EJI0010.1177/1721727X15590946European Journal of InflammationWu et al. 590946research-article2015

Original article

European Journal of Inflammation 2015, Vol. 13(2) 82­–90 Bioinformatics analysis of transcription © The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav profiling of sepsis DOI: 10.1177/1721727X15590946 eji.sagepub.com

Yanfeng Wu, Peng Xia and Changjun Zheng

Abstract Sepsis is a fatal whole-body inflammatory response that complicates a serious infection. To elucidate the molecular mechanism of sepsis, transcription profile data of GSE12624 which included a total of 70 samples (34 sepsis samples and 36 non-sepsis samples) was downloaded. The t test based on Bayes method in limma package was used to identify differentially expressed (DEGs) between sepsis and non-sepsis samples (criterion: P value <0.05). Gene Ontology (GO) enrichment analysis was conducted to investigate the biological processes involved DEGs. -protein interaction (PPI) network and sub-network analysis were conducted to investigate the interactions between DEGs. A total of 894 DEGs, including 479 downregulated DEGs and 415 upregulated DEGs, were identified in sepsis samples comparing with non-sepsis samples. GO enrichment analysis showed that DEGs mainly involved in cellular metabolic process, primary metabolic process, and response to organic cyclic compound. In the PPI network, four genes of CDC2, GTF2F2, PCNA, and SMAD4 with degrees more than 10 were identified. Subsequently, four sub-networks, in which genes of PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45 had relative high degrees were identified from the PPI network. After the discussion referring to previous studies, we suggested that CDC2, GTF2F2, PCNA, SMAD4 PSMA3, PTBP1, and GADD45 might be used as new therapeutic targets for sepsis. However, experiments should be further performed to prove the practical utility of these candidates.

Keywords differentially expressed genes, functional enrichment analysis, protein-protein interaction network, sepsis

