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Abstract

Multiple trauma can induce sepsis and organ failure, even threaten people’s lives. To further study the mechanisms of multiple trauma, we analyzed microarray of GSE5760. GSE5760 was downloaded from the Gene Expression Omnibus including a total of 58 peripheral blood transcriptome from patients without (WT, n = 30) and carrying (MUT, n = 28) the tumor necrosis factor (TNF) rs1800629 A variant. The differentially expressed genes (DEGs) were screened using the limma package in R and the Benjamin and Hochberg method in a multi-test package. Then, functional enrichment analysis of DEGs was performed. Also, transcription factors significantly related to DEGs were searched using WebGestalt and interaction network of transcription factors and DEGs were constructed using STRING online software. Furthermore, pathway enrichment analysis for the DEGs in the interaction network was conducted using KO-Based Annotation System (KOBAS). We screened 39 DEGs including 27 upregulated and 12 downregulated genes. The enriched functions were associated with biological process (BP) (such as response to hypoxia, P value = 0.039803), cell components (CC) (such as mitochondrial part, P value = 0.043857), and molecular function (MF) (such as structural constituent of ribosome, P value = 0.008735). Besides, RPS7 and RPL17 were associated with ribosome and participated in ribosome pathway. PPP2R2B was related to mitochondrion. KCNMA1, ALAS2 and SOCS3 were associated with hypoxia. Moreover, transcription factors of LEF1, CHX10, ELK1, SP1, and MAZ were significantly related to DEGs. RPS7, RPL17, PPP2R2B, KCNMA1, ALAS2, and SOCS3 might relate to multiple trauma. And TNF-α mutation could cause sepsis in patients with multiple trauma by changing the expression of these genes.

Keywords

differentially expressed genes multiple trauma TNF-α mutation

Introduction

Multiple trauma is a serious injury involving multi-sites and organs. Patients with multiple trauma are often suffering from bleeding, shock, and severe physiological disorders, some life-threatening.¹ The injuries can be induced by intentional causes (domestic violence, suicides, homicides, and war) and non-intentional ones (sports-related injuries, falls, traffic crashes, near-drowning, burns, and accidental poisoning).² More than 5 million deaths per year resulted from injuries,³ and about 50% of young children with non-intentional injuries are left with disabilities.⁴ Thus, it is very urgent to study the molecular mechanisms of multiple trauma and develop therapeutic schedules.

In multiple trauma, cytokines have many physiological and pathological roles. The major cause of late death after multiple trauma is organ failure, and sepsis is the main reason for organ failure.⁵ TNF-α is a cytokine produced by the macrophage and monocyte activation, and plays a key role in the inflammatory response, cell immunity, tumor immunity, and other physiological and pathological processes.^6,7 Through the regulation of granulocyte-macrophage colony-stimulating factor (GM-CSF), TNF-α synthesis is elevated in the blood of patients with multiple injuries or sepsis.⁸ Angiopoietin-2 (Ang-2) is regulated by proinflammatory stimuli and is associated with sepsis pathophysiology. Furthermore, serum TNF-α has a strong relationship with serum Ang-2 and it may play a role in the regulation of Ang-2 production in sepsis.⁹

Menges et al. analyzed serial blood samples from patients on the first day after trauma and during follow-up, patients with sepsis syndrome, and patients with a fatal outcome. They found that carriers of the TNF rs1800629 A allele have higher TNF-α serum concentrations, which are related to sepsis syndrome and death after severe injury.¹⁰ However, they neither have screened differentially expressed genes (DEGs), nor have conducted functional and pathway enrichment analyses, which can further study the mechanisms of how TNF-α mutation affects sepsis syndrome and death after severe injury. The molecular mechanisms of multiple trauma still remain unclear.

Using the data from Menges et al.,¹⁰ we screened the DEGs between samples with and without the TNF rs1800629 A variant and their potential functions were analyzed by Gene Ontology (GO) and pathway enrichment analysis. Also, transcription factors significantly related to DEGs were searched, and then the interaction relationships between transcription factors and DEGs were further investigated using network analysis. Key genes identified in this study might have some correlation with multiple trauma and sepsis. However, their potential therapeutic value still needed further validations.

