miR‑148 family members are putative biomarkers for sepsis.

Sepsis is a type of systemic inflammatory response caused by infection. The present study aimed to identify novel targets for the treatment of sepsis. We conducted bioinformatic analysis of the microarray Gene Expression Omnibus dataset GSE12624, which includes data on 34 patients with sepsis and 36 healthy individuals without sepsis. Differentially expressed genes (DEGs) in sepsis patients were identified using Bayesian methods included in the limma package in R. Correlations among the expression values of DEGs were analyzed using the weighted gene co‑expression network analysis (WGCNA) to construct a co‑expression network. Subsequently, the generated co‑expression network was visualized using Cytoscape 3.3 software. Additionally, a protein‑protein interaction (PPI) network was constructed based on all the DEGs using STRING. Finally, the integrated regulatory network was constructed based on DEGs, microRNAs (miRNAs) and transcription factors (TFs). A total of 407 DEGs were identified in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. WGCNA grouped the DEGs into 13 co‑expressed modules. Additionally, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the PPI network comprised 48 nodes and 112 edges, which included the pairs MAP3K8‑RPS6KA5, MAP3K8‑IL10, RPS6KA5‑EXOSC4 and EXOSC4‑EXOSC5. Lastly, the TF‑miRNA‑target DEG regulatory network was constructed based on eight TFs (NF‑κB), seven miRNAs (miR152, miR‑148A/B), and 52 TF‑miRNA‑target gene triplets (17 upregulated genes, including MAP3K8, and 10 downregulated genes, including RPS6KA5). Our analysis showed that the members of the miR‑148 family (miR‑148A/B and miR‑152) are candidate biomarkers for sepsis.

Introduction

Sepsis is a type of systemic inflammatory response syndrome (SIRS) and is mediated by an immune response triggered by infection, which can progress from sepsis to severe sepsis and septic shock (1). Sepsis can lead to symptoms, including fever, increased heart rate, breathing rate and confusion (2). In 2015, the incidence rates of sepsis and severe sepsis in high-income countries were 0.44 and 0.27%, respectively (3). Meanwhile, the mortality rates for sepsis were reported to be 3 and 75 cases per 1,000 individuals in two Chinese military hospitals (4). Sepsis remains difficult to predict, diagnose and treat (5). Thus, there is an urgent need to identify target genes and microRNAs (miRNAs) that can seve as biomarkers for sepsis.

Previous studies have demonstrated that proinflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), are key mediators of inflammation during sepsis (6). A previous study showed that IL-6, IL-1β, IL-8 and TNF-α levels were significantly upregulated in culture-proven sepsis groups relative to those in the control groups (7). Multiple studies have reported that target genes and miRNAs are involved in sepsis. For example, Nrf2 is a basic leucine zipper transcription factor (TF) that mediates the response to lipopolysaccharides (LPS) and TNF-α by activating NF-κB production during experimental sepsis (8). In addition, procalcitonin (PCT) is elevated in patients with SIRS, and has been approved by the Food and Drug Administration (U.S. FDA) for the assessment of risk for developing severe sepsis in patients (9). Although PCT is closely associated with inflammation, there are some limitations specific for infection resulting in questionable efficacy as PCT can also be increased in noninfectious disease conditions (10). Generally, the concentration value of PCT <0.5 ng/ml indicates a low risk while values of 0.5–2.0 ng/ml represent an intermediate likelihood of sepsis and/or septic shock. Wacker et al reported (11) that PCT had a modest diagnostic performance with 77% sensitivity and 79% specificity. Therefore, PCT is not specific for diagnosis in patients with values in the intermediate range. Importantly, multiple miRNAs have various biological functions in inflammation, metabolism and tumor progression. These candidate miRNAs show high accuracy and sensitivity, and are expected to be ideal biomarkers for sepsis. Evidence suggests that the sensitivity and specificity of miR-223 for predicting the occurrence of sepsis after urinary operation were higher than those of PCT (12,13). In addition, miR-155 has been suggested to directly target key genes that are involved in LPS signaling, such as Fas-associated death domain protein, IκB kinase ε, and the receptor (TNFR superfamily)-interacting serine-threonine kinase 1 to enhance TNF-α production (14). Nevertheless, miR-125b targets the 3′-untranslated region of the TNF-α transcript (14). However, the fundamental mechanisms underlying the pathogenesis of sepsis remain unclear. Multiple mechanisms involving complex systemic inflammation networks, genetic polymorphisms, immune dysfunction, abnormal coagulant function, and host response to pathogenic microorganisms and their toxins are likely to be involved in sepsis. Therefore, the pathogenesis of sepsis warrants further investigation.

