Skip to main content
PMC home page
3 Biotech logoLink to 3 Biotech
. 2017 Apr 25;7(1):46. doi: 10.1007/s13205-017-0713-x

Unveiling differentially expressed genes upon regulation of transcription factors in sepsis

Junli Zhang 1,#, Yuelei Cheng 1,#, Minmin Duan 2, Nannan Qi 2, Jian Liu 1,✉
PMCID: PMC5428098  PMID: 28444588

Abstract

In this study, we integrated the gene expression data of sepsis to reveal more precise genome-wide expression signature to shed light on the pathological mechanism of sepsis. Differentially expressed genes via integrating five microarray datasets from the Gene Expression Omnibus database were obtained. The gene function and involved pathways of differentially expressed genes (DEGs) were detected by GeneCodis3. Transcription factors (TFs) targeting top 20 dysregulated DEGs (including up- and downregulated genes) were found based on the TRANSFAC. A total of 1339 DEGs were detected including 788 upregulated and 551 downregulated genes. These genes were mostly involved in DNA-dependent transcription regulation, blood coagulation, and innate immune response, pathogenic escherichia coli infection, epithelial cell signaling in helicobacter pylori infection, and chemokine signaling pathway. TFs bioinformatic analysis of 20 DEGs generated 374 pairs of TF-target gene involving 47 TFs. At last, we found that five top ten upregulated DEGs (S100A8, S100A9, S100A12, PGLYRP1 and MMP9) and three downregulated DEGs (ZNF84, CYB561A3 and BST1) were under the regulation of three hub TFs of Pax-4, POU2F1, and Nkx2–5. The identified eight DEGs may be regarded as the diagnosis marker and drug target for sepsis.

Electronic supplementary material

The online version of this article (doi:10.1007/s13205-017-0713-x) contains supplementary material, which is available to authorized users.

Keywords: Sepsis, Differentially expressed gene, Transcription factor, Pathological mechanism

Introduction

Sepsis is a complex clinical syndrome induced by the interaction between the host and pathogens products, such as endotoxin and the host immune system (Brunkhorst and Reinhart 2009). Sepsis leads to various organ dysfunctions including acute renal, liver injury, and acute respiratory distress syndrome which is the main cause of increased mortality in patients (de Montmollin and Annane 2011). Although great process is performed in various researches, the pathological mechanism of sepsis remains unclear. Additionally, the incidence and mortality of sepsis are still high (Angus et al. 2001; Annane et al. 2003). Thus, discovery of important mediators involved in the process of sepsis is urgently needed.

Microarray strategies (Calvano et al. 2005) seem to be a powerful option for finding crucial mediators involving in disease of humans. Based on the superiority of large sample and reliable result, integration analysis of multiple microarrays may be useful to provide evidence for understanding the mechanism of sepsis. Additionally, the TFs and its target genes have been the study focus of system biology. The regulatory network between TFs and target genes is valuable in the field of biomedicine. Several TFs such as NF-κB, AP1, p53, PPAR, CREB, STAT, and E2F were closely related to some important diseases (inflammation and cancer) (Hoesel and Schmid 2013; Kesh et al. 2015; Lopez-Bergami et al. 2010; Mullen and Gonzalez-Perez 2016; Pal et al. 2014; Ramana et al. 2010; Wang and DuBois 2010). This may provide the drug target for different diseases.

In this study, we conducted an integrated analysis of sepsis microarray data, and identified differentially expressed genes (DEGs) between sepsis and normal control samples. Moreover, we also obtained significantly enriched functions of these DEGs to reveal the biological processes and signaling pathways associated with sepsis. Finally, some transcription factors (TFs) that targeted top 20 DEGs were found. We hope that this integrated analysis may provide additional understanding of sepsis that was helpful to explore novel diagnostic marker of sepsis and drug targets for future therapeutic tests.

Materials and methods

Microarray data

In this study, we searched datasets from the GEO (http://www.ncbi.nlm.nih.gov/geo/) database with the keywords “sepsis” [MeSH Terms] or sepsis [All Fields] and “Homo sapiens” [porgn] AND “gse” [Filter]. The study type was described as “expression profiling by array.” All selected datasets were genome-wide expression sequencing data in blood of sepsis group and/or normal group (no drug stimulation or being transfected treatment) samples. And these datasets were standardized and original, and were downloaded by R (3.2.1) GEO query. Finally, there were five datasets obtained in this study.

Detection of DEGs and meta-analysis

Limmapackage (3.2.1) (Diboun et al. 2006) and Meta-MA package were used to identify the DEGs between sepsis and normal group. Limma is used to calculate the P value and FDR value of one gene in one dataset. The metaMA is applied to combine the P values of one gene in different datasets by inverse normal method. The gene with FDR < 0.05 was deemed to indicate a DEG.

