Bioinformatic data analysis – comparison from three human studies using different Affymetrix platforms
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Talk presented in Vancouver 2007 at the Biomarker Conference.
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Bioinformatic data analysis – comparison from three human studies using different Affymetrix platforms
- 1. Dr. Agnieszka Lichanska, UQ
- 2. Bioinformatic data analysis – comparison from three human studies using different Affymetrix platforms Agnieszka M. Lichanska1 , Sheryl Maher2,3 , Nguyen Pham1 , Timothy Pan1,2 , and Saso Ivanovski4 1 School of Dentistry, 2 Institute for Molecular Biosciences, 3 Australian Biosecurity CRC for Emerging Infectious Diseases The University of Queensland, and School of Dentistry Griffith University Brisbane, Australia
- 3. Dr. Agnieszka Lichanska, UQ Overview • Studies and starting hypothesis • Analysis tools • Results from bioinformatics • Validation • Future studies • How well the new exon arrays characterize the gene expression?
- 4. Dr. Agnieszka Lichanska, UQ Studies 1. Comparison of gene expression between periodontal ligament cells and gingival cells 2. Functions of nuclear IGFBP5 3. Identification of biological processes induced in osteoblasts by LPS
- 5. Dr. Agnieszka Lichanska, UQ Array analysis • Affymetrix Platform – Hu133A arrays - using Ambion MessageAmp and Enzo IVT kit – Human ST1.0 exon arrays - using new GeneChip WT cDNA amplification kit • Analysis – MAS, DMT, Spotfire, – Partek – GO Browser (Affymetrix), Pathway Miner, DAVID, Onto-Tools, Clover, PAINT, MSCAN, CpGPro, • Data validation – Real time PCR, cell-based assays, other methods
- 6. Dr. Agnieszka Lichanska, UQ Study 1 - periodontal ligament cells and gingival cells • The objective was to identify the markers for periodontal ligament cells which can be used for development of periodontitis therapies. • Limited knowledge about the regulation of gene expression in those tissues. • Extensive functional knowledge about the role of the different cells in periodontium. • Hu133A arrays
- 7. Dr. Agnieszka Lichanska, UQ Questions 1. What regulates the differential gene expression in those tissues? 2. Is the differential methylation playing a role in expression regulation? 3. Can we identify markers for each of the tissues?
- 8. Dr. Agnieszka Lichanska, UQ Identification of differentially expressed genes Total differentially expressed genes - 292 Genes with CpG islands – 121 Up in Ligament – 112 genes Down in Ligament – 180 genes Genes with CpG islands – 70 Identification of differentially expressed genes: MAS 5.0 – presence/absence calls DMT - number of concordant changes Spotfire - ANOVA analysis
- 9. Dr. Agnieszka Lichanska, UQ Biological processes Up in ligament Down in ligament DAVID functional annotation tool
- 10. Dr. Agnieszka Lichanska, UQ Elk-1 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 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 PAINT analysis MSCAN analysis
- 11. Dr. Agnieszka Lichanska, UQ Broad-complex Elk-1HMG-IV bZIP911 Elk-1 FREAC7 HMG-IV HMG-IV SRYPAX4 DOF3 AP2 alpha 1 2000 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 Clover analysis LSM3 gene
- 12. Dr. Agnieszka Lichanska, UQ Conclusions • A lot of additional information can be mined from the array datasets, such as what can regulate differential gene expression • The promoter analysis can be particularly useful in cases when little in know about the system • Similarly to all of other analyses multiple tools have to be used as none of them provides all the information. • The output formats can be difficult to manipulate • The hypothesis has to be obviously validated in vitro
- 13. Dr. Agnieszka Lichanska, UQ Exon arrays
- 14. Dr. Agnieszka Lichanska, UQ Study 2 - Functions of nuclear IGFBP5 in osteoblasts • The objective was to identify the genes regulated by nuclear translocation of IGFBP5 • IGFBP5 Functions: – It is the main IGFBP in the bone – It induces proliferation of osteoblasts in vitro – It can act through IGF-dependent or IGF-independent mechanisms – It is also associated with breast cancer progression – It is known to interact with FHL2 and RAR/RXR in the nucleus – The target genes regulated by IGFBP5 are not known • Array platform Human ST1.0 exon arrays
