Overview Radboudumc Center for Proteomics, Glycomics and Metabolomics april 2015

Translational Metabolic Laboratory Using Proteomics, Glycomics and Metabolomics to translate Research to Biomarkers to Diagnostics

April 2015

Translational Metabolic Laboratory, Department of Laboratory Medicine https://www.radboudumc.nl/Research/ProteomicsMetabolomicsGlycomics/

Radboudumc • Mission: “To have a significant impact on healthcare” • Strategic focus on Personalized Healthcare through “the

patient as partner” • Core activities:

• Patient care • Research • Education

• 11.000 colleagues • 50 departments • 3.000 students • 1.000 beds • First academic centre outside US to fully implement EPIC

Patient

Radboud Personalized Healthcare

A significant impact

on healthcare

Molecule

Population

3

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities Select personalized therapy

Population

Patient

Molecule

4

www.radboudumc.nl/research/technologycenters

Genomics

Bioinformatics

Animal studies

Stem cells

Translational neuroscience

Image-guided treatment

Imaging

Microscopy

Biobank

Health economics

Mass Spectrometry

Radboudumc Technology

Centers Investigational

products

Clinical trials

EHR-based research

Statistics

Human physiology

Data stewardship

Molecule

Flow cytometry

March 2015

Opening Radboud Research Facilities, 2nd Oct 2014

Point of contact: Alain van Gool

About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia

www.radboudumc.nl/research/technologycenters/

6

Genomics

Bioinformatics

Animal studies

Stem cells

Translational neuroscience

Image-guided treatment

Imaging

Microscopy

Biobank

Health economics

Mass Spectrometry

Radboudumc Technology

Centers Investigational

products

Clinical trials

EHR-based research

Statistics

Human physiology

Data stewardship

Molecule

Flow cytometry

7

About 250 dedicated people working in 18 Technology Centers, ~1600 users (internal, external), ~140 consortia

www.radboudumc.nl/research/technologycenters/

• Proteins • Metabolites • Drugs • PK-PD

• Preclinical • Clinical

• Behavioural • Preclinical

• Animal facility • Systematic review

• Cell analysis • Sorting

• Pediatric • Adult • Phase 1, 2, 3, 4

• Vaccines • Pharmaceutics • Radio-isotopes • Malaria parasites

• Management • Analysis • Sharing • Cloud computing

• DNA • RNA

• Internal • External

• Early HTA • Evidence-based

surgery • Field lab

• Statistics • Biological • Structural

• Preclinical • Clinical • Economic

viability • Decision

analysis

• Experimental design • Biostatistical advice

• Electronic Health Records • Big Data • Best practice

• In vivo • Functional

diagnostics

• iPSC • Organoids

Translational medicine @ Radboudumc

Research Biomarkers Diagnostics

Department of Laboratory Medicine, Radboudumc Integrated Translational Research and Diagnostic Laboratory, 220 fte, yearly budget ~ 28M euro. Close interaction with Dept of Genetics, Pathology and Medical Microbiology

Specialities: • Proteomics, glycomics, metabolomics • Enzymatic assays • Neurochemistry • Cellulair immunotherapy • Immunomonitoring

Areas of disease: • Metabolic diseases • Mitochondrial diseases • Lysosomal /glycosylation disorders • Neuroscience • Nefrology • Iron metabolism • Autoimmunity • Immunodeficiency • Transplantation

In development: • ~500 Biomarkers • Early and late stage • Analytical development • Clinical validation

Assay formats: • Immunoassay • Turbidicity assays • Flow cytometry • DNA sequencing • Mass spectrometry • Experimental human (-ized)

invitro and invivo models for inflammation and immunosuppression

Validated assays*: • ~ 1000 assays • 3.000.000 tests/year

Areas of application: • Personalized healthcare • Diagnosis • Prognosis • Mechanism of disease • Mechanism of drug action

Department of Laboratory Medicine

*CCKL accreditation/RvA/EFI

www.laboratorymedicine.nl

9

One genome → multiple proteomes/metabolomes

• The proteomes and metabolomes are the functional output of the genome

• 21.000 genes → approximately 500.000 possible proteins and isoforms and biochemical metabolites