Materials and methods

Microarray data

Expression profile of GSE5760 deposited by Menges et al.¹⁰ was downloaded from Gene Expression Omnibus (GEO, see http://www.ncbi.nlm.nih.gov/geo/). GSE5760 included a collective of 30 peripheral blood transcriptome from patients without the TNF rs1800629 A variant (WT, n = 30) and 28 peripheral blood transcriptome from patients carrying the TNF rs1800629 A variant (MUT, n = 28), which were collected from 12 patients without the TNF rs1800629 A variant and 10 patients carrying the TNF rs1800629 A variant.

DEGs screening

After GSE5760 was downloaded, we first transformed the probe numbers into the corresponding gene names. The average value of multiple probes mapped with one gene was obtained as ultimate gene expression value. Then, we conducted Log 2 conversion.¹¹ After that, the data were analyzed using the limma (linear models for microarray data) package in R language.¹² The Benjamini and Hochberg (BH) method in a multi-test package¹³ was used to adjust the raw P values into a false discovery rate (FDR),¹⁴ while, log fold-change (FC) was calculated. The FDR <0.05 and |logFC| >1 were used as the cutoff criteria.

Functional enrichment analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID), which is used for functional annotation analysis of large gene lists, consists of a comprehensive set of tools.¹⁵ Using the DAVID software, biological process (BP), molecular function (MF), and cell components (CC) enrichment analysis were performed for the DEGs. The P value <0.05 was used as the cutoff criterion.

Searching for transcription factors significantly related to DEGs

As a web-based integrated data mining system, the Web-based Gene Set Enrichment Analysis Toolkit (WebGestalt, see http://genereg.ornl.gov/webgestalt/) includes four modules: information retrieval, organization/visualization, statistics, and gene set management.¹⁶ The WebGestalt software was used to search transcription factors significantly related to DEGs. The P value <0.05 was used as the cutoff criterion.

Network analysis

The STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) online software¹⁷ was used to search interrelationships of all the DEGs. The cutoff criterion was set at 0.4, and the other parameters were set to the default values. Besides, combing with the interactions between the transcription factors and the DEGs, interaction network was constructed and visualized using the Cytoscape.¹⁸

Pathway enrichment analysis of DEGs in network

The KO-Based Annotation System (KOBAS) is a web-based platform for pathway identification and automated annotation.¹⁹ In this study, the KOBAS was used to conduct Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the DEGs in the interaction network. The cumulative hypergeometric distribution was used to perform statistical analysis. The P value <0.05 was used as the cutoff criterion.

Results

DEGs analysis

A total of 39 DEGs were screened, including 12 downregulated genes (e.g. suppressor of cytokine signaling 3 [SOCS3], thyroid hormone receptor, beta [THRB], complement component [3b/4b] receptor 1 CR1, and aminolevulinate, delta-, symthase 2 [ALAS2]) and 27 upregulated genes (e.g. ribosomal protein L17 [RPL17], spectrin repeat containing, nuclear envelope 2 [SYNE2], inversin [INVS], and ribosomal protein S7 [RPS7]) (Table 1). Moreover, there were more upregulated genes than downregulated genes.

Table 1.

The screened upregulated and downregulated genes.