- 15. Dr. Agnieszka Lichanska, UQ Model cells 0 0.5 1 1.5 2 2.5 3 IGFBP5 0.000645792 1 2.414514008 0.298656771 0.276601969 0.849916633 MG63 Saos-2 OBN OBK OBJ OBL osteosarcoma Primary osteoblasts 0 0.0002 0.0004 0.0006 0.0008 0.001 0.0012 MG63 # 1 MG63 # 2 MG63 # 3 FoldChange
- 16. Dr. Agnieszka Lichanska, UQ Time course of IGFBP5 A. 2 Hour C. 8 Hour E. 48 Hour B. 4 Hour D. 24 Hour F. No Treatment Concentration of IGFBP-5, 625ng/mL α-nucleolin Isotype control α-IGFBP5 Confocal Z- sections
- 17. Dr. Agnieszka Lichanska, UQ Affymetrix Human Exon 1.0 ST Array • Exon-level detection: differentiate differentially spliced transcripts of each gene • Gene-level detection: all probesets are summarised into an expression value of all transcripts from the same gene • Each exon comprises one probeset which contains 4 probes • Each gene contains around 40 probes (www.affymetrix.com)
- 18. Dr. Agnieszka Lichanska, UQ (www.affymetrix.com) rRNA reduction step 2nd cycle cDNA synthesis Sample preparation of 3’UTR vs exon arrays
- 19. Dr. Agnieszka Lichanska, UQ Analysis of the new Affymetrix exon arrays •Experimental QC –rRNA reduction –IVT yield –cDNA yield –Fragmentation of cDNA •Analytical QC –Box plot - actually best done in Expression console (Affymetrix) –Histogram analysis –PCA analysis •Analysis –Exon alternative splicing (visualized with gene model) –Gene level analysis (visualized with a bar chart) •Output –Splicing - gives Transcript cluster ID –Gene level - gives Probeset Ids –Entrez Gene ID has to be retrieved from Affymetrix to use in functional analysis •Functional analysis –GO analysis –Pathway mapping –Promoter analysis –CpG islands
- 20. Dr. Agnieszka Lichanska, UQ Preliminary analysis of the dataset PCA PCA colored by p-value
- 21. Dr. Agnieszka Lichanska, UQ Up-regulated Genes Functional Groups Gene Name Fold Change Transcription Factors ATBF1 AT-binding transcription factor 1 2.1 TTF1 transcription termination factor, RNA polymerase I 2.6 Extracellular Matrix COL4A6 collagen, type IV, alpha 6 2.5 Signalling Proteins SOCS3 suppressor of cytokine signaling 3 2.1 Transporters NUP210 nucleoporin 210kDa 2.8 Receptors RYR1 ryanodine receptor 1 (skeletal) 2.8 GPR109B G protein-coupled receptor 109B 2.8 GRIN2C glutamate receptor, ionotropic, N-methyl D- aspartate 2C 2.9 Growth Factors FGF10 fibroblast growth factor 10 2.4 Cell Cycle/Division BAD BCL2-antagonist of cell death 2.2 CDC6 cell division cycle 6 homolog (S. cerevisiae) 2.3 RBBP6 retinoblastoma binding protein 6 2.3 CENPK centromere protein K 2.4 BIRC5 baculoviral IAP repeat-containing 5 (survivin) 2.6 BCAR1 breast cancer anti-estrogen resistance 1 2.9 AURKB aurora kinase B 7.2 TOP2A topoisomerase (DNA) II alpha 170kDa 7.3
- 22. Dr. Agnieszka Lichanska, UQ Down-regulated Genes Functional Groups Gene Name Fold Change HNRPF heterogeneous nuclear ribonucleoprotein F -2.5 MBD1 methyl-CpG binding domain protein 1 -3.2 HOXA1 homeobox A1 -3.6 MEF2A myocyte enhancer factor 2A -4.2 TCF4 transcription factor 4 -4.2 RBM12 RNA binding motif protein 12 -5.2 KLF5 Kruppel-like factor 5 (intestinal) -4.1 Extracellular Matrix Col4A5 collagen, type IV, alpha 5 -2.2 Col2A1 collagen, type II, alpha 1 -2.7 Col27A1 collagen, type XXVII, alpha 1 -2.8 Col9A1 collagen, type IX, alpha 1 -3.1 LAMA2 laminin, alpha 2 -2.4 LAMC1 laminin, gamma 1 -2.2 Signalling Proteins CYFIP1 cytoplasmic FMR1 interacting protein 1 -2.1 ENPEP glutamyl aminopeptidase (aminopeptidase A) -3.5 Transporter SLC3A2 solute carrier family 3 (activators of dibasic and neutral amino acid transport), member 2 -3.4 SLC26A9 solute carrier family 26, member 9 -5.1 ABCA1 ATP-binding cassette, sub-family A (ABC1), member 1 -5.2 NUP133 nucleoporin 133kDa -5.3 Receptors RARG retinoic acid receptor, gamma -2.1 INSR insulin receptor -2.5 CD47 CD47 molecule -3.2 Growth Factors GRB10 growth factor receptor-bound protein 10 -2.5 RLN1 relaxin 1 -3.1 GFI1B growth factor independent 1B -3.2 Cell Cycle/Division CDY2A chromodomain protein, Y-linked, 2A -3.5 Transcription Factors / Nuclear Proteins
- 23. Dr. Agnieszka Lichanska, UQ Functional analysis up down
