Responses to UV and IR in PBL
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Bioinformatics analysis for identifying responses to ultraviolet radiation and ionizing radiation in peripheral blood lymphocytes
Top 100 feature genes were classified into 4 clusters.
p53 signaling pathway and aminoacyl-tRNA biosynthesis pathway were involved in responses to UV and IR.
CDKN1A, GADD45A, CDK4 and SHMT2 were hub nodes in PPI network.
Purpose: This study was aimed to explore the underlyingmechanisms of responses to ultraviolet radiation (UV) and ionizing radiation (IR) in peripheral blood lymphocytes (PBL) by bioinformatics analysis.
Methods: Gene expression profile GSE1977 was downloaded from the Gene Expression Omnibus. Peripheral blood lymphocytes (PBL) samples collected from 15 healthy individuals were divided into three aliquots for mock (Mock), UV and IR treatment. Feature genes were identified by interquartile range, a repeated measures analysis of variance and random forest in R. The top 100 feature genes were shown with heat map. Cluster analysis and functional enrichment analysis for top 100 feature genes were performed. Then protein-protein interaction (PPI) networks were constructed by Cytoscape software.
Results: Total 826 feature genes were obtained. The top 100 feature genes were classified into 4 clusters. Feature genes in cluster 1 were significantly enriched in p53 signaling pathway. In cluster 2, feature genes were mainly enriched in aminoacyl-tRNA biosynthesis pathway. Cyclin-dependent kinase inhibitor 1A (CDKN1A) and growth arrest and DNA-damage-inducible α (GADD45A) were hub nodes in PPI network of cluster 1[MF1]. Cyclin-dependent kinase 4 (CDK4) and serine hydroxymethyltransferase 2 (SHMT2) were hub nodes in PPI network of cluster 2.
Conclusions: p53 signaling pathway and aminoacyl-tRNA biosynthesis pathway may be associated with UV- and IR-induced responses in PBL. CDKN1A, GADD45A, CDK4 and SHMT2 may be potential detection genes for UV and IR treatment in PBL.
Key words: peripheral blood lymphocytes, ultraviolet radiation, ionizing radiation
In recent year, there has been a large number of external stimuli, such as ultraviolet radiation (UV) and ionizing radiation (IR), affect individuals. These external stimuli lead to DNA damage, cell death, even body injury . Peripheral blood lymphocytes (PBL) as an extremely sensitive indicator are used for detecting body changes in several diseases [3,4]. PBL are mature lymphocytes and distribute throughout the body, rather than localising to organs (such as the spleen or lymph nodes) . PBL comprise T lymphocytes, natural killer (NK) cells and B lymphocytes .[MF2]
Excessive exposure to UV and IR can result in acute and chronic harmful effects on the eye, skin and immune system [6,7]. UV and IR trigger adaptive changes in gene expression , damage collagen fibers  and cause DNA damage . Christopher et al. showed oxidative DNA damage induced by UV light using Chinese hamster ovary cells . Declan et al. used bladder cancer cell-lines to study DNA damage and repair in telomerase reverse transcriptase (TERT) gene following IR . Moreover, in rat mesenchymal stem cells, low-dose IR and UV stimulate cell proliferation through activation of the MAPK/ERK pathway . Tan et al. reported that up-regulation of miR-125b served as a negative feedback mechanism to control p38α activity and promote cell survival upon UV radiation . However, UV- and IR-induced responses in PBL and underlying molecular mechanisms are still not clearly demonstrated.
Rieger et al. utilized GSE1977 to investigate the transcriptional response of 10,000 genes in DNA damage to IR and UV radiation. In this study, we downloaded GSE1977 andidentified the feature genes between UV and IR exposed samples to explain the responses in PBL.[MF3][MF4] Besides, cluster analysis, functional enrichment analysis and protein-protein interaction (PPI) networks were constructed to study and identify several genes in these responses. We hope this study can give us a systematic perspective to understand the mechanisms and discover potential genes in the responses to UV and IR radiation.[MF5]
Data and methods
Affymetrix microarray analysis
The array data of GSE1977 was downloaded from Gene Expression Omnibus (GEO) database, which was deposited by Rieger KE et al . Peripheral blood lymphocytes (PBL) samples were collected from 15 healthy individuals, ages 21-36. Lymphoblastoid cells were divided into three aliquots for mock (Mock), ultra-violet radiation (UV) and ionizing radiation (IR) treatment. The raw CEL data and annotation files were downloaded based on the platform of GPL8300 (Affymetrix Human Genome U95 Version 2 Array) for further analysis.
