Open Access

Gene expression profile analysis of ventilator-associated pneumonia

  • Authors:
    • Xiaoli Xu
    • Bo Yuan
    • Quan Liang
    • Huimin Huang
    • Xiangyi Yin
    • Xiaoyue Sheng
    • Niuyan Nie
    • Hongmei Fang
  • View Affiliations

  • Published online on: September 29, 2015     https://doi.org/10.3892/mmr.2015.4389
  • Pages: 7455-7462
  • Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up‑ and down‑regulated genes. The up‑ and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein‑protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up‑ and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

Introduction

Ventilator-associated pneumonia (VAP), the most common type of hospital-acquired pneumonia (HAP), is defined as bacterial pneumonia that develops in patients who have received mechanical ventilation for >48 h (1). VAP occurs in 6–52% of mechanically ventilated patients and in certain specific settings, the incidence is up to 76% (2). The incidence of VAP varies depending on the type of population studied, the intensity of preventive measures implemented and the presence or absence of risk factors. With an estimated attributable mortality of 9%, VAP usually increases the time of mechanical ventilation and time spent in the intensive care unit (ICU) by >10 days and its treatment accounts for at least 50% of antibiotics prescribed in ICUs, producing a huge cost (3). The expected burden of VAP in the coming years may increase with the increasing age of the population, intensification of care and the growing prevalence of severe underlying diseases in ICU patients (4). As the diagnosis as well as the treatment of VAP remain challenging, studies are required to gain a comprehensive understanding of VAP.

A number of previous studies have shown that up- or down-regulation of the expression of certain genes is closely associated with the development of infection. For instance, a study on the polymorphism of tumor necrosis factor (TNF)-α reported that overexpression of TNF-α is associated with a 2.1-13-fold increase in the incidence of severe sepsis from all causes, including pneumonia (5). PI3, which encodes an elastase-specific inhibitor with antimicrobial peptide activity, has proven to promote early clearance of Pseudomonas aeruginosa by activating macrophages and recruiting neutrophils (6). PIK3R3, which encodes the protein of phosphoinositide 3-kinase regulatory sub-unit gamma, was shown to be predominately expressed in immune cells and to be involved in chemoattractant-induced cell migration (7). None of these genes alone, however, is sufficient enough to answer the fundamental question why certain patients develop VAP, while other similar patients do not.

As a genome-wide screening approach, microarray analysis may be effective for identifying novel genetic factors or gene expression profiles associated with the development of infection in VAP. With the goal to construct a model for the prediction of susceptibility to VAP in critically-injured trauma patients, Swanson et al (8) analyzed the gene expression profiles of patients who developed VAP and those who were never affected. By comparing the differentially expressed genes (DEGs) between the two groups, a logistic regression model was constructed, which comprised five genes (PIK3R3, ATP2A1, PI3, ADAM8 and HCN4) and was able to accurately categorize 95% of patients that developed VAP.

The present study identified DEGs between patients with VAP and those without VAP based on the expression profiling data provided by Swanson et al (8) and then performed functional annotation and enrichment analyses in order to elucidate the potential molecular mechanisms of VAP. In addition, a protein-protein interaction (PPI) network was constructed and the functional modules in this network were analyzed. The novel view on VAP provided by the present study indicates that VAP requires further study.

Materials and methods

Derivation of genetic data

The gene expression profile dataset GSE30385 deposited by Swanson et al (8) was downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo), a public functional genomics data repository. The annotation platform was the GPL201 (HG-Focus) Affymetrix Human HG-Focus Target Array platform (Affymetrix, Inc., Santa Clara, CA, USA) updated on May 10th, 2013. A total of 20 samples from critically ill trauma patients were available, including 10 patients who developed ventilator-associated pneumonia (VAP group) and 10 who did not (NoVAP group).

Data processing

The Robust Multichip Average method of the R/Bioconductor Affy package (http://www.bioconductor.org/; Affymetrix, Inc.) was used to pre-process the downloaded raw data via background adjustment, quintile normalization and summarization (9). Using the R/Bioconductor software v 3.1 (http://www.bioconductor.org/), probe serial numbers in the matrix were transformed into gene names. As certain different probes can be mapped to the same gene, the average probe expression value was calculated as the final expression value of the corresponding gene.

