Identification of risk factors for sepsis-associated mortality by gene expression profiling analysis
- Authors:
- Published online on: January 25, 2018 https://doi.org/10.3892/mmr.2018.8491
- Pages: 5350-5355
Abstract
Introduction
Sepsis is a systemic inflammatory response syndrome that occurs when a bacterial, viral or fungal infection spreads to the bloodstream and induces life-threatening organ dysfunction (1). Without immediate and aggressive treatment, sepsis can rapidly cause tissue damage, organ failure, and even death. More than 5 million people die from sepsis every year worldwide (2). Despite numerous advances in fundamental and clinical research, the mortality rate for sepsis remains high (3–4). Over the last 30 years, over 100 clinical trials have failed to indicate a survival benefit for patients with severe sepsis, and the failure is due, at least partially, to host heterogeneity (5–8). Patients vary in their circumstances, and a more precise assessment of sepsis is required.
Risk factors associated with deterioration or mortality may be used to diagnose and evaluate the severity of sepsis. Thereby, the subset of patients with a high risk of mortality for sepsis may receive additional, aggressive therapies. Thus far, a number of biomarkers have been widely used for the diagnosis, prognosis and treatment of sepsis (5,10–12). For example, C-reactive protein (CRP) is an acute phase protein, which increases rapidly in response to most forms of inflammation, infection, and tissue damage. High levels of CRP are associated with the risk of sepsis, cardiovascular disease and stroke (12–14). While CRP is broadly used for clinical diagnosis of acute sepsis, it lacks the capacity to differentiate between infective and non-infective inflammation and have low specificity for severe sepsis (15,16). Severe sepsis is often attributed to immune dysregulation, and the imbalance between pro- and anti-inflammatory cytokines may serve a crucial role in the pathogenesis of sepsis (17). Patients who express high levels of interleukin (IL)-6 have an increased risk of mortality (18,19). Although the combination of IL-6 and CRP plasma biomarkers may be helpful in sepsis diagnosis, recent studies demonstrated that IL-6 and CRP are not ideal as they lack sufficient sensitivity and specificity (20–22).
Genome expression profiling is a potential approach to discover novel risk factors based on microarray technology and bioinformatics (23). Microarray technology enables researchers to gain insights into signaling pathways and gene networks that may participate in sepsis development (24–26). The present study focused on the identification of risk factors for predicting sepsis deterioration by genome-wide expression profiling. Differentially expressed genes (DEGs) between sepsis survivors and nonsurvivors were analyzed, and type I interferon (IFN-I) signaling was identified as an important risk factor for sepsis-associated mortality.
Materials and methods
Gene expression profiles
Two gene expression profiles (GSE54514 and GSE3140) were downloaded from public functional genomics data repository Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/) (27–28). The array data from GSE54514 were performed on the platform of Illumina Human HT-12 v3.0 Expression BeadChip (GPL6947; Illumina, Inc., San Diego, CA, USA). This dataset contained 53 blood samples, including 26 samples from sepsis survivors, 9 samples from sepsis nonsurvivors and 18 samples from healthy controls.
The gene expression profile of GSE3140 was performed on the platform of an Affymetrix GeneChip Human HG-Focus Target Array (GPL201; Affymetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA). Prior to the mRNA expression profiling, blood samples were collected from 6 healthy adult volunteers and 6 healthy, full-term infants, and RNA was isolated from cord blood and adult peripheral blood mononuclear cells of blood samples following incubation with or without lipopolysaccharide (LPS).
Screening of DEGs
DEGs were identified using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/). GEO2R is an R-based web tool for performing differential gene expression analysis in the GEO data repository (29). The adjusted P-values (adj. P) were adjusted using the Benjamini and Hochberg false discovery rate method (30). Genes with adj. P<0.05 and |logFC|>1 were considered to be DEGs, and the heatmap of DEGs was generated using the heatmap visualization tool Morpheus (software.broadinstitute.org/morpheus/).
Functional enrichment of DEGs
The Gene Ontology (GO) defines classes used to describe gene function and associations between biology concepts (31). It classifies functions according to three aspects: Molecular Function, Cellular Component and Biological Process. In the present study, GO enrichment analysis of DEGs was performed using Gene Ontology Consortium (www.geneontology.org/), with P<0.05 indicating a significantly enriched term (32).
Protein-protein interaction (PPI) network
STRING is a database and web resource of experimental and predicted PPIs. STRING provides a score for each interaction, and these scores are indicators of confidence and rank from 0–1. The online STRING 10.5 database (string-db.org/) was used to analyze protein interactions and a confidence score >0.4 was used as the cut-off criterion (33).