Date received: 9 February 2015; accepted: 18 May 2015

Introduction Sepsis is a fatal whole-body inflammatory response shock patients is the highest mortality compared that complicates a serious infection, most com- with other early stages.3 The Surviving Sepsis monly with bacteria, fungi, viruses, and parasites in Campaign recently issued guidance for the clinician respiratory, genitourinary, would/soft tissue, and caring for patients with severe sepsis or septic central nervous system.1 The spectrum of sepsis shock.4 The guidelines are mainly organized into syndromes includes systemic inflammatory two ‘bundles’ of care: an initial management bun- response syndrome (SIRS), sepsis, severe sepsis, dle to be accomplished within 6 h of the patient’s and septic shock.2 Sepsis and severe sepsis are now presentation; and a management bundle to be major causes of high mortality among critically ill patients in non-coronary intensive care units (ICU). In the USA, the incidence of severe sepsis is The Department of Respiratory Medicine, the Second Hospital of Jilin approximately 300 cases per 100,000 population. University, Changchun 130041, PR China Notably, about 50% of patients suffers sepsis out- Corresponding author: side the ICU and 25% of patients developing into Changjun Zheng, Department of Spine Surgery, The Second Hospital of Jilin University, Ziqiang Street 218, Changchun, Jilin Province, 130041, severe sepsis will die during their hospitalization. PR China. Besides, the approximately 50% death rate of septic Email: [email protected] Wu et al. 83 accomplished in the ICU.5 Notably, patients treated Materials and methods with ‘bundles’ of illustrated improved guideline compliance showed lower hospital mortality.6,7 mRNA expression profile data However, searching for effective new therapies and The mRNA expression profile dataset GSE12624,21 reliable predictors depending on underlying bio- which is based on the platform GPL4204, was logic features of sepsis is still the emphasis of obtained from the Expression Omnibus today’s studies.8 (GEO, http://www.ncbi.nlm.nih.gov/geo/) of At the early stage, sepsis was considered as a National Center of Biotechnology Information result of uncontrolled inflammatory response and (NCBI). The dataset includes a total of 70 subdata several clinical trials attempted to find effective from traumatized patients in intensive care units therapies through blocking inflammatory cascades, (ICU), including 36 non-sepsis samples and 34 such as (TNF)-α antagonist,9 sepsis samples. anti-endotoxin,10 and steroids.11 However, immu- nosuppression is now suggested as a key pathogen- DEGs screening esis related with sepsis mortality.8 An apparent decrease in lipopolysaccharide (LPS)-stimulated The original files were downloaded using secretion of mediators and in immune Geoquery package22 in R language. The original effector cells were detected.12 Furthermore, several data at probe symbol level were first converted clinical trials have proved that immune-enhancing into expression values at gene symbol level. The therapies are related with positive effect.13,14 missing data were imputed using nearest neighbor Therefore, finding drugs to improve the immune averaging (KNN) method (k = 10) and normal- system may be new therapeutic methods. ized to a linear model using quantile normaliza- Hall et al. illustrated that granulocyte mac- tion of robust multichip averaging (RMA).23 The rophage colony stimulating factor (GM-CSF) had limma package24 in R language with t test based the function of inducing productions of neutrophils on Bayes methods25 was employed to identify and macrophages, which is beneficial for sepsis DEGs between traumatized patients with and patients.15 Another immunotherapeutic agent is without sepsis. P value <0.05 was used as the interleukin 7 which could support replenishment of threshold for DEGs inclusion. lymphocytes by promoting proliferation of T cells.16 Recently, Charalampos et al. retrieved 3370 studies Go enrichment analysis of DEGs from the entire Medline database and found 178 To investigate the DEGs at functional level, Gene different biomarkers have been evaluated. Among Ontology (GO)26 functional enrichment analysis the 178 biomarkers, 18 had been identified through was performed through the Gene set Analysis experimental study, 58 through both experimental Toolkit V2 platform27,28 and further adjust was per- and clinical studies, and 101 through clinical stud- formed using multiple testing correction based on ies, respectively.17 Notably, coagulation, comple- the Benjamini-Hochberg method.29 GO function ment, contact system activation, inflammation, and terms in three categories of molecular function were all proved to be related with sepsis. (MF), cellular component (CC), and biological Despite the extensive researches, the pathology of process (BP) were investigated. In this study, sepsis still remains unclear. adjusted P value <0.05 was set as the cutoff crite- Identification of differentially expressed genes rion for functional categories. (DEGs) is a fundamental step for performing genome wide expression profiling,18 and analyses of function enrichment and protein-protein interac- PPI network construction and module