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PROTÉOMIQUE

Application de la protéomique à la cartographie de l’interactome et

la découverte de biomarqueurs

BENOIT COULOMBE, PhD

Laboratoire de protéomique & transcription génique

BCM2003, mars 2012

Biomarkers and personalized medicine

Early diagnosis and prognosis of disease

Monitoring the response of patients to treatment

- Known therapies - Development of new drugs

Stratification of patient cohorts in order to provide personalized treatments for specific disease phenotypes (ex: IRESSA and lung cancer)

Process of biomarker discovery can also identify new targets for drug discovery, and develop new knowledge on mechanisms of disease

The sequencing of the human genome has provided the list

and linear sequence of all (most) human proteins

Genome

Proteins

Proteins rarely work alone, but rather assemble into protein

complexes that integrate the function of multiple gene products

Genome

Proteins

Complexes

Machineries

Function

Large-scale genomic studies identify an increasing number of

genes associated with disease phenotypes

Genome

Genome

How the products of these genes participate in

the establishment and the progression of disease

is often unknown!

Large-scale genomic studies identify an increasing number of

genes associated with disease phenotypes

The interaction partners of disease-associated proteins can

reveal their role in disease (guilt by association)

Genome

Proteins

Complexes

Machineries

Role in disease

The interaction partners of disease-associated proteins are

likely to act as modulators of the disease phenotype

Genome

Proteins

Complexes

Machineries

Role in disease

Modulators

The disease-associated proteins and their interactors are

putative biomarkers and drug targets

Genome

Proteins

Complexes

Machineries

Role in disease

Biomarkers Drug targets

P3MD

Computational classifier

PROTEUS

Computational tools

AP-MS

Cell Lines Patient Samples

SRM

List of Interactions

(Network Maps)

List of Validated

Biomarkers

Candidate

Biomarkers

Selection of

Disease-Associated

Proteins (Baits)

The P3MD platform Infrastructure, expertise, and data

Selection of

Peptides

(transitions)

3. Data

2. Software Applications

1. Laboratory Pipelines

A. INTERACTOME B. BIOMARKERS

Candidate

Biomarker

Selection

Engine

The human proteome

Identification of an association between a protein and a disease The case of hypothetical protein D

D

The protein D interactome

D

High-quality generic map of the protein D interactome Affinity purification coupled with MS

D

Components of the protein D interactome are candidate

drug targets and biomarkers

D

Identification of interactors using LC-MS/MS

+ Determination of a ‘‘probability’’ (False Discovery

Rate or FDR) for each interaction to be valid using

computational algorithm trained using machine

learning to differentiate valid vs. invalid interactions



D



D

I

I



D

I

Preliminary validation of candidate biomarkers Modulations of components of protein D interactome in disease cells (SRM)

D

Upregulated

Isoform

Downregulated

Selected Reaction Monitoring (SRM, also termed MRM)

Protein quantification is possible by spiking known amounts of chemically

synthesized heavy-labelled peptides in the samples (standard)

Proof of concept

RNAP II

Cellular machineries involved in mRNA synthesis

5’

CHROMATIN-REMODELING MACHINERY

- ATP dependent remodeling

- Histone-modifying enzymes

TFIIS

PRE-mRNA PROCESSING

MACHINERY

snRNPs U1-U6

hnRNPs

PRE-mRNA

Capping enzymes

Cleavage/polyA factors

P-TEFb

TFIID

RNAP II

TATAAA

TFIIB TFIIE

TFIIF

TFIIH

Mediator

+1

GENERAL TRANSCRIPTION MACHINERY

- RNA polymerase II (RNAP II)

- General transcription factors

- Elongation factors

TFIIF

3’

Main Features of the Procedure: 1) Expression at physiological level 2) Cell lysis in mild conditions (soluble) 3) Purification in native conditions 4) Protein identification by sensitive MS 5) Efficient data analysis and validation

