNK, and ROS pathways. Am J Physiol Gastrointest Liver Physiol 290: G583-9

7. Vemula M, Berthiaume F, Jayaraman A, Yarmush ML. 2004. Expression profiling analysis of the metabolic and inflammatory changes following burn injury in rats. Physiol Genomics 18: 87-98

8. Yang E, Berthiaume F, Yarmush ML, Androulakis IP. 2006. An integrative systems biology approach for analyzing liver hypermetabolism. Presented at 9th Int. Symp. Process Systems Engineering and 16th European Symp. Computer Aided Process Engineering, Garmisch-Partenkirchen / Germany