	ID	Gene symbol	FDR	P value	Log FC	Chromosome location
Downregulated genes	1904	SOCS3	0.012294	0.000389	−1.2678	17q25.3
Downregulated genes	499	THRB	0.014299	0.000511	−1.2161	3p24.2
	4463	CR1	0.004991	7.73E-05	−1.20918	1q32
	4945	SELENBP1	0.044165	0.00317	−1.06225	1q21-q22
	1151	ALAS2	0.008394	0.000191	−1.05324	Xp11.21
	1430	OSBPL8	0.014299	0.000511	−1.05125	12q14
	4246	CYP26B1	0.005468	8.89E-05	−1.05037	2p13.3
	5893	TP53TG3	0.032388	0.00186	−1.046	16p13
	3386	CD47	0.002252	2.37E-05	−1.02803	3q13.1-q13.2
	3577	PGD	0.022152	0.000988	−1.02657	1p36.3-p36.13
	4312	SC4MOL	0.037658	0.00237	−1.02606	4q32-q34
	232	PGGT1B	0.00888	0.000214	−1.02344	5q22.3
Upregulated genes	8161	PPP2R2B	0.000879	5.84E-06	1.022279	5q31-q32
	5112	HSPC171	0.000461	1.79E-06	1.039284	16q22.1
	10315	ECH1	5.98E-05	7.7E-08	1.045919	19q13.1
	6727	MRPL42	0.000673	4.1E-06	1.057413	12q22
	8883	TBCE	4.08E-05	2.87E-08	1.061843	1q42.3
	3910	CLEC2B	0.002215	2.13E-05	1.069088	12p13-p12
	10409	MRPL1	0.004991	7.77E-05	1.07656	4q21.1
	7446	NOL7	5.52E-05	5.81E-08	1.076916	6p23
	6907	KCNMA1	0.039382	0.00259	1.077016	10q22.3
	3137	MCTS1	5.74E-06	2.69E-09	1.079365	Xq22-q24
	1143	AUH	0.000918	6.23E-06	1.085167	9q22.31
	6464	GGH	0.000247	7.31E-07	1.174049	8q12.3
	8422	UBE2V2	0.000481	2.08E-06	1.177649	8q11.21
	6363	HCFC2	9.66E-05	1.89E-07	1.203536	12q23.3
	4799	FLJ42562	9.66E-05	1.83E-07	1.216401	2p16.1
	6911	RPP38	0.000598	2.98E-06	1.227402	10p13
	6521	CPSF6	0.010989	0.000326	1.23909	12q15
	7307	SLC39A8	0.002244	2.18E-05	1.324429	4q22-q24
	10395	TFB1M	0.002248	0.000023	1.407495	6q25.1-q25.3
	3524	CETN3	0.002846	3.33E-05	1.419168	5q14.3
	1516	PFDN4	0.000673	3.99E-06	1.441344	20q13.2
	10078	RPL17	0.000461	1.89E-06	1.443268	18q21
	7484	USP16	0.001975	1.83E-05	1.481331	21q22.11
	6492	MYOM2	0.008931	0.000219	1.709715	8p23.3
	1314	INVS	0.043083	0.00303	1.726591	9q31
	9306	RPS7	4.73E-06	1.66E-09	1.771533	2p25
	840	SYNE2	0.00816	0.000178	1.920928	14q23.2

Functional enrichment analysis

DAVID was used for functional enrichment analysis of the DEGs. The enriched functions were listed in Table 2. We enriched four functions in BP category, including RNA processing (P value= 0.038809), response to hypoxia (P value = 0.039803), translation (P value = 0.042681), and response to oxygen levels (P value = 0.043641). Functions related to CC included ribosomal subunit (P value = 0.002963), non-membrane-bounded organelle (P value = 0.007812), and mitochondrial part (P value = 0.043857). Functions associated with MF included structural constituent of ribosome (P value = 0.008735) and enoyl-CoA hydratase activity (P value = 0.015157).

Table 2.

The enriched gene ontology (GO) terms for the DEGs.

Name		Gene number	P value	Gene symbol
Biological process
	RNA processing	5	0.038809	RPP38, MRPL1, CPSF6, TFB1M, RPS7
	Response to hypoxia	3	0.039803	KCNMA1, ALAS2, SOCS3
	Translation	4	0.042681	MRPL1, RPL17, MRPL42, RPS7
	Response to oxygen levels	3	0.043641	KCNMA1, ALAS2, SOCS3
Cellular component
	Ribosomal subunit	4	0.002963	MRPL1, RPL17, MRPL42, RPS7
	Ribonucleoprotein complex	6	0.005546	RPP38, MRPL1, RPL17, MRPL42, CPSF6, RPS7
	Intracellular non-membrane-bounded organelle	13	0.007812	RPP38, RPL17, MRPL1, MRPL42, NOL7, CETN3, TBCE, RPS7, SYNE2, MYOM2, INVS, TFB1M, PPP2R2B
	Non-membrane-bounded organelle	13	0.007812	RPP38, RPL17, MRPL1, MRPL42, NOL7, CETN3, TBCE, RPS7, SYNE2, MYOM2, INVS, TFB1M, PPP2R2B
	Ribosome	4	0.012427	MRPL1, RPL17, MRPL42, RPS7
	Mitochondrial lumen	4	0.014373	MRPL1, MRPL42, ALAS2, TFB1M
	Mitochondrial matrix	4	0.014373	MRPL1, MRPL42, ALAS2, TFB1M
	Mitochondrion	7	0.0325	MRPL1, MRPL42, ALAS2, ECH1, PPP2R2B, TFB1M, AUH
	Mitochondrial part	5	0.043857	MRPL1, MRPL42, ALAS2, PPP2R2B, TFB1M
Molecular function
	Structural constituent of ribosome	4	0.008735	MRPL1, RPL17, MRPL42, RPS7
	Enoyl-CoA hydratase activity	2	0.015157	ECH1, AUH

