Open Access

Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation

  • Authors:
    • Zhenliang Li
    • Ying Zhang
    • Yaling Liu
    • Yanchun Liu
    • Youyi Li
  • View Affiliations

  • Published online on: July 15, 2017     https://doi.org/10.3892/mmr.2017.7013
  • Pages: 3133-3146
  • Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria.

Introduction

Sepsis is a systemic and deleterious inflammatory response to noxious infection (1,2). Sepsis causes ~18 million new cases and millions of deaths worldwide annually; therefore, it is a major cause of morbidity and mortality globally in critically ill patients (3,4). The excessive activation of inflammation, complement and coagulation systems may damage the host's own tissues and organs, leading to multiple organ failure and death (5). In a group of patients diagnosed with sepsis, the most common causative agents are Gram-positive and Gram-negative bacteria (6,7).

Tang et al (8) used the microarray expression profile of GSE6535 to identify the differentially expressed genes (DEGs) between patients with Gram-positive and Gram-negative sepsis with univariate F test according to the cut-off criteria of false discovery rate (FDR) <0.05 and |log fold-change (FC)|>1.5 and determined that Gram-positive sepsis and Gram-negative sepsis had a common host response at the transcriptome level in critically ill patients (8). However, a previous study illustrated the different mechanisms of sepsis caused by Gram-positive bacteria and Gram-negative bacteria. Hypoxia-inducible factor 1α and Kruppel-like factor 2 have been identified to be involved in Gram-positive endotoxin-mediated sepsis by regulating cellular motility and proinflammatory gene expression in myeloid cells (9). In Gram-negative bacteria-induced sepsis, it has been determined that the inhibition of caspase-1 and defective interleukin 1β production are important immunological features (10). Additionally, α2-antiplasmin has been identified to be a protective mediator during Gram-negative sepsis by inhibiting bacterial growth, inflammation, tissue injury and coagulation (11). Furthermore, thrombomodulin-mediated protein C activation may contribute to protective immunity in severe Gram-negative sepsis by regulating inflammatory and procoagulant response (12). Despite the clinical importance of the disease and extensive research, no specific treatment is available for sepsis caused by Gram-positive and Gram-negative bacteria. Therefore, it is necessary to screen the biomarkers for sepsis.

The present study aimed to use the microarray data of Tang et al (8) to screen the DEGs in Gram-positive and Gram-negative samples compared with control samples using the limma package based on a wide range of thresholds (P<0.05 and |log2FC|>0.8). In addition, specific genes were collected as biomarkers for sepsis caused by Gram-positive and Gram-negative bacteria. A previous study has proposed that analyses based on differential statistical tests may lead to different outcomes (13). Therefore, the findings of the present study may differ to those of Tang et al (8).

Materials and methods

Microarray data

The microarray dataset of GSE6535 (8) was downloaded from the database of gene expression omnibus (www.ncbi.nlm.nih.gov/geo), which was sequenced on the platform GPL4274 NHICU Human 19K version 1.0. Probe annotation information for mapping the probes into gene symbols was also downloaded. From GSE6535 dataset, 17 neutrophil samples from patients without sepsis, 18 neutrophil samples from patients with Gram-positive sepsis, and 25 neutrophil samples from patients with Gram-negative sepsis were selected. Tang et al (8) obtained whole blood samples from critically ill patients on admission to the intensive care unit of Nepean Hospital (Sydney, Australia). Using Ficoll-Paque density gradient separation, neutrophils were isolated from the whole blood. The patients with sepsis were diagnosed retrospectively according to their medical record. According to the criteria established by Calandra and Cohen (14), the patients with sepsis were divided into Gram-positive and Gram-negative sepsis groups through assessing various clinical features, including physical examination and history and microbiological cultures, such as bronchoalveolar washings, urine, blood and cerebrospinal fluid. GSE6535 was deposited by Tang et al (8). The study of Tang et al was approved by the Ethics Committee of Nepean Hospital and written informed consent was provided by the patients or their families (8).

Data preprocessing and differential expression analysis

Based on the probe annotation information, probe IDs were converted into their corresponding gene symbols. The average value of multiple probes (that were corresponding to the same gene) was used as the gene expression value. To eliminate inherent expression differences between genes, the gene expression values were performed with Z-score normalization as previously described (15). Subsequently, the limma package version 3.32.2 in R (16) was used to screen the DEGs in the Gram-positive and Gram-negative samples compared with the control samples. The P<0.05 and |log2FC| >0.8 were used as the cut-off criteria for screening DEGs. Using the VennDiagram in R (17), the common DEGs between Gram-positive and Gram-negative samples, as well as the specific DEGs in Gram-positive samples or Gram-negative samples were identified. Gene Ontology (GO; www.geneontology.org) is a bioinformatics resource that may be used to classify gene product function using controlled, structured vocabularies (18). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (19), GO functional enrichment analysis was performed on the common DEGs. The hierarchical cluster analysis of the specific DEGs in Gram-positive or Gram-negative samples was conducted using cluster version 3.0 software (20) and then visualized using TreeView tool version 3 (21).

Similarity network construction

Pearson's correlation coefficient (PCC) (22), which determines the correlation between two variables, was used to identify the positive or negative correlations among different samples, with the threshold of |PCC|>0.5. Using Cytoscape version 2.8 software (23), a similarity network was constructed for the Gram-positive, Gram-negative and control samples.

Functional and pathway enrichment analyses

Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.jp/kegg/), which integrates genomic, chemical and systemic functional information, is a useful resource for pathway mapping (24). Using the online tool DAVID (19), GO functional and KEGG pathway enrichment analyses were conducted for the DEGs. P<0.05 was used as the threshold.

Identification of significantly differential functions using stochastic perturbations

The average expression value in Gram-positive or Gram-negative samples was calculated for each gene enriched in the same term (GO functions or KEGG pathways). Euclidean distance (25) was used to calculate the difference between the levels of all the terms between Gram-positive and Gram-negative samples, according to the following equation:

distance=∑i=1T(X¯Pi–X¯Ni)2

Where distance represents the Euclidean distance between Gram-positive samples and Gram-negative samples; X¯Pi stands for the average expression value of gene i in Gram-positive samples; X¯Pi represents the average expression value of gene i in Gram-negative samples; and T indicates the gene number in each term.

