Dynamics of hepatic gene expression profile in a rat cecal ligation and puncture model.

BACKGROUND Sepsis remains a major clinical challenge in intensive care units. The difficulty in developing new and more effective treatments for sepsis exemplifies our incomplete understanding of the underlying pathophysiology of it. One of the more widely used rodent models for studying polymicrobial sepsis is cecal ligation and puncture (CLP). While a number of CLP studies investigated the ensuing systemic inflammatory response, they usually focus on a single time point post-CLP and therefore fail to describe the dynamics of the response. Furthermore, previous studies mostly use surgery without infection (herein referred to as sham CLP, SCLP) as a control for the CLP model, however, SCLP represents an aseptic injurious event that also stimulates a systemic inflammatory response. Thus, there is a need to better understand the dynamics and expression patterns of both injury- and sepsis-induced gene expression alterations to identify potential regulatory targets. In this direction, we characterized the response of the liver within the first 24 h in a rat model of SCLP and CLP using a time series of microarray gene expression data. METHODS Rats were randomly divided into three groups: sham, SCLP, and CLP. Rats in SCLP group are subjected to laparotomy, cecal ligation, and puncture while those in CLP group are subjected to the similar procedures without cecal ligation and puncture. 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. Unoperated animals (Sham) serve as negative controls. After identifying differentially expressed probesets between sham and SCLP or CLP conditions over time, the concatenated data sets corresponding to these differentially expressed probesets in sham and SCLP or CLP groups were combined and analyzed using a "consensus clustering" approach. Promoters of genes that share common characteristics were extracted and compared with gene batteries comprised of co-expressed genes to identify putatative transcription factors, which could be responsible for the co-regulation of those genes. RESULTS The SCLP/CLP genes whose expression patterns significantly changed compared with sham over time were identified, clustered, and finally analyzed for pathway enrichment. Our results indicate that both CLP and SCLP triggered the activation of a proinflammatory response, enhanced synthesis of acute-phase proteins, increased metabolism, and tissue damage markers. Genes triggered by CLP, which can be directly linked to bacteria removal functions, were absent in SCLP injury. In addition, genes relevant to oxidative stress induced damage were unique to CLP injury, which may be due to the increased severity of CLP injury versus SCLP injury. Pathway enrichment identified pathways with similar functionality but different dynamics in the two injury models, indicating that the functions controlled by those pathways are under the influence of different transcription factors and regulatory mechanisms. Putatively identified transcription factors, notably including cAMP response element-binding (CREB), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and signal transducer and activator of transcription (STAT), were obtained through analysis of the promoter regions in the SCLP/CLP genes. Our results show that while transcription factors such as NF-κB, homeodomain transcription factor (HOMF), and GATA transcription factor (GATA) were common in both injuries for the IL-6 signaling pathway, there were many other transcription factors associated with that pathway which were unique to CLP, including forkhead (FKHD), hairy/enhancer of split family (HESF), and interferon regulatory factor family (IRFF). There were 17 transcription factors that were identified as important in at least two pathways in the CLP injury, but only seven transcription factors with that property in the SCLP injury. This also supports the hypothesis of unique regulatory modules that govern the pathways present in both the CLP and SCLP response. CONCLUSIONS By using microarrays to assess multiple genes in a high throughput manner, we demonstrate that an inflammatory response involving different dynamics and different genes is triggered by SCLP and CLP. From our analysis of the CLP data, the key characteristics of sepsis are a proinflammatory response, which drives hypermetabolism, immune cell activation, and damage from oxidative stress. This contrasts with SCLP, which triggers a modified inflammatory response leading to no immune cell activation, decreased detoxification potential, and hyper metabolism. Many of the identified transcription factors that drive the CLP-induced response are not found in the SCLP group, suggesting that SCLP and CLP induce different types of inflammatory responses via different regulatory pathways.