A Novel Approach for Cigarette Smoke-Induced Differential Gene Expression in Blood Cells from Monozygotic Twin Pairs

A Novel Approach for Cigarette Smoke-Induced Differential Gene Expression in Blood Cells from Monozygotic Twin Pairs

Akshatha M1

Bioinformatics (BBI) GM Institute of Technology

Davangere, India.

Prof. Manjunath Dammalli3 Department of Biotechnology Siddaganga Institute of Technology Tumkur, India.

Prof. Dr. H. Gurumurthy2 HOD, Dept. of Biotechnology GM Institute of Technology Davangere, India.

Abstract Cigarette smoke is a well known source of chemical carcinogens and comprises a complex mixture of over sixty proven, probable and possible carcinogenic agents. Cigarette smoking alone is directly responsible for approximately thirty percent of all cancer deaths. Microarray data analysis provides information on disease and therapeutic approaches for cancer. Samples are downloaded from GEO database and imported to Bioconductor R to do statistical analysis. Expression analysis is performed to Identify and rank common differentially expressed genes in smokers compared to non-smokers, responsible to cause an incredible variety of cancer.

Keywords Cigarette smoking, Tobacco, Monozygotic twins, Bioconductor R, gene expression profiles, Cancer.

1. INTRODUCTION

   A good health demands a healthy environment. Over the last decades it has been a major interest to understand who and what extent human influence or pollute, their environment and how in turn, the environment influences the human health. The development of civilization and the emergence of industries over the preceding centuries greatly contributed to the increased presence of complex mixtures of hazardous toxic and carcinogenic chemicals in the human environment, many of which are suspected or have been proven to cause cancer. Human exposure to carcinogens may result from many sources, related to the general environment, occupational settings and dietary or life style habits(e.g.: cigarette smoking).Today cancer is one of the leading cause for the death worldwide, with annually increasing number of patients[1]. The word cigare is derived from the Mayan word sikar which means to smoke. The word tobacco is derived from a Spanish word tobaca which is a Y-shaped instrument used by early American Indians to inhale snuff. The term to smoke was introduced during the late sixteenth century by Sir Walter Raleigh to UK [2].

   Tobacco smoking has been in vogue for hundreds of years. Chemical carcinogenesis induced by lifestyle factors like cigarette smoking is a major research area in epidemiology. Cigarette smoke is a well known source of chemical carcinogens and comprises a complex mixture of over 60 proven, probable and possible carcinogenic agents.

   Monozygotic twin pairs are referred to as identical twins, but they are not perfectly identical, although they can be very similar. Monozygotic twins DNA is very similar, but there are differences, such as copy number variations that indicate a difference in the number of copies of certain parts of the DNA. These differences in DNA can result in slight differences in appearance as well as differences in other physical characteristics, medical conditions or susceptibility to certain diseases. Cigarette smoking alone is directly responsible for approximately 30% of all cancer deaths. Cigarette smoking also contributes to lung disease, heart disease, stroke, and the development of low birth weight babies. Cigarette smoking causes 87% of lung cancer deaths. Lung cancer is the leading cause of cancer death in both men and women. The health risks caused by cigarette smoking are not limited to smokers – exposure to second- hand smoke, or environmental tobacco smoke ETS (environmental tobacco smoke), significantly increases a non-smokers risk of developing lung cancer[1] [2].

   Cancer is a public health problem worldwide. It affects all people from the young to the old, the rich to the poor, men, women and children. Of the several causes investigated for cancer, the use of tobacco has shown strong and consistent associations with cancer at several sites of the body. Presently, more than 10 million people globally are diagnosed with cancer every year due to smoking. It is estimated that by 2020, there will be 15 million new cases every year. Cancer causes 6 million deaths every year, or 12% of deaths worldwide [4]. Microarrays generate large

   amounts of numeric data that should be analyzed effectively. Microarrays are used to measure gene expression levels in different ways. An experiment is designed by which the microarray experiment is carried out and data are generated. The analysis of microarray data to produce lists of differentially expressed genes has several steps which can differ based on the type of data being assayed. However, all data follows the same general pipeline which involves reading raw data, quality assessing the data, removing bad spots/arrays from further analysis, preprocessing the data and calculating differential expression by statistical analysis.

2. MATERIALS AND METHODS

   The current study planned to take gene expression data from Gene Expression Omnibus (GEO) and aimed to get genes and biomarkers which were not found by the studies conducted previously.