Results
Identification of DEGs between sepsis survivors and nonsurvivors
To determine the early risk factors for sepsis-associated mortality, the GSE54514 dataset was downloaded from the GEO database and GEO2R was used to identify the DEGs between blood samples from sepsis survivors and nonsurvivors. A heatmap of DEGs was subsequently generated by Morpheus. The results demonstrated that a total of 18 DEGs were identified, including 14 upregulated genes and 4 downregulated genes in nonsurvivors compared with survivors (Fig. 1).
IFN-I signaling is upregulated in nonsurvivors of sepsis
The identified DEGs were functionally enriched by GO analysis using the GOC website with P<0.05 as the threshold. As demonstrated in Table I, DEGs that were upregulated in the nonsurvivors group compared with the survivors group were highly enriched in 15 pathways, including ‘type I interferon signaling pathway’, ‘cellular response to type I interferon’, ‘response to type I interferon’, ‘negative regulation of viral genome replication’ and ‘regulation of viral genome replication’. To further investigate the association between the upregulated genes, STRING was used to construct the PPI network. As demonstrated in Fig. 2, a set of proteins from IFN-I signaling pathway closely interacted with each other, which indicates that they may be implicated in sepsis biology due to their interactions. These results indicated that upregulated IFN-I signaling may be a risk factor for sepsis-associated mortality.
Table I.Enriched pathways of differentially expressed genes that were upregulated in the whole blood of sepsis nonsurvivors compared with survivors. |
IFN-I signaling is upregulated in blood samples from LPS-treated healthy neonates
IFN-I signaling is crucial for the host defense; however, its role in sepsis remains controversial. The results of the present study indicated that, during early sepsis, upregulated IFN-I signaling may be a marker for an increased risk of mortality. Therefore, the present study also investigated whether this signaling pathway was dysregulated in a subset of patients with a high risk of sepsis-induced mortality (34). It is widely accepted that neonates suffer higher sepsis mortality rates compared with adults (35), therefore, DEGs between the cord blood mononuclear cells of healthy neonates and adult peripheral blood mononuclear cells were analyzed and numerous DEGs were identified by GEO2R. However, instead of IFN-1 signaling, DEGs upregulated in healthy neonates compared with healthy adults were largely enriched in ‘protoporphyrinogen IX metabolic process’, ‘gas transport’, ‘oxygen transport’, ‘erythrocyte development’ and ‘porphyrin-containing compound biosynthetic process’, among others (Table II).
Table II.Enriched pathways of differentially expressed genes that were upregulated in the untreated cord blood mononuclear cells of healthy neonates compared with the untreated peripheral blood mononuclear cells of healthy adults. |
Furthermore, upregulated DEGs between the LPS-treated cord blood mononuclear cells of healthy neonates and LPS-treated peripheral blood mononuclear cells of healthy adults were identified and analyzed by GO enrichment. Consistent with the hypothesis that the IFN-I pathway may be upregulated in a subset of patients with sepsis that normally exhibit a higher risk of mortality, ‘type I interferon signaling pathway’ and ‘response to interferon-α’ were upregulated in the LPS-treated blood mononuclear cells of healthy neonates compared with adults (Table III). Subsequently, the associations among the upregulated genes were analyzed by STRING; 183 proteins were demonstrated to be involved in the interaction network and were separated into several clusters, and the proteins involved in IFN-I signaling appeared to exhibit a higher number of interactions (Fig. 3). These results indicated that IFN-I signaling may be upregulated in a subset of patients with sepsis, in this case neonates, that have a higher risk of mortality.
Table III.Enriched pathways of differentially expressed genes that were upregulated in the LPS-treated cord blood mononuclear cells of healthy neonates compared with LPS-treated peripheral blood mononuclear cells of healthy adults. |
Discussion
Sepsis is an important cause of mortality from infection, and although numerous studies have been performed concerning this life-threatening condition, there remains a lack of effective treatment and an assessment of risk factors to assist in the diagnostic process. The present study focused on the identification of potential risk factors for sepsis-associated mortality by genome-wide expression profiling. The results demonstrated that DEGs that were upregulated in sepsis nonsurvivors compared with survivors were highly enriched in the IFN-I signaling pathway. The associations between the upregulated genes were analyzed by STRING and the results demonstrated that the proteins were also highly associated with IFN-I signaling pathway. Therefore, it was hypothesized that a dysregulated IFN-I signaling pathway may be associated with a high risk of sepsis-associated mortality.