Subsequently, stochastic perturbations were used (26) to determine the significance findings. The 18 Gram-positive and 25 Gram-negative samples were randomly sorted. Subsequently, 18 samples were randomly selected and defined as Gram-positive samples and the remaining 25 samples were defined as Gram-negative samples. The Euclidean distance between the newly defined Gram-positive samples and Gram-negative samples was recalculated. This was repeated for 10,000 times and the Euclidean distance for 10,000 perturbations were sorted from small to large and used as the background distribution. The ranking order of the initial Euclidean distance in the background distribution was calculated and converted to a P-value. The terms with P<0.05 were considered significantly differential functions between Gram-positive and Gram-negative samples.

Results

DEGs analysis

The gene distribution of Gram-negative (Fig. 1A) and Gram-positive samples (Fig. 1B) are presented using a volcano plot. Using the P<0.05 and |log2FC|>0.8 as thresholds, a total of 340 DEGs, including 181 upregulated genes, including large tumor suppressor kinase 2 (LATS2), NADH:ubiquinone oxidoreductase subunit S4 (NDUFS4) and 159 downregulated genes, including myeloid cell leukemia 1 (MCL1) and chitinase-like 1, were obtained in Gram-positive samples compared with control samples. A total of 485 DEGs were identified, 324 upregulated genes, including NDUFS4 and NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2) and 161 downregulated genes, including MCL1 and ecotropic viral integration site 2B, were identified in Gram-negative samples compared with the control samples. The top 10 significantly upregulated genes and downregulated genes in the Gram-negative and Gram-positive samples are presented in Table I.

Table I.

Top 10 upregulated and downregulated genes in patients with Gram-negative and Gram-positive sepsis.

Table I.

Top 10 upregulated and downregulated genes in patients with Gram-negative and Gram-positive sepsis.

A, Gram-negative

DEGs-log2 (P-value)logFC
Upregulated genes
  RPL273.735442   1.654937
  TM4SF12.691156   1.541493
  SEC11A2.258718   1.518767
  PLOD22.255042   1.494549
  UQCRH2.154078   1.446315
  AFP1.889918   1.429409
  CDK5RAP23.965291   1.413672
  EPB41L4A-AS14.122053   1.411759
  SOD12.91784   1.40033
  CANX4.013587   1.392226
Downregulated genes
  EVI2B4.312471−2.08489
  MME5.521434−1.73605
  ZBP15.974694−1.56361
  LITAF3.113137−1.54349
  CYTH42.874971−1.53818
  FBXL52.808339−1.53
  CHI3L14.498941−1.45407
  QPCT4.411504−1.45154
  TREM14.12983−1.43918
  MXD13.392031−1.41323

B, Gram-positive

DEGs−log2 (P-value)logFC

Upregulated genes
  SSBP11.896196279   1.5341469
  LAIR13.8569852   1.4491332
  MRPS18A2.377785977   1.4338711
  NDUFC23.935542011   1.4093212
  CTSC3.982966661   1.3851966
  MT1L2.991399828   1.3843636
  TM4SF12.249491605   1.3772427
  FCHSD21.694648631   1.301164
  CYP1B12.460923901   1.297874
  NDUFA41.876148359   1.267824
Downregulated genes
  CHI3L16.359519−1.92246
  EVI2B3.271646−1.71993
  MME4.36251−1.68036
  KCNB12.300162−1.56111
  LITAF2.415669−1.44533
  FUS2.767004−1.42285
  QPCT3.090444−1.38819
  CKAP44.251812−1.35146
  MCL14.221126−1.34034
  EFHC23.458421−1.32892

[i] DEGS, differentially expressed genes; FC, fold-change.

A total of 188 common DEGs, including 120 upregulated and 68 downregulated were identified between Gram-positive and Gram-negative samples. Additionally, 152 specific DEGs, including 61 upregulated and 91 downregulated genes in the Gram-positive samples and 297 specific DEGs, including 204 upregulated and 93 downregulated genes in Gram-negative samples were also screened (Fig. 2). GO functional enrichment analysis was performed on the common DEGs, consisted of 120 upregulated and 68 downregulated genes in the Gram-positive and Gram-negative samples and the top 5 terms for each sample type were presented in Fig. 2. The findings revealed that the common upregulated genes were primarily associated with the regulation of apoptosis and cell death, whereas the common downregulated genes were primarily associated with cellular respiration (Fig. 2). Hierarchical cluster analysis of the specific DEGs revealed that there were significant differences between control and sepsis samples. However, no significant difference was identified between the Gram-positive and Gram-negative samples (Fig. 3).

Similarity network analysis

In the similarity network, positive associations were identified between the majority of the control and sepsis samples. However, negative associations were also identified between the control and sepsis samples (Fig. 4).

Functional and pathway enrichment analyses

Functional enrichment analysis was performed on the upregulated and downregulated genes in the Gram-positive or Gram-negative samples separately. For the downregulated genes in the Gram-positive samples and Gram-negative samples, MCL1 was significantly associated with the functions of apoptosis and programmed cell death regulation. For the upregulated genes in the Gram-positive and Gram-negative samples, NDUFS4 was significantly associated with mitochondrial respiratory chain complex I assembly. Additionally, NDUFB2, NDUFB8 and ubiquinol-cytochrome c reductase hinge protein (UQCRH) were significantly enriched in the functions of cellular respiration, ATP synthesis coupled electron transport and respiratory electron transport chain in Gram-negative samples. LATS2 was significantly associated with the G1/S transition of the mitotic cell cycle in Gram-positive samples (Tables II and III). KEGG pathway enrichment analysis was also conducted for up and downregulated genes in Gram-positive and Gram-negative samples (Tables IV and V). NDUFS4 was significantly enriched in the pathway of oxidative phosphorylation.