Screening of DEGs

To screen DEGs between the VAP group and the NoVAP group, the Linear Models for Microarray Data (Limma) package in R/Bioconductor software 3.1 (http://www.bioconductor.org/packages/release/bioc/html/limma.html) was used (10). The Benjamini-Hochberg false discovery rate (FDR) was used to adjust the raw P-value in the multiple testing (11,12). DEGs were screened with cutoff values of FDR<0.05 and |log (fold change)|≥1.5 (13,14). The eligible DEGs were grouped into upregulated and downregulated genes. To guarantee that the identified DEGs represent the two types of specimen, cluster analysis of DEGs was also performed using the Pheatmap package of R (http://cran.r-project.org/web/packages/pheatmap/index.html).

Functional annotation and pathway enrichment analysis

The Database for Annotation Visualization and Integrated Discovery (DAVID) provides a comprehensive set of functional annotation tools to understand the biological meaning of large lists of genes by using a hypergeometric distribution algorithm (15). To functionally annotate DEGs identified between the VAP group and the NoVAP group, DAVID was used to analyze the significantly enriched Gene Ontology (GO) terms (16) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (17) for upregulated and down-regulated genes, respectively (adjusted P-value <0.05).

Computational identification of transcription factors (TF), tumor suppressor genes (TSGs) and oncogenes

TRANSFAC version 7.0 (http://www.gene-regulation.com/pub/databases.html), which was initially a data collection, has evolved into the basis for a complex platform for the description and analysis of gene regulatory events and networks, including TFs as well as their DNA-binding sites and profiles (18). The TSGene database was designed by integrating TSGs with large-scale experimental evidence to offer a comprehensive resource for further investigation of TSGs and their molecular mechanisms in cancer (19). The tumor-associated gene (TAG) database was developed by integrating information from well-characterized oncogenes and tumor suppressor genes to facilitate cancer research (20). Based on these three databases, DEGs identified between the VAP group and the NoVAP group were analyzed for the screening of TFs, TSGs and TAGs.

Protein-protein interaction (PPI) network construction

The Search Tool for the Retrieval of Interacting Genes (STRING version 1.0; http://string-db.org/) is an online database which includes experimental as well as predicted interaction information and comprises >1,100 completely sequenced organisms (21). The DEGs identified above were directly mapped to the STRING database for acquiring significant PPI pairs from a range of sources, including data from experimental studies and data retrieved by text mining and homology searches. PPI pairs with the combined score of ≥0.4 were retained for the construction of the PPI network.

Functional module analysis

CFinder (http://www.cfinder.org/) is a fast program for locating and visualizing overlapping, densely interconnected groups of nodes (defined as modules) in undirected graphs, and allows for easy navigation between the original graph and the network of these groups (22). Using the default parameter of k=3, modules were screened from the PPI network constructed as described above using CFinder. The top two sub-modules identified by modularity analysis were then selected for later functional enrichment by DAVID (FDR<0.05).

Results

Identification of DEGs

With a FDR<0.05 and |log (fold change)|≥1.5, 69 DEGs between the VAP group and the NoVAP group were obtained. Of these DEGs, 33 (47.83%) were upregulated and 36 (52.17%) were downregulated in the VAP group compared with the NoVAP group. The results of the clustering analysis of the DEGs are shown in Fig. 1.

Functional annotation and pathway enrichment analysis

To identify the functions of these DEGs, all of the up- and downregulated genes were mapped to terms of the GO database, which consists of three ontologies, namely biological processes (BP), cellular components (CC) and molecular function (MF). In the three categories of the GO terms, 'immune system processes', 'immune response', 'kinase activity' were dominant for the upregulated genes, while 'response to stress', 'programmed cell death' and 'peptidase inhibitor activity' were dominant among the downregulated genes (Table I).

Table I

Classification of differentially expressed genes in ventilator-associated pneumonia according to GO terms with P<0.05.

Table I

Classification of differentially expressed genes in ventilator-associated pneumonia according to GO terms with P<0.05.