Functional and biological pathway analysis of DEGs

In order to investigate the functional changes of DEGs, the gene function annotation analysis tool GeneCodis3 (http://genecodis.cnb.csic.es/analysis/) (Tabas-Madrid et al. 2012) was used to find the biological meaning of groups of genes, including gene ontology (GO) categories (Young et al. 2010) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation (Kanehisa and Goto 2000).

Analysis of potential TFs to target DEGs

TFs play an important role in regulating gene expression. We downloaded the TFs in the human genome and the motifs of genomic binding sites from the TRANSFAC (Wingender 2008). Moreover, the 2 KB sequence in the upstream promoter region of DEGs was downloaded from UCSC (http://www.genome.ucsc.edu/cgi-bin/hgTables). Target TFs were subsequently obtained by the method of minimize false-positive matches. Finally, the transcriptional regulatory network was visualized by cytoscape software (Smoot et al. 2011).

Results

Identification of DEGs in sepsis

We downloaded five gene expression datasets of sepsis from microarray experiments. GEO IDs were GSE69528, GSE67652, GSE57065, GSE26378, and GSE26440. In summary, there were 293 sepsis samples and 118 normal samples, respectively. The characteristics of these five datasets are provided in Table 1. Based on the integrated analysis of five microarray datasets, 8332 genes were identified. A total of 1339 DEGs were screened under the statistical significance threshold of FDR < 0.05. Among which, there were 788 upregulated and 551 downregulated DEGs. The heat map of top 100 DEGs is shown in Fig. 1. And the heat map of sample clustering is shown in supplementary Fig. 1. The top ten upregulated or downregulated DEGs between sepsis and normal sample are shown in Table 2.

Table 1.

Characteristics of the five microarray datasets for integrated analysis

GEO ID Platform Gene number Sample count (N:P) Notes
GSE69528 GPL10558Illumina humanht-12 V4.0 expression beadchip 18939 101 (28:73) Cazalis et al. (2014)
GSE67652 GPL16699Agilent-039494 sureprint G3 human GE v2 8 × 60 K microarray 039381 (feature number version) 62976 24 (12:12) Pellegrina et al. (2015)
GSE57065 GPL570[HG-U133_Plus_2] affymetrix human genome U133 plus 2.0 array 54675 53 (25:28) Speake et al. (2015)
GSE26378 GPL570[HG-U133_Plus_2] affymetrix human genome U133 plus 2.0 array 54675 103 (21:82) Standage and Wong (2011)
GSE26440 GPL570[HG-U133_Plus_2] affymetrix human genome U133 plus 2.0 array 54675 130 (32:98) Standage and Wong (2011)
Open in a new tab

Fig. 1.

Fig. 1

Open in a new tab

Heat map of top 100 DEGs that were significantly upregulated or downregulated (P value < 0.05) in sepsis compared to relatively healthy controls

Table 2.

Top 20 upregulated or downregulated DEGs between sepsis and normal controls

ID Symbol Combined.ES P value FDR Regulation
6283 S100A12 3.77042 0.00000 0.00000 Up
6280 S100A9 3.02895 0.00000 0.00000 Up
8291 DYSF 2.66283 0.00000 0.00000 Up
6279 S100A8 2.62050 0.00000 0.00000 Up
266747 RGL4 2.52765 0.00000 0.00000 Up
683 BST1 2.52574 0.00000 0.00000 Up
8993 PGLYRP1 2.50512 0.00000 0.00000 Up
4318 MMP9 2.49083 0.00000 0.00000 Up
7295 TXN 2.35942 0.00000 0.00000 Up
116844 LRG1 2.28872 0.00000 0.00000 Up
221061 FAM171A1 −2.00105 0.00000 0.00000 Down
25875 LETMD1 −1.90124 0.00000 0.00000 Down
79673 ZNF329 −1.86837 0.00000 0.00000 Down
7637 ZNF84 −1.81614 0.00000 0.00000 Down
10194 TSHZ1 −1.77981 0.00000 0.00000 Down
220002 CYB561A3 −1.77349 0.00000 0.00000 Down
56916 SMARCAD1 −1.73668 0.00000 0.00000 Down
4216 MAP3K4 −1.70413 0.00000 0.00000 Down
25894 PLEKHG4 −1.66204 0.00000 0.00000 Down
55114 ARHGAP17 −1.63496 0.00000 0.00000 Down
Open in a new tab

GO enrichment and KEGG signaling pathway analysis of DEGs

Among 1339 DEGs, a total of 1243 DEGs were recognized in GO and KEGG signaling pathway enrichment analysis. GO enrichment analysis showed that these DEGs were significantly enriched in the apoptotic process (GO: 0006915, FDR = 4.78E−14), mitotic cell cycle (GO: 0000278, FDR = 4.44E−09), regulation of transcription depending on DNA (GO: 0006355, FDR = 4.51E−09), blood coagulation (GO: 0007596, FDR = 3.80E−07), innate immune response (GO: 0045087, FDR = 6.18E−07) and so on under the biological process category. The top fifteen significantly enriched GO functions of DEGs are listed in Table 3. Furthermore, KEGG pathway enrichment analysis indicated that these DEGs were significantly involved in huntington’s disease (FDR = 9.50E−09), alzheimer’s disease (FDR = 1.01E−08), pathogenic escherichia coli infection (FDR = 1.61E−07), phagosome (FDR = 1.74E−07), epithelial cell signaling in helicobacter pylori infection (FDR = 3.57E−07), parkinson’s disease (FDR = 9.47E−07), and chemokine signaling pathway (FDR = 1.75E−05). The top fifteen remarkably enriched KEGG signaling pathways of DEGs are presented in Table 4.