Table II.

Top 10 enriched GO terms for the upregulated and the downregulated genes in Gram-positive samples.

Table II.

Top 10 enriched GO terms for the upregulated and the downregulated genes in Gram-positive samples.

A, Upregulated genes

TermFunctionCountP-valueGene symbol
GO:0006412Translation22 3.52×10−11AIMP1, EEF1A2, GARS, MRPS10, RPS27L, MRPS18C, MRPL15, MRPS18A, RPL22, MRPL27, EEF1E1, NARS2, EIF2S2, HARS, MRPL19, MRPL36, RPL10, RPL11, RSL24D1, MRPL32, EEFSEC, MRPL33
GO:0006120Mitochondrial electron transport, NADH to ubiquinone  7 4.78×10−6NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2
GO:0042773ATP synthesis coupled electron transport  7 2.62×10−5NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2
GO:0022904Respiratory electron transport chain  7 5.65×10−5NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2
GO:0045333Cellular respiration  8 7.34×10−5NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2, MDH1
GO:0044267Cellular protein metabolic process44 1.09×10−4FASTK, FKBP3, PRDX4, MRPS10, RPS27L, CANX, LATS2, VRK1, PSMB7, PSMB6, MRPL15, PLOD2, NARS2, MRPL36, B3GNT1, MRPL19, RPL10, RPL11, RSL24D1, MRPL32, LOXL1, FGF2, MRPL33, HSP90AA1, AIMP1, EEF1A2, GARS, SOD1, LRPAP1, IKBKE, MAST4, HSP90B1, PPIH, MRPS18C, PSMA5, MRPL27, RPL22, MRPS18A, EEF1E1, EIF2S2, HARS, DSP, EEFSEC, FKBP2
GO:0016310 Phosphorylation19 1.87×10−3NDUFA4, NDUFA8, MVD, NDUFA9, FASTK, HK2, NDUFC2, PRDX4, SOD1, LATS2, NDUFS6, IKBKE, MAST4, VRK1, NDUFS5, NDUFS4, ATP5C1, ATP5A1, FGF2
GO:0000079Regulation of cyclin-dependent protein kinase activity  5 2.51×10−3GTPBP4, CDKN2C, CKS2, CDKN3, LATS2
GO:0033365Protein localization in organelle  7 4.53×10−3PPIH, MTX2, NDUFA13, FGF2, TIMM44, SEC61G, NR5A1
GO:0010257NADH dehydrogenase complex assembly  3 4.73×10−3NDUFAF4, NDUFS5, NDUFS4

B, Downregulated genes

TermFunctionCountP-valueGene symbol

GO:0010942Positive regulation of cell death17 1.51×10−6PTGS2, PREX1, STK17B, RRAGA, PRKDC, NLRP3, NLRP1, SERINC3, NOTCH1, EI24, SSTR3, CASP4, DUSP1, CASP8, BNIP3L, FGD3, KALRN
GO:0043065Positive regulation of apoptosis16 6.31×10−6PTGS2, PREX1, STK17B, PRKDC, NLRP3, NLRP1, SERINC3, NOTCH1, EI24, SSTR3, CASP4, DUSP1, CASP8, BNIP3L, FGD3, KALRN
GO:0043068Positive regulation of programmed cell death16 6.86×10−6PTGS2, PREX1, STK17B, PRKDC, NLRP3, NLRP1, SERINC3, NOTCH1, EI24, SSTR3, CASP4, DUSP1, CASP8, BNIP3L, FGD3, KALRN
GO:0042981Regulation of apoptosis20 8.53×10−5PTGS2, MCL1, PREX1, STK17B, PRKDC, PIM2, NLRP3, NLRP1, SERINC3, NOTCH1, EI24, SSTR3, CASP4, DUSP1, IGF2R, BNIP3L, CASP8, DLG5, FGD3, KALRN
GO:0043067Regulation of programmed cell death20 9.73×10−5PTGS2, MCL1, PREX1, STK17B, PRKDC, PIM2, NLRP3, NLRP1, SERINC3, NOTCH1, EI24, SSTR3, CASP4, DUSP1, IGF2R, BNIP3L, CASP8, DLG5, FGD3, KALRN
GO:0012502Induction of programmed cell death12 1.38×10−4SERINC3, EI24, CASP4, SSTR3, PREX1, BNIP3L, CASP8, STK17B, NLRP3, NLRP1, FGD3, KALRN
GO:0009966Regulation of signal transduction20 2.68×10−4LITAF, PREX1, KLK5, CYTH4, RABGAP1L, PIM2, TBC1D22A, OSM, ECE1, CXCR4, SOSTDC1, CASP8, GPSM1, RAMP1, RAPGEF1, RUNX2, ARAP1, FGD3, GNG7, KALRN
GO:0008277Regulation of G-protein coupled receptor protein signaling pathway  5 1.10×10−3ECE1, KLK5, GPSM1, RAMP1, GNG7
GO:0051056Regulation of small GTPase mediated signal transduction  8 7.45×10−3PREX1, CYTH4, RABGAP1L, RAPGEF1, FGD3, ARAP1, TBC1D22A, KALRN
GO:0010647Positive regulation of cell communication  8 2.82×10−2OSM, LAMA2, ECE1, PTGS2, LITAF, KLK5, CASP8, PIM2

[i] GO, gene ontology.

Table III.

Top 10 enriched GO terms for the upregulated and the downregulated genes in Gram-negative samples.

Table III.

Top 10 enriched GO terms for the upregulated and the downregulated genes in Gram-negative samples.