IFN-Is, which include IFN-α and IFN-β, trigger IFN-I signaling by binding to the IFN-α/β receptor (IFNAR), stimulating the Janus kinase/signal transducer and activator of transcription pathway and initiating the transcription of IFN-stimulated genes, which mediate various anticellular effects by modulating cell viability and function (36). The IFN-I signaling pathway is well known for its protective roles in the majority of viral infections, while its functions in bacterial infection remain controversial. This controversy arises as certain studies have reported that IFN-I signaling served a critical role in host protection against bacterial infection and that the development of bacteremia during sepsis was enhanced in the mice that lack IFNAR (37,38), while other studies indicated that IFNAR-deficient mice were partially protected against lethality in multiple inflammatory models, including endotoxemia-induced shock, cecal ligation and puncture-induced sepsis, and colon ascendens stent peritonitis-induced sepsis (39–41). In addition, IFN-I signaling may exert toxic effects during sepsis by negatively regulating neutrophil recruitment and suppressing adaptive immunity, leading to inefficient control of infections and eventual mortality (40,42,43).
Although these findings highlight the critical role of the IFN-I signaling pathway in sepsis, there remains a lack of reliable evidence from clinical studies. The results of the present study demonstrated that upregulated IFN-I signaling may be a high-risk factor for sepsis-associated mortality; however, it remains to be elucidated whether dysregulated IFN-I signaling may affect the mortality of patients with sepsis. Although a potential risk factor for short-term mortality in sepsis is provided, the survivors still suffer a high risk of long-term mortality for months or years. Although IFN-I signaling was upregulated in several survivors compared with the others, no information concerning their long-term mortality is available due to a lack of data, therefore a more comprehensive scrutiny of clinical research concerning the effect of the IFN-I signaling pathway in sepsis is required.
As the initial results of the present study indicated that upregulated IFN-I signaling may be a potential marker for a higher risk of sepsis-associated mortality, it was further investigated whether this signaling pathway was dysregulated in a subset of patients that possess a higher risk of sepsis-associated mortality. It is established that neonates suffer a higher rate of sepsis-associated mortality compared with adults; therefore, DEGs between the untreated cord blood mononuclear cells of healthy neonates and untreated peripheral blood mononuclear cells of adults were analyzed (44). There were numerous DEGs between neonates and adults; however, the DEGs were not enriched in the IFN-1 signaling pathway. Subsequently, upregulated DEGs between LPS-treated cord blood mononuclear cells of healthy neonates and LPS-treated peripheral blood mononuclear cells of adults were identified and, consistent with the hypothesis, ‘type I interferon signaling pathway’ and ‘response to interferon-α’ were upregulated in the LPS-treated cells of healthy neonates compared with adults. Furthermore, the associations among the upregulated genes were analyzed and the results demonstrated that the proteins associated with the IFN-I signaling possessed a higher number of interactions and may function together in pathological processes.
In conclusion, the present findings suggest that upregulated IFN-I signaling pathway may be a risk factor for sepsis-associated mortality, but further studies are needed to confirm the current results.
Acknowledgements
The present study was supported by the National Natural Science Foundation of China (grant nos. 31500738 and 81273199), the Health and Family Planning Commission of Jilin Province (grant no. 2014Z067) and the Union Projects of Jilin University and Xinjiang Medical University (for Liu Z).
References
Singer CS, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al: The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 315:801–810. 2016. View Article : Google Scholar : PubMed/NCBI | |
Rudd KE, Delaney A and Finfer S: Counting sepsis, an imprecise but improving science. JAMA. 318:1228–1229. 2017. View Article : Google Scholar : PubMed/NCBI | |