Protein Identification

Computational Analysis (Reliability, Connectivity)

Cell Lysis in Mild Conditions

Soluble fraction (supernatant)

Insoluble fraction (pellet)

Collection of 293 cell lines engineered to express physiological levels of affinity tagged polypeptides upon induction

A platform for the systematic characterization of protein

interaction networks in human cells

A platform for the systematic characterization of protein interaction networks in human cells

Human cDNA cloned into an ecdysone-inducible expression vector

encoding the TAP tag

Transfection in EcR-293 cells and selection of expressing clones

Induction with ponasterone A to obtain near physiological expression levels

Cell lysis in mild conditions

Centrifugation

Soluble fraction (WCE)


Tandem Affinity Purification

SDS gel analysis (high resolution)

Protein identification (Orbitrap MS: high sensitivity & mass precision)

The ecdysone-inducible mammalian expression system

Modified from

Vickers and Sharrocks, 2002

Advantages:

• very low basal expression

• ecdysone analogs are inert in mammalian cells

• high control on expression levels (physiological)

pVgEcR

pIND

Western blot







Centrifugation






Double-affinity tagged protein

extract

IgG sepharose

TEV protease

Calmodulin Beads

Purified

Protein Complexes

Unbound Bound

Beads Eluate

Unbound Bound

EGTA

Beads Eluate

IgG

Sepharose

Calmodulin

Beads

Protein A IgG-binding sites

TEV protease cleavage site

Calmodulin binding peptide (CBP)

Protein of interest

Double-affinity Tag

EGTA

TEV

Tandem affinity purification in native conditions







Centrifugation






Identification of proteins by mass spectrometry In-gel

digestion

Yarmush and Jayaraman, 2002

SDS-

PAGE

Excised

proteins

Trypsin

digestion

Peptides

mixture

N-I I

Mass spectrometry

LC-MS/MS Computer Cluster

Peptide Sequence







Centrifugation






Affinity purification of protein complexes







Centrifugation






Reciprocal tagging

Reciprocal tagging allows to validate some interactions and to enrich the data set







Centrifugation






Computational analysis (reliability & connectivity)

Reciprocal tagging






Centrifugation






Computational analysis (reliability & connectivity)

Reciprocal tagging

Comprehensive Maps of Human Protein Interaction

Networks


The protein interaction database Proteus

C. Poitras & D. Bergeron

Computational data analysis and validation

A B

C

Computational data analysis and validation

• keeping 83% of the interactions supported by the literature

• excluding 83% of the interactions judged likely false-positives

805 distinct interactions were selected as high-confidence

protein interactions

Summary of our survey of protein complexes

(2007)

Jeronimo et al (2007) Mol Cell 27, 262

High-quality protein interaction network for the

RNA polymerase II transcription machinery

Proof of concept

Myopathies

The myopathies are a heterogeneous group of diseases of skeletal

muscle

They include:

Sporadic Inclusion-Body Myositis (sIBM)

Dermatomyositis

Polymyositis

Myofibrillar myopathies

Neurogenic muscular athrophy

IBMPFD

And many others

The RNA Polymerase II-Associated Proteins (RPAPs)

are previously uncharacterized proteins

Expression of components of the RNAPII network is specifically

modulated in tissues of patients suffering from

various muscular diseases

Muscle disease

N° biopsies

RPAPx

RPAPy

RPAPz

Normal controls

15

Normal

Normal

Normal

Disease Phenotype A

12

Increased *

Normal

Normal

Disease Phenotype B

8

Increased

Increased *

Increased

Disease Phenotype C

10

Increased

Normal

Increased

Increased expression of RPAPs is the only marker today that permits to

discriminate between two specific muscular disorders

The RPAPs are dysregulated in muscular diseases

Diseases under study

Myopathies

Leucodystrophies

Type 2 diabetes and insulin resistance

Hypercholesterolemia

Cancer

Thank you !

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