Some of the DEGs we obtained were associated with the function and structure of ribosome, especially the upregulated ribosome protein RPS7 and RPL17 (such as translation, ribosomal subunit, and ribonucleoprotein). Meanwhile, large conductance calcium-activated potassium channel alpha subunit (KCNMA1), ALAS2, and SOCS3 were related to the functions of response to hypoxia and response to oxygen levels. Also, phosphatase 2 regulatory subunit B-beta isoform (PPP2R2B) was associated with functions associated with CC, such as non-membrane-bounded organelle and mitochondrial part. Moreover, enoyl-CoA hydratase 1, peroxisomal (ECH1) was related to the functions of mitochondrion and enoyl-CoA hydratase activity.

Searching for transcription factors and network analysis

We obtained five transcription factors significantly related to DEGs: lymphoid enhancer-binding factor 1 (LEF1), cation/H(+) antiporter 10 (CHX10), ELK1, member of ETS oncogene family (ELK1), Sp1 transcription factor (SP1), and MYC-associated zinc finger protein (MAZ) (Table 3). Using the STRING online software, we searched inter-relationships of all the DEGs, combing the interactions between the transcription factors and the DEGs, and the interaction network was constructed and visualized using the Cytoscape (Figure 1). In total, we obtained 17 interactions among the DEGs. There were 32 nodes and 51 edges in the network. Also, KCNMA1 was regulated by SP1 and MAZ. Moreover, ECH1 and PPP2R2B, PPP2R2B and RPL17, RPL17 and RPS7 had inter-relationships, respectively. In addition, PPP2R2B were targeted by LEF1, CHX10, SP1, and MAZ.

Table 3.

The transcription factors significantly related to DEGs.

TF	ID	Parameters	Targeted genes
hsa_CTTTGA_V$LEF1	DB_ID:2427	O = 8;r awP = 5.58e-06; adjP = 4.46e-05	MRPL1, PPP2R2B, HCFC2, OSBPL8, INVS, SLC39A8, SYNE2, MCTS1
hsa_TAATTA_V$CHX10	DB_ID:2408	O = 6; rawP = 4.75e-05; adjP = 0.0001	PPP2R2B, OSBPL8, INVS, KCNMA1, SYNE2, MCTS1
hsa_SCGGAAGY_V$ELK1	DB_ID:2413	O = 7; rawP = 4.60e-05; adjP = 0.0001	CETN3, UBE2V2, NOL7, INVS, TBCE, USP16, MCTS1
hsa_GGGCGGR_V$SP1	DB_ID:2452	O = 7; rawP = 0.0090; adjP = 0.0115	CYP26B1, MRPL1, PPP2R2B, HCFC2, ECH1, KCNMA1, MCTS1
hsa_GGGAGGRR_V$MAZ	DB_ID:2430	O = 6; rawP = 0.0101; adjP = 0.0115	THRB, PPP2R2B, HCFC2, CD47, KCNMA1, SYNE2

adjP: adjusted P value O: observed DEGs number; rawP: original P value.

Figure 1.

Interaction network of transcription factors and their corresponding DEGs. Circles and squares indicate the up- and downregulated genes, respectively. Triangles represent five transcription factors significantly related to DEGs.

Pathway enrichment analysis of DEGs in the network

By the KOBAS, we conduct KEGG pathway enrichment analysis for the DEGs in the interaction network. We obtained a ribosome pathway (FDR = 0.0029), which involved two DEGs (RPS7 and RPL17). Moreover, the two genes were both upregulated genes (Figure 2).

Figure 2.

RPS7 and RPL17 expression in samples. The horizontal axis represents the two genes, the vertical axis shows the ratio MUT/WT. MUT: peripheral blood transcriptome from patients carrying the TNF rs1800629 A variant; WT: peripheral blood transcriptome from patients without the TNF rs1800629 A variant.