Gene categoryGO IDFunctionCountP-value
Upregulated genes
 BPGO:0009617Response to bacterium9 6.66×10−8
GO:0006955Immune response14 2.12×10−7
GO:0051707Response to other organism9 5.28×10−6
GO:0009607Response to biotic stimulus9 7.59×10−6
GO:0002376Immune system process15 9.69×10−6
 CCGO:0005576Extracellular region130.0001
GO:0071682Endocytic vesicle lumen20.0004
GO:0030139Endocytic vesicle40.0005
GO:0005615Extracellular space60.0052
GO:0045335Phagocytic vesicle20.0076
 MFGO:0004908Interleukin-1 receptor activity2 8.24×10−5
GO:0035251 UDP-glucosyltransferase activity20.0001
GO:0046527Glucosyltransferase activity20.0002
GO:0004708Kinase activity20.0005
GO:0004712Protein serine/threonine/tyrosine kinase activity20.0023
Downregulated genes
 BPGO:0050873Brown fat cell differentiation3 5.08×10−5
GO:0006950Response to stress180.0001
GO:0050790Regulation of catalytic activity120.0001
GO:0006915Apoptotic process120.0002
GO:0012501Programmed cell death120.0002
 CCGO:0044445Cytosolic part40.0005
GO:0022626Cytosolic ribosome30.0009
GO:0044421Extracellular region part80.0019
GO:0044391Ribosomal subunit30.0026
GO:0005829Cytosol120.0036
 MFGO:0042277Peptide binding40.0004
GO:0033218Amide binding40.0005
GO:0004866Endopeptidase inhibitor activity40.0006
GO:0061135Endopeptidase regulator activity40.0006
GO:0030414Peptidase inhibitor activity40.0006

[i] GO, gene ontology; BP, biological process; CC, cellular components; MF, molecular function.

KEGG was used to further identify the altered biological functions arising from these DEGs. The upregulated genes were mainly enriched in ten pathways, including the neurotrophin signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway, while the downregulated genes were enriched in nine pathways, which included complement and coagulation cascades as well as pathways in cancer and ribosomes (Table II).

Table II

Enrichment of KEGG pathways in ventilator-associated pneumonia with P<0.05.

Table II

Enrichment of KEGG pathways in ventilator-associated pneumonia with P<0.05.

Gene categoryKEGG IDFunctionCountP-value
Upregulated genes4722Neurotrophin signaling pathway30.0064
4010Mitogen-activated protein kinase signaling40.0078
5014Amyotrophic lateral sclerosis20.0112
4621Nucleotide-binding oligomerization domain20.0133
receptor signaling pathway
4664Fc epsilon RI signaling pathway20.0238
4640Hematopoietic cell lineage20.0291
4912 Gonadotropin-releasing hormone signaling20.0375
4620Toll-like receptor signaling pathway20.0382
5146Amoebiasis20.0410
4060Cytokine-cytokine receptor interaction30.0449
Downregulated genes4610Complement and coagulation cascades40.0001
3010Ribosome30.0053
5150Staphylococcus aureus infection20.0192
5131Shigellosis20.0234
5140Leishmaniasis20.0318
5220Chronic myeloid leukemia20.0326
5200Pathways in cancer40.0358
5222Small cell lung cancer20.0431
4012ErbB signaling pathway20.0449

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Identification of TFs, TSGs and oncogenes

According to the TRANSFAC, TSGene and TAG databases, a total of four TFs, two oncogenes, four TSGs and two other TAGs were further screened from the DEGs preliminarily identified between the VAP group and the NoVAP group (Table III).

Table III

Annotation for TFs, TSGs, oncogenes and other TAGs in ventilator-associated pneumonia.

Table III

Annotation for TFs, TSGs, oncogenes and other TAGs in ventilator-associated pneumonia.

Gene categoryTFsOncogenesTSGsOther TAGs
Upregulated genesKLF7CD24LTFNA
Downregulated genesEGR3, FOSB, NCOR2CRKBTG2, CDKN1A, THBDRGS2, CTSZ

[i] TF, transcription factor; TSG, tumor suppressor gene; TAG, tumor-associated gene.

PPI construction

The PPI network contained 64 PPI pairs and 44 nodes (20 upregulated genes and 24 downregulated genes in the VAP group) (Fig. 2). The five node proteins with a connection degree of >5 were ELANE (connection degree, 10), PTGS2 (connection degree, 9), MAPK14 (connection degree, 8), LTF (connection degree, 7) and MMP8 (connection degree, 6).

Functional module analysis

High aggregation, which reflects the high modularization of a gene network, is an important characteristic of biological networks. To distinguish the modules with specific functions and different sizes, the constructed network was usually divided into relatively independent sub-modules prior to analysis. By using CFinder, three models were obtained using the default parameter of k=3 and the top two modules are shown in Fig. 3.

The significantly enriched GO terms and KEGG pathways for Module 1 and Module 2 are displayed in Tables IV and V, respectively. Genes in Module 1 were mainly classified into the GO terms of multi-organism processes, extracellular region and serine-type endopeptidase activity, while genes in Module 2 were mainly classified into the GO terms negative regulation of biological processes, nucleus and protein kinase activity. Genes in Module 2 were enriched in 12 pathways, including the MAPK, p53 and VEGF signaling pathways, while no significant pathway was identified for genes in Module 1 at FDR<0.05.

