Identification of Gene Signatures in Rejected Lung Transplants
Researchers are making substantial advances in identification of Gene Signatures. A significant event in the post-genomics era was the development of high throughput technologies such as microarrays that provide huge volumes of complex data. Studying the differential expression of genes coding for various factors, mapping genes onto biological pathways, understanding their function and interactions enable scientists to identify expression patterns or signatures associated with diseases and drug response.
Microarrays are now all set to revolutionize the field of transplantation medicine. A case study on lung transplant using Genowiz™ a microarray data analysis tool developed by Ocimum Biosolutions has been illustrated in the following article.
Lung transplantation is the only succor to patients suffering from acute lung diseases. The limitation that most transplants face is the rejection of the transplanted organ. Several immuno-suppressive therapies have been formulated to lessen or inhibit transplant rejection, but the ratio of transplant rejections are still high. This generates a need for understanding the mechanisms and immune responses leading to transplant rejections. A differential expression analysis of samples taken from rejected and functional transplants can help researchers arrive at characteristic gene signatures that will help in the identification of possible drug targets.
How would a researcher interpret differential expression of genes to identify gene signatures?
- Cluster (if necessary) to look at expression patterns of interest among the differentially expressed genes
- Perform functional classification of genes
- Look into gene interactions using pathways
- Literature mining
- Interpretations
Genowiz™ makes use of all these methods to arrive at characteristic gene signatures after arriving at a set of differentially expressed genes. This has been illustrated by taking a publicly available dataset dealing with rejected lung transplant samples versus accepted or functional transplants.
The GDS999.soft dataset was taken from GEO database at NCBI. This dataset contains 27 samples with no rejection and 7 samples with acute rejection. To identify gene signatures associated with the cases of acute rejection, after uploading the data into Genowiz™ , the data was log transformed. Non significant genes were eliminated using t-test at a p value of 0.05 . FDR was applied to eliminate false positives. This resulted in 24 most differentially expressed genes between the rejected and accepted groups.
Figure 1 Differentially expressed genes
Figure 2 Most genes expressed more in rejected samples than accepted ones
To draw biological interpretations, these 24 genes were functionally classified using Gene Ontology feature of Genowiz™. In order to functionally classify the genes, select Bio Analysis > Build Gene Ontology. To view the functions, processes or components most affected in the data, select Bio Analysis > View GO Report. This report illustrates the functions affected in the data along with their z-scores. Higher the z-scores, more is the function affected. The report revealed that IFN-gamma receptor binding activity, cytokine biosynthesis, antigen binding, T- cell receptor binding and positive regulation of T cell proliferation were the major functions affected.
Figure 3 Gene Ontology Report
A pathway analysis of these genes mapped expression data mainly onto the following pathways: T cell receptor signaling pathway, JAK STAT pathway, NK cell mediated cytotoxicity pathway, TGF beta pathway, CAM, and cytokine cytokine receptor interactions.
Figure 4 Pathway Map
IFN-gamma was found to be involved in many of the pathways like T cell receptor signaling, NK cell cytotoxicity and TGF beta pathway, thus making it a likely gene to form a gene signature. Scientific abstracts obtained by connecting to PubMed from Bio Analysis > PubMed also revealed the presence of IFN gamma in various transplant rejections. Similarly other genes like CD 28 and CD 3E were also identified; thus, arriving at a gene signature which consists of IFN gamma, CD 3E and CD 28 genes in the rejected lung transplants as opposed to that of the accepted transplants. Identification of gene signatures, such as this one, would pave the way for biomarker discovery and target identification for drug design.
References:
- Matsumura Y, Zuo XJ, Prehn J, Linsley PS, Marchevsky A, Kass RM, Matloff JM, Jordan SC. Soluble CTLA4Ig Modifies Parameters of Acute Inflammation in Rat Lung Allograft Rejection Without Altering Lymphocytic Infiltration or Transcription of Key Cytokines, Transplantation, 1995 Feb 27; 59(4): 551-8.
- Hodge G, Hodge S, Reynolds PN, Holmes M. Increased Intracellular Pro- and Anti-Inflammatory Cytokines in Bronchoalveolar Lavage T-Cells of Stable Lung Transplant Patients. Transplantation 2005 Oct 27; 80(8): 1040-5.
