Bioinformatic data analysis – comparison from three human studies using different Affymetrix...

Dr. Agnieszka Lichanska, UQ

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

1School of Dentistry, 2Institute for Molecular Biosciences, 3 Australian Biosecurity CRC for Emerging Infectious Diseases The University of

Queensland, and School of Dentistry Griffith University Brisbane, Australia


Overview

• Studies and starting hypothesis• Analysis tools• Results from bioinformatics• Validation• Future studies• How well the new exon arrays

characterize the gene expression?


Studies

1. Comparison of gene expression between periodontal ligament cells and gingival cells

2. Functions of nuclear IGFBP53. Identification of biological processes

induced in osteoblasts by LPS


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


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


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?


Identification of differentially expressed genes

Total differentially expressed genes - 292

Genes with CpG islands – 121

Up in Ligament – 112 genesDown in Ligament – 180 genes

Genes with CpG islands – 70

Identification of differentially expressed genes: MAS 5.0 – presence/absence callsDMT - number of concordant changesSpotfire - ANOVA analysis


Biological processes

Up in ligament

Down in ligament

DAVID functional annotation tool


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


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


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


Exon arrays


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


Model cells

osteosarcoma Primary osteoblasts


Time course of IGFBP5A. 2 Hour C. 8 Hour E. 48 Hour

B. 4 Hour D. 24 Hour F. No Treatment

Concentration of IGFBP-5, 625ng/mL

-nucleolin Isotype control -IGFBP5

Confocal Z-sections


Affymetrix Human Exon 1.0 ST ArrayAffymetrix 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

• 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)


(www.affymetrix.com)

rRNA reduction step

2nd cycle cDNA synthesis

Sample preparation of 3’UTR vs exon arrays


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


Preliminary analysis of the datasetPCA

PCA colored by p-value


Up-regulated GenesUp-regulated Genes


Down-regulated GenesDown-regulated Genes


Functional analysis updown


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


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


Volcano plot analysis for gene selection


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


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


How do the new exon arrays compare with old 3’ UTR arrays?


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.


Splicing data - Differences between strong inducer - LPS and weak inducer - IGFBP5

IGFBP5LPS


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.


Acknowledgements

QBI: Virginia Nink, Paul Beatus

IMB:Sheryl Maher,Elisabetta d’Aniello

School of Dentistry:Thor FriisTimothy PanNguyen Pham

Griffith University:Dr Saso Ivanovski

Millenium Science: Robert Henke,Jeremy PrestonSpotfire: Andrew Khoo, Partek: Michael Venezia

This work was supported by the UQ ECR grant, ADRF and Eli Lilly Foundation grant


ComBio 22-26th September, Sydney

MGED/AMATA - 3-5th September, Brisbane

Date post:	29-Nov-2014
Category:	Health & Medicine
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Bioinformatic data analysis – comparison from three human studies using different Affymetrix...

Health & Medicine