Rhodes A, Evans LE, Alhazzani W, Levy MM, Antonelli M, Ferrer R, Kumar A, Sevransky JE, Sprung CL, Nunnally ME, et al: Surviving sepsis campaign: International guidelines for management of sepsis and septic shock: 2016. Crit Care Med. 45:486–552. 2017. View Article : Google Scholar : PubMed/NCBI | |
Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, Angus DC, Rubenfeld GD and Singer M; Sepsis Definitions Task Force, : Developing a new definition and assessing new clinical criteria for septic shock: For the third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 315:775–787. 2016. View Article : Google Scholar : PubMed/NCBI | |
van der Poll T, van de Veerdonk FL, Scicluna BP and Netea MG: The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol. 17:407–420. 2017. View Article : Google Scholar : PubMed/NCBI | |
Seeley EJ and Bernard GR: Therapeutic targets in sepsis: Past, present, and future. Clin Chest Med. 37:181–189. 2016. View Article : Google Scholar : PubMed/NCBI | |
Hotchkiss RS, Monneret G and Payen D: Immunosuppression in sepsis: A novel understanding of the disorder and a new therapeutic approach. Lancet Infect Dis. 13:260–268. 2013. View Article : Google Scholar : PubMed/NCBI | |
Dellinger RP: Severe sepsis trials: Why have they failed? Minerva Anestesiol. 65:340–345. 1999.PubMed/NCBI | |
Gotts JE and Matthay MA: Sepsis: Pathophysiology and clinical management. BMJ. 353:i15852016. View Article : Google Scholar : PubMed/NCBI | |
Seymour CW and Rosengart MR: Septic shock: Advances in diagnosis and treatment. JAMA. 314:708–717. 2015. View Article : Google Scholar : PubMed/NCBI | |
McLean AS, Tang B and Huang SJ: Investigating sepsis with biomarkers. BMJ. 350:h2542015. View Article : Google Scholar : PubMed/NCBI | |
Vashist SK, Venkatesh AG, Marion Schneider E, Beaudoin C, Luppa PB and Luong JH: Bioanalytical advances in assays for C-reactive protein. Biotechnol Adv. 34:272–290. 2016. View Article : Google Scholar : PubMed/NCBI | |
Ridker PM: A test in context: High-sensitivity c-reactive protein. J Am Coll Cardiol. 67:712–723. 2016. View Article : Google Scholar : PubMed/NCBI | |
Póvoa P, Coelho L, Almeida E, Fernandes A, Mealha R, Moreira P and Sabino H: Pilot study evaluating C-reactive protein levels in the assessment of response to treatment of severe bloodstream infection. Clin Infect Dis. 40:1855–1857. 2005. View Article : Google Scholar : PubMed/NCBI | |
Fraunberger P, Wang Y, Holler E, Parhofer KG, Nagel D, Walli AK and Seidel D: Prognostic value of interleukin 6, procalcitonin, and C-reactive protein levels in intensive care unit patients during first increase of fever. Shock. 26:10–12. 2006. View Article : Google Scholar : PubMed/NCBI | |
Schneider CP, Yilmaz Y, Kleespies A, Jauch KW and Hartl WH: Accuracy of procalcitonin for outcome prediction in unselected postoperative critically ill patients. Shock. 31:568–573. 2009. View Article : Google Scholar : PubMed/NCBI | |
Chousterman BG, Swirski FK and Weber GF: Cytokine storm and sepsis disease pathogenesis. Semin Immunopathol. 39:517–528. 2017. View Article : Google Scholar : PubMed/NCBI | |
Mikacenic C, Price BL, Harju-Baker S, O'Mahony DS, Robinson-Cohen C, Radella F, Hahn WO, Katz R, Christiani DC, Himmelfarb J, et al: A Two-biomarker model predicts mortality in the critically III with Sepsis. Am J Respir Crit Care Med. 196:1004–1011. 2017. View Article : Google Scholar : PubMed/NCBI | |
Qi Y, Ge J, Ma C, Wu N, Cui X and Liu Z: Activin A regulates activation of mouse neutrophils by Smad3 signalling. Open Biol. 7:1603422017. View Article : Google Scholar : PubMed/NCBI | |
Ettinger M, Calliess T, Kielstein JT, Sibai J, Brückner T, Lichtinghagen R, Windhagen H and Lukasz A: Circulating biomarkers for discrimination between aseptic joint failure, low-grade infection, and high-grade septic failure. Clin Infect Dis. 61:332–341. 2015. View Article : Google Scholar : PubMed/NCBI | |
Buttaro MA, Tanoira I, Comba F and Piccaluga F: Combining C-reactive protein and interleukin-6 may be useful to detect periprosthetic hip infection. Clin Orthop Relat Res. 468:3263–3267. 2010. View Article : Google Scholar : PubMed/NCBI | |
Elgeidi A, Elganainy AE, Abou Elkhier N and Rakha S: Interleukin-6 and other inflammatory markers in diagnosis of periprosthetic joint infection. Int Orthop. 38:2591–2595. 2014. View Article : Google Scholar : PubMed/NCBI | |
Rung J and Brazma A: Reuse of public genome-wide gene expression data. Nat Rev Genet. 14:89–99. 2013. View Article : Google Scholar : PubMed/NCBI | |