A, Upregulated genes

TermFunctionCountP-valueGene symbol
GO:0045333Cellular respiration15 8.97×10−9UQCRC2, NDUFA8, NDUFB8, NDUFA6, NDUFA7, CYCS, NDUFC2, NDUFB2, NDUFS6, UQCR10, NDUFS5, NDUFS4, UQCRH, UQCRB, MDH1
GO:0042773ATP synthesis coupled electron transport12 1.31×10−8NDUFS6, NDUFS5, UQCR10, NDUFS4, NDUFA8, UQCRH, NDUFB8, NDUFA6, NDUFA7, NDUFC2, UQCRB, NDUFB2
GO:0022904Respiratory electron transport chain12 5.68×10−8NDUFS6, NDUFS5, UQCR10, NDUFS4, NDUFA8, UQCRH, NDUFB8, NDUFA6, NDUFA7, NDUFC2, UQCRB, NDUFB2
GO:0006412Translation24 2.78×10−7MRPL3, RPL19, EEF1B2, EEF1A2, HBS1L, RPL15, MRPS10, RPL27, RPS27L, RPL22L1, IARS2, RPS7, MRPS18C, MRPL15, MRPS18A, RPL22, MRPL27, EEF1E1, MRPL19, RPL11, RSL24D1, MRPL32, MRPL33, RPS23
GO:0006457Protein folding17 6.33×10−7HSP90AA1, FKBP5, FKBP4, FKBP3, TTC9, PDIA5, CCT3, LMAN1, CANX, LRPAP1, CCT7, PPIH, HSP90B1, SIL1, RUVBL2, HSPD1, FKBP2
GO:0006120Mitochondrial electron transport, NADH to ubiquinone  9 1.70×10−6NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFB8, NDUFA6, NDUFA7, NDUFC2, NDUFB2
GO:0051437Positive regulation of ubiquitin-protein ligase activity during mitotic cell cycle  9 6.75×10−5CDK1, PSMB7, PSMD14, PSMB6, PSMA6, PSMA5, PSMC2, PSMA4, PSMD1
GO:0051351Positive regulation of ligase activity  9 1.12×10−4CDK1, PSMB7, PSMD14, PSMB6, PSMA6, PSMA5, PSMC2, PSMA4, PSMD1
GO:0051438Regulation of ubiquitin-protein ligase activity  9 1.80×10−4CDK1, PSMB7, PSMD14, PSMB6, PSMA6, PSMA5, PSMC2, PSMA4, PSMD1
GO:0051436Negative regulation of ubiquitin-protein ligase activity during mitotic cell cycle  8 3.37×10−4PSMB7, PSMD14, PSMB6, PSMA6, PSMA5, PSMC2, PSMA4, PSMD1

B, Downregulated genes

TermFunctionCountP-valueGene symbol

GO:0043067Regulation of programmed cell death22 3.47×10−6IFIH1, PTGS2, MCL1, PREX1, TGFBR1, BCL2A1, STK17B, IFI16, NLRP3, NLRP1, TNFRSF9, CASP4, DUSP1, BTG2, IGF2R, BNIP3L, CHST11, CASP8, LRRK2, MX1, IFI6, KALRN
GO:0010942Positive regulation of cell death16 3.57×10−6PTGS2, PREX1, TGFBR1, STK17B, RRAGA, IFI16, NLRP3, NLRP1, TNFRSF9, CASP4, DUSP1, CASP8, BNIP3L, MX1, LRRK2, KALRN
GO:0042981Regulation of apoptosis21 1.10×10−5IFIH1, PTGS2, MCL1, PREX1, TGFBR1, BCL2A1, STK17B, IFI16, NLRP3, NLRP1, TNFRSF9, CASP4, DUSP1, BTG2, IGF2R, BNIP3L, CHST11, CASP8, MX1, IFI6, KALRN
GO:0043068Positive regulation of programmed cell death15 1.61×10−5PTGS2, PREX1, TGFBR1, STK17B, IFI16, NLRP3, NLRP1, TNFRSF9, CASP4, DUSP1, CASP8, BNIP3L, MX1, LRRK2, KALRN
GO:0043065Positive regulation of apoptosis14 6.62×10−5PTGS2, TGFBR1, PREX1, STK17B, IFI16, NLRP3, NLRP1, TNFRSF9, CASP4, DUSP1, CASP8, BNIP3L, MX1, KALRN
GO:0012502Induction of programmed cell death12 8.32×10−5TNFRSF9, CASP4, PREX1, TGFBR1, BNIP3L, CASP8, STK17B, IFI16, MX1, NLRP3, NLRP1, KALRN
GO:0031401Positive regulation of protein modification process  7 5.07×10−3OSM, CCND3, TGFBR1, CD4, RICTOR, UBE2D1, LRRK2
GO:0010562Positive regulation of phosphorus metabolic process  5 1.02×10−2OSM, CCND3, TGFBR1, CD4, RICTOR
GO:0045937Positive regulation of phosphate metabolic process  5 1.02×10−2OSM, CCND3, TGFBR1, CD4, RICTOR
GO:0019048Virus-host interaction  3 1.22×10−2IRF7, RRAGA, CD4

Table IV.

Enriched pathways for the upregulated and the downregulated genes in Gram-negative samples.

Table IV.

Enriched pathways for the upregulated and the downregulated genes in Gram-negative samples.