Table 3.

Enriched top 15 GO terms of DEGs between sepsis and normal samples

GO ID GO term No. of genes FDR
Biological process
 GO:0006915 Apoptotic process 69 4.78E−14
 GO:0042981 Regulation of apoptotic process 33 8.32E−10
 GO:0015031 Protein transport 47 4.41E−09
 GO:0000278 Mitotic cell cycle 39 4.44E−09
 GO:0006355 Regulation of transcription, DNA-dependent 114 4.51E−09
 GO:0016192 Vesicle-mediated transport 30 6.59E−09
 GO:0007049 Cell cycle 47 1.16E−08
 GO:0000084 S phase of mitotic cell cycle 21 1.71E−07
 GO:0022904 Respiratory electron transport chain 18 3.59E−07
 GO:0006281 DNA repair 34 3.59E−07
 GO:0007596 Blood coagulation 45 3.80E−07
 GO:0045087 Innate immune response 35 6.18E−07
 GO:0006916 Anti-apoptosis 27 8.42E−07
 GO:0051301 Cell division 33 9.31E−07
 GO:0000075 Cell cycle checkpoint 21 1.73E−06
Molecular function
 GO:0005515 Protein binding 432 3.42E−86
 GO:0000166 Nucleotide binding 176 1.69E−22
 GO:0046872 Metal ion binding 207 2.45E−19
 GO:0005524 ATP binding 123 3.84E−15
 GO:0008270 Zinc ion binding 141 5.10E−13
 GO:0003677 DNA binding 128 2.45E−11
 GO:0016740 Transferase activity 62 8.39E−11
 GO:0016787 Hydrolase activity 77 1.87E−08
 GO:0003676 Nucleic acid binding 62 3.63E−06
 GO:0004672 Protein kinase activity 30 1.18E−05
 GO:0047485 Protein N-terminus binding 15 1.57E−05
 GO:0003723 RNA binding 50 1.58E−05
 GO:0042802 Identical protein binding 30 4.38E−05
 GO:0004197 Cysteine-type endopeptidase activity 12 5.98E−05
 GO:0016301 Kinase activity 25 6.31E−05
Cellular component
 GO:0005634 Nucleus 460 2.15E−73
 GO:0005737 Cytoplasm 450 3.48E−72
 GO:0005829 Cytosol 231 1.19E−49
 GO:0005739 Mitochondrion 141 2.64E−26
 GO:0016020 Membrane 278 2.14E−24
 GO:0005730 Nucleolus 139 1.82E−23
 GO:0005654 Nucleoplasm 102 6.78E−23
 GO:0005783 Endoplasmic reticulum 85 2.65E−11
 GO:0005886 Plasma membrane 209 1.03E−10
 GO:0005794 Golgi apparatus 81 1.12E−10
 GO:0005743 Mitochondrial inner membrane 37 9.65E−09
 GO:0048471 Perinuclear region of cytoplasm 44 1.28E−08
 GO:0005625 Soluble fraction 42 1.59E−08
 GO:0005694 Chromosome 33 2.80E−08
 GO:0005856 Cytoskeleton 66 1.99E−07
Open in a new tab

Table 4.