detection tion (PPI) network inform people of how molecules PPI network analysis is an important method for or genes work.19,20 In this study, we first down- comprehensively understanding intracellular pro- loaded the expressional microarray data, and then cess. The IntAct,30 DIP,31 BIND,32 and HPRD33 identified the DEGs between sepsis and non-sepsis databases, containing known and predicted pro- samples. Besides, pathway analyses were per- tein-protein interactions, were used to construct the formed to demonstrate the function of these DEGs. PPI network. The identification of functional mod- Finally, a PPI network of DEGs was constructed ules (significantly differentially expressed sub- and module was detected. networks) within the PPI network was primarily 84 European Journal of Inflammation 13(2) performed using MCODE software34 for module Discussion analysis. Severe sepsis and septic shock are leading causes of death, covering approximately 30% to 50% of Results hospital-reported mortality.12 However, the treat- DEGs screening ment of sepsis is still unsolved. In this study, bioin- formatics analyses were performed to investigate T test based on Bayes method was used to identify the potential molecular mechanism of sepsis. the DEGs in mRNA expression profile data, with Consequently, 894 DEGs were identified between the criterion of P value <0.05. Accordingly, a total sepsis samples and non-sepsis samples. Functional of 894 DEGs were identified in sepsis samples enrichment analysis showed the DEGs were mainly comparing with non-sepsis samples. Among those participated in cellular metabolic process, primary 894 DEGs, 479 DEGs were downregulated (e.g. metabolic process, and response to organic cyclic PSMA9, PSMA10, PCNA, EIF3S4, and EIF3S12) compound. Subsequently, DEGs with relative high and 415 DEGs were upregulated (e.g. CDC2, degrees in the PPI network (e.g. CDC2, GTF2F2, CCNA2, GADD45A, PSMA3, PSMA6, PTBP1, PCNA, and SMAD4) and four sub-networks (e.g. RTL6, and GT2F2) in sepsis samples comparing PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and with those in non-sepsis samples, respectively. GADD45) were identified. Cell division cycle 2 homolog (CDC2) gene Enrichment analysis of DEGs encodes a 34 kDa catalytic subunit of a regulated protein kinase which is essential for ini- Functional classification was performed through tiation of DNA replication and entry into mitosis.35 the Gene set Analysis Toolkit V2 platform. Figure 1 The upregulation of CDC2 in sepsis sample was illustrates the categories of CC, MF, and BP asso- determined in this study, this was in consistent with ciated with DEGs. The DEGs were mainly located finding of Wang et al.36 Besides, several studies in cytoplasm and intracellular membrane-bounded have demonstrated that increased activity of CDC2 organelle (Figure 1a); were associated with the are related to response to several apoptotic stimuli, MFs of protein and anion binding, hydrolase such as TNF-α37 and transforming growth factor activity, and receptor activity (Figure 1b); and (TGF)-β1.38 As shown previously, increased apop- mainly participated in BPs of phosphate-contain- totic processes have a close relationship with sep- ing compound metabolic process, response to sis syndromes.39 Therefore, we speculated that the lipid, response to organic cyclic compound, and increasing activity of CDC2 might affect sepsis interact with host (Figure 1c). through the apoptotic pathways. GTF2F2 gene encodes General IIF, PPI network construction and sub-network Polypeptide 2, which could promote transcription detection elongation by binding to RNA polymerase II. Our Based on IntAct, DIP, BIND, and HPRD databases, analysis demonstrated that gene GTF2F2 was the PPI network of DEGs was constructed (Figure upregulated in sepsis samples compared with those 2). The PPI network was composed of 191 nodes in non-sepsis samples. Sepsis upregulated many (DEGs) and 61,263 edges (interactions). Table 1 genes involved in different physiological pro- shows the DEGs with relative high degrees in the cess.17,40 Therefore, the upregulated genes were PPI network, such as CDC2 (degree = 14), GTF2F2 essential for sepsis, demonstrating the vital role of (degree = 11), PCNA (degree = 11), and SMAD4 GTF2F2 in sepsis. (degree = 10). Sub-network analysis was per- PCNA gene encodes a proliferating cell nuclear formed using MCODE software, and four sub-net- antigen associated with the cell cycle. We identi- works (Figure 3) were identified from the PPI fied that the PCNA gene was downregulated in network. Genes with relative high degrees such as sepsis samples. Research of Hali et al. has demon- PTBP1 (degree = 7), PSMA3 (degree = 6), PSMA6 strated that PCNA immunolocalization can be used (degree = 4), PSMB9 (degree = 4), PSMB10 as an index of cell proliferation.41 Furthermore, (degree = 4), and GADD45 (degree = 4) were iden- Guo et al. identified that GO functions associated tified in these four sub-networks. with PCNA were involved in negative regulation of Wu et al. 85 GO analysis for differentially expressed genes. (a) Categories of cellular components; (b) categories molecular function; a nd (c) biological process. Figure 1. 86 European Journal of Inflammation 13(2)