A, Upregulated genes

TermFunctionCountP-valueGene symbol
hsa05012Parkinson's disease19 9.00×10−9UQCRC2, NDUFA8, SLC25A5, NDUFA4L2, NDUFB8, SLC25A6, NDUFA6, COX7B, NDUFA7, CYCS, NDUFC2, NDUFB2, NDUFS6, UQCR10, NDUFS5, NDUFS4, UQCRH, ATP5A1, UQCRB
hsa05016Huntington's disease21 7.73×10−8UQCRC2, NDUFA8, SLC25A5, NDUFA4L2, NDUFB8, POLR2K, SLC25A6, NDUFA6, CYCS, COX7B, NDUFA7, NDUFC2, SOD1, NDUFB2, NDUFS6, UQCR10, NDUFS5, NDUFS4, UQCRH, ATP5A1, UQCRB
hsa00190Oxidative phosphorylation17 4.25×10−7UQCRC2, NDUFA8, NDUFA4L2, NDUFB8, NDUFA6, COX7B, NDUFA7, NDUFC2, NDUFB2, NDUFS6, UQCR10, NDUFS5, NDUFS4, UQCRH, ATP5A1, ATP5I, UQCRB
hsa05010Alzheimer's disease18 1.96×10−6UQCRC2, NDUFA8, NDUFA4L2, NDUFB8, NDUFA6, COX7B, NDUFA7, CYCS, NDUFC2, NAE1, NDUFB2, NDUFS6, UQCR10, NDUFS5, NDUFS4, UQCRH, ATP5A1, UQCRB
hsa03050Proteasome9 3.61×10−5PSMB7, PSMD14, PSMB6, PSMA6, PSMA5, PSMC2, PSMA4, SHFM1, PSMD1
hsa03010Ribosome10 6.12×10−4RPL19, RPL22, RPL15, RPL27, RPS27L, RPL11, RSL24D1, RPL22L1, RPS23, RPS7
hsa00620Pyruvate metabolism6 4.60×10−3LDHA, ACYP1, GLO1, ACAT2, PCK1, MDH1
hsa04260Cardiac muscle contraction7 2.05×10−2UQCRC2, UQCR10, UQCRH, COX7B, ATP1A2, TNNI3, UQCRB
hsa04110Cell cycle9 2.21×10−2CDK1, YWHAG, CDKN2C, YWHAQ, TFDP2, PCNA, CDK6, GADD45A, SMC3
hsa04115p53 signaling pathway6 3.94×10−2CDK1, CYCS, CDK6, PERP, IGFBP3, GADD45A

B, Downregulated genes

TermFunctionCountP-valueGene symbol

hsa04622RIG-I-like receptor signaling pathway5 5.32×10−3IFIH1, ISG15, IRF7, CASP8, IFNA8
hsa04612Antigen processing and presentation59.21×10–3HSPA6, CD4, IFNA8, CTSS, HLA-F
hsa04620Toll-like receptor signaling pathway4 1.99×10−2IRF7, CASP8, IFNA8, CD14
hsa04660T cell receptor signaling pathway4 2.33×10−2PTPN6, RAF1, CD4, MAP3K14

Table V.

Enriched pathways for the upregulated and the downregulated genes in Gram-positive samples.

Table V.

Enriched pathways for the upregulated and the downregulated genes in Gram-positive samples.

A, Upregulated genes

TermFunctionCountP-valueGene symbol
hsa05012Parkinson's disease10 3.37×10−5NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, SLC25A5, NDUFA9, NDUFC2, ATP5C1, ATP5A1
hsa05016Huntington's disease11 9.07×10−5NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, SLC25A5, NDUFA9, NDUFC2, ATP5C1, ATP5A1, SOD1
hsa00190Oxidative phosphorylation  9 2.43×10−4NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2, ATP5C1, ATP5A1
hsa05010Alzheimer's disease  9 1.11×10−3NDUFA4, NDUFS6, NDUFS5, NDUFS4, NDUFA8, NDUFA9, NDUFC2, ATP5C1, ATP5A1
hsa03010Ribosome  5 2.58×10−2RPL22, RPL10, RPS27L, RPL11, RSL24D1
hsa04612Antigen processing and presentation  4 9.23×10−2HSP90AA1, IFI30, HSPA4, CANX
hsa00970:Aminoacyl-tRNA biosynthesis  39.85×10–2NARS2, HARS, GARS

B, Downregulated genes

TermFunctionCountP-valueGene symbol

hsa04650Natural killer cell mediated cytotoxicity  6 9.37×10−3PTPN6, ICAM2, RAF1, IFNA8, NFATC2, KLRD1
hsa04621NOD-like receptor signaling pathway  4 2.25×10−2IL8, CASP8, NLRP3, NLRP1
hsa04660T cell receptor signaling pathway  4 2.91×10−2PTPN6, RAF1, NFATC2, MAP3K14
Significantly differential functions screening

Based on the Euclidean distance of the biological functions, as well as the P-values of the 10,000 stochastic perturbations between Gram-positive samples and Gram-negative samples, a total of 10 significantly differential functions were obtained, including cellular respiration (P<1.00×10−8, Euclidean distance=1.156277), ATP synthesis coupled electron transport (P<1.00×10−8, Euclidean distance=1.156277) and G1/S transition of mitotic cell cycle (P=0.015, Euclidean distance = 0.554799; Table VI).

Table VI.

Top 10 significant differential functions between Gram-negative samples and Gram-positive samples.

Table VI.

Top 10 significant differential functions between Gram-negative samples and Gram-positive samples.