   Figure 1: Methodology of Flowchart

   The Gene Expression Omnibus is a microarray database that allows users to download experiments and curated gene expression profiles provided by NCBI. The samples for

   different smokers and non-smokers are downloaded from this database in order to perform expression analysis. The series matrix of the sample is downloaded and they are saved in ZIP/winRAR format. Bioconductor R is used to preprocess and normalize data. jvenn is a an integrative tool for comparing lists with Venn Diagrams. The Database for Annotation, Visualization and Integrated Discovery (DAVID) provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes. Cytoscape is an open source software platform for visualizing molecular interaction networks and biological pathways. profiler used for functional profiling of gene lists from large-scale experiments. GeneCards is a database of human gene that provides genomic, proteomic, transcriptomics, genetic and functional information on all known and predicted human genes KEGG pathway maps for biological interpretation of higher-level systemic functions.

3. RESULTS AND DISCUSSION

Differential gene expression caused by cigarette smoking in blood cells from monozygotic twins discordant in smoking behaviour. There are some parameters to be considered in the selection of dataset that is the organism of the dataset should be Homo sapiens, experiment type should be expression profiling by array and the data sets should based on Affymetrix human genome platform. The dataset should be in CEL format and there should be more than 2 groups within the dataset.

Different cigarette smokers samples are downloaded from GEO database in order to perform expression analysis. We only used the data sets based on Affymetrix human genome platform. Thats, expression profiles for the dataset GSE30660, GSE7434, GSE3212,GSE12585,GSE4635 were

downloaded including all samples.

R is free software which will be downloaded from the link. After a successful R installation, Bioconductor should be installed. After installing the required libraries and packages set the working directory. The datasets which are retrieved in Raw.tar format will be unzipped by using R statistical software into CEL files.

1. Pre-processing

   In order to perform meaningful statistical analysis and inferences fromthe data, we need to ensure that samples are comparable. Systematic differences between the samples that are likely to be noise, biological variability should be removed. To examine and compare the overall distribution of the transformed expression values in the samples Box plot are used.

   Affymetrix CEL-files contain slightly processed raw data of these probe intensities. Functions for reading Affymetrix data are available in the package affy. The function ReadAffy() reads in the raw data files, and stores

   the data as an AffyBatch object. By default, all CEL-files in the same directory are read. Next it compares the overall distribution of the transformed expression values in the samples.

2. Normalization

   Normalization is a broad term for methods that are used for removing systematic variation from DNA microarray data. Normalization makes the measurements from different arrays inter-comparable. Normalization is a process in which random elements of the datasets as well as background corrections will be done and it also calculates each of the expression. It is just one part of Affymetrix data processing before estimates of gene expression are ready for further analyses. If we compare the two box plots, differences is seen. If the sample seems quite different from others the dataset like this should be removed considering its bad quality sample.

3. Quality Control

   The next step in the quality control is to check whether the overall variability of the samples reflect their grouping. This can be done by hierarchical clustering of the samples to see if the samples cluster in the groups we expect. The samples are grouped according to the number of datasets are available.GSE30660 dataset equal number of samples in both groups (4 in each). The groups will be specified with the name as group1 and group2.In order to check the random samples within the two groups, group2 will be subtracted with the group1. Than p-value which is a measure that allows us to control how big a proportion of false positives (genes that we think are differentially expressed but really are not) are willing to accept. Normally the p-value should be less than 0.1 to 0.25. In This significant p-value and adj. p-value has been set to 0.1.Next step is to set the column names for each of the sample after that colour code will be specified for the dataset. In the hierarchical clustering map Red color specifies Highly Expressed Genes and Green Color specifies Low Expressed Genes. Black line shows which of the samples are clustered (Grouped) to one another.

   Figure 2: Hierarchical Clustering Map of GSE30660

4. Differentially Expressed Genes

   Statistical tests are carried out that will be used to identify the genes that are differentially expressed between the two groups. The two significant p-values parameters are selected. For this analysis p-value is used which is a measure that allows us to control how big a proportion of false positives we are willing to accept. To do a more refined selection of the genes that we believe to do list differentially expressed genes. The significant p-values for the GSE30660 dataset has been set to 0.1. Often the results of microarray experiments are verified using other methods, and then we want to filter out genes that exhibit differences in expression that are so small that we will not be able to verify them with another method. This is done by adding one last criterion to the filter. Difference should have an significant value higher than 0 or lower than 0, as we are working with log transformed data, the group mean difference is really the fold change, so this filter means that we require a fold change above 0 and below 0. Note that the significant value > is important because the difference could be negative as well as positive. The result is that we end up with a list of genes that are likely candidates to exhibit differential expression in the two groups [7]. A number of summary statistics are computed for each gene. The log-fold change is the log expression level for that gene. The AveExpr is the average expression level for that gene across all the arrays and channels. The moderated t-statistic (t) is the ratio of the M value to its standard error. Each p-value has an adjacent p-value for each of the gene. The log odd statistics (B) is shown for each gene. Figure 5.6 & 5.7 shows the list of up-regulated and down-regulated gene list.