Enriched top 15 KEGG pathways of DEGs between sepsis and normal samples

KEGG ID KEGG term Count FDR Genes
hsa00190 Oxidative phosphorylation 27 3.52E−11 ATP5J, NDUFA2, COX17, COX6A1, ATP6V1D, COX5A, ATP6V0A1, COX6B1, COX5B, NDUFB11, ATP5J2, ATP6V0E1, ATP6V1E1, SDHB, TCIRG1, NDUFA4, COX8A, NDUFA13, COX7A2, COX7B, NDUFA1, ATP6V1E2, ATP6V0B, UQCR11, UQCRQ, ATP6V0D1, ATP5B
hsa00240 Pyrimidine metabolism 20 8.20E−09 POLR2J, POLR2H, POLR1C, POLD2, POLR2L, NME4, AK3, TYMP, POLA1, NME7, NT5C2, POLD1, NME6, PRIM1, POLR3A, NME1, NT5E, CDA, NT5M, POLR3F
hsa04380 Osteoclast differentiation 23 8.54E−09 TYROBP, MAPK3, MAPK13, IKBKG, SIRPA, NFKBIA, TNFRSF1A, SIRPB1, LILRB3, SYK, CYBA, FCGR2A, IFNGR1, LILRA2, SPI1, NCF4, LILRB2, NCF2, IFNAR2, JUNB, PIK3CG, LILRA6, PPARG
hsa05016 Huntington’s disease 28 9.50E−09 POLR2J, ATP5J, POLR2H, NDUFA2, AP2S1, COX6A1, POLR2L, COX5A, COX6B1, COX5B, NDUFB11, CASP9, SDHB, NDUFA4, COX8A, CLTCL1, SOD2, NDUFA13, COX7A2, HIP1, COX7B, NDUFA1, DNAL4, UQCR11, PPARG, UQCRQ, TBPL1, ATP5B
hsa05010 Alzheimer’s disease 26 1.01E−08 ATP5J, MAPK3, NDUFA2, COX6A1, TNFRSF1A, FAS, COX5A, COX6B1, COX5B, PSENEN, NDUFB11, CASP9, PPP3R1, NAE1, SDHB, NDUFA4, COX8A, GRIN2A, NDUFA13, COX7A2, COX7B, NDUFA1, UQCR11, UQCRQ, ATP5B, PSEN1
hsa05130 Pathogenic Escherichia coli infection 14 1.61E−07 CDC42, ARPC2, ARPC4, ABL1, YWHAZ, YWHAQ, RHOA, LY96, TUBA1B, CD14, ARPC3, TUBA1C, TUBA1A, TUBA4A
hsa04145 Phagosome 22 1.74E−07 CTSS, TLR2, ATP6V1D, ATP6V0A1, PIK3C3, ATP6V0E1, ATP6V1E1, CYBA, FCGR2A, TCIRG1, TUBA1B, CD14, RAB5A, NCF4, NCF2, ATP6V1E2, ATP6V0B, VAMP3, ATP6V0D1, TUBA1C, TUBA1A, TUBA4A
hsa05120 Epithelial cell signaling in Helicobacter pylori infection 15 3.57E−07 CDC42, MAPK13, IKBKG, NFKBIA, ATP6V1D, ATP6V0A1, CXCL1, ATP6V0E1, ATP6V1E1, CXCR1, LYN, TCIRG1, ATP6V1E2, ATP6V0B, ATP6V0D1
hsa04666 Fc gamma R-mediated phagocytosis 17 7.49E−07 CDC42, ARPC2, VAV1, MAPK3, ARPC4, PRKCD, RAC2, DNM2, SYK, GSN, FCGR2A, LYN, PTPRC, ARPC3, PIK3CG, HCK, LIMK2
hsa05012 Parkinson’s disease 20 9.47E−07 ATP5J, NDUFA2, COX6A1, COX5A, COX6B1, COX5B, NDUFB11, CASP9, SDHB, NDUFA4, COX8A, NDUFA13, COX7A2, COX7B, NDUFA1, HTRA2, UQCR11, PINK1, UQCRQ, ATP5B
hsa05152 Tuberculosis 23 1.48E−06 MAPK3, CTSS, MAPK13, ITGAX, TLR2, NFYA, IL10RB, TNFRSF1A, ATP6V0A1, RHOA, SYK, PIK3C3, CASP9, RFXANK, PPP3R1, FCGR2A, TCIRG1, IFNGR1, CD14, RAB5A, ATP6V0B, CEBPB, ATP6V0D1
hsa00230 Purine metabolism 21 5.46E−06 POLR2J, POLR2H, POLR1C, POLD2, AK1, POLR2L, NME4, IMPDH1, POLA1, NME7, NT5C2, POLD1, NME6, PFAS, PRIM1, POLR3A, AMPD3, NME1, NT5E, NT5M, POLR3F
hsa04110 Cell cycle 18 8.22E−06 MDM2, ABL1, YWHAZ, FZR1, PTTG1, CDC7, YWHAQ, ANAPC4, ORC2, SKP1, CDC16, CDKN2D, ANAPC11, ZBTB17, RBL1, CCND3, MAD1L1, E2F4
hsa04621 NOD-like receptor signaling pathway 12 1.16E−05 TAB3, MAPK3, CASP5, MAPK13, IKBKG, BIRC2, CASP1, NFKBIA, CXCL1, BIRC3, PYCARD, PSTPIP1
hsa04062 Chemokine signaling pathway 22 1.75E−05 CDC42, VAV1, MAPK3, IKBKG, PTK2B, PRKCD, GRK6, RAC2, NFKBIA, GNB4, CXCL1, RHOA, ARRB2, GNG10, CXCR1, LYN, CCR1, GNB2, PIK3CG, HCK, FGR, CCR6
Open in a new tab

Establishment of TF-target genes regulatory network for sepsis

In order to disclose the TF-target genes′ regulatory network for sepsis, we utilized TRANSFAC to obtain TFs regulating top 20 upregulated or downregulated DEGs. Finally, we obtained transcriptional regulatory networks consisting of 374 pairs of TFs genes involving 47 TFs. In the network, the three most downstream genes covered by TFs were ZNF84, CYB561A3, and BST1. The three hub TFs were Pax-4 (degree = 13), POU2F1 (degree = 12), and Nkx2–5 (degree = 11) (Table 5). Among the top 20 upregulated or downregulated DEGs, five upregulated genes (S100A8, S100A9, S100A12, PGLYRP1, and MMP9) and three downregulated genes (ZNF84, CYB561A3, and BST1) were co-regulated by Pax-4, POU2F1, and Nkx2–5.