GO IDTermEuclidean distanceP-valueGene symbols
GO:0006120Mitochondrial electron transport, NADH to ubiquinone1.156277 <1.00×10−8NDUFB8, NDUFB2, NDUFA6, NDUFA7, NDUFA9, NDUFA4
GO:0042773ATP synthesis coupled electron transport1.156277 <1.00×10−8UQCRH, NDUFB8, NDUFB2, UQCRB, NDUFA6, NDUFA7, UQCR10, NDUFA9, NDUFA4
GO:0022904Respiratory electron transport chain1.156277 <1.00×10−8UQCRH, NDUFB8, NDUFB2, UQCRB, NDUFA6, NDUFA7, UQCR10, NDUFA9, NDUFA4
GO:0045333Cellular respiration1.156277 <1.00×10−8UQCRH, NDUFB8, NDUFB2, UQCRC2, UQCRB, NDUFA6, NDUFA7, UQCR10, CYCS, NDUFA9, NDUFA4
GO:0016310 Phosphorylation1.413364 1.00×10−3UQCRH, PAK4, FGFR1, NDUFB8, NDUFB2, CDK1, CDK6, UQCRC2, GHR, IGFBP3, UQCRB, NDUFA6, NDUFA7, UQCR10, MET, ATP5I, MVD, NDUFA9, LATS2, MAST4, NDUFA4, IKBKE, ATP5C1
GO:0042981Regulation of apoptosis1.508092 2.70×10−3IFI16, TNFRSF9, IFIH1, MX1, BTG2, CHST11, TGFBR1, BCL2A1, IFI6, LGALS1, PRDX1, PHLDA1, HSPD1, SORT1, MAL, DHCR24, GLO1, ITGB3BP, CDK1, GHR, SERPINB2, NQO1, ANXA1, IGFBP3, SMO, CADM1, CD44, KRT18, CYCS, NAE1, PERP, DLG5, EI24, PRKDC, NOTCH1, SERINC3, FGD3, PIM2, SSTR3
GO:0043067Regulation of programmed cell death1.517301 3.60×10−3IFI16, TNFRSF9, IFIH1, MX1, BTG2, CHST11, LRRK2, TGFBR1, BCL2A1, IFI6, LGALS1, PRDX1, PHLDA1, HSPD1, SORT1, MAL, DHCR24, GLO1, ITGB3BP, CDK1, GHR, SERPINB2, NQO1, ANXA1, IGFBP3, SMO, CADM1, CD44, KRT18, CYCS, NAE1, PERP, DLG5, EI24, PRKDC, NOTCH1, SERINC3, FGD3, PIM2, SSTR3
GO:0000082G1/S transition of mitotic cell cycle0.554799 1.50×10−2LATS2
GO:0044267Cellular protein metabolic process2.267426 2.37×10−2RPS23, IARS2, HSPD1, PAK4, FGFR1, SEC11A, HEXB, FKBP5, PSMA4, MRPL3, RPL27, PSMC2, SIL1, SUPT3H, RPL27, RPS7, RPL19, FKBP4, LMAN1, PTPN1, CDK1, CDK6, GHR, RPL22L1, PDIA5, CCT7, FBXO7, ANXA1, IGFBP3, PSMA6, PSMD1, CCT3, HERC3, TTC9, MET, NAE1, PSMD14, HBS1L, RABGGTB, EEF1B2, RPL15, RUVBL2, AIMP1, LATS2, MAST4, EEFSEC, LOXL1, NARS2, HARS, B3GNT1, MRPL36, IKBKE, EIF2S2, DSP, RPL10, GARS
GO:0007517Muscle organ development1.050407 4.15×10−2FAM65B, LAMA2, ANKRD2, FOXP1, CACNB4

[i] GO, gene ontology.

Discussion

In line with the results of Tang et al (8), the present study determined that there was no significant difference in the expression profile between Gram-positive and gram-negative samples from hierarchical clustering analysis. In the Gram-positive and Gram-negative samples, the GO functional enrichment analysis revealed that MCL1 was significantly associated with the regulation of apoptosis and programmed cell death. A previous study has determined that the apoptosis of T-cells may induce the breakdown of defense mechanisms resulting in sepsis (27). Additionally, the inhibition of programmed cell death may reverse T-cell exhaustion and thus eradicate the invading pathogens which cause sepsis (28). Additionally, MCL1 may also be associated with the reduction of apoptosis of neutrophils in patients with sepsis (29). Therefore, it is possible for MCL1 to be involved in sepsis via the regulation of T-cell apoptosis and programmed T-cell death in both Gram-positive and Gram-negative sepsis.

Additionally, the present study also determined that NDUFS4 was significantly associated with mitochondrial respiratory chain complex I assembly. Mitochondrial dysfunction may lead to oxidative stress and failure of energy production, which may result in organ dysfunction in sepsis (30). The KEGG pathway enrichment analysis revealed that NDUFS4 was significantly enriched in oxidative phosphorylation. Lee and Hüttemann (31) have determined that the inhibition of oxidative phosphorylation may lead to a reduction of the mitochondrial membrane potential, resulting in a lack of energy, which may cause organ failure and death in septic patients (31). NDUFS4 has been previously reported to be an important subunit of complex I which has a key role in oxidative phosphorylation (32). Additionally, NDUFS4 may participate in the regulation of sepsis induced by Gram-negative and Gram-positive bacteria through regulation of oxidative phosphorylation.

However, the present study identified specific DEGs in Gram-positive and Gram-negative samples compared with normal samples. According to the Euclidean distance and the stochastic perturbations performed between Gram-positive and Gram-negative samples, NDUFB2, NDUFB8 and UQCRH were significantly upregulated in the Gram-negative samples, whereas they were not upregulated in the Gram-positive samples. In addition, functional annotation revealed that they were significantly associated with cellular respiration, ATP synthesis coupled electron transport and mitochondrial electron transport, ubiquinol to cytochrome c. NDUFB2 and NDUFB8 are parts of the multisubunit mitochondrial NADH ubiquinone oxidoreductase (complex I) which has an important role in mitochondrial functioning (33,34). A previous study determined that a dysfunction of respiratory chain complex I may be associated with reactive oxygen species (ROS) production (35). Additionally, previous studies reported that ROS are toxic oxygen-containing molecules that may damage the cells and the antioxidant defense system, which is the pathogenesis of sepsis (36,37). UQCRH, which encodes the cytochrome b-c1 complex subunit 6 of complexes III (cytochrome c-oxidoreductase), is involved in the mitochondrial oxidative phosphorylation and the dysfunction of UQCRH may lead to breast and ovarian cancer by altering the function of the mitochondria (38,39). To the best of our knowledge, this is the first study investigating the functions of NDUFB2, NDUFB8 and UQCRH in Gram-negative bacteria-induced sepsis. The present study concluded that NDUFB2, NDUFB8 and UQCRH may be involved in the Gram-negative bacteria-induced sepsis by altering mitochondrial oxidative phosphorylation and may also be potential targets for the treatment of Gram-negative bacterial sepsis.

In addition, the function of the G1/S transition of the mitotic cell cycle was also determined to be significantly different between the Gram-positive and Gram-negative samples. LATS2 was enriched in this function and was significantly upregulated in patients with Gram-positive sepsis, whereas it was not significantly expressed in Gram-negative patients. LATS2, encoding serine/threonine-protein kinase, has been identified to inhibit the G1/S transition in the cell cycle of tumor cells (40). Additionally, G1 cell cycle arrest may be important for the initiation of kidney injury in sepsis (41). Therefore, LATS2 may be associated with Gram-negative bacterial sepsis by the modulation of G1/S transition in cell cycle.

