A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues

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A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues Sam Robson MOAC DTC, Senate House, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL

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A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues. Sam Robson MOAC DTC, Senate House, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL. Cancer. Normal Mitosis. Abnormal Mitosis. = Healthy Cell. = Healthy Cell. = Cancer Cell. - PowerPoint PPT Presentation

Transcript of A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues

Page 1: A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues

A Comparison of c-Myc Regulated Gene Networks Involved in

Tumourigenesis of Two Distinct Tissues

Sam Robson

MOAC DTC, Senate House, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL

Page 2: A Comparison of c-Myc Regulated Gene Networks Involved in Tumourigenesis of Two Distinct Tissues

CancerNormal Mitosis Abnormal Mitosis

Genetic defects prevent apoptotic pathways from activating, allowing abnormal cells to proliferate. With no proliferative control, tumours can form.

Cell duplicates to form two identical daughter cells. Errors in DNA replication result in cell suicide (apoptosis) to avoid passing aberrant DNA to progeny.

= Healthy Cell

= Cancer Cell

= Apoptosis

= Healthy Cell

= Cancer Cell

= Apoptosis

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C-Myc

C-Myc Protein

45 63 129 143 355 368 410 4391

Carboxyl terminal

Amino terminal

Transactivation domain

Myc Box IMyc Box IIBasic

Helix-Loop-HelixLeucine Zipper

Legend

Max Protein

Image adapted from Pelengaris et al. (2002).

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Transgenic Model

Myc Box IMyc Box IIBasic

Helix-Loop-HelixLeucine ZipperEstrogen Receptor

Legend

ERTAM HSP90

MycInactive MycERTAM

Bound Heat Shock Protein 90

4-Hydroxytamoxifen

4-OHT binds estrogen receptor opening up bHLHz domain.

Max Max binds Myc at leucine helix-loop-helix zipper region

Active MycERTAM

Myc-Max complex binds E-box sequence of target gene

TR

RA

P

Transformation-Transcription domain Associated Protein (TRRAP) binds to MBII with help from MBI

HA

T

RNA Polymerase

TRRAP recruits a histone acetyltransferase (HAT). This acetylates nucleosomal histones resulting in chromatin remodelling, allowing access by RNA Polymerase for gene transcription

CACGTG

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Skin

Image taken from http://kidshealth.org/kid/body/skin_noSW.html

• Cells proliferate from epidermal stem cells in basal layer.

• Migration towards surface – cells become keratinized.

• Stratum corneus layer made up of highly keratinized nuclei-free cells – squames.

• Squames constantly shed from epidermal surface.

• Homeostasis within the skin very important.

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Pancreas

• Homogenous groupings of cells within the exocrine – Islets of Langerhans.

• Islets contain predominately β-cells – sole source of insulin

• Insulin responsible for glucose metabolism.

• Loss of insulin leads to Type I diabetes.

• Pancreatic ducts transport pancreatic enzymes.

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c-MycERTAM Activation in Pancreas – Apoptosis

c-MycERTAM Activation in Skin – Proliferation

INACTIVE ACTIVE

Pancreatic Islet β-Cells

Proliferation and Apoptosis

Apoptosis outweighs

proliferation

Islet involution

Skin epidermis Constant renewal of

cells from basal layer. Keratinised ‘squame’ cells lost from surface

Proliferation Increase in basal cell

proliferation. More cells migrate through to squamous layer

INACTIVE ACTIVE

+4OHT

+4OHT

Unchecked proliferation leads to many hallmarks of carcinoma.

Tumour is localised with no metastasis seen.

Cell Migration

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Microarray

•Features measure one nucleotide sequence (25mers).

•Hundreds of identical 25mers per feature.

•11-20 features per gene.

•25mer sequence specifically binds biotin labelled cDNA.

•Fluorescence readings give relative RNA concentration – equivalent to gene expression.

Images courtesy of Affymetrix - www.affymetrix.com

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Microarray HybridizationTotal RNA

Reverse TranscriptionAAAA

AAAA

AAAA

AAAA

cDNA

In Vitro Transcription

B

B

B

Biotin-labelled cRNA

Fragmentation, biotin labelling

and hybridization.

Analysis in Genespring

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Cell cycle – Cancer reversal

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Cell Death – Cancer reversal

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Laser Capture Microscopy

Laser Glass Slide Support

Membrane Slide

LCM Ependorf

Tube

Tissue

• Tissue section bound to membrane of LCM slide. Glass slide used as support on LCM platform.

• “Sticky” ependorf tube lid lowered onto membrane. Laser cuts designated area for dissection. Raising lid lifts cut material from LCM platform for RNA extraction.

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Laser Capture Microscopy

Sweat gland

Path of laser

1:

4:3:

2:

Sweat gland

removed

Laser captured

sweat gland

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Problems with pancreatic RNA

Freshsection

Airdriedsection

Fixed section

Stained section

Control sample

18s and 28s peaks similar heights

18s and 28s peaks different heights

18s and 28s peaks non-existant

18s and 28s peaks non-existant

18s and 28s peaks non-existant

A EDCBFreshsection

Airdriedsection

Fixed section

Stained section

Freshsection

Airdriedsection

Fixed section

Stained section

Control sample

18s and 28s peaks similar heights

18s and 28s peaks different heights

18s and 28s peaks non-existant

18s and 28s peaks non-existant

18s and 28s peaks non-existant

A EDCB

• RNA degraded naturally in cells by the enzyme RNase.

• Pancreas rife with RNase activity.

• Integrity of RNA gradually decreases throughout LCM procedure.

• RNA fully degraded by the time of tissue collection.

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Problems with pancreatic RNA

A Fresh Sample

Islet RNA

Exocrine RNA

B C

18S and 28S peaks different heights

18S and 28S peaks different heights

Large unknown peak

• RNA integrity in islets is good compared to RNA integrity in exocrine tissue.

• Implies that islet RNA is not subject to same degradation as that in the exocrine.

• Possible that structure of islets protects islet cell RNA from ductal RNases.

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Network Analysis

Image from Schäfer et al., 2005.

•Empirical Bayesian approach estimates gene network structure from microarray data.

•Problems – Sample size small for number of nodes (genes).

•Allows estimation of gene interactions in complex system.

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Generalised Linear Models• Unsupervised linear regression technique.• Models data as a linear combination of variables:

...321

332211 nnn xbxbxbay

• Gives the most statistically relevant variables.

• Implementation in Genespring for public use.

• Makes no assumptions of data and works with unbalanced experiments – Useful for clinical data.

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Conclusion• c-Myc known to be very important in cancer

formation.• c-Myc function in cancer onset still not fully

understood.• In vivo analysis of early c-Myc activity will help to

disentangle the web of c-Myc functionality.• Understanding of the route of tumourigenesis

will hopefully aid in development of gene specific cancer therapies.

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Acknowledgements

Project Supervisors:

Mike Khan

David Epstein

Stella Pelengaris

Group members:

Sylvie Abouna

Linda Cheung

Vicky Ifandi

Göran MattsonSpecial thanks:

Helen Bird, Sue Davis, Lesley Ward, David Pritlove, Sean James, Paul Anderson