In search of tissue specific regulators in periodontium - a bioinformatic approach.


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Poster presented at ESHG in Nice, France in 2007.

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In search of tissue specific regulators in periodontium - a bioinformatic approach.

  1. 1. Introduction: Tissue specific gene expression can be regulated by tissue specific promoters, enhancers, silencers, transcription factors, differential methylation, tissue specific alternative splicing, as well as other transcriptional and post- transcriptional factors. There are multiple methods used for studying the regulatory elements, however, they are useful in cases where some information about the promoters active in a given tissue is available. This information is lacking for the regulation of gene expression in the tissues maturing after the tooth development is complete is unclear. Periodontal ligament tissue (PDL) is essential for structural support of the teeth (attaches root to the bone) (Figure 1) while gingiva offers protection from external factors. Expression profiling data of the primary cell cultures of periodontal ligament tissue and outer gum tissue (gingiva) was performed using Affymetrix HU133A arrays. The analysis has identified 292 genes differentially regulated in these tissues. This set of genes was then subjected to promoter analysis to identify the CpG islands and promoter binding sites. We have used a number of bioinformatic tools, such as Promoter-Express, PAINT, MScan, Clover, CpGProD, and CpG plot to generate an overview of the promoters of the differentially regulated genes. As a result we identified signature promoter features of these differentially expressed genes. Conclusions: The CpG island analysis has identified a number of genes with potential methylation sites, these will be investigated further in the current global scan using the biopsies of the two tissues. It will be important to confirm experimentally if these genes are methylated in the tissues. The identification of potential transcription factors (TFs) involved in the regulation of gene expression has indicated that Elk-1 is a potential but not only regulator of expression in the ligament. It was clear from CLOVER analysis that there are multiple sites for many TFs and this information can now be used for experimental analysis of the promoters. Results: Figure 3. Bar graph of Gene Ontology (GO) analysis using DAVID software. The two sets of differentially regulated genes were categorised in Gene Ontology biological process with DAVID software. Those processes with P<0.05 are displayed in the graph for the set of genes up and down in human periodontal ligament in comparison to gingiva . Methods: CpGProD was employed to predict the presence of CpG islands associated with the promoter regions of each of the genes . The region 2000 bp upstream from the transcription start site of each gene was first masked using Repeat masker and then processed by CpGProd (Fig. 2). The fasta format of the regulatory regions was extracted using PAINT program . The functional groups were identified using DAVID software. The genelists of differentially expressed genes consisting of Affymetrix probe ids were uploaded to the program to determine the biological processes they might be involved in (Fig.3) with a p-value cut-off at 0.05. Over expressed transcription factor binding site clusters were predicted by PAINT (Fig. 4) and CLOVER (Table 2, Fig 5 & 6) programs. Over represented clusters were predicted within the sequence 2000 base pairs upstream from the transcription start site of each of the differentially regulated genes (P< 0.05). A comparison of identified over expressed cluster (Elk-1) in genes up in periodontal was performed by the MSCAN software (Table 1) using JASPAR Elk-1 position frequency matrices. Figure 2. Computational prediction of the presence of CpG islands using CpGProD. The the presence, location and size of CpG islands within the region 2000 base pairs upstream from the transcription start site of each of the genes was predicted using CpGProD. The same analysis has been performed using CpG plot and the results were consistent across 80% of the CpG islands identified. Gene Name Predicted Elk-1 Cluster PAINT MSCAN CYP51A1     EGR1     HSPE1     KPNB1     MAGOH     MET     PAWR     PLCB4     PPP1CB     RNF5     SNRPD1     SNRPG     TAF11     TDG     GLG1     SIP1     FUBP3     ADAMTS1     KIAA0152     COX17     CDC42EP3     PDLIM5     PAPOLA     EBNA1BP2     U2AF2     DHRS7B     C14orf109     LSM3     TPRKB     C14orf111     MRPL35     LSM8     ENAH     C13orf10     YRDC     ZNF587     Figure 4. PAINT transcription factor binding site cluster analysis of genes up-regulated in ligament. PAINT was employed to predict transcription factor binding site clusters within the 2000 bp upstream of transcription start site of genes down- and up-regulated in ligament. The same analysis was also performed using various GO groupings from DAVID analysis to identify if any of the biological processes are associated with particular sets of TF binding sites. Table 1. Comparision of PAINT and MSCAN prediction for the presence of ELK-1 transcription factor binding sites. Acknowledgments: This work was supported by UQ Early Career Grant. Elk-1 Legends: Enamel Gingival epithelium Gingiva Cementum Alveolar bone Periodontal ligament Root of the tooth References: ALKEMA, W. B. et al (2004) Nucleic Acids Res, 32, W195 -8. DENNIS, G., JR., et al (2003) Genome Biol, 4, P3. PONGER, L. & MOUCHIROUD, D. (2002) Bioinformatics, 18 , 631-3. VADIGEPALLI, R., et al (2003) Omics, 7, 235 -52. FU, Y., et al (2004) Nucleic Acids Res, 32, W420 -3. Total differentially expressed genes - 292 Genes with CpG islands – 121 Up in Ligament – 112 genesDown in Ligament – 180 genes Genes with CpG islands – 70 GObiologicalprocessterms Number of Genes Prediction of Elk-1 Transcription factor binding site clusters in gene by both PAINT and MSCAN Prediction of Elk-1 Transcription factor binding site clusters in gene by PAINT only Prediction of Elk-1 Transcription factor binding site clusters in gene by MSCAN only Biological processes of genes up in periodontal ligament Biological processes of genes down in periodontal ligament Table 2. CLOVER analysis of promoters of genes up-regulated in ligament Transcription factor P-value Broad complex 0 SRY 0.001 AP2 alpha 0.001 FREAC-7 0.002 ELK-1 0.002 DOF-3 0.003 UBX 0.006 bZIP911 0.006 PAX4 0.008 HMG-IV 0.01 HFH-1 0.01 Figure 5. ELK-1 and other TF sites in the 2000bp upstream of the LSM3 gene. Analysis was performed using CLOVER software. Only the TFs with p-value<0.05 are indicated. In search of tissue specific regulators in periodontium - a bioinformatic approach. Agnieszka M. Lichanska and Nguyen Pham Department of Oral Biology and Pathology, University of Queensland, St Lucia, Australia. Figure 1. Structure of the periodontium Elk-1HMG-IV bZIP911 Elk-1 Elk-1 HMG-IV HMG-IV SRY PAX4 DOF3 AP2 alpha Broad- complex 1 2000