Table 5.

Top eight TFs targeting more than five DEGs

Transcription factor Count Genes
Pax-4 13 ZNF84, ZNF329, SMARCAD1, S100A9, PLEKHG4, PGLYRP1, MAP3K4, LRG1, LETMD1, FAM171A1, CYB561A3, BST1, ARHGAP17
POU2F1 12 ZNF84, ZNF329, TXN, SMARCAD1, S100A8, S100A12, RGL4, PGLYRP1, DYSF, CYB561A3, BST1, ARHGAP17
Nkx2–5 11 ZNF84, ZNF329, SMARCAD1, RGL4, PGLYRP1, MMP9, MAP3K4, LETMD1, CYB561A3, BST1, ARHGAP17
AP-1 7 ZNF84, TXN, SMARCAD1, S100A8, RGL4, PLEKHG4, LRG1
HNF-1 7 ZNF329, SMARCAD1, S100A8, S100A12, PGLYRP1, LRG1, CYB561A3
COMP1 6 TSHZ1, PGLYRP1, DYSF, CYB561A3, BST1, ARHGAP17
HNF-4 6 ZNF84, S100A9, S100A8, S100A12, PLEKHG4, MMP9
Elk-1 5 ZNF84, SMARCAD1, RGL4, PLEKHG4, CYB561A3
Open in a new tab

Discussion

Sepsis is associated with substantial morbidity and is a rapidly growing public health concern in older people. The understanding of molecular mechanisms underlying sepsis will lead to new therapies that improve survival. TF-target genes′ regulatory network may be available to investigate the underlying mechanisms and provide additional evidence for the therapeutic method of sepsis. In our study, based on the integrated analysis of five microarray datasets for sepsis, we identified 1339 DEGs including 788 upregulated and 551 downregulated DEGs. It is well known that inflammation is a fundamental component of sepsis pathogenesis and severe sepsis is associated with blood coagulation and immune system dysfunctions. In this study, these DEGs were significantly enriched in the GO term for biological processes especially in regulation of blood coagulation and innate immune response. Furthermore, the KEGG pathway of pathogenic escherichia coli infection was also significantly enriched. At last, we found that five top ten upregulated DEGs (S100A8, S100A9, S100A12, PGLYRP1, and MMP9) and three downregulated DEGs (ZNF84, CYB561A3, and BST1) were under the regulation of the three hub TFs of Pax-4, POU2F1, and Nkx2–5. These eight DEGs played a significant role in sepsis.

As the family members of S100 proteins, S100A8, S100A9, and S100A12 have relationship with inflammatory diseases and aggravate inflammatory response (Roth et al. 2003). The heterodimer S100A8/A9, also known as calprotectin, is elevated in the circulation and has been severed as a potential diagnostic marker of sepsis (Terrin et al. 2011). S100A12 released from granulocytosis is over-expressed and can activate human monocytes via toll-like receptor 4 during clinical and experimental sepsis (Foell et al. 2013). In this study, S100A8, S100A9, and S100A12 were upregulated in sepsis, which were in accordance with the previous reports.

PGLYRP1 (also called PGRP-S) is said to be participated in neutrophil production of reactive oxygen species, as well as regulation of immune responses in vivo (Dziarski et al. 2003; Park et al. 2013). It is strongly expressed in bone marrow and polymorphonuclear leukocytes and has direct antibacterial effect in vitro (Kang et al. 1998; Liu et al. 2000, 2001; Tydell et al. 2002). In our study, we found PGLYRP1 was upregulated, which may result in dysfunction of immune response in sepsis.

MMP9 is a multi-domain zinc metalloproteinase released by inflammatory cells. MMP9 regulates the activity of numerous cytokines that are critical to inflammation (Galliera et al. 2015; Vandooren et al. 2013). It is noted that MMP9 levels in plasma are elevated in patients with septic shock and severe sepsis, and it may cause severe sepsis (Nakamura et al. 1998). In this study, we found that MMP9 was upregulated in sepsis that was consistent with previous reports.

Zincfinger (ZNF) genes are the largest family of TFs in mammalian genomes. It is suggested that ZNF84 is upregulated in mature MII oocytes and human ES cells compared the gene expression profile of somatic tissues (Assou et al. 2009). However, very few researches about ZNF84 existed in sepsis. In this study, the expression of ZNF84 decreased, which may serve as a novel insight into the relationship between ZNF84 and sepsis.