- 24. Dr. Agnieszka Lichanska, UQ Study 3 - Identification of biological processes induced in osteoblasts by LPS • The objective was to determine how LPS modulates function of osteoblasts • Osteoblasts express Toll-like receptors 2, 3, 4, 5 and 9, with TLR4 the being the main receptor for bacterial LPS • In periodontitis tissue loss includes bone loss but the changes induced by bacteria remain unclear • LPS is used in this study as a model for infection • What transcriptional events are induced by LPS? • What is the mechanism of induction of apoptosis in osteoblasts in response to LPS? Questions
- 25. Dr. Agnieszka Lichanska, UQ PCA analysis of the LPS experiment QC analysis using PCA PCA analysis not really useful for separating dataset the into groups after the ANOVA analysis
- 26. Dr. Agnieszka Lichanska, UQ Volcano plot analysis for gene selection
- 27. Dr. Agnieszka Lichanska, UQ Biological Processes regulated in LPS treated cells • Up-regulated genes – Actin cytoskeleton • Down-regulated genes – Mitosis – Cell cycle – Regulation of cell processes – Cellular physiological processes
- 28. Dr. Agnieszka Lichanska, UQ Conclusions • IGFBP5 study has identified biological processes – expected to be regulated by IGFBP5 treatment - cell cycle, proliferation – Also some unexpected ones - RNA splicing and transcriptional regulators • LPS study has provided us with clues as to the mechanisms by which LPS regulates osteoblast function – There is an upregulation of osteoclast stimulating factors,e.g. CSF-1 – There is downregulation of genes involved in proliferation – There is also upregulation of apoptotic genes – This suggests that a number of mechanisms can be potentially be involved in apoptosis known to occur in osteoblasts in response to LPS
- 29. Dr. Agnieszka Lichanska, UQ How do the new exon arrays compare with old 3’ UTR arrays?
- 30. Dr. Agnieszka Lichanska, UQ 3’UTR arrays These arrays let us to analyze the gene levels for each gene only and as the probe sets were selected mainly in 3’UTR region thus giving us limited information about gene expression.
- 31. Dr. Agnieszka Lichanska, UQ Splicing data - Differences between strong inducer - LPS and weak inducer - IGFBP5 IGFBP5LPS
- 32. Dr. Agnieszka Lichanska, UQ Final conclusions • Exon arrays provide us with much more information than 3’UTR ones • The analysis of new whole genome arrays and exon arrays can be combined by using the same hybridization cocktail • The new probe synthesis method has eliminated the need for using Test arrays, required by using cRNA on the arrays. • The data analysis can use the entire dataset, can focus on alternative splicing or gene levels • The output at the moment is difficult to manipulate • Gene Ontology and pathway mapping of exon arrays can be done through the same tool, DAVID Functional annotation tool. • Not all public tools are yet catering for the exon arrays.
- 33. Dr. Agnieszka Lichanska, UQ Acknowledgements QBI: Virginia Nink, Paul Beatus IMB: Sheryl Maher, Elisabetta d’Aniello School of Dentistry: Thor Friis Timothy Pan Nguyen Pham Griffith University: Dr Saso Ivanovski Millenium Science: Robert Henke,Jeremy Preston Spotfire: Andrew Khoo, Partek: Michael Venezia This work was supported by the UQ ECR grant, ADRF and Eli Lilly Foundation grant
- 34. Dr. Agnieszka Lichanska, UQ ComBio 22-26th September, Sydney MGED/AMATA - 3-5th September, Brisbane
Editor's Notes
- 292 genes less than -2 and more than 2 fold changed 60% have CpG islands (that is average in genome)
- Only processes with p-values less than 0.05 are shown. Up-regulation of RNA metabolism and biopolymer metabolism seem to support the fact that Periodontal ligament (PDL) produces high levels proteins, especially ECM (extracellular matrix) proteins.
- As one of the objectives was to identify what regulates the differential gene expression. We used a number of tools to analyze all 292 promoter regions. PAINT relies on TransFac database to identify potential Transcription factor binding sites. It provides a clustering type of output as well as list of potential factors. We have identified ELK-1 as overrepresented in a number of genes, and NKX2.5 as under-represented TF. As a confirmation we wanted to use a different method to identify binding sites. MSCAN identified ELK-1 sites in some of the same genes.