The probe IDs were converted into corresponding gene names based on the annotation information on the platform. If multiple probes corresponded to a same gene, the average expression value was calculated as the expression value of this gene. The missing values were imputed using k-nearest neighbor averaging  method with theimpute package  in R. The raw microarray data were quantile normalized with the PreprocessCore package  in R[MF6] and presented by box plot.
Feature genes analysis
In order to identify feature genes, different methods were used to filter genes. At first, interquartile range (IQR)  was used to filter genes based on gene expression levels distribution. All genes whose variability closely to 0 [MF7]were eliminated. Thenthe mean values among groups were compared according to a repeated measures analysis of variance (ANOVA) using genefilter package  in R. The genes in inter-group with significant difference and p-value < 0.01 were selected.[MF8] Finally, random forest method was applied to weigh the construction of each gene on the classification for samples [MF9]using randomForest package  in R. Hierarchical cluster analysis was constructed using the top 100 genes and visualized using heat map by the gplots package  in R.
Functional enrichment analysis
Gene Ontology (GO) database  is a collection of a large number of gene annotation terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) knowledge database  is applied to identify the functional and metabolic pathway. Database for Annotation, Visualization and Integrated Discovery (DAVID) [25,26] is a tool that provides a comprehensive set of functional annotation for large list of genes. GO enrichment analysis and KEGG pathway enrichment analysis were conducted for the feature genes with DAVID. The p-value < 0.05 was the cutoff criterion for the gene enrichment analysis.
Protein-protein interaction (PPI) network construction
Search Tool for the Retrieval of Interacting Genes (STRING)  is an online database which collects comprehensive information of proteins [MF10]. The interactions of protein pairs in STRING database were displayed with a combined score. The genes in different gene clusters [MF11]were mapped into PPIs based on the information of STRING database, respectively. The protein pairs with confidence score > 0.4 were considered to be significant. PPI network was visualized in the application of Cytoscape software .
After preprocessing, total 9012 genes were obtained from 45 chips data. Normalization result was shown in Figure 1.
Feature genes analysis
Figure 2A showed the distribution of IQR of raw data before filtering.[MF12] Filtered with ANOVA, 826 genes were retained according to the criteria of p-value < 0.01 (Figure 2B). Moreover, top 100 feature genes were identified by random forest based on their contributions to classification.
Heat map and cluster analysis[MF13]
The heat map for top 100 feature genes was shown in Figure 3A. The cluster analysis results showed that feature genes were classified into 4 clusters (Figure 3B). Feature genes in cluster 1 were up-regulated in UV and IR, but down-regulated in Mock. In cluster 2, feature genes were up-regulated in IR and Mock, but down-regulated in UV. Cluster 3 contained feature genes up-regulated in UV and down-regulated in Mock and IR. Cluster 4 included feature genes up-regulated in Mock, down-regulated in IR, but inconsistent changes in UV. Feature genes in cluster 4 were ignored due to weak separating capacity.[MF14]
Functional enrichment analysis
The GO enrichment analysis was shown in Table 1. Feature genes in cluster 1 were significantly enriched in GO terms of DNA damage stimulus and cell death; feature genes in cluster 2 were mainly enriched in tRNA and ncRNA metabolic processes; feature genes in cluster 3 were enriched in regulation of cell proliferation.
Total 4 pathways were obtained in KEGG enrichment analysis (Table 2). The feature genes in cluster 1 were enriched in “p53 signaling pathway”, “nucleotide excision repair” and “apoptosis”; feature genes in cluster 2 were enriched in “aminoacyl-tRNA biosynthesis”.
Protein-protein interaction (PPI) network construction
Only two PPI networks were constructed between feature genes and their interactive genes in cluster 1 (Figure 4A) and cluster 2 (Figure 4B), respectively.