In mammals, lysosome cytochrome b561 (CYB561A3) is expressed at the plasma membrane and intracellular sites (McKie et al. 2001). Lysosome iron pools play an important part in oxidative stress (Kurz et al. 2006), which involves CYB561A3-Fe3+-reductase activity. It is pointed out that the expression of CYB561A3 is found in chronic lymphocytic leukemia (Yepes et al. 2015). In this study, we found that CYB561A3 was downregulated and may have a role of oxidative stress in sepsis.

Bone marrow stromal cell antigen-1 (BST1, also called CD157) is highly conserved and has been found in organisms of mammals. BST1 plays numerous roles in humoral immune response, neutrophil transmigration, and hematopoietic stem cell support (Ishihara and Hirano 2000). It is reported that BST1 is involved in inflammatory pathways of alzheimer’s disease (Chang et al. 2015). Also, BST1 is expressed in epithelial ovarian carcinoma and its high expression is associated with variations in tumor cell morphology, decreased cell–cell interactions, motility, and mesothelial invasion (Morone et al. 2012). Herein, we found that the expression of BST1 was reduced and may be involved in sepsis via mediating immune response.

Depending on the results of TF-target genes regulation network, we identified 47 TFs in sepsis. In the network, top three TFs regulating the most downstream target genes were Pax-4, POU2F1, and Nkx2–5. Pax-4 belongs to the PAX family and contains the paired domain and homeodomain (Inoue et al. 1998; Walther et al. 1991), which are potential DNA-binding domains. POU family is a class of DNA-binding transcription factors that function in cell specification and developmental regulation (Phillips and Luisi 2000; Ryan and Rosenfeld 1997). POU2F1 is a member of the POU family that has been reported as being both a positive and negative regulator of transcription and can regulate transcription by interacting with other transcription factors (Mordvinov et al. 1999). The homeobox transcription factor Nkx2–5 is a critical regulator of cardiac gene expression and heart development (Komuro and Izumo 1993; Lints et al. 1993). The regulation of these three TFs for above eight DEGs also played a part in the process of sepsis.

In a word, our integrated analysis found a number of DEGs in sepsis. Furthermore, the results of GO enrichment analysis and KEGG signal pathway analysis revealed that some biological functions or pathways may be closely linked to the development of sepsis. The constructed transcriptional regulatory network uncovered top three TFs (Pax-4, POU2F1, and Nkx2–5) regulating most downstream target genes including S100A8, S100A9, S100A12, PGLYRP1, MMP9 ZNF84, CYB561A3, and BST1. All these genes appeared to play vital roles in the development of disease and may therefore be potential diagnostic marker of sepsis and drug targets for the treatment of sepsis.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 221 kb) (221.5KB, pdf)

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Footnotes

Electronic supplementary material

The online version of this article (doi:10.1007/s13205-017-0713-x) contains supplementary material, which is available to authorized users.

Junli Zhang and Yuelei Cheng contributed equally to this paper and should be considered as co-first authors