- As we wanted some statistical validation of the identified sites. CLOVER provides both statistical output and graphical output. Here is just an example of one of the genes with a number of TF binding sites identified, only the ones with a p-value<0.05 are indicated on the graph. ELK-1 was also identified through this method, in addition FREAC7 binding site, DOF3, PAX4, HMG-IV. Differential expression analysis identified FREAC2 as being differentially expressed so it is possible that this transcription factor is upregulated and can also regulate expression of some of other genes through the FREAC7 binding sites.
- IGFBP5 is one of the 6 IGFBPs and the main IGFBP in the bone and has been shown to affect osteoblasts, osteoclasts and chondrocytes. As IGFBP5 has nuclear localisation signal and has been shown to be present in the nucleus we wanted to identify the effects it has on gene expression. In vitro IGFBP5 has been shown to induce osteoblast proliferation but being down regulated during differentiation. IGFBP5 has also been associated with breast and prostate cancer. FHL2 (Transcription factor), RAR/RXR (retinoic acid receptors) identified by Yeast two hybrid screen. IGFBP5 does not bind to the DNA so it has to bind to a TF.
- As we wanted to assay effects of exogenous IGFBP5 on osteoblast function and gene expression. So we needed a model system, therefore we checked the expression levels of IGFBP5 by real time PCR in a range of cells and identified MG63 cells as the best model for our study.
- We needed to perform dose and time response as nothing of that has been identified previously. We determined 625ng/ml as the best dose. Using this amount we performed time course. The protein is just detectable at 2 hours and its concentration in the nucleus over next few hours and by 8 hours it appears to be localized to nucleoli. To determine if that is a likely answer we have used anti-nucleolin antibody but as both antibodies are mouse monoclonal we could not do a colocalization study. In order to show that the IGFBP5 is in the nucleus we used confocal microscopy and it showed that IGFBP5 is present in the nucleus and in the perinuclear region (DO NOT SAY THAT TO THEM BUT IF SOMEONE ASKS IT COULD BE GOLGI BUT I AM NOT FULLY CONVINCED, WE WOULD NEED TO HAVE SOME MARKERS, THIS WILL BE HOPEFULLY DONE BY LUCAS WITH LABELLED IGFBP5).
- Comparison of 3 ’ UTR arrays (Hu133A) with probes designed in the 3 ’ UTR and exon arrays with probes designed in all exons
- Sample preparation comparison
- QC analysis is similar to other Affy arrays and in fact any other arrays in fact. The exon arrays allow analysis of alternative splicing or gene level analysis depending what we want to look at. Moreover we can either analyze either just known genes or all of the genes which are on the arrays, this includes predicted and hypothetical genes. There are two types of outputs, transcript cluster IDs or Probeset IDs, however, they cannot be so far used in the web-based tools, which require the Affy Ids, Entrez IDs, GeneBank accession numbers or other identifiers. The Entrez IDs have to be retrieved from Affy website. Now let ’ s see what we are able to do with those arrays.
- We used Partek Genomics Suite for analysis. PCA (principal component analysis) has showed that the two datasets can be separated from each other, Below is the PCA for the ANOVA analyzed data (bottom graph). An interesting way of looking at the data is the chromosomal view. It shows that there are no any particular areas of the genome that are linked to regulation of gene expression by IGFBP5. This view is particularly useful if the methylation study is done at the same time.
- Here are just some of the data we identified in the analysis. POINT TO THE CELL DIVISION/CELL CYCLE, PROLIFERATION LINKED GENES
- Not surprisingly there was downregulation of ECM markers linked to differentiation, which was not unexpected. In addition to the GO analysis we wanted to identify the pathways involved but because of the output format we could not use Pathway Miner, which requires GeneBank Accession numbers and signal log ratios. Instead we used DAVID Functional annotation tool which allows to use Entrez IDs as input.
- One of the top scoring pathways was the regulation of cytoskeleton shown here. It indicates that in response to growth factors, such as FGF there will be alteration of actin polymerization.
- The last study I will show here was also done on the exon arrays and it was aimed at a continuation of the first one as the rationale was based on the periodontal LPS from Salmonella minessotta rough mutant
- PCA analysis was useful in the first study we did but in some cases such as this one it is not useful.
- The volcano plot‚ arrange genes along dimensions of biological and statistical significance. The first (horizontal) dimension is the fold change between the two groups (on a log scale, so that up and down regulation appear symmetric), and the second (vertical) axis represents the p-value for a t-test of differences between samples (most conveniently on a negative log scale ‚ so smaller p-values appear higher up). The first axis indicates biological impact of the change; the second indicates the statistical evidence, or reliability of the change. The researcher can then make judgements about the most promising candidates for follow-up studies, by trading off both these criteria by eye. With a good interactive program, it is possible to attach names to genes that appear promising.
- Exon arrays allow to look at the gene level or at the differential splicing. Changes can be either significant as in LPS or just in some exons in case of IGFBP5.