In the present study, we performed bioinformatics analysis to identify the differentially expressed genes (DEGs) in sepsis, as well as the TFs and miRNAs of these DEGs. Subsequently, an integrated regulatory network was constructed based on the DEGs, miRNAs and TFs. Finally, we investigated the interactions among the DEGs and TFs/miRNAs and their corresponding functions. Our current findings provided insights into the pathogenesis of sepsis and identified novel targets for the treatment of sepsis.

Materials and methods

Microarray data

The GSE12624 dataset was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo/) and contains gene expression data of 34 sepsis patients and 36 healthy individuals without sepsis. The inclusion criteria for the study are described in (15). The microarray platform was GPL4204 GE Healthcare/Amersham Biosciences CodeLink UniSet Human I Bioarray. Raw data were available in TXT format.

Data preprocessing and identification of DEGs

The probes corresponded to gene symbols according to the latest annotation file from the NCBI gene database. When more than one probe corresponded to the same gene symbol, the expression level of the gene was calculated as the median of the two expression values. Subsequently, the data were fitted to a log-normal distribution using the log2 function, normalized using the median function, and compared with septic samples and non-septic samples using Bayesian methods from the limma package in R (Linear Models for Microarray Data, http://www.bioconductor.org/packages/release/bioc/html/limma.html). Finally, |log fold change (FC)| >0.585 and adjusted P-value <0.05 were used as the threshold values for considering the DEGs.

Identification of sepsis-related genes and modules based on WGCNA

WGCNA is a systematic method for identifying putative target genes involved in a disease. It is used to describe the correlation among genes by finding significant modules from high-throughput sequencing data (16). In the present study, WGCNA was performed based on the following analysis workflow. i) The correlations among the expression values of DEGs in the dataset were determined. A higher correlation value indicates higher consistency of gene expression in each dataset, which is a prerequisite for the construction of a WGCNA network. ii) The correlation matrix of gene co-expression values was constructed based on Smn = |cor(m,n)|, where Smn indicates the correlation coefficient of co-expression patterns between genes m and n. iii) The adjacency is defined as amn = power(Smn,β), which measures the pairwise correlation between the expression levels of two genes. iv) Adjacency functions for both weighted and unweighted networks require the user to choose threshold parameters. The threshold of ≥0.9 was considered for the correlation coefficient between log2 k (node count) and log2 p(k) (frequency of node). v) The correlation matrix Smn was transformed to the adjacency matrix amn. Afterwards, the adjacency matrix amn was transformed to a topological matrix using the following equation:

where lmn indicates the sum of adjacency coefficient of the common edge between genes m and n and km indicates sum of connection strengths of m with the other network genes. vi) Gene significance (GS) measures were used to incorporate external information into the co-expression network. Module significance was determined by calculating the average |GS| for all genes in a module.

Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the key modules

The Database for Annotation, Visualization and Integration Discovery (DAVID, a public high-throughput functional annotation tool (version 6.8, http://david-d.ncifcrf.gov/) is an online bioinformatics tool that can be used for functional annotation and microarray analysis by integrating data mining environments and analyzing gene lists (17). DEGs in the modules were used as input for DAVID, and GO and KEGG enrichment analyses were conducted using MEblue and MEyellow DEGs. P-value <0.05 and the enriched gene count ≥2 were considered significant.

Construction of the PPI network and module analysis

The PPI network was constructed based on all the DEGs using STRING from a well-known online server (version 10.0, http://www.string-db.org/) (18). A combined score of >0.4 was defined as the threshold value for constructing the PPI network. The PPI network was visualized using Cytoscape software (version 3.2.0, http://cytoscape.org/) (19). In addition, MCODE (version 1.4.2, http://apps.cytoscape.org/apps/MCODE) in Cytoscape software was used to analyze the most significant module, with the threshold value of 5 (20).

Construction of the TF-miRNA-target DEGs regulatory network

The miRNA-target DEGs and TF-target DEGs were predicted using Overrepresentation Enrichment Analysis enrichment method in WebGestal (http://www.webgestalt.org/). Gene pairs with P-value <0.05 were integrated into the TF-miRNA-target DEGs regulatory network, which was visualized using Cytoscape.

