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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 genes (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-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

Introduction

Sepsis is a fatal whole-body inflammatory response that complicates a serious infection, most commonly with bacteria, fungi, viruses, and parasites in respiratory, genitourinary, would/soft tissue, and central nervous system.¹ The spectrum of sepsis syndromes includes systemic inflammatory response syndrome (SIRS), sepsis, severe sepsis, and septic shock.² Sepsis and severe sepsis are now 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 approximately 300 cases per 100,000 population. Notably, about 50% of patients suffers sepsis outside the ICU and 25% of patients developing into severe sepsis will die during their hospitalization. Besides, the approximately 50% death rate of septic shock patients is the highest mortality compared with other early stages.³ The Surviving Sepsis Campaign recently issued guidance for the clinician caring for patients with severe sepsis or septic shock.⁴ The guidelines are mainly organized into two ‘bundles’ of care: an initial management bundle to be accomplished within 6 h of the patient’s presentation; and a management bundle to be accomplished in the ICU.⁵ Notably, patients treated with ‘bundles’ of illustrated improved guideline compliance showed lower hospital mortality.^6,7 However, searching for effective new therapies and reliable predictors depending on underlying biologic features of sepsis is still the emphasis of today’s studies.⁸

At the early stage, sepsis was considered as a result of uncontrolled inflammatory response and several clinical trials attempted to find effective therapies through blocking inflammatory cascades, such as tumor necrosis factor (TNF)-α antagonist,⁹ anti-endotoxin,¹⁰ and steroids.¹¹ However, immunosuppression is now suggested as a key pathogenesis related with sepsis mortality.⁸ An apparent decrease in lipopolysaccharide (LPS)-stimulated cytokine secretion of mediators and in immune effector cells were detected.¹² Furthermore, several clinical trials have proved that immune-enhancing therapies are related with positive effect.^13,14 Therefore, finding drugs to improve the immune system may be new therapeutic methods.

Hall et al. illustrated that granulocyte macrophage colony stimulating factor (GM-CSF) had the function of inducing productions of neutrophils and macrophages, which is beneficial for sepsis patients.¹⁵ Another immunotherapeutic agent is interleukin 7 which could support replenishment of lymphocytes by promoting proliferation of T cells.¹⁶ Recently, Charalampos et al. retrieved 3370 studies from the entire Medline database and found 178 different biomarkers have been evaluated. Among the 178 biomarkers, 18 had been identified through experimental study, 58 through both experimental and clinical studies, and 101 through clinical studies, respectively.¹⁷ Notably, coagulation, complement, contact system activation, inflammation, and apoptosis were all proved to be related with sepsis. Despite the extensive researches, the pathology of sepsis still remains unclear.

Identification of differentially expressed genes (DEGs) is a fundamental step for performing genome wide expression profiling,¹⁸ and analyses of function enrichment and protein-protein interaction (PPI) network inform people of how molecules or genes work.^19,20 In this study, we first downloaded the expressional microarray data, and then identified the DEGs between sepsis and non-sepsis samples. Besides, pathway analyses were performed to demonstrate the function of these DEGs. Finally, a PPI network of DEGs was constructed and module was detected.

Materials and methods

mRNA expression profile data

The mRNA expression profile dataset GSE12624,²¹ which is based on the platform GPL4204, was obtained from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) of National Center of Biotechnology Information (NCBI). The dataset includes a total of 70 subdata from traumatized patients in intensive care units (ICU), including 36 non-sepsis samples and 34 sepsis samples.

DEGs screening

The original files were downloaded using Geoquery package²² in R language. The original data at probe symbol level were first converted into expression values at gene symbol level. The missing data were imputed using nearest neighbor averaging (KNN) method (k = 10) and normalized to a linear model using quantile normalization of robust multichip averaging (RMA).²³ The limma package²⁴ in R language with t test based on Bayes methods²⁵ was employed to identify DEGs between traumatized patients with and without sepsis. P value <0.05 was used as the threshold for DEGs inclusion.

Go enrichment analysis of DEGs

To investigate the DEGs at functional level, Gene Ontology (GO)²⁶ functional enrichment analysis was performed through the Gene set Analysis Toolkit V2 platform^27,28 and further adjust was performed using multiple testing correction based on the Benjamini-Hochberg method.²⁹ GO function terms in three categories of molecular function (MF), cellular component (CC), and biological process (BP) were investigated. In this study, adjusted P value <0.05 was set as the cutoff criterion for functional categories.

PPI network construction and module detection

PPI network analysis is an important method for comprehensively understanding intracellular process. The IntAct,³⁰ DIP,³¹ BIND,³² and HPRD³³ databases, containing known and predicted protein-protein interactions, were used to construct the PPI network. The identification of functional modules (significantly differentially expressed sub-networks) within the PPI network was primarily performed using MCODE software³⁴ for module analysis.