References

  1. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–1310. doi: 10.1097/00003246-200107000-00002. [DOI] [PubMed] [Google Scholar]
  2. Annane D, Aegerter P, Jars-Guincestre MC, Guidet B. Current epidemiology of septic shock: the CUB-Rea network. Am J Respir Crit Care Med. 2003;168:165–172. doi: 10.1164/rccm.2201087. [DOI] [PubMed] [Google Scholar]
  3. Assou S, et al. A gene expression signature shared by human mature oocytes and embryonic stem cells. BMC Genom. 2009;10:10. doi: 10.1186/1471-2164-10-10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Brunkhorst FM, Reinhart K. Diagnosis and causal treatment of sepsis. Der Internist. 2009;50:810–816. doi: 10.1007/s00108-008-2287-5. [DOI] [PubMed] [Google Scholar]
  5. Calvano SE, et al. A network-based analysis of systemic inflammation in humans. Nature. 2005;437:1032–1037. doi: 10.1038/nature03985. [DOI] [PubMed] [Google Scholar]
  6. Cazalis MA, et al. Early and dynamic changes in gene expression in septic shock patients: a genome-wide approach. Intensive Care Med Exp. 2014;2:20. doi: 10.1186/s40635-014-0020-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chang KH, Wu YR, Chen YC, Fung HC, Lee-Chen GJ, Chen CM. STK39, But Not BST1, HLA-DQB1, and SPPL2B polymorphism, is associated with Han-Chinese Parkinson’s disease in Taiwan. Medicine. 2015;94:e1690. doi: 10.1097/MD.0000000000001690. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. de Montmollin E, Annane D. Year in review 2010: critical care-multiple organ dysfunction and sepsis. Crit care. 2011;15:236. doi: 10.1186/cc10359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Diboun I, Wernisch L, Orengo CA, Koltzenburg M. Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genom. 2006;7:252. doi: 10.1186/1471-2164-7-252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dziarski R, Platt KA, Gelius E, Steiner H, Gupta D. Defect in neutrophil killing and increased susceptibility to infection with nonpathogenic gram-positive bacteria in peptidoglycan recognition protein-S (PGRP-S)-deficient mice. Blood. 2003;102:689–697. doi: 10.1182/blood-2002-12-3853. [DOI] [PubMed] [Google Scholar]
  11. Foell D, et al. Proinflammatory S100A12 can activate human monocytes via Toll-like receptor 4. Am J Respir Crit Care Med. 2013;187:1324–1334. doi: 10.1164/rccm.201209-1602OC. [DOI] [PubMed] [Google Scholar]
  12. Galliera E, Tacchini L, Corsi Romanelli MM. Matrix metalloproteinases as biomarkers of disease: updates and new insights. Clin Chem Lab Med. 2015;53:349–355. doi: 10.1515/cclm-2014-0520. [DOI] [PubMed] [Google Scholar]
  13. Hoesel B, Schmid JA. The complexity of NF-kappaB signaling in inflammation and cancer. Mol cancer. 2013;12:86. doi: 10.1186/1476-4598-12-86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Inoue H, Nomiyama J, Nakai K, Matsutani A, Tanizawa Y, Oka Y. Isolation of full-length cDNA of mouse PAX4 gene and identification of its human homologue. Biochem Biophys Res Commun. 1998;243:628–633. doi: 10.1006/bbrc.1998.8144. [DOI] [PubMed] [Google Scholar]
  15. Ishihara K, Hirano T. BST-1/CD157 regulates the humoral immune responses in vivo. Chem Immunol. 2000;75:235–255. doi: 10.1159/000058772. [DOI] [PubMed] [Google Scholar]
  16. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kang D, Liu G, Lundstrom A, Gelius E, Steiner H. A peptidoglycan recognition protein in innate immunity conserved from insects to humans. Proc Natl Acad Sci USA. 1998;95:10078–10082. doi: 10.1073/pnas.95.17.10078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kesh K, et al. Association of MMP7-181A→G promoter polymorphism with gastric cancer risk: influence of nicotine in differential allele-specific transcription via increased phosphorylation of cAMP-response element-binding protein (CREB) J Biol Chem. 2015;290:14391–14406. doi: 10.1074/jbc.M114.630129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Komuro I, Izumo S. Csx: a murine homeobox-containing gene specifically expressed in the developing heart. Proc Natl Acad Sci USA. 1993;90:8145–8149. doi: 10.1073/pnas.90.17.8145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kurz T, Gustafsson B, Brunk UT. Intralysosomal iron chelation protects against oxidative stress-induced cellular damage. FEBS J. 2006;273:3106–3117. doi: 10.1111/j.1742-4658.2006.05321.x. [DOI] [PubMed] [Google Scholar]
  21. Lints TJ, Parsons LM, Hartley L, Lyons I, Harvey RP. Nkx-2.5: a novel murine homeobox gene expressed in early heart progenitor cells and their myogenic descendants. Development. 1993;119:419–431. doi: 10.1242/dev.119.2.419. [DOI] [PubMed] [Google Scholar]
  22. Liu C, Gelius E, Liu G, Steiner H, Dziarski R. Mammalian peptidoglycan recognition protein binds peptidoglycan with high affinity, is expressed in neutrophils, and inhibits bacterial growth. J Biol Chem. 2000;275:24490–24499. doi: 10.1074/jbc.M001239200. [DOI] [PubMed] [Google Scholar]
  23. Liu C, Xu Z, Gupta D, Dziarski R. Peptidoglycan recognition proteins: a novel family of four human innate immunity pattern recognition molecules. J Biol Chem. 2001;276:34686–34694. doi: 10.1074/jbc.M105566200. [DOI] [PubMed] [Google Scholar]
  24. Lopez-Bergami P, Lau E, Ronai Z. Emerging roles of ATF2 and the dynamic AP1 network in cancer. Nat Rev Cancer. 2010;10:65–76. doi: 10.1038/nrc2681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. McKie AT, et al. An iron-regulated ferric reductase associated with the absorption of dietary iron. Science. 2001;291:1755–1759. doi: 10.1126/science.1057206. [DOI] [PubMed] [Google Scholar]