Results

Sepsis-related genes and modules

A total of 407 DEGs, including 227 upregulated DEGs and 180 downregulated DEGs, were identified. According to the standard scale-free network model, the power value was set to 12 when the square of correlation coefficient was 0.9 (Fig. 1). The network conformed to a scale-free model when the square of the correlation coefficient square was set to the highest value. Subsequently, the WGCNA network was constructed under power = 12. Gene cluster dendrogram was obtained according to dissTOM using the hierarchical clustering method. The dynamic tree cut method was employed to estimate the number of clusters in the dataset. Finally, the DEGs were divided into 13 co-expressed modules (Fig. 2), and genes in the grey module contained genes that could not clustered under the other modules. Subsequently, the most highly connected intramodular hub gene in each module was considered as the module representative. The analysis identified a total of 7 modules with correlation coefficients >0.5. The correlation coefficients of the MEblue and MEyellow modules were higher than 0.6. To ensure the reliability of the key network module, the |GS| was used to further identify two key modules (Fig. 3). Finally, the MEblue (Fig. 3A) and MEyellow (Fig. 3B) modules were defined as the key modules. All of DEGs in the two modules are shown in the Table I.

Figure 1.

Power values (weighted threshold parameters) of the adjacency matrix. x-axis indicates the power value; y-axis indicates the square of the correlation coefficient between log2 k (node count) and log2 p(k) (frequency of node). The network approaches a scale-free network model as the square of correlation coefficient is increased. The red line indicates a square of correlation coefficient of 0.9.

Figure 2.

The 13 sepsis-related co-expressed modules after weighted gene co-expression network analysis. The horizontal axis represents each different color module; the vertical axis represents the correlation coefficient between genes in each module and disease status.

Figure 3.

The two key modules [(A) MEblue and (B) MEyellow] were identified by calculating |Gene significance (GS)| values.

Table I.

Differentially expressed genes in the MEblue and MEyellow modules.

Genes	Module	Description	Genes	Module	Description
ACN9	blue	up	ADAMTS5	yellow	down
ANPEP	blue	down	CALCRL	yellow	up
CA4	blue	up	DGKG	yellow	down
CDADC1	blue	up	DHRS9	yellow	up
COQ2	blue	up	ERLIN1	yellow	up
CPNE5	blue	up	FAM105A	yellow	up
CYP19A1	blue	up	FAR2	yellow	up
EFNB3	blue	up	GADD45A	yellow	up
EMR3	blue	down	HMGB2	yellow	up
EXOSC4	blue	up	IL18RAP	yellow	up
EXOSC5	blue	down	KLHL2	yellow	up
F8	blue	up	LEPROT	yellow	up
FOXN2	blue	up	LRPPRC	yellow	down
FZD6	blue	up	MAP3K8	yellow	up
HAS3	blue	up	NAALADL1	yellow	down
HPGD	blue	up	PECR	yellow	up
IDI1	blue	up	PROS1	yellow	up
IDNK	blue	up	RPS6KA5	yellow	down
IL10	blue	up	SAMSN1	yellow	up
IPO11	blue	up	SDPR	yellow	up
JUND	blue	up	SIPA1L2	yellow	up
LOC643641	blue	up	SP140	yellow	down
MREG	blue	up	SPIB	yellow	down
NOV	blue	down	UBE2H	yellow	up
OGN	blue	up	URGCP	yellow	down
PDK2	blue	down	USP46	yellow	down
PIDD	blue	down	VNN1	yellow	up
PLAU	blue	down
PNMAL1	blue	up
PPEF1	blue	up
PSTPIP2	blue	up
RABGEF1	blue	up
RCC1	blue	up
SERPINB1	blue	up
SERPING1	blue	down
SLC25A1	blue	down
SLC51A	blue	up
SPON2	blue	down
STARD10	blue	down

Up, upregulated; down, downregulated.

GO function and KEGG pathway analysis

A total of 66 DEGs were identified in the MEblue and MEyellow modules, including 46 upregulated DEGs and 20 downregulated DEGs. F8, PLAU and SERPING1 in the MEblue module were enriched with complement and coagulation cascades. EXOSC4 and EXOSC5 in the MEblue module were enriched in the RNA degradation pathway. MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. The GO functions of the genes in the two modules are shown in Table II.

Table II.

Gene Ontology functions for genes in the two modules.

