Severe sepsis is defined as a potentially deadly medical condition which is characterized by a whole-body inflammatory state 1. Cecal ligation and puncture (CLP) model is an animal model that mimics the physiological changes of human sepsis, which is considered as the gold standard for sepsis researches 2. Following CLP, animals generally develop bacteremia, hypothermia, hypotension, and hyper-metabolic and catabolic state at whole body level. Given the fact that liver controls the metabolic activity of body and produces acute phase proteins during the systemic inflammation, it is one of the target organs to understand the underlying molecular mechanisms of the septic state and to propose therapeutic approaches. Therefore, genome-wide microarray technology has been already applied to reveal transcriptional changes in liver following the CLP treatment 3-5. However, these studies either focus on a single time point (24 h post-CLP) which probably misses the critical early response or do not take the time scale into account, i.e. the inherent ordering and spacing provided by the time points are ignored. Thus, the aim of this study is to get a better understanding of the hepatic transcriptional response to CLP by generating a rich time series as well as utilizing bioinformatics tools to identify the dynamic patterns of CLP induced-genes showing distinct expression profiles over time compared to a time dependent control.
In this study, the infection was induced by applying CLP treatment. Rats were first anesthetized, and then the analgesic buprenorphrine (0.01 to 0.05 mg/kg) and Bupivicaine (0.125% to 0.25%) were given subcutaneously. The abdominal cavity was cut open by a 2 cm midline incision. The cecum of the rat was exposed and ligated just below the ileocecal valve so that intestinal obstruction was not produced. We took care to not ligate the cecal branch of the ileocecal artery, thus preserving viability of the cecum itself, in order to increase the survival rate. The cecum was punctured 4 times with a 20 gauge needle and replaced in the peritoneum. The abdominal incision was then sutured in layers using interrupted monofilament sutures. The animal received 10 mL/kg saline intraperitoneally for resuscitation. Negative controls (sham CLP or SCLP) consist of animals treated identically without receiving cecal ligation and puncture. Rats were single caged after the treatments and given standard rat chow and water ad libitum until sacrifice. Animals are sacrificed (starting at 9am) at different time points (0, 2, 4, 8, 12, 16, 20 and 24hr post-treatment) 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 SCLP and CLP expression profiles, gene expression data analysis includes normalization, filtering, 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 CLP and SCLP groups were investigated to identify the temporally and differentially expressed probesets by comparing the overall-AUC (area under the gene expression–time curve in CLP group) with the baseline-AUC (area under the gene expression–time curve in SCLP group) for each gene 6 in order to explore significant net responses of gene expression profiles following the CLP. Finally concatenated data sets corresponding to differentially expressed probesets in CLP and SCLP groups were combined to form one single matrix, which was then clustered using the approach of “consensus clustering” 7. The goal was to identify subsets of transcripts with coherent expression pattern in CLP and SCLP. In total 1987 CLP-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 8 as well as analyzed the functions of each individual gene 9.
Consensus clustering identifies 4 statistically dominant transcriptional clusters composed of 474, 565, 287, 661 probe sets respectively. Functional characterization elucidated that these responses are mainly associated with inflammation (cluster 1 and cluster 2) and xenobiotics biodegradation (cluster 3 and cluster 4). The results show that the first 2 clusters exhibit up-regulation following CLP treatment when compared to the control group, which are both related to inflammatory response. More specifically, genes in cluster 1 are important in natural killer cell mediated cytotoxicity (Gzmb, Icam1, Nfat5, Nfatc4, Shc2), MAPK signaling pathway (Cacna1a, Dusp1, Fgf12, Fos, Il1a, Nfatc4, Rasgrp3, Taok1, Tp53), and B-cell receptor signaling pathway (Fos, Nfat5, Nfatc4, Rasgrp3). Genes involved in cluster 2 participate in Jak-STAT signaling pathway (Cblc, Grb2, Ifnar1, Ifnb1, Il10rb, Il7r, Jak3, Lepr, Socs3), B cell receptor signaling pathway (Bcl10, Fos, Grb2, Nfkb1, Nfkbie, Syk), and Toll-like receptor pathway (Fos, Ifnar1, Ifnb1, Lbp, Nfkb1, Tlr2). On the other hand, the last 2 clusters showing down-regulation following the CLP compared to control are associated with liver metabolism. They are both enriched in xenobiotics biodegradation pathway (Cyp1a2, Cyp2b1, Cyp2c12, Cyp3a23/3a1, Cyp1a2, Cyp2b1, Cyp2c12, Cyp3a23/3a1, Adh1, Cyp2c22, Cyp2c23, Cyp2e1, Cyp3a2, Ephx1, Gsta3, mGsta4, Gstm1, Gstm2, Gstm4, Gstz1, Mgst3, Ugt1a6, Yc2).