Figure 2. PPI network of differentially expressed genes (DEGs) between sepsis samples and non-sepsis samples. Nodes represent DEGs and edges represent interactions between DEGs.

42 Table 1. Top 10 genes with relative high degree in the PPI cell proliferation. Therefore, downregulated network. expression of PCNA gene in sepsis suggested Gene symbol Degree Gene symbol Degree PCNA might be an important gene involving in sepsis. SMAD4 gene encodes a protein of SMAD CDC2 15 PAPOLA 9 family member 4 which is activated by transmem- GTF2F2 11 RXRA 9 brane serine-threonine receptor kinases as TGF- PCNA 11 SMAD2 8 β1. In this study, we indicated that the expression SMAD4 10 CCNA2 8 of SMAD4 gene was upregulated in sepsis, which RPA2 9 PTBP1 7 was consistent with the findings of Hu et al.43 Wu et al. 87

Figure 3. Modules of PPI network. Nodes represent differentially expressed genes and lines represent interactions between differentially expressed genes.

SMAD4 is a mediator of signal transduction by (BIM) pre-splicing which is an attractive approach TGF-β1, which is a potential anti-inflammatory to modulate apoptosis.45 Furthermore, it has been cytokine in sepsis.44 Therefore, we speculated that shown that dysregulated apoptosis leads to multi- SMAD4 gene might play a role in sepsis through ple diseases including sepsis.17 Our results showed TGF-β1 signal transduction. that the expression of PTBP1 gene was upregu- Four sub-networks were identified from the PPI lated in sepsis patients. Therefore, a new therapeu- network, and genes such as PTBP1, PSMA3, tic target might be focused on gene PTBP1. PSMA6, PSMB9, PSMB10, and GADD45 with rel- Gene GADD45 encodes growth arrest and ative high degrees were identified in these four DNA-damage-inducible protein 45. As shown sub-networks. Almost all the DEGs in the sub-net- before, increased apoptotic processes may play a works participated in the decrease of cell activity. determining role in the outcome of sepsis syn- The PTBP1 gene encodes polypyrimidine tract- dromes.39 Evidence has demonstrated that the binding protein 1 which belongs to the subfamily expression of gene GADD45 was upregulated dur- of ubiquitously expressed heterogeneous nuclear ing apoptosis,46,47 which is similar with our results. ribonucleoproteins (hnRNPs). PTBP1 appears to Moreover, studies have shown that GADD45 play influence pre-mRNA processing and other aspects a role in apoptosis and sepsis may through the of mRNA metabolism and transport, and downreg- activation of the JNK (c-Jun N-terminal kinase) ulation of PTBP1 could inhibit BCL-2 like 11 and/or p38 MAPK (mitogen-activated protein 88 European Journal of Inflammation 13(2) kinase) signaling pathways.48,49 Therefore, of TLR4 (Toll-like receptors) and GADD45 might be treated as a new target. NF-κ.58 Genes PSMA3 ( Subunit Alpha 3), Overall, GO enrichment analysis showed that PSMA6 (Proteasome Subunit Alpha 6), PSMB9 the DEGs between sepsis and non-sepsis patients (Proteasome Subunit Beta 9), and PSMB10 were mainly involved in cellular metabolic pro- (Proteasome Subunit Beta 10) encode the 20S pro- cesses. After discussion, we speculated that genes teasome of humans.50 Several studies have proved such as PTBP1, PSMA3, PSMA6, PSMB9, that sepsis stimulates breakdown of - PSMB10, and GADD45 with relative high degrees dependent protein in skeletal muscle, and this could in the PPI network and sub-networks might be be caused by activation of an ubiquitin-proteasome used as the new therapeutic targets for sepsis. pathway.51,52 Hobler et al. further found that 20S However, all