Module	GO-ID-Name	Count	P-value	Genes
MEblue
GO_BP	GO:0007596~blood coagulation	3	1.75E-02	F8, SERPING1, PLAU
	GO:0050817~coagulation	3	1.75E-02	F8, SERPING1, PLAU
	GO:0007599~hemostasis	3	1.95E-02	F8, SERPING1, PLAU
	GO:0050878~regulation of body fluid levels	3	3.19E-02	F8, SERPING1, PLAU
	GO:0008299~isoprenoid biosynthetic process	2	3.92E-02	COQ2, IDI1
	GO:0032101~regulation of response to external stimulus	3	3.98E-02	SERPING1, IL10, PLAU
	GO:0045861~negative regulation of proteolysis	2	4.30E-02	SERPING1, IL10
GO_CC	GO:0031983~vesicle lumen	3	4.84E-03	F8, ANPEP, SERPING1
	GO:0044421~extracellular region part	7	1.88E-02	NOV, OGN, F8, SERPING1, EMR3, SPON2, IL10
	GO:0000178~exosome (RNase complex)	2	2.69E-02	EXOSC4, EXOSC5
GO_MF	GO:0000175~3′-5′-exoribonuclease activity	2	2.74E-02	EXOSC4, EXOSC5
	GO:0004532~exoribonuclease activity	2	2.96E-02	EXOSC4, EXOSC5
	GO:0016896~exoribonuclease activity, producing 5′-phosphomonoesters	2	2.96E-02	EXOSC4, EXOSC5
	GO:0016796~exonuclease activity, active with either ribo- or deoxyribonucleic acids and producing 5′-phosphomonoesters	2	4.52E-02	EXOSC4, EXOSC5
MEyellow
GO_BP	GO:0006508~proteolysis	6	1.64E-02	RPS6KA5, USP46, ERLIN1, UBE2H, NAALADL1, ADAMTS5
	GO:0006511~ubiquitin-dependent protein catabolic process	3	4.90E-02	USP46, ERLIN1, UBE2H
GO_MF	GO:0008237~metallopeptidase activity	3	2.89E-02	RPS6KA5, NAALADL1, ADAMTS5
	GO:0070011~peptidase activity, acting on L-amino acid peptides	4	4.40E-02	RPS6KA5, USP46, NAALADL1, ADAMTS5
	GO:0008233~peptidase activity	4	4.92E-02	RPS6KA5, USP46, NAALADL1, ADAMTS5

GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function.

PPI network based on the MEblue and MEyellow modules

The PPI network (Fig. 4) contained 48 nodes (genes) and 112 edges (protein-protein interrelations), such as MAP3K8-RPS6KA5, MAP3K8-IL10, RPS6KA5-EXOSC4 and EXOSC4-EXOSC5). One sub-network (hub module) had an MCODE score ≥5 and comprised 7 nodes (e.g. IL10) and 15 edges (Fig. 4).

Figure 4.

The protein-protein interaction (PPI) network based on the differentially expressed genes (DEGs) in the MEblue and MEyellow modules. Triangles indicate upregulated DEGs, and arrows indicate downregulated DEGs. Yellow and blue colors indicate genes in the MEyellow and MEblue modules, respectively.

miRNA-TF-target gene regulatory network

Overall, the analysis identified 8 TFs (NF-κB) and 7 miRNAs (miR152 and miR-148A/B), which comprised 52 TF-miRNA-target gene pairs (17 upregulated genes, such as MAP3K8 and 10 downregulated genes, such as RPS6KA5) and were used to construct an miRNA-TF-target gene regulatory network (Fig. 5).

Figure 5.

Constructed TF-miRNA-target DEG regulatory network. Triangles indicate upregulated DEGs; arrows indicate downregulated DEGs; hexagons indicate TFs; and the circles indicate miRNAs. Yellow and blue colors indicate genes in the MEyellow and MEblue modules, respectively. Arrows indicate the direction of regulation. TF, transcription factor; DEG, differentially expressed gene.

Discussion

In the present study, we identified a total of 407 DEGs in the sepsis samples, including 227 upregulated DEGs and 180 downregulated DEGs. Subsequently, these DEGs were grouped into 13 co-expressed modules after WGCNA. Additionally, MEblue and MEyellow modules with a correlation coefficient >0.6 were defined as the key modules; these modules included 6 upregulated and 20 downregulated DEGs. EXOSC4 and EXOSC5 in the MEblue module were enriched in the RNA degradation pathway. MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, the resulting PPI network comprised 48 nodes and 112 edges such as MAP3K8-RPS6KA5, MAP3K8-IL10, RPS6KA5-EXOSC4 and EXOSC4-EXOSC5. Finally, the analysis identified 8 TFs (NF-κB) and 7 miRNAs (miR-152 and miR-148A/B) that corresponded to 52 TF-miRNA-target gene pairs (17 upregulated genes, such as MAP3K8 and 10 downregulated genes, such as RPS6KA5).

