Transcriptomics applied to Obesity

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Transcriptomics applied to Obesity

• Nathalie Viguerie
• 2007 June 7th

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Contents

• Genes, genomics and nutrigenomics
  introduction 3 - 8
• Techniques and technologies in transcriptomics 9
  - 32
• Gene ontology 33 - 39
• Microarray workflow process 40 - 43
• Transcriptomics applied to obesity 44 - 56
• Quantitation of mRNA levels overview 57
• 7. Abbreviations used 58

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Only a fraction of all genes are turned on.It
is the subset that is "expressed" that confers
unique properties to each cell type.

• "Gene expression" is the term used to describe
  the transcription of the information contained
  within genomic DNA into messenger RNA which is
  then translated into the proteins that perform
  most of the critical functions of cells.

4
Genomics

• refers to the comprehensive study of genes and
  their function as a whole
• based on high-throughput techniques allowing a
  wide picture of gene characteristics
• understanding of the molecular mechanisms
  underlying normal and dysfunctional biological
  processes
• Gene expression (messenger RNA) profiles
  (transcriptome) offer a multidimensional view of
  metabolic diseases and can provide a basis for
  choosing between therapies yielding a common
  clinical end point

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Some questions for the golden age of genomics

• How gene expression differs in different cell
  types?
• How gene expression changes when the organism
  develops and cells are differentiating?
• How gene expression differs in a normal and
  diseased (e.g., insulin-resistant, cancerous)
  cell?
• How gene expression changes when a cell is
  treated by a drug?
• How gene expression is regulated which genes
  regulate which, and how?

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Nutrigenomics

• supermarket of today pharmacy of tomorrow?
• ability to optimize nutrition and maintain a
  state of good health through longer periods of
  life

• focuses on the interaction between bioactive
  dietary components and the genome
• guidelines may be ideal for only a relatively
  small proportion of the population
•  Right diet (drug), right person, right time  

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Two strategies of nutrigenomics research

• identifies candidate genes (Transcription Factors
  as nutrient sensors) and targets
• elucidates signaling pathways involved
• in response to nutrients

• develop biomarkers of early metabolic
  dysregulation and susceptibility (stress
  signatures) that are influenced by diet

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D1
1 gene leads to many gene products
3 billion DNA bp
23-30000 genes
70-100000 transcripts
gt 100000 proteins
gt 1000000 peptides
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50 years of technology development
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D2
The DNA double helix
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Assessment of mRNA levels
Microbiopsies of subcutaneous adipose tissue (0.5
g) of skeletal muscle (50 mg) of liver (50 mg)
Optimization of total RNA extraction
Quality control of total RNA
RT- real time PCR
DNA chip
High number of subjects Quantitative
measurement Limitations in gene number
Complete transcriptome or gene selection Limitatio
ns in number of subjects
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D2
Typical yield of total RNA from human tissues
Tissues/cells
50 mg
1 g
50 mg
50 mg
200 µg -1mg
Total RNA preparation
5-10 µg
50 µg
50-300 µg
175 µg
40 µg
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Challenges /alternatives

• Control for sample heterogeneity
• Careful subject selection, consistent sample
  collection, careful and rapid processing and
  storage.
• Amount of tissue limits quantity of total RNA
• RNA amplification
• Overcoming the complexity of the tissue
• microbeads isolated cells
• Laser dissection

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D3
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Total RNA check
Yield purity
( 1U 40 µg/ml)
Nanodrop
Quality
Agarose gel electrophoresis
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Microfabricated chip-based electrophoresis

• - lab-on-a-chip
• low sample consumption (lt2 µL)

Agilent 2100 Bioanalyzer
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DNA microarrays basics
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DNA arrays
Clontech Macro-array (nylon) 1176 human cDNA
Micro-array (plastic) 8 000 human oligos
13 cm
13 cm
Macroarrays 8 X 13 cm nylon polypropylene Mi
croarrays nylon 2.7 X 1.8 cm glass 2.5 X 7.5
cm or less
8 cm
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Probe selection strategies
Prerequisite for quality of the arrays careful
probe design
Accuracy specificity
throughput
Selected transcript regions (oligonucleotides,
PCR products)
Selected transcripts (IMAGE clones, PCR products)
ESTs (IMAGE clones) Spotting without prior
sequencing
Importance of sensitivity and specificity for
detection of weak mRNA levels diferences
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Micro-arrays on glass slides (gt10,000 depots/cm2)
2 cm
arrayer
8 cm
Jaw pins
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Affymetrix Technology
In situ oligonucleotide synthesis with
light-induced coupling (de-) protection
chemistry
1 transcript 40 oligonucleotides 20 Perfect
Match 20 MisMatch
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Agilent In Situ Oligo Synthesis Ink-jet printing
method
60-mer oligonucleotide microarrays constructed
base-by-base
Pins
Sureprint Inkjet Process