In conclusion, MCL1, NDUFS5 and NDUFS4 may be potential target genes for the treatment of Gram-positive and Gram-negative bacterial sepsis. Additionally, NDUFB2, NDUFB8 and UQCRH may also be associated with Gram-negative bacterial sepsis. LATS2 may contribute to the progression of Gram-negative bacterial sepsis. However, further studies are still required in order to elucidate their action mechanisms in sepsis.

Glossary

Abbreviations

Abbreviations:

DEGs

differentially expressed genes

FDR

false discovery rate

FC

fold-change

ICU

intensive care unit

PCC

Pearson's correlation coefficient

KEGG

Kyoto Encyclopedia of Genes and Genomes

References

1 

Taylor FB Jr, Kinasewitz GT and Lupu F: Pathophysiology, staging and therapy of severe sepsis in baboon models. J Cell Mol Med. 16:672–682. 2012. View Article : Google Scholar : PubMed/NCBI

2 

Brodská H, Malíčková K, Adámková V, Benáková H, Šťastná MM and Zima T: Significantly higher procalcitonin levels could differentiate Gram-negative sepsis from Gram-positive and fungal sepsis. Clin Exp Med. 13:165–170. 2013. View Article : Google Scholar : PubMed/NCBI

3 

Deutschman C and Tracey K: Sepsis: Current dogma and new perspectives. Immunity. 40:463–475. 2014. View Article : Google Scholar : PubMed/NCBI

4 

Lyle NH, Pena OM, Boyd JH and Hancock REW: Barriers to the effective treatment of sepsis: Antimicrobial agents, sepsis definitions, and host-directed therapies. Ann N Y Acad Sci. 1323:101–114. 2014. View Article : Google Scholar : PubMed/NCBI

5 

Hotchkiss RS and Karl IE: The pathophysiology and treatment of sepsis. N Engl J Med. 348:138–150. 2003. View Article : Google Scholar : PubMed/NCBI

6 

Koh EM, Lee SG, Kim CK, Kim M, Yong D, Lee K, Kim JM, Kim DS and Chong Y: Microorganisms isolated from blood cultures and their antimicrobial susceptibility patterns at a university hospital during 1994–2003. Korean J Lab Med. 27:265–275. 2007.(In Korean). View Article : Google Scholar : PubMed/NCBI

7 

Glik J, Kawecki M, Gaździk T and Nowak M: The impact of the types of microorganisms isolated from blood and wounds on the results of treatment in burn patients with sepsis. Pol Przegl Chir. 84:6–16. 2012.PubMed/NCBI

8 

Tang BM, McLean AS, Dawes IW, Huang SJ, Cowley MJ and Lin RC: Gene-expression profiling of gram-positive and gram-negative sepsis in critically ill patients. Crit Care Med. 36:1125–1128. 2008. View Article : Google Scholar : PubMed/NCBI

9 

Mahabeleshwar GH, Qureshi MA, Takami Y, Sharma N, Lingrel JB and Jain MK: A myeloid hypoxia-inducible factor 1α-Krüppel-like factor 2 pathway regulates gram-positive endotoxin-mediated sepsis. J Biol Chem. 287:1448–1457. 2012. View Article : Google Scholar : PubMed/NCBI

10 

Giamarellos-Bourboulis EJ, van de Veerdonk FL, Mouktaroudi M, Raftogiannis M, Antonopoulou A, Joosten LA, Pickkers P, Savva A, Georgitsi M, van der Meer JW and Netea MG: Inhibition of caspase-1 activation in Gram-negative sepsis and experimental endotoxemia. Crit Care. 15:R272011. View Article : Google Scholar : PubMed/NCBI

11 

Kager LM, Weehuizen TA, Wiersinga WJ, Roelofs JJ, Meijers JC, Dondorp AM, van't Veer C and van der Poll T: Endogenous α2-antiplasmin is protective during severe gram-negative sepsis (melioidosis). Am J Respir Crit Care Med. 188:967–975. 2013. View Article : Google Scholar : PubMed/NCBI

12 

Kager LM, Wiersinga WJ, Roelofs JJ, de Boer OJ, Weiler H, van't Veer C and van der Poll T: A thrombomodulin mutation that impairs active protein C generation is detrimental in severe pneumonia-derived gram-negative sepsis (melioidosis). PLoS Negl Trop Dis. 8:e28192014. View Article : Google Scholar : PubMed/NCBI

13 

Afsari B, Geman D and Fertig EJ: Learning dysregulated pathways in cancers from differential variability analysis. Cancer Inform. 13:(Suppl 5). S61–S67. 2014.

14 

Calandra T, Cohen J, et al: International Sepsis Forum Definition of Infection in the ICU Consensus Conference: The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Crit Care Med. 33:1538–1548. 2005. View Article : Google Scholar : PubMed/NCBI

15 

Murie C, Barette C, Lafanechère L and Nadon R: Control-plate regression (CPR) normalization for high-throughput screens with many active features. J Biomol Screen. 19:661–671. 2014. View Article : Google Scholar : PubMed/NCBI

16 

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI

17 

Chen H and Boutros PC: VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics. 12:352011. View Article : Google Scholar : PubMed/NCBI

18 

Gene Ontology Consortium, . Blake JA, Dolan M, Drabkin H, Hill DP, Li N, Sitnikov D, Bridges S, Burgess S, Buza T, et al: Gene ontology annotations and resources. Nucleic Acids Res. 41:(Database issue). D530–D535. 2013. View Article : Google Scholar : PubMed/NCBI

19 

da Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2008. View Article : Google Scholar

20 

Zhou J, Dong X, Zhou Q, Wang H, Qian Y, Tian W, Ma D and Li X: microRNA expression profiling of heart tissue during fetal development. Int J Mol Med. 33:1250–1260. 2014.PubMed/NCBI

21 

Zhai Y, Tchieu J and Saier MH Jr: A web-based tree view (TV) program for the visualization of phylogenetic trees. J Mol Microbiol Biotechnol. 4:69–70. 2002.PubMed/NCBI

22 

Weaver B and Wuensch KL: SPSS and SAS programs for comparing Pearson correlations and OLS regression coefficients. Behav Res Methods. 45:880–895. 2013. View Article : Google Scholar : PubMed/NCBI

23 

Demchak B, Hull T, Reich M, Liefeld T, Smoot M, Ideker T and Mesirov JP: Cytoscape: The network visualization tool for GenomeSpace workflows. F1000Res. 3:1512014.PubMed/NCBI

24 

Kotera M, Moriya Y, Tokimatsu T, Kanehisa M and Goto S: KEGG and GenomeNet, New Developments. Metagenomic Analysis. 329–339. 2015.