MAP3K8 is a serine-threonine kinase that plays a critical role in innate immunity and is known to induce tumor necrosis factor (TNF) production by activating ERK (21). In addition, TNF-α has been implicated as a key mediator in inflammation, morbidity and mortality associated with sepsis. TNF-α has been demonstrated to be responsible for the initial hypothermia and lethality in septic mice. (22). In addition, host reactions during sepsis, septic shock, and multiple organ failure are associated with increased TNF production in humans (23). TNF-α is a strong pro-inflammatory cytokine associated with septic patients and has been considered as a target for the treatment of sepsis (24). In the present study, MAP3K8 expression levels were found to be upregulated in sepsis samples relative to those of the control samples. As indicated above, MAP3K8 induces TNF production, consistent with increased TNF levels in the sepsis samples in the present study. Importantly, MAP3K8 and RPS6KA5 in the MEyellow module were enriched in the MAPK and TNF signaling pathways. In addition, MAP3K8 interacts with IL10 based on the constructed PPI network. IL10 is an anti-inflammatory agent that can improve disease outcome in the model of sepsis syndrome (25). Therefore, these findings indicate that MAP3K8 is involved in sepsis through the MAPK and TNF signaling pathways. The nuclear transcription factor NF-κB is known to be activated following hemorrhagic shock and sepsis (26). In the present study, NF-κB acts as the upstream TF of the MAP3K8 gene. Proinflammatory cytokines, such as TNF-α and IL-1, activate important signaling pathways. In particular, cytokines activate members of the NF-κB group of TFs, which play central roles in inflammation and innate immunity (27). Activation of NF-κB and other TFs involved in the innate immune/inflammatory response can upregulate the expression of various genes, such as MMP-9, VEGF and TNF (28). Therefore, MAP3K8 is potentially involved in sepsis through the activation of NF-κB and is likely to be involved in the MAPK and TNF signaling pathways.

In the PPI network, RPS6KA5 interacted with MAP3K8, and these two genes were enriched in the MAPK and TNF signaling pathways. RPS6KA5, also known as mitogen- and stress-activated protein kinase 1 (MSK1), is a downstream target of both p38 and ERK1/2 (29). RPS6KA5 stimulates the transcription of various pro-inflammatory genes, such as IL-6, IL-8 and TNF-α, by activating TFs (30). Therefore, RPS6KA5 was associated with sepsis through the MAPK and TNF signaling pathways. In the miRNA-TF-target gene regulatory network, RPS6KA5 was the target gene of miR-152, miR-148A and miR-148B. A previous study indicated that members of the miR-148 family (miR-148A, miR-148B and miR-152) negatively regulated antigen presentation and Toll-like receptor (TLR)-triggered cytokine secretion in dendritic cells (31). TLRs are a class of proteins that play key roles in the innate immune system and secrete proinflammatory cytokines, such as TNF-α, IL-6 and IL-12 (32,33). In addition, soluble TLR2 is a biomarker for sepsis in critically ill patients with multi-organ failure within 12 h of ICU admission (34). Although there was no direct evidence to identify that miR-148 is better than PCT or TLR2, miRNAs with high accuracy and sensitivity, are expected to be ideal biomarkers for sepsis (13). Thus, the receiver operating characteristic (ROC) curve of the miR-148 family (including sensitivity and specificity) should be compared with those of PCT or TLR2 in diagnostic performance of sepsis patients. It is one of the limitation of the present study. Therefore, members of the miR-148 family (miR-148A, miR-148B and miR-152) may be candidate biomarkers for sepsis.

The present study has certain limitations. First, limited samples were collected from the sepsis patients, and experimental validation of the results was not performed. PCR or western blotting will be performed in subsequent studies to verify the findings. In addition, experiments should be conducted to verify whether RPS6KA5 is a target of miR-148A/B and miR-152 in sepsis. In addition, the microarray dataset GSE12624 from the Gene Expression Omnibus only included 34 patients with sepsis and 36 healthy individuals without sepsis. Therefore, correlation among the miR-148 family (miR-148A/B and miR-152), and the type of infection was not performed. In addition, an ROC curve of the miR-148 family should be assayed in the diagnostic performance of sepsis patients. However, the present results will not be affected by these limitations.

Therefore, MAP3K8 is potentially induced during sepsis through NF-κB activation and is potentially involved in the MAPK and TNF signaling pathways. Meanwhile, RPS6KA5 interacted with MAP3K8 in the PPI network and was also found to be enriched in the MAPK and TNF signaling pathways. Members of the miR-148 family (miR-148A/B and miR-152) are candidate biomarkers for sepsis.