Maslove DM and Wong HR: Gene expression profiling in sepsis: Timing, tissue, and translational considerations. Trends Mol Med. 20:204–213. 2014. View Article : Google Scholar : PubMed/NCBI | |
Wong HR: Clinical review: Sepsis and septic shock-the potential of gene arrays. Crit Care. 16:2042012. View Article : Google Scholar : PubMed/NCBI | |
Tang BM, Huang SJ and McLean AS: Genome-wide transcription profiling of human sepsis: A systematic review. Crit Care. 14:R2372010. View Article : Google Scholar : PubMed/NCBI | |
Parnell GP, Tang BM, Nalos M, Armstrong NJ, Huang SJ, Booth DR and McLean AS: Identifying key regulatory genes in the whole blood of septic patients to monitor underlying immune dysfunctions. Shock. 40:166–174. 2013. View Article : Google Scholar : PubMed/NCBI | |
Koch L, Linderkamp O, Ittrich C, Benner A and Poeschl J: Gene expression profiles of adult peripheral and cord blood mononuclear cells altered by lipopolysaccharide. Neonatology. 93:1–100. 2008. View Article : Google Scholar : PubMed/NCBI | |
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41(Database Issue): D991–D995. 2013.PubMed/NCBI | |
Benjamini Y, Drai D, Elmer G, Kafkafi N and Golani I: Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 125:279–284. 2001. View Article : Google Scholar : PubMed/NCBI | |
Rhee SY, Wood V, Dolinski K and Draghici S: Use and misuse of the gene ontology annotations. Nat Rev Genet. 9:509–515. 2008. View Article : Google Scholar : PubMed/NCBI | |
Munoz-Torres M and Carbon S: Get GO! Retrieving GO data using AmiGO, QuickGO, API, Files, and tools. Methods Mol Biol. 1446:149–160. 2017. View Article : Google Scholar : PubMed/NCBI | |
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in 2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45(D1): D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI | |
Trinchieri G: Type I interferon: Friend or foe? J Exp Med. 207:2053–2063. 2010. View Article : Google Scholar : PubMed/NCBI | |
Kan B, Razzaghian HR and Lavoie PM: An immunological perspective on neonatal sepsis. Trends Mol Med. 22:290–302. 2016. View Article : Google Scholar : PubMed/NCBI | |
Schreiber G and Piehler J: The molecular basis for functional plasticity in type I interferon signaling. Trends Immunol. 36:139–149. 2015. View Article : Google Scholar : PubMed/NCBI | |
LeMessurier KS, Häcker H, Chi L, Tuomanen E and Redecke V: Type I interferon protects against pneumococcal invasive disease by inhibiting bacterial transmigration across the lung. PLoS Pathog. 9:e10037272013. View Article : Google Scholar : PubMed/NCBI | |
Mancuso G, Midiri A, Biondo C, Beninati C, Zummo S, Galbo R, Tomasello F, Gambuzza M, Macrì G, Ruggeri A, et al: Type I IFN signaling is crucial for host resistance against different species of pathogenic bacteria. J Immunol. 178:3126–3133. 2007. View Article : Google Scholar : PubMed/NCBI | |
Mahieu T, Park JM, Revets H, Pasche B, Lengeling A, Staelens J, Wullaert A, Vanlaere I, Hochepied T, van Roy F, et al: The wild-derived inbred mouse strain SPRET/Ei is resistant to LPS and defective in IFN-beta production. Proc Natl Acad Sci USA. 103:pp. 2292–2297. 2006; View Article : Google Scholar : PubMed/NCBI | |
Dejager L, Vandevyver S, Ballegeer M, Van Wonterghem E, An LL, Riggs J, Kolbeck R and Libert C: Pharmacological inhibition of type I interferon signaling protects mice against lethal sepsis. J Infect Dis. 209:960–970. 2014. View Article : Google Scholar : PubMed/NCBI | |
Weighardt H, Kaiser-Moore S, Schlautkötter S, Rossmann-Bloeck T, Schleicher U, Bogdan C and Holzmann B: Type I IFN modulates host defense and late hyperinflammation in septic peritonitis. J Immunol. 177:5623–5630. 2006. View Article : Google Scholar : PubMed/NCBI | |
Schwandt T, Schumak B, Gielen GH, Jüngerkes F, Schmidbauer P, Klocke K, Staratschek-Jox A, van Rooijen N, Kraal G, Ludwig-Portugall I, et al: Expression of type I interferon by splenic macrophages suppresses adaptive immunity during sepsis. EMBO J. 31:201–213. 2012. View Article : Google Scholar : PubMed/NCBI | |
McNab F, Mayer-Barber K, Sher A, Wack A and O'Garra A: Type I interferons in infectious disease. Nat Rev Immunol. 15:87–103. 2015. View Article : Google Scholar : PubMed/NCBI | |
Ginsburg AS, Meulen AS and Klugman KP: Prevention of neonatal pneumonia and sepsis via maternal immunisation. Lancet Glob Health. 2:e679–e680. 2014. View Article : Google Scholar : PubMed/NCBI |