5. Common Gene List

   Next step is to paste the probe-id of upregulated and down regulated id in each of the box. Name will be specified for each of the box, In order to identify which of the datasets have the common elements in the five datasets. Jvenn diagram shows which of the genes are overlapped with one another and the graph below the Jvenn shows number of probe-ids taken from each of the dataset.

   Figure 3: Venn diagram showing overlap among the five datasets upregulated and Downregulated genes in smokers

   Than these probe-id s are mapped to DAVID open source database. To determine Gene name of each probe-id

   Gene Accession Conversion Tool is used. Same method is followed for all the five datasets. In the DAVID database six random gene names are removed and it gives 101 gene names of differentially expressed genes. Below is the table showing the probe-id of both upregulated and Downregulated common genes along with their gene name, gene-id and from to location along with the function of each gene.

   The below table shows the Common Gene List.

   Table 1: Common Gene List

6. Functional Annotation of Differentially Expressed Genes The differentially expressed genes were mapped to

   their pathway. This gave the information about the genes and the pathway on which the gene acts. The total differentially expressed genes; Up regulated and Down regulated were mapped to DAVID open source database, this indexing will give curated evidence and confirmation of these genes as differentially expressed.

   Following are the screen shots for DAVID database which is used for annotation of common probe id and finding various information. The annotation results show that the probe id list have three functional categories and three protein domains. And all the details of the differentially expressed genes are given in DAVID annotation table.

7. Functional Annotation of Differentially Expressed Genes

   The differentially expressed genes were mapped to their pathway. This gave the information about the genes and the pathway on which the gene acts. The total differentially expressed genes; Up regulated and Down regulated were

   mapped to DAVID open source database, this indexing will give curated evidence and confirmation of these genes as differentially expressed.

   Following are the screen shots for DAVID database which is used for annotation of common probe id and finding various information. The annotation results show that the probe id list have three functional categories and three protein domains. And all the details of the differentially expressed genes are given in DAVID annotation table.

8. Functional Annotation of Differentially Expressed Genes The differentially expressed genes were mapped to

   their pathway. This gave the information about the genes and the pathway on which the gene acts. The total differentially expressed genes; Up regulated and Down

   regulated were mapped to DAVID open source database, this indexing will give curated evidence and confirmation of these genes as differentially expressed.

   Following are the screen shots for DAVID database which is used for annotation of common probe id and finding various information. The annotation results show that the probe id list have three functional categories and three protein domains. And all the details of the differentially expressed genes are given in DAVID annotation table. Again the file has to be imported to Cytoscape and the columns are set as the source and target. But now the selection of source will be probe-id and targt will gene symbol. The network parameters are set to visualize the network. Set the node size and node color along with that edge size and edge color. In this node color is described from low to high i.e. from orange to red. The edges and nodes are shown in the network. The arrows between the nodes can be directed or undirected. The highly expressed genes are shown in red colour and the low expressed genes are shown in orange colour. The analysis of the network statistics are shown in the result.

   1. List of Common Genes

For each of the gene Gene Ontology-id can be known in the Cytoscape. Select the highly expressed gene. When we select the gene UGT1A1 node color changes to yellow. Then select external links in that choose ontology click on Gene Ontology (Quick by name).it shows the list in which it gives information about a gene that in which of the species these genes are present.

J. Identified Common Genes and Diseases

In five dataset of monozygotic twin pairs discordant for smoking and non-smoking, we investigated whether cigarette smoking causes differential gene expression of toxicologically relevant genes in peripheral blood cells. By analyzing cigarette smoke-induced

differential gene expression in monozygotic twins, we reduced the impact of interindividual variability due to variation in genetic background. It also provided the opportunity to perform pair-wise analyses, adding statistical power to the study. The analyses revealed several genes to be reproducibly differentially expressed due to cigarette smoking. Genes which were differentially expressed in smokers compared to non-smokers were identified by a combination Jvenn and Cytoscape software. The genes that were found by all approaches are considered the most discriminatively relevant genes. Resulting genes are shown in the Table 2 Identified Common Genes and Diseases.

Table 2: Identified common genes and diseases.

CONCLUSION

Tobacco use causes a wide range of major diseases which impact nearly every organ of the body. The work investigated the genes which are differentially expressed in monozygotic twin pairs (smokers and non smokers). By analyzing both the up &down regulated results, ninety six highly expressed genes and eighteen common genes are investigated.

ACKNOWLEDGMENT

The authors would like to say thank Siddaganga Institute of Technology, Tumkur, GM Institute of Technology, for their Technical support and, Davangere for their support and guidance.

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