• Non-contact printing
• Uniform feature size/shape
• No donuts
• Precision printing/volume control
• Improved pixel statistics
• 2-colour hybridisation

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Illumina Sentrix Human-6 Expression
BeadChipprobe coated small beads
3µ Bead arrays
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Nylon microarrays Labelling using radioactive
probes (32P, 33P) Best incorporation rate
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Labelling using fluorescent probes
green)
red)
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DNA chip basic principles
Control
Diet
Total RNA
Amplified RNA
Labelling

0
Cy5dUTP
Cy3dUTP
Purification, quantitation
Signal analysis
log2 Cy5/Cy3
44 K oligochips
Statistical analysis (SAM)
Data analysis Annotation (GO) Clustering Target
analysis
Hybridization, washing steps
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Differentially expressed genes
Gene under-expressed
Genes over-expressed
Genes expressed at low level
Exp 4
Exp 3
Exp 2
Exp1
Exp Gène
Signal from equally expressed genes
Ratio1,4 Cy5/Cy3
Ratio1,3 Cy5/Cy3
Ratio1,2 Cy5/Cy3
Ratio1,1 Cy5/Cy3
Gène1
-
-
-
-
-
Feature extraction Genepix,
-
-
-
-
-
Ratio i,4 Cy5/Cy3
Ratio i,3 Cy5/Cy3
Ratio i,2 Cy5/Cy3
Ratio i,1 Cy5/Cy3
Gène i
Mean intensity RNA from controls
Mean intensity RNA from treated or restricted
subjects
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Data analysis
Clustered display of data from time course of
cell treatment Time-course Dose-response Subject
s comparison
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Microarray Gene Expression Data Society
MGEDOntology Working Group Progressguidelines
in the form of MIAME
www.mged.org
www.mged.org
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Ontologies Working Group Goals

• Collect controlled vocabularies for sample
  descriptions.
• Define microarray concepts and their
  relationships.
• Provide bridge to ontologies from other knowledge
  domains.

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MIAMEMinimum Information About a Microarray
Experiment

• Describes the Minimum Information About a
  Microarray Experiment that is needed to enable
  the interpretation of the results of the
  experiment unambiguously, and potentially to
  reproduce the experiment.

• Underlying motivation
• to enable the establishment of public
  repositories for microarray data
• to serve as a basis for designing a microarray
  data exchange format
• Recommended for submitting microarray data to
  public repositories
• ArrayExpress
• Gene Expression Omnibus (GEO)

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Ontologies in Gene Expression Databases
Deciphering the biological meaning of data will
be facilitated by annotating genes/genes products
using an controlled vocabulary. eg. Gene Ontology
TM Consortium.

• Controlled vocabulary
• Define relationships through hierarchy (e.g.,
  taxonomy)
• Knowledge representation
• Link to other domains (gene sequence annotation,
  gene and protein roles, pathways). Facilitate
  data exchange.
• Literature (MeSH), Phenotypes, Others
• Gene descriptions (Gene Ontology)

Molecular Function TEXT the tasks performed by
individual gene products examples are
transcription factor and enzyme Biological
Process TEXT broad biological goals, such as
TCA cycle or steroid metabolism, that are
accomplished by ordered assemblies of molecular
functions Cellular Component TEXT subcellular
structures, locations, and macromolecular
complexes examples include nucleus, telomere,
and origin recognition complex
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Glycolytic pathway
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Glycolytic pathway
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Microarray workflow process major phases of
microarray analysis and their connectivity
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Microarray data confirmationReal-time PCR
End point
Cycle threshold (real-time)
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Dedicated arrays
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Transcriptomics applied to obesity

• Use microarrays strategy to map changes in gene
  expression related to
• changes in energy expenditure (skeletal muscle)
• Nutritional challenges (adipose tissue)
• Calorie restriction
• Dietary composition during restriction
• nutrient sensitive genes
• Predictors of weight loss or complications