  26. Mordvinov VA, et al. Binding of YY1 and Oct1 to a novel element that downregulates expression of IL-5 in human T cells. J Allergy Clin Immunol. 1999;103:1125–1135. doi: 10.1016/S0091-6749(99)70188-0. [DOI] [PubMed] [Google Scholar]
  27. Morone S, et al. Overexpression of CD157 contributes to epithelial ovarian cancer progression by promoting mesenchymal differentiation. PLoS One. 2012;7:e43649. doi: 10.1371/journal.pone.0043649. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mullen M, Gonzalez-Perez RR. Leptin-Induced JAK/STAT signaling and cancer growth. Vaccines. 2016;4:26. doi: 10.3390/vaccines4030026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Nakamura T, Ebihara I, Shimada N, Shoji H, Koide H. Modulation of plasma metalloproteinase-9 concentrations and peripheral blood monocyte mRNA levels in patients with septic shock: effect of fiber-immobilized polymyxin B treatment. ‎Am J Med Sci. 1998;316:355–360. doi: 10.1097/00000441-199812000-00001. [DOI] [PubMed] [Google Scholar]
  30. Pal S, Bhattacharjee A, Ali A, Mandal NC, Mandal SC, Pal M. Chronic inflammation and cancer: potential chemoprevention through nuclear factor kappa B and p53 mutual antagonism. J Immunol. 2014;11:23. doi: 10.1186/1476-9255-11-23. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Park SY, Jing X, Gupta D, Dziarski R. Peptidoglycan recognition protein 1 enhances experimental asthma by promoting Th2 and Th17 and limiting regulatory T cell and plasmacytoid dendritic cell responses. J Immunol. 2013;190:3480–3492. doi: 10.4049/jimmunol.1202675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Pellegrina DV, Severino P, Machado MC, Pinheiro da Silva F, Reis EM. Microarray gene expression analysis of neutrophils from elderly septic patients. Genom Data. 2015;6:51–53. doi: 10.1016/j.gdata.2015.08.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Phillips K, Luisi B. The virtuoso of versatility: POU proteins that flex to fit. J Mol Biol. 2000;302:1023–1039. doi: 10.1006/jmbi.2000.4107. [DOI] [PubMed] [Google Scholar]
  34. Ramana KV, Tammali R, Srivastava SK. Inhibition of aldose reductase prevents growth factor-induced G1-S phase transition through the AKT/phosphoinositide 3-kinase/E2F-1 pathway in human colon cancer cells. Mol Cancer Ther. 2010;9:813–824. doi: 10.1158/1535-7163.MCT-09-0795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Roth J, Vogl T, Sorg C, Sunderkotter C. Phagocyte-specific S100 proteins: a novel group of proinflammatory molecules. Trends Immunol. 2003;24:155–158. doi: 10.1016/S1471-4906(03)00062-0. [DOI] [PubMed] [Google Scholar]
  36. Ryan AK, Rosenfeld MG. POU domain family values: flexibility, partnerships, and developmental codes. Genes Dev. 1997;11:1207–1225. doi: 10.1101/gad.11.10.1207. [DOI] [PubMed] [Google Scholar]
  37. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27:431–432. doi: 10.1093/bioinformatics/btq675. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Speake C, et al. An interactive web application for the dissemination of human systems immunology data. J Trans Med. 2015;13:196. doi: 10.1186/s12967-015-0541-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Standage SW, Wong HR. Biomarkers for pediatric sepsis and septic shock. Expert Rev Anti Infect Ther. 2011;9:71–79. doi: 10.1586/eri.10.154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A. GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 2012;40:W478–W483. doi: 10.1093/nar/gks402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Terrin G, et al. Serum calprotectin: an antimicrobial peptide as a new marker for the diagnosis of sepsis in very low birth weight newborns. Clin Dev Immunol. 2011;2011:291085. doi: 10.1155/2011/291085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Tydell CC, Yount N, Tran D, Yuan J, Selsted ME. Isolation, characterization, and antimicrobial properties of bovine oligosaccharide-binding protein. A microbicidal granule protein of eosinophils and neutrophils. J Biol Chem. 2002;277:19658–19664. doi: 10.1074/jbc.M200659200. [DOI] [PubMed] [Google Scholar]
  43. Vandooren J, Van den Steen PE, Opdenakker G. Biochemistry and molecular biology of gelatinase B or matrix metalloproteinase-9 (MMP-9): the next decade. Crit Rev Biochem Mol Biol. 2013;48:222–272. doi: 10.3109/10409238.2013.770819. [DOI] [PubMed] [Google Scholar]
  44. Walther C, et al. Pax: a murine multigene family of paired box-containing genes. Genomics. 1991;11:424–434. doi: 10.1016/0888-7543(91)90151-4. [DOI] [PubMed] [Google Scholar]
  45. Wang D, DuBois RN. Therapeutic potential of peroxisome proliferator-activated receptors in chronic inflammation and colorectal cancer. Gastroenterol Clin North Am. 2010;39:697–707. doi: 10.1016/j.gtc.2010.08.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Wingender E. The TRANSFAC project as an example of framework technology that supports the analysis of genomic regulation. Brief Bioinform. 2008;9:326–332. doi: 10.1093/bib/bbn016. [DOI] [PubMed] [Google Scholar]
  47. Yepes S, Torres MM, Andrade RE. Clustering of expression data in chronic lymphocytic leukemia reveals new molecular subdivisions. PLoS One. 2015;10:e0137132. doi: 10.1371/journal.pone.0137132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14. doi: 10.1186/gb-2010-11-2-r14. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1 (PDF 221 kb) (221.5KB, pdf)

Articles from 3 Biotech are provided here courtesy of Springer

RESOURCES