In Figure 4A, the PPI network of cluster 1 included 17 nodes and 25 edges. Cyclin-dependent kinase inhibitor 1A (p21) (CDKN1A) and growth arrest and DNA-damage-inducible α (GADD45A) were hub nodes in this network. The PPI network of cluster 2 comprised 25 nodes and 29 edges. Cyclin-dependent kinase 4 (CDK4) and serine hydroxymethyltransferase 2 (mitochondrial) (SHMT2) were hub nodes in this network. In addition, glycyl-tRNA synthetase (GARS), alanyl-tRNA synthetase (AARS), tyrosyl-tRNA synthetase (YARS) and methionine-tRNA synthetase (MARS) composed a small module[MF15].
In this study, we aimed to identify feature genes that were particularly importance to UV and IR response.[MF16] For the identification of feature genes with the publicly available microarray database (GSE1977), we used ANOVA and random forest analysis. As a result, total 826 genes were identified according to the criteria of p-value < 0.01. Top 100 feature genes were classified into 4 clusters. The genes in cluster 1 were enriched in p53 signaling pathway and genes in cluster 2 were enriched in aminoacyl-tRNA biosynthesis pathway. Furthermore, PPI network results showed that CDKN1A and GADD45A were hub nodes in cluster 1. CDK4 and SHMT2 were hub nodes in cluster 2.
P53 is a protein that in human is encoded by tumor protein p53 gene . It regulates cell cycle and acts as a tumor suppressor preventing cancer . The p53 signaling pathway is stimulated by DNA damage . In this study, CDKN1A and GADD45A enriched in p53 signaling pathway were up-regulated in cluster 1 with UV and IR treatment. CDKN1A and GADD45A were also hub nodes in PPI network. They were significantly enriched in GO term of response to DNA damage stimulus, which were consistent with previous studies. [MF17]CDKN1A (the gene encoding p21) encodes a potent cyclin-dependent kinase inhibitor. Waldman et al. reported that p21/CDKN1A was necessary for the p53-mediated G1 arrest in human cancer cells . It involved in cell response to DNA damage mediated through transcriptional activation by p53 . Moreover, GADD45A, a member of GADD45 family genes, is a p53-regulated stress protein. GADD45A has been characterized as one of the important players that participate in cellular response to a variety of DNA damage agents . As a result, we infer that CDKN1A and GADD45A may regulate DNA damage induced by UV and IR treatment through p53 pathway.
In cluster 1, genes were significantly enriched in p53 signaling pathway. While, in cluster 2, genes were enriched in aminoacyl-tRNA biosynthesis pathway. Aminoacyl-tRNA is substrate for translation. The function of aminoacyl-tRNA biosynthesis is to precisely match amino acids with tRNAs containing the corresponding anticodon . Mihail et al. reported that UV-induced damage to translation inhibition was through impairing aminoacyl-tRNA binding[MF18] . In this study, GARS, AARS, YARS and MARS were enriched in this pathway. These genes were aminoacyl-tRNA synthetases. Yao et al. reported that UV irradiation induced phosphorylation of MARS by general control nonrepressed-2 . Thus, phosphorylation of MARS inhibited the methionine combined with its tRNA.[MF19] In addition, these four genes composed a small module[MF20] in PPI network. It suggested that these four genes interacted with each other in aminoacyl-tRNA biosynthesis pathway.
CDK4 is a member of the cyclin-dependent kinase family. In this study, cluster analysis of feature genes revealed that CDK4 was down-regulated in UV treatment, which was agreed with previous studies. Kim et al. reported that UV induced cell cycle arrest was mediated by CDK4 downregulation . In addition, SHMT2 was also down-regulated in UV-exposed in cluster 2. SHMT2 encodes the mitochondrial form of a pyridoxal phosphate-dependent enzyme. Fox et al. reported that SHMT2 transcript was highly susceptible to UV-induced DNA damage . Thus, CDK4 and SHMT2 were closely associated with responses induced by UV. They may be unique characteristic genes for UV treatment.
In conclusion, our data provides a comprehensive bioinformatics analysis of feature genes which may be involved in UV- and IR-induced responses in PBL. The genes of CDKN1A and GADD45A may regulate DNA damage induced by UV and IR treatment through p53 signaling pathway. The genes of GARS, AARS, YARS and MARS may be associated with response induced by UV via aminoacyl-tRNA biosynthesis pathway. CDK4 and SHMT2 may be potential detection genes for UV treatment in PBL. However, further experiments are still needed to confirm our results.
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