25 

Liberti L, Lavor C, Maculan N and Mucherino A: Euclidean distance geometry and applications. Quantitative Biol. 56:3–69. 2012.

26 

Bódai T, Altmann EG and Endler A: Stochastic perturbations in open chaotic systems: Random versus noisy maps. Phys Rev E Stat Nonlin Soft Matter Phys. 87:0429022013. View Article : Google Scholar : PubMed/NCBI

27 

Schmidt MV, Paulus P, Kuhn AM, Weigert A, Morbitzer V, Zacharowski K, Kempf VA, Brüne B and von Knethen A: Peroxisome proliferator-activated receptor γ-induced T cell apoptosis reduces survival during polymicrobial sepsis. Am J Respir Crit Care Med. 184:64–74. 2011. View Article : Google Scholar : PubMed/NCBI

28 

Chang K, Svabek C, Vazquez-Guillamet C, Sato B, Rasche D, Wilson S, Robbins P, Ulbrandt N, Suzich J, Green J, et al: Targeting the programmed cell death 1: Programmed cell death ligand 1 pathway reverses T cell exhaustion in patients with sepsis. Crit Care. 18:R32014. View Article : Google Scholar : PubMed/NCBI

29 

Härter L, Mica L, Stocker R, Trentz O and Keel M: Mcl-1 correlates with reduced apoptosis in neutrophils from patients with sepsis. J Am Coll Surg. 197:964–973. 2003. View Article : Google Scholar : PubMed/NCBI

30 

Galley H: Oxidative stress and mitochondrial dysfunction in sepsis. Br J Anaesth. 107:57–64. 2011. View Article : Google Scholar : PubMed/NCBI

31 

Lee I and Hüttemann M: Energy crisis: The role of oxidative phosphorylation in acute inflammation and sepsis. Biochim Biophys Acta. 1842:1579–1586. 2014. View Article : Google Scholar : PubMed/NCBI

32 

Petruzzella V, Vergari R, Puzziferri I, Boffoli D, Lamantea E, Zeviani M and Papa S: A nonsense mutation in the NDUFS4 gene encoding the 18 kDa (AQDQ) subunit of complex I abolishes assembly and activity of the complex in a patient with Leigh-like syndrome. Hum Mol Genet. 10:529–535. 2001. View Article : Google Scholar : PubMed/NCBI

33 

Ugalde C, Hinttala R, Timal S, Smeets R, Rodenburg RJ, Uusimaa J, van Heuvel LP, Nijtmans LG, Majamaa K and Smeitink JA: Mutated ND2 impairs mitochondrial complex I assembly and leads to Leigh syndrome. Mol Genet Metab. 90:10–14. 2007. View Article : Google Scholar : PubMed/NCBI

34 

Bottley A, Phillips NM, Webb TE, Willis AE and Spriggs KA: eIF4A inhibition allows translational regulation of mRNAs encoding proteins involved in Alzheimer's disease. PLoS One. 5:pii: e13030. 2010. View Article : Google Scholar : PubMed/NCBI

35 

Dröse S and Brandt U: Molecular mechanisms of superoxide production by the mitochondrial respiratory chainMitochondrial Oxidative Phosphorylation. Springer; pp. 145–169. 2012, View Article : Google Scholar

36 

Van Raamsdonk JM and Hekimi S: Superoxide dismutase is dispensable for normal animal lifespan. Proc Natl Acad Sci USA. 109:5785–5790. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Schulte J, Struck J, Köhrle J and Müller B: Circulating levels of peroxiredoxin 4 as a novel biomarker of oxidative stress in patients with sepsis. Shock. 35:460–465. 2011. View Article : Google Scholar : PubMed/NCBI

38 

Owens KM, Kulawiec M, Desouki MM, Vanniarajan A and Singh KK: Impaired OXPHOS complex III in breast cancer. PLoS One. 6:e238462011. View Article : Google Scholar : PubMed/NCBI

39 

Modena P, Testi MA, Facchinetti F, Mezzanzanica D, Radice MT, Pilotti S and Sozzi G: UQCRH gene encoding mitochondrial Hinge protein is interrupted by a translocation in a soft-tissue sarcoma and epigenetically inactivated in some cancer cell lines. Oncogene. 22:4586–4593. 2003. View Article : Google Scholar : PubMed/NCBI

40 

Li Y, Pei J, Xia H, Ke H, Wang H and Tao W: Lats2, a putative tumor suppressor, inhibits G1/S transition. Oncogene. 22:4398–4405. 2003. View Article : Google Scholar : PubMed/NCBI

41 

Yang QH, Liu DW, Long Y, Liu HZ, Chai WZ and Wang XT: Acute renal failure during sepsis: Potential role of cell cycle regulation. J Infect. 58:459–464. 2009. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

September 2017
Volume 16 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
APA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., & Li, Y. (2017). Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation. Molecular Medicine Reports, 16, 3133-3146. https://doi.org/10.3892/mmr.2017.7013
MLA
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16.3 (2017): 3133-3146.
Chicago
Li, Z., Zhang, Y., Liu, Y., Liu, Y., Li, Y."Identification of key genes in Gram‑positive and Gram‑negative sepsis using stochastic perturbation". Molecular Medicine Reports 16, no. 3 (2017): 3133-3146. https://doi.org/10.3892/mmr.2017.7013