Discussion

In this study, we screened a total of 39 DEGs, in which there were 12 downregulated genes and 27 upregulated genes. The functional annotations for BP, CC, and MF suggested that the DEGs we obtained were associated with the function and structure of ribosome (such as RNA processing, ribosomal subunit, and structural constituent of ribosome). In particular, the upregulated ribosome proteins RPS7 and RPL17 not only had functions associated with ribosome (such as translation, ribosomal subunit, and ribonucleoprotein), but also participated in the ribosome pathway. Thus, TNF-α mutation caused significant expression changes of genes associated with ribosome functions. Besides, the main function of ribosome is synthesis of proteins, which is the nature of antibody involved in immune response.²⁰ What’s more, patients with multiple trauma may have inflammatory disorders subsequently.²¹ A critical cause of morbidity and mortality in patients with multiple trauma is infection,²² so RPS7 and RPL17 might be associated with occurrence and aggravation of sepsis in multiple trauma patients. Also, we screened five transcription factors (LEF1, CHX10, ELK1, SP1, and MAZ) significantly related to DEGs.

Besides, RPS7 and RPL17, and RPL17 and PPP2R2B had inter-relationships, respectively. PPP2R2B were targeted by LEF1, CHX10, SP1, and MAZ. The glycine 90 to aspartate alteration in the PPP2R1B causes a deficit in protein function.²³ In this study, PPP2R2B was associated with functions of mitochondrion and non-membrane-bounded organelle (which includes ribosome). Also, PPP2R2B and ECH1 had inter-relationships. Also, ECH1 was related to the functions of mitochondrion and enoyl-CoA hydratase activity. By constantly monitoring intracellular protons, calcium ions, redox stare, nitric oxide, reactive oxygen species, and gate potential to regulate electron transfer activity, mitochondria occupy the primacy position in energy production and metabolism.^24,25 In patients with trauma and shock, mitochondria can open pores that leak contents into the host cell’s cytoplasm and trigger necrosis or programmed cell death.²⁶ Therefore, PPP2R2B might have correlation with multiple trauma.

Additionally, KCNMA1, ALAS2, and SOCS3 were related to functions associated with response to hypoxia and response to oxygen levels. In survivors of severe trauma, increased oxygen delivery index, oxygen consumption index, and cardiac index are primary compensations that have survival value and augmentation of these compensations can decrease mortality.²⁷ Oxygen consumption and reduction of oxygen delivery to below a critical level produces ischemic metabolic insufficiency and then causes increasing of lactate generation.²⁸ Furthermore, blood lactate levels are tightly related to outcome in critically ill trauma patients.²⁹ Thus, KCNMA1, ALAS2, and SOCS3 might have correlation with multiple trauma. Moreover, we speculated that TNF-α mutation could cause sepsis in patients with multiple trauma by changing the expression of these genes.

In conclusion, we performed a comprehensive bioinformatics analysis of genes which may have relation to multiple trauma. We screened a total of 39 DEGs. Besides, RPS7, RPL17, PPP2R2B, KCNMA1, ALAS2, and SOCS3 might have correlation with multiple trauma and be related to occurrence and aggravation of sepsis in multiple trauma patients. Moreover, TNF-α mutation could cause sepsis in patients with multiple trauma by changing the expression of these genes.

Footnotes

Authors’ contributions

GL, XL, and YL participated in the design of this study. ZL and QL both performed the statistical analysis. GC, GL, and NH carried out the study, and together with WW, collected important background information, and drafted the manuscript. YW, YC, and GS conceived of this study, participated in the design, and helped to draft the manuscript. All authors read and approved the final manuscript.

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 study was supported by Shanghai medical key subject construction project (No. ZK2012A28) and Key National Clinical Discipline construction project and Outstanding young medical talents in Pudong district (PWRq2012-13).

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TNF-α mutation affects the gene expression profiles of patients with multiple trauma

Abstract

Keywords

Introduction

Materials and methods

Microarray data

DEGs screening

Functional enrichment analysis

Searching for transcription factors significantly related to DEGs

Network analysis

Pathway enrichment analysis of DEGs in network

Results

DEGs analysis

Functional enrichment analysis

Searching for transcription factors and network analysis

Pathway enrichment analysis of DEGs in the network

Discussion

Footnotes

Authors’ contributions

Declaration of conflicting interests

Funding

References