Results

DEGs screening

T test based on Bayes method was used to identify the DEGs in mRNA expression profile data, with the criterion of P value <0.05. Accordingly, a total of 894 DEGs were identified in sepsis samples comparing with non-sepsis samples. Among those 894 DEGs, 479 DEGs were downregulated (e.g. PSMA9, PSMA10, PCNA, EIF3S4, and EIF3S12) and 415 DEGs were upregulated (e.g. CDC2, CCNA2, GADD45A, PSMA3, PSMA6, PTBP1, RTL6, and GT2F2) in sepsis samples comparing with those in non-sepsis samples, respectively.

Enrichment analysis of DEGs

Functional classification was performed through the Gene set Analysis Toolkit V2 platform. Figure 1 illustrates the categories of CC, MF, and BP asso-ciated with DEGs. The DEGs were mainly located in cytoplasm and intracellular membrane-bounded organelle (Figure 1a); were associated with the MFs of protein and anion binding, hydrolase activity, and receptor activity (Figure 1b); and mainly participated in BPs of phosphate-containing compound metabolic process, response to lipid, response to organic cyclic compound, and interact with host (Figure 1c).

Figure 1.

GO analysis for differentially expressed genes. (a) Categories of cellular components; (b) categories of molecular function; and (c) categories of biological process.

PPI network construction and sub-network detection

Based on IntAct, DIP, BIND, and HPRD databases, the PPI network of DEGs was constructed (Figure 2). The PPI network was composed of 191 nodes (DEGs) and 61,263 edges (interactions). Table 1 shows the DEGs with relative high degrees in the PPI network, such as CDC2 (degree = 14), GTF2F2 (degree = 11), PCNA (degree = 11), and SMAD4 (degree = 10). Sub-network analysis was performed using MCODE software, and four sub-networks (Figure 3) were identified from the PPI network. Genes with relative high degrees such as PTBP1 (degree = 7), PSMA3 (degree = 6), PSMA6 (degree = 4), PSMB9 (degree = 4), PSMB10 (degree = 4), and GADD45 (degree = 4) were identified in these four sub-networks.

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.

Table 1.

Top 10 genes with relative high degree in the PPI network.

Gene symbol	Degree	Gene symbol	Degree
CDC2	15	PAPOLA	9
GTF2F2	11	RXRA	9
PCNA	11	SMAD2	8
SMAD4	10	CCNA2	8
RPA2	9	PTBP1	7

Figure 3.

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

Discussion

Severe sepsis and septic shock are leading causes of death, covering approximately 30% to 50% of hospital-reported mortality.¹² However, the treatment of sepsis is still unsolved. In this study, bioinformatics analyses were performed to investigate the potential molecular mechanism of sepsis. Consequently, 894 DEGs were identified between sepsis samples and non-sepsis samples. Functional enrichment analysis showed the DEGs were mainly participated in cellular metabolic process, primary metabolic process, and response to organic cyclic compound. Subsequently, DEGs with relative high degrees in the PPI network (e.g. CDC2, GTF2F2, PCNA, and SMAD4) and four sub-networks (e.g. PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45) were identified.

Cell division cycle 2 homolog (CDC2) gene encodes a 34 kDa catalytic subunit of a cell cycle regulated protein kinase which is essential for initiation of DNA replication and entry into mitosis.³⁵ The upregulation of CDC2 in sepsis sample was determined in this study, this was in consistent with finding of Wang et al.³⁶ Besides, several studies have demonstrated that increased activity of CDC2 are related to response to several apoptotic stimuli, such as TNF-α³⁷ and transforming growth factor (TGF)-β1.³⁸ As shown previously, increased apoptotic processes have a close relationship with sepsis syndromes.³⁹ Therefore, we speculated that the increasing activity of CDC2 might affect sepsis through the apoptotic pathways. GTF2F2 gene encodes General Transcription Factor IIF, Polypeptide 2, which could promote transcription elongation by binding to RNA polymerase II. Our analysis demonstrated that gene GTF2F2 was upregulated in sepsis samples compared with those in non-sepsis samples. Sepsis upregulated many genes involved in different physiological process.^17,40 Therefore, the upregulated genes were essential for sepsis, demonstrating the vital role of GTF2F2 in sepsis.

PCNA gene encodes a proliferating cell nuclear antigen associated with the cell cycle. We identified that the PCNA gene was downregulated in sepsis samples. Research of Hali et al. has demonstrated that PCNA immunolocalization can be used as an index of cell proliferation.⁴¹ Furthermore, Guo et al. identified that GO functions associated with PCNA were involved in negative regulation of cell proliferation.⁴² Therefore, downregulated expression of PCNA gene in sepsis suggested PCNA might be an important gene involving in sepsis. SMAD4 gene encodes a protein of SMAD family member 4 which is activated by transmembrane serine-threonine receptor kinases as TGF-β1. In this study, we indicated that the expression of SMAD4 gene was upregulated in sepsis, which was consistent with the findings of Hu et al.⁴³ SMAD4 is a mediator of signal transduction by TGF-β1, which is a potential anti-inflammatory cytokine in sepsis.⁴⁴ Therefore, we speculated that SMAD4 gene might play a role in sepsis through TGF-β1 signal transduction.