Table IV

GO annotation for genes in the top two modules.

Table IV

GO annotation for genes in the top two modules.

CategoryGO IDFunctionCountP-value
Module 1
 BPGO:0050832Defense response to fungus3 3.87×10−7
GO:0009620Response to fungus3 1.42×10−6
GO:0009617Response to bacterium5 1.96×10−6
GO:0051704Multi-organism process7 5.46×10−6
GO:0051707Response to other organisms5 2.53×10−5
 CCGO:0005576Extracellular region9 8.31×10−8
GO:0031012Extracellular matrix30.0016
GO:0044421Extracellular region part40.0031
GO:0030141Secretory granule20.0103
GO:0005615Extracellular space30.0114
 MFGO:0004252Serine-type endopeptidase activity3 5.05×10−5
GO:0008236Serine-type peptidase activity3 7.44×10−5
GO:0017171Serine hydrolase activity3 7.70×10−5
GO:0004175Endopeptidase activity30.0007
GO:0004867Serine-type endopeptidase inhibitor activity20.00103
Module 2
 BPGO:0071478Cellular response to radiation4 4.48×10−7
GO:2000379Positive regulation of reactive oxygen species metabolic process3 9.14×10−7
GO:0048519Negative regulation of biological processes9 1.27×10−6
GO:0009605Response to external stimuli7 1.63×10−6
GO:0071479Cellular response to ionizing radiation3 2.02×10−6
 CCGO:0005634Nucleus70.0038
GO:0005654Nucleoplasm30.0219
 MFGO:0004708Mitogen-activated protein kinase kinase activity2 3.65×10−5
GO:0004712Protein serine/threonine/tyrosine kinase activity20.00018
GO:0004672Protein kinase activity30.0038
GO:0016773Phosphotransferase activity, alcohol group as acceptor30.0064
GO:0016301Kinase activity30.0079

[i] GO, gene ontology; BP, biological process; CC, cellular components; MF, molecular function.

Table V

KEGG pathway enrichment for genes in Module 2.

Table V

KEGG pathway enrichment for genes in Module 2.

KEGG IDFunctionCountP-value
4380Osteoclast differentiation30.0005
5014Amyotrophic lateral sclerosis20.0022
4115P53 signaling pathway20.0035
5140Leishmaniasis20.0040
4370Vascular endothelial growth factor signaling pathway20.0044
4010Mitogen-activated protein kinase signaling pathway30.0044
4664Fc epsilon RI signaling pathway20.0048
4912 Gonadotropin-releasing hormone signaling pathway20.0077
4620Toll-like receptor signaling pathway20.0078
4110Cell cycle20.0114
5145Toxoplasmosis20.0129
5160Hepatitis C20.0132

[i] KEGG, Kyoto Encyclopedia of Genes and Genomes.

Discussion

Due to its high prevalence and mortality, there is an urgent requirement to investigate the pathogenic mechanism of VAP. The present study analyzed the gene expression profiles of patients who developed VAP and those who were not affected in order to identify possible functions and signaling pathways of DEGs between the two groups. After the construction of a PPI network, sub-modules in the network were mined and the functions of the top two modules were annotated.

In the present study, a total of 69 DEGs were identified between patients with VAP and those not affected. This result differs from that of Swanson et al (8), who provided the gene expression profile and identified 810 DEGs between the two groups. The inconsistency between the present study and that by Swanson et al (8) is likely to be attributed to the different statistical methods employed for detecting DEGs. Swanson et al (8) screened the DEGs using Partek software and the analysis of variance method, which calculates a q-value as described by Storey and Tibshirani (23), while the present study used the Limma package in R language, which is at present most commonly applied for identifying DEGs. To avoid false-positive results, relatively high cutoff values of FDR<0.05 and |log (fold change)|≥1.5 were set, which ensured high accuracy of the results.