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Molecular basis for energy metabolism in humans
Energy balance
Weight

• Nutritional challenges
• VLCD
• Nutrient composition
• Weight stabilization

Physical activity
Lipids
Hormonal stimulation of resting energy expenditure
D.I.T. / TR
Proteins

-
Stable
Cho
DIT diet-induced thermogenesis REE resting
energy expenditure TR thermogenic response
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Very Low Calorie Diet 28 days, 800Kcal/d
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Very Low Calorie Diet 28d, 800Kcal/d
1923 differentially expressed genes (FDR 2.5)
Nb genes () Functional categories
170 Genes linked to inflammatory processes
TNF a SAA Haptoglobin a2 macroglobul.
IL12A IL18 MMP9
FASEB J, 2004
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Validation using RT-qPCR of microarray data
Microarray comparison of adipocyte vs. SVF
During/after VLCD
Adipocyte/SVF
Serum amyloid A4
22 (5)
0.6 (0.6)
Carboxylesterase 1
26 (5)
0.6 (0.4)
Prostaglandin E receptor 3
11 (2)
0.8 (0.7)
Gene expression in adipocytes AND stromavascular
fraction cells in adipose tissue is modified by
VLCD
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Resident macrophages in the SVF are responsible
for inflammatory-related gene expression (RT-qPCR)
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Conclusions

• Microarray analysis coupled to real-time PCR
  allows identification of new groups of genes
  related to clinical parameters
• REE
• inflammatory status
• Weight loss in adipose tissue of obese subjects
• decreases the expression of inflammatory markers
• increases the expression of molecules with
  anti-inflammatory properties.
• SVF cells and particularly macrophages are
  sensitive to caloric restriction

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Gain insight into disease processes (Insight in
molecular mechanisms in obesity development or
complications) Help in gene function
identification (identification of molecular
markers for prediction of outcome
complications) Identify new target for
treatments Classification (patients, tumor
) Prognosis (weight loss, tumor
progression) Other applications Exon-specific
assays (alternative splicing) ChIP-on-chip
(promoter DNA binding sites)
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Transcriptomics applied to obesity

• Other techniques
• Differential display (DD)
• AdipoQ is a novel adipose-specific gene
  dysregulated in obesity.Erding Hu,  Peng Liang
  ,  Bruce M. Spiegelman
• J Biol Chem. 1996 May 3271(18)10697-703
• Serial Analysis of Gene Expression (SAGE) .
• Full transcriptome analysis of rhabdomyosarcoma,
  normal, and fetal skeletal muscle statistical
  comparison of multiple SAGE libraries .Schaaf GJ
  et al. FASEB J. 2005 19(3) 404-406

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Differential Display I
mRNA
5'
3'
NBAAAAAAAAAAAAA(A)n
MVTTTTTTTTTTT
3'
5'
reverse transcription
mRNA
NBAAAAAAAAAAAAA(A)n
cDNA
MVTTTTTTTTTTT
Arbitrary decamer
AMPLIFICATION
MVTTT
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Differential Display II
mRNA
5'
3'
NBAAAAAAAAAAAAA(A)n
MVTTTTTTTTTTT
3'
5'
reverse transcription
mRNA
NBAAAAAAAAAAAAA(A)n
cDNA
MVTTTTTTTTTTT
Arbitrary decamer
AMPLIFICATION
MVTTT
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SAGE Serial Analysis of Gene Expression
RNA extraction
sample
sequencing
Tag number
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Quantitation of mRNA levels overview
Complete genome or gene selection Limitations in
number of subjects
High number of subjects Quantitative
measurement Limitations in gene number
cDNA microarray
RT-quantitative PCR
Microarray data validation
Differential display
RNAse Protection Assay RPA
Northern / Dot blot
SAGE
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Abbreviations used
aRNA Amplified RNA
EBI European Bioinformatics Institute
EST Expressed sequence tag
FDR False detection rate
GO Gene ontology
IL Interleukin
IMAGE Integrated Molecular Analysis of Genomes and their Expression (consortiums)
MCP-1 Monocyte chemotactic protein 1
MeSH Medical Subject Headings
MMP Matrix metalloproteinase
ORF Open reading frame
PCR Polymerase chain reaction
RT Reverse transcriptase
SAA Serum amyloid A
SAM Statistical analysis of microarrays
SVF Stromovascular fraction
TNF Tumour necrosis factor
VLCD Very low calorie diet