Interestingly, though the first two clusters are involved in similar functions, their dynamic profiles are significantly different. Our results indicate that the proinflammatory response in cluster 2 is rapidly up-regulated within the first 2 hours following CLP injury. Similarly, the inflammatory response in cluster 1 is also activated compared to the time-dependent control, which would otherwise not be considered as an upregulation if control group was assumed to be a constant value (0 time point). Following CLP injury, it was observed that the genes involved in xenobiotics degradation and metabolism in cluster 3 and 4 were suppressed starting around 2h post-CLP compared to dynamic gene expression profiles of control group. The fact that xenobiotics metabolism associated genes are observed to be downregulated in response to inflammation indicates an impairment in the liver function of detoxification, implying a CLP induced challenge to the drug-metabolism 10.
In conclusion, the methodology used in this study which allows to characterize the dynamic patterns of both CLP and SCLP group elucidated that CLP resulted in activation of certain signaling pathways as well as the impairment of the xenobiotics metabolism in the liver.
1. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. 1992. Chest. 2009; 136(5 Suppl): e28.
2. Wichterman KA, Baue AE, Chaudry IH. Sepsis and septic shock--a review of laboratory models and a proposal. J Surg Res. 1980; 29(2): 189-201.
3. Cobb JP, Laramie JM, Stormo GD, Morrissey JJ, Shannon WD, Qiu Y, Karl IE, Buchman TG, Hotchkiss RS. Sepsis gene expression profiling: murine splenic compared with hepatic responses determined by using complementary DNA microarrays. Crit Care Med. 2002; 30(12): 2711-21.
4. Li ZJ, Li YP, Gai HR, Xue YL, Feng XZ. [Research of gene expression profile of liver tissue in rat sepsis model]. Zhongguo Wei Zhong Bing Ji Jiu Yi Xue. 2007; 19(3): 156-9.
5. Chinnaiyan AM, Huber-Lang M, Kumar-Sinha C, Barrette TR, Shankar-Sinha S, Sarma VJ, Padgaonkar VA, Ward PA. Molecular signatures of sepsis: multiorgan gene expression profiles of systemic inflammation. Am J Pathol. 2001; 159(4): 1199-209.
6. Scheff J, Almon R, DuBois D, Jusko W, Androulakis I. Assessment of Pharmacologic Area Under the Curve When Baselines are Variable. Pharmaceutical Research. 2011: 1-9.
7. Nguyen TT, Nowakowski RS, Androulakis IP. Unsupervised selection of highly coexpressed and noncoexpressed genes using a consensus clustering approach. Omics. 2009; 13(3): 219-37.
8. Tong W, Cao X, Harris S, Sun H, Fang H, Fuscoe J, Harris A, Hong H, Xie Q, Perkins R. ArrayTrack--supporting toxicogenomic research at the US Food and Drug Administration National Center for Toxicological Research. Environmental health perspectives. 2003; 111(15): 1819-1826.
9. Twigger S, Lu J, Shimoyama M, Chen D, Pasko D, Long H, Ginster J, Chen CF, Nigam R, Kwitek A, Eppig J, Maltais L, Maglott D, Schuler G, Jacob H, Tonellato PJ. Rat Genome Database (RGD): mapping disease onto the genome. Nucleic Acids Res. 2002; 30(1): 125-8.
10. Morgan ET. Regulation of cytochrome p450 by inflammatory mediators: why and how? Drug Metab Dispos. 2001; 29(3): 207-12.
See more of this Group/Topical: Food, Pharmaceutical & Bioengineering Division