these results and assumptions have proteasome was increased in skeletal muscle, and not been verified, thus more than one experimental this supported the role of 20S proteasome in regula- verification is needed to prove the practical utility tion of sepsis-induced muscle .53 of these candidates. After being analyzed by functional enrichment, the DEGs were mainly associated with BPs includ- Declaration of conflicting interests ing cellular metabolic process, primary metabolic The author(s) declared no potential conflicts of interest process, and response to organic cyclic compound. with respect to the research, authorship, and/or publication A lot of metabolic changes have been related with of this article. sepsis. Levy et al. demonstrated that mitochondrial dysfunction played an important role in sepsis- Funding induced organ dysfunction because of inability of This research received no specific grant from any fund- using molecular oxygen for ing agency in the public, commercial, or not-for-profit production caused by defect in oxidative phospho- sectors. rylation.54 Besides, Luiking et al. illustrated lower plasma arginine levels caused by the changes of References metabolism of arginine were related with sepsis 1 Annane D, Bellissant E and Cavaillon J-M (2005) and supplement of arginine was beneficial for sep- Septic shock. Lancet 365: 63–78. sis patients.55 Furthermore, Wu et al. identified that 2 Soong J and Soni N (2012) Sepsis: Recognition and decreased levels of high-density lipoproteins treatment. Clinical Medicine 12: 276–80. (HDLs) were related with sepsis and the magnitude 3 Mayr FB, Yende S and Angus DC (2014) of reduction was positively with the severity of the Epidemiology of severe sepsis. Virulence 5: 4. 4 Dellinger RP, Levy MM, Rhodes A, et al. (2013) illness. They also demonstrated that HDL acted as Surviving Sepsis Campaign: International guidelines potent anti-inflammatory effects and might be use- for management of severe sepsis and septic shock, 56 ful in the treatment of sepsis. 2012. Intensive Care Medicine 39: 165–228. The corresponding MFs related to DEGs were 5 Angus DC and van der Poll T (2013) Severe sepsis catalytic activity, hydrolase activity, transferase and septic shock. New England Journal of Medicine activity, cytidylyltransferase activity, and receptor 369: 840–851. activity. Gene ADAM10 encodes metallopeptidase 6 Ferrer R, Artigas A, Levy MM, et al. (2008) domain 10 protein which is cell surface protein Improvement in process of care and outcome after associated with structure processing potential a multicenter severe sepsis educational program in adhesion and protease domains. ADAM10 protein Spain. Journal of the America Medical Association cleaves many including TNF-α and solu- 299: 2294–2303. ble vascular endothelial (sVE)-cadherin. Flemming 7 Levy MM, Dellinger RP, Townsend SR, et al. (2010) The Surviving Sepsis Campaign: Results of an inter- et al. demonstrated that sVE-cadherin cleaved by national guideline-based performance improvement ADAM10 is a clinical diagnostic tool for detection program targeting severe sepsis. Intensive Care 57 of endothelial barrier disruption in septic patients. Medicine 36: 222–231. Zhang et al. demonstrated that TGFB1, involving 8 Hotchkiss RS, Monneret G and Payen D (2013) in protein binding, could promote inflammatory Immunosuppression in sepsis: A novel understand- response by facilitating the formation of pro- ing of the disorder and a new therapeutic approach. inflammatory mediator TNF-α and inhibiting the Lancet Infectious Diseases 13: 260–268. Wu et al. 89