The upregulated genes in the VAP group identified in the present study were mainly enriched in the GO terms of immune system processes and immune responses, which have been verified to have a pivotal role in the development of VAP (24). The immune system has a central role in controlling the duration and amplitude of the inflammatory response (25). A previous study on sepsis revealed that the innate and adaptive immune response is considerably different between patients with VAP and those without this type of nosocomial infection (26). A cohort study including 90 patients with VAP mainly caused by Gram-negative bacteria demonstrated an association between immune system disorders and mortality, as patients with early monocyte apoptosis of >50% were less likely to succumb to sepsis compared with those exhibiting monocyte apoptosis of l<50% (27). The present study revealed that GO:0002376 (immune system processes) and GO:0006955 (immune responses) were significantly abnormal in patients with VAP, and the two GO categories included the genes ELANE and LTF. ELANE is the gene encoding neutrophil elastase, a serine protease expressed in myelomonocytic cells and their precursors (28). Mutations in the ELANE gene are proven to be the most common genetic cause of congenital neutropenia, as ELANE mutations are associated with functional deficiencies in neutrophils and further contribute to the risk of infection (29,30). In addition, lactoferrin, encoded by LTF, was suggested to affect the regulation of the inflammatory response by modulating cellular iron homeostasis (31). Furthermore, the present study showed that ELANE and LTF were the significant nodes in the resulting PPI network. It is therefore inferred that ELANE and LTF may be involved in the susceptibility of patients to VAP by affecting the immune response.

The present study also observed that the upregulated genes in patients with VAP were significantly enriched in the MAPK signaling pathway, which was also the most significant pathway enriched among genes in Module 2 mined from the PPI network. The MAPK signaling pathway is a three-tiered cascade that includes a MAP kinase kinase kinase (MAP3K), MAP kinase kinase (MAP2K) and MAPK, which regulate numerous cellular functions, including proliferation, differentiation, migration and apoptosis (32). The four major MAPKs are extracellular regulated kinases 1 and 2 (ERK1/2), c-Jun-N-terminal kinases, p38 and ERK5 (33). Functional analysis performed in the present study demonstrated that four DEGs (GADD45A, MAPK14, IL1R2 and MAP2K6) were significantly enriched in this pathway, among which MAPK14 is also one of the nodes with the highest connection degree in the PPI network. MAPK14 encodes the MAPK14 protein that is also referred to as p38α (34). p38α is the first identified member of the p38 MAPK family and can be targeted by pyridinylimidazole drugs that inhibit the production of pro-inflammatory cytokines (35). Höcker et al (36) have demonstrated that following its activation by the GADD45B-MAP3K4 signaling complex, MAPK14 is directed to autophagosomes, where it impairs autophagosome-lysosome fusion and thus autophagy. It has been indicated that activation of autophagy in macrophages mediates the early lung inflammation during mechanical ventilation via NLRP3 inflammasome signaling (37). These findings combined with the results of the present study lead to the hypothesis that the p38 MAPK signaling pathway may be involved in the development of VAP and MAPK14 may be one of the key genes in this process.

In conclusion, the present study analyzed the gene expression profiles between patients with VAP and those not affected by this disease using a computational bioinformatics approach. Functional annotation of the DEGs into GO terms and KEGG pathways was performed, and a PPI network was constructed, followed by module mining. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway. The present study provided a novel, genetic perspective on VAP, which may aid in the development of strategies to prevent or treat VAP in patients in ICUs.

Acknowledgments

The present study was supported by the Nosocomial Infection Control Research Fund of The Chinese Preventative Association (grant no. ZHYG2014-0037) and the Jinling Hospital Research Fund of the Jinling Affiliated Hospital of Nanjing University Medical School (grant no. YYMS2014017).

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November-2015
Volume 12 Issue 5

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

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Copy and paste a formatted citation
x
Spandidos Publications style
Xu X, Yuan B, Liang Q, Huang H, Yin X, Sheng X, Nie N and Fang H: Gene expression profile analysis of ventilator-associated pneumonia. Mol Med Rep 12: 7455-7462, 2015
APA
Xu, X., Yuan, B., Liang, Q., Huang, H., Yin, X., Sheng, X. ... Fang, H. (2015). Gene expression profile analysis of ventilator-associated pneumonia. Molecular Medicine Reports, 12, 7455-7462. https://doi.org/10.3892/mmr.2015.4389
MLA
Xu, X., Yuan, B., Liang, Q., Huang, H., Yin, X., Sheng, X., Nie, N., Fang, H."Gene expression profile analysis of ventilator-associated pneumonia". Molecular Medicine Reports 12.5 (2015): 7455-7462.
Chicago
Xu, X., Yuan, B., Liang, Q., Huang, H., Yin, X., Sheng, X., Nie, N., Fang, H."Gene expression profile analysis of ventilator-associated pneumonia". Molecular Medicine Reports 12, no. 5 (2015): 7455-7462. https://doi.org/10.3892/mmr.2015.4389