Four sub-networks were identified from the PPI network, and genes such as PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45 with relative high degrees were identified in these four sub-networks. Almost all the DEGs in the sub-networks participated in the decrease of cell activity. The PTBP1 gene encodes polypyrimidine tract-binding protein 1 which belongs to the subfamily of ubiquitously expressed heterogeneous nuclear ribonucleoproteins (hnRNPs). PTBP1 appears to influence pre-mRNA processing and other aspects of mRNA metabolism and transport, and downregulation of PTBP1 could inhibit BCL-2 like 11 (BIM) pre-splicing which is an attractive approach to modulate apoptosis.⁴⁵ Furthermore, it has been shown that dysregulated apoptosis leads to multiple diseases including sepsis.¹⁷ Our results showed that the expression of PTBP1 gene was upregulated in sepsis patients. Therefore, a new therapeutic target might be focused on gene PTBP1.

Gene GADD45 encodes growth arrest and DNA-damage-inducible protein 45. As shown before, increased apoptotic processes may play a determining role in the outcome of sepsis syndromes.³⁹ Evidence has demonstrated that the expression of gene GADD45 was upregulated during apoptosis,^46,47 which is similar with our results. Moreover, studies have shown that GADD45 play a role in apoptosis and sepsis may through the activation of the JNK (c-Jun N-terminal kinase) and/or p38 MAPK (mitogen-activated protein kinase) signaling pathways.^48,49 Therefore, gene GADD45 might be treated as a new target.

Genes PSMA3 (Proteasome Subunit Alpha 3), PSMA6 (Proteasome Subunit Alpha 6), PSMB9 (Proteasome Subunit Beta 9), and PSMB10 (Proteasome Subunit Beta 10) encode the 20S proteasome of humans.⁵⁰ Several studies have proved that sepsis stimulates breakdown of ubiquitin-dependent protein in skeletal muscle, and this could be caused by activation of an ubiquitin-proteasome pathway.^51,52 Hobler et al. further found that 20S proteasome was increased in skeletal muscle, and this supported the role of 20S proteasome in regulation of sepsis-induced muscle proteolysis.⁵³

After being analyzed by functional enrichment, the DEGs were mainly associated with BPs including cellular metabolic process, primary metabolic process, and response to organic cyclic compound. A lot of metabolic changes have been related with sepsis. Levy et al. demonstrated that mitochondrial dysfunction played an important role in sepsis-induced organ dysfunction because of inability of using molecular oxygen for adenosine triphosphate production caused by defect in oxidative phosphorylation.⁵⁴ Besides, Luiking et al. illustrated lower plasma arginine levels caused by the changes of metabolism of arginine were related with sepsis and supplement of arginine was beneficial for sepsis patients.⁵⁵ Furthermore, Wu et al. identified that decreased levels of high-density lipoproteins (HDLs) were related with sepsis and the magnitude of reduction was positively with the severity of the illness. They also demonstrated that HDL acted as potent anti-inflammatory effects and might be useful in the treatment of sepsis.⁵⁶

The corresponding MFs related to DEGs were catalytic activity, hydrolase activity, transferase activity, cytidylyltransferase activity, and receptor activity. Gene ADAM10 encodes metallopeptidase domain 10 protein which is cell surface protein associated with structure processing potential adhesion and protease domains. ADAM10 protein cleaves many proteins including TNF-α and soluble vascular endothelial (sVE)-cadherin. Flemming et al. demonstrated that sVE-cadherin cleaved by ADAM10 is a clinical diagnostic tool for detection of endothelial barrier disruption in septic patients.⁵⁷ Zhang et al. demonstrated that TGFB1, involving in protein binding, could promote inflammatory response by facilitating the formation of pro-inflammatory mediator TNF-α and inhibiting the expression of TLR4 (Toll-like receptors) and NF-κ.⁵⁸

Overall, GO enrichment analysis showed that the DEGs between sepsis and non-sepsis patients were mainly involved in cellular metabolic processes. After discussion, we speculated that genes such as PTBP1, PSMA3, PSMA6, PSMB9, PSMB10, and GADD45 with relative high degrees in the PPI network and sub-networks might be used as the new therapeutic targets for sepsis. However, all these results and assumptions have not been verified, thus more than one experimental verification is needed to prove the practical utility of these candidates.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Bioinformatics analysis of transcription profiling of sepsis

Abstract

Keywords

Introduction

Materials and methods

mRNA expression profile data

DEGs screening

Go enrichment analysis of DEGs

PPI network construction and module detection

Results

DEGs screening

Enrichment analysis of DEGs

PPI network construction and sub-network detection

Discussion

Footnotes

Declaration of conflicting interests

Funding

References