9 Abraham E, Wunderink R, Silverman H, et al. (1995) density oligonucleotide array probe level data. Efficacy and safety of monoclonal antibody to human Biostatistics 4: 249–264. tumor necrosis factor α in patients with sepsis syn- 24 Diboun I, Wernisch L, Orengo CA, et al. (2006) drome: A randomized, controlled, double-blind, mul- Microarray analysis after RNA amplification can ticenter clinical trial. Journal of the American Medical detect pronounced differences in gene expression Association 273: 934–941. using limma. BMC Genomics 7: 252. 10 Shoji H (2003) Extracorporeal endotoxin removal 25 Smyth GK (2004) Linear models and empirical bayes for the treatment of sepsis: Endotoxin adsorption methods for assessing differential expression in cartridge (Toraymyxin). Therapeutic Apheresis and microarray experiments. Statistical Applications in Dialysis 7: 108–114. Genetics and Molecular Biology 3: 3. 11 Minneci PC, Deans KJ, Banks SM, et al. (2004) Meta- 26 Ashburner M, Ball CA, Blake JA, et al. (2000) Gene analysis: The effect of steroids on survival and shock Ontology: Tool for the unification of biology. Nature during sepsis depends on the dose. Annals of Internal Genetics 25: 25–29. Medicine 141: 47–56. 27 Zhang B, Kirov S and Snoddy J (2005) WebGestalt: 12 Cho S-Y and Choi J-H (2014) Biomarkers of sepsis. An integrated system for exploring gene sets in vari- Infection & Chemotherapy 46: 1–12. ous biological contexts. Nucleic Acids Research 33: 13 Meisel C, Schefold JC, Pschowski R, et al. (2009) W741–W748. Granulocyte–macrophage colony-stimulating factor 28 Duncan D, Prodduturi N and Zhang B (2010) to reverse sepsis-associated immunosuppression: A WebGestalt2: An updated and expanded version double-blind, randomized, placebo-controlled mul- of the Web-based Gene Set Analysis Toolkit. BMC ticenter trial. American Journal of Respiratory and Bioinformatics 11: P10. Critical Care Medicine 180: 640–648. 29 Reiner A, Yekutieli D and Benjamini Y (2003) 14 Unsinger J, McGlynn M, Kasten KR, et al. (2010) Identifying differentially expressed genes using false IL-7 promotes T cell viability, trafficking, and func- discovery rate controlling procedures. Bioinformatics tionality and improves survival in sepsis. Journal of 19: 368–375. Immunology 184: 3768–3779. 30 Kerrien S, Aranda B, Breuza L, et al. (2012) The 15 Hall MW, Knatz NL, Vetterly C, et al. (2011) IntAct molecular interaction database in 2012. Nucleic Immunoparalysis and nosocomial infection in chil- Acids Research 40: D841–D846. dren with multiple organ dysfunction syndrome. 31 Xenarios I, Salwinski L, Duan XJ, et al. (2002) DIP, Intensive Care Medicine 37: 525–532. the Database of Interacting Proteins: A research tool 16 Boomer JS, To K, Chang KC, et al. (2011) for studying cellular networks of protein interactions. Immunosuppression in patients who die of sepsis Nucleic Acids Research 30: 303–305. and multiple organ failure. Journal of the American 32 Bader GD, Betel D and Hogue CW (2003) BIND: The Medical Association 306: 2594–2605. biomolecular interaction network database. Nucleic 17 Pierrakos C and Vincent J-L (2010) Sepsis biomark- Acids Research 31: 248–250. ers: A review. Critical Care 14: R15. 33 Peri S, Navarro JD, Kristiansen TZ, et al. (2004) 18 Clark NR, Hu KS, Feldmann AS, et al. (2014) The Human protein reference database as a discovery characteristic direction: A geometrical approach resource for proteomics. Nucleic Acids Research 32: to identify differentially expressed genes. BMC D497–D501. Bioinformatics 15: 1–16. 34 Bader GD and Hogue CW (2003) An automated 19 Kanehisa M and Goto S (2000) KEGG: Kyoto method for finding molecular complexes in large pro- encyclopedia of genes and genomes. Nucleic Acids tein interaction networks. BMC Bioinformatics 4: 2. Research 28: 27–30. 35 Aleem E, Kiyokawa H and Kaldis P (2005) Cdc2– 20 Szklarczyk D, Franceschini A, Kuhn M, et al. (2011) cyclin E complexes regulate the G1/S phase transi- The STRING database in 2011: Functional interaction tion. Nature Cell Biology 7: 831–836. networks of proteins, globally integrated and scored. 36 Wang S, Hasham MG, Isordia-Salas I, et al. (2003) Nucleic Acids Research 39: D561–D568. Upregulation of Cdc2 and cyclin A during apop- 21 Shen Z, Guo J and Li D (2014) Screening of differen- tosis of endothelial cells induced by cleaved high- tially expressed genes related to severe sepsis induced molecular-weight kininogen. American Journal of by multiple trauma with DNA microarray. European Physiology-Heart and Circulatory Physiology 284: Review for Medical and Pharmacological Sciences H1917–H1923. 18: 734–739. 37 Meikrantz W, Gisselbrecht S, Tam SW, et al. (1994) 22 Davis S and Davis MS (2013) Package ‘GEOquery’. Activation of cyclin A-dependent protein kinases dur- 23 Irizarry RA, Hobbs B, Collin F, et al. (2003) ing apoptosis. Proceedings of the National Academy Exploration, normalization, and summaries of high of Sciences 91: 3754–3758. 90 European Journal of Inflammation 13(2)

38 Choi KS, Eom YW, Kang Y, et al. (1999) Cdc2 48 Takekawa M and Saito H (1998) A family of stress- and Cdk2 kinase activated by transforming growth inducible GADD45-like proteins mediate activation factor-β1 trigger apoptosis through the phosphoryla- of the stress-responsive MTK1/MEKK4 MAPKKK. tion of protein in FaO hepatoma cells. Cell 95: 521–530. Journal of Biological Chemistry 274: 31775–31783. 49 Harkin DP, Bean JM, Miklos D, et al. (1999) Induction 39 Oberholzer C, Oberholzer A, Clare-Salzler M, et al. of GADD45 and JNK/SAPK-dependent apoptosis (2001) Apoptosis in sepsis: A new target for therapeu- following inducible expression of BRCA1. Cell 97: tic exploration. FASEB Journal 15: 879–892. 575–586. 40 Cao Z and Robinson RA (2014) The role of proteom- 50 Coux O, Tanaka K and Goldberg AL (1996) Structure ics in understanding biological mechanisms of sepsis. and functions of the 20S and 26S . Annual PROTEOMICS-Clinical Applications 8: 35–52. Review of Biochemistry 65: 801–847. 41 Hall P, Levison D, Woods A, et al. (1990) Proliferating 51 Tiao G, Fagan JM, Samuels N, et al. (1994) Sepsis cell nuclear antigen (PCNA) immunolocalization in stimulates nonlysosomal, energy-dependent prote- paraffin sections: An index of cell proliferation with olysis and increases ubiquitin mRNA levels in rat evidence of deregulated expression in some, neo- skeletal muscle. Journal of Clinical Investigation plasms. Journal of Pathology 162: 285–294. 94: 2255. 42 Guo Z, Zhao C and Wang Z (2014) Gene expression 52 Tiao G, Hobler S, Wang JJ, et al. (1997) Sepsis is profiles analysis identifies key genes for acute lung associated with increased mRNAs of the ubiquitin- injury in patients with sepsis. Diagnostic Pathology 9: proteasome proteolytic pathway in human skeletal 176. muscle. Journal of Clinical Investigation 99: 163. 43 Hu W-C (2013) Sepsis is a syndrome with hyperactiv- 53 Hobler SC, Williams A, Fischer D, et al. (1999) ity of TH17-like innate immunity and hypoactivity of Activity and expression of the 20S proteasome are adaptive immunity. arXiv 1311.4747. increased in skeletal muscle during sepsis. American 44 Hafez HM, Berwanger CS, Lintott P, et al. (2000) Journal of Physiology-Regulatory, Integrative and CLINICAL RESEARCH STUDIES-Endotoxemia Comparative Physiology 277: R434–R440. during supraceliac aortic crossdamping is associated 54 Levy RJ (2007) Mitochondrial dysfunction, bioener- with suppression of the monocyte CD14 mecha- getic impairment, and metabolic down-regulation in nism: Possible role of transforming growth factor-b1. sepsis. Shock 28: 24–28. Journal of Vascular Surgery 31: 520–531. 55 Luiking YC, Poeze M, Ramsay G, et al. (2005) The 45 Juan WC, Roca X and Ong ST (2014) Identification role of arginine in infection and sepsis. Journal of of cis-acting elements and splicing factors involved in Parenteral and Enteral Nutrition 29: S70–S74. the regulation of BIM pre-mRNA splicing. PloS One 56 Wu A, Hinds CJ and Thiemermann C (2004) High- 9: e95210. density lipoproteins in sepsis and septic shock: 46 Hollander M, Alamo I, Jackman J, et al. (1993) Metabolism, actions, and therapeutic applications. Analysis of the mammalian gadd45 gene and its Shock 21: 210–221. response to DNA damage. Journal of Biological 57 Flemming S, Renschler M, Meir M, et al. (2014) Chemistry 268: 24385–24393. sVE-cadherin as a diagnostic marker for detection 47 Rishi AK, Sun R-J, Gao Y, et al. (1999) Post- of endothelial barrier breakdown in sepsis? (676.2). transcriptional regulation of the DNA damage-induc- FASEB Journal 28: 676.2. ible gadd45 gene in human breast carcinoma cells 58 Zhang Y and Xing J (2011) Effect of transforming exposed to a novel retinoid CD437. Nucleic Acids growth factor-β1 on monocyte Toll-like receptor 4 Research 27: 3111–3119. expression in septic rats. World 2: 228–231.