• Proteomes define and reflect the functional state of a cell or organism at a certain time under certain conditions

• Proteomes and metabolome change depending on stimuli and challenges; most cell/tissue signalling occurs through rapid protein changes

• Proteomics and metabolomics are strong approaches to identify and analyse metabolic changes of cell/tissue/organism

• Unique added value of proteomics: • Protein expression • Post-translational modifications • Protein complex formation + function

One genome → multiple proteomes

Body fluids

Tissues

Cells

Plasma Urine CSF

Lung Colon Adrenal gland

THP-1 Jurkat Granulosa cells

Proteomics Metabolomics Glycomics

Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department of Laboratory Medicine), close interaction with Radboudumc scientists and external partners

Translational Metabolic Laboratory – Laboratory Medicine

Ron Wevers, Jolein Gloerich, Alain van Gool, Leo Kluijtmans, Dirk Lefeber, Hans Wessels, et al


Mass spectrometry – NMR based, 20 dedicated fte, part of diagnostic laboratory (Department Laboratory Medicine), close interaction with Radboudumc scientists and external partners

Key experts: Proteomics Jolein Gloerich Hans Wessels Alain van Gool Glycomics Monique Scherpenzeel Dirk Lefeber Metabolomics Leo Kluijtmans Ron Wevers


Research • Projects • Service

External • Projects • Service

Patient care • Health care focus • Biomarkers, diagnostics • Consortia (NL, EU)

Key features: • Expertise centre rather than service facility • Focus to translate Research to Biomarkers to Diagnostics • Application of many years Omics expertise to customer’s specific needs • Ambition to grow with long-term strategic projects, collaborations, staff and impact


Radboud Proteomics Center

Bottom up proteomics

Top down proteomics

Targeted proteomics

Peptide-based Differential Protein Profiling Relative Quantitation

Intact protein-based Post Translational Modifications


Peptide-based Selected biomarkers Quantitative analysis

Proteomics techniques

• Peptide-based identification of proteins • Differential protein expression profiling (labelfree/labeled) • Suitable for very complex samples (in combination with fractionation) • Focus on research

Whole proteome analysis

Protein complex isolation and characterization

Bottom up Proteomics

Applications • Differential protein expression in:

• Health/disease

• Time

• Before/after treatment

• Protein-protein interactions:

• Protein complexes

• Protein correlation profiling

Up regulated Down regulated

Instruments:


Proteins Peptides Data Analysis

Ph

ase

1

RP pH2.7 LC-MS/MS

Trypsin

1D LC MS/MS workflow

CONTROLS

CONDITION 1

CONDITION 2

• Body fluids • Circulating vesicles • Tissues • Cells • Organelles • Membranes • Protein complexes • Single proteins

Samples:


Example cellular proteome profiling

Sample: HEK293 whole cell proteome (1 µg tryptic digest of urea extract)

1D LC-M/MS proteomics analysis

Retention time

m/z

400

600

800

1000

1200

1400

m/z

10 20 30 40 50 60 Time [min]

Blue: signal intensity in MS Pink dots: precursors selected for MS/MS

Detected peaks in MS spectra 1.584.599

Detected isotope patterns in MS spectra 130.172

Total number of MS/MS spectra 22.743

Av. Absolute Mass Deviation [ppm] 2,8972

Matched MS/MS spectra 5.603

Identified NR peptides 4.537

Identified proteins 1.321

False Discovery Rate 0,98%


In 1 scan:

Proteins Peptides RP pH10 UPLC 20 fractions

Ph

ase

1

Ph

ase

2

20 fractions RP pH2.7 LC-MS/MS

Data processing Statistical analysis

400

600

800

1000

1200

m/z

20 30 40 50 Time [min]

Trypsin

CONTROLS

CONDITION 1

CONDITION 2

2D LC (RP x RP) MS/MS workflow


719

94

109

41

246

845

1107

2D LC-MS/MS 60min gradients

1D LC-MS/MS 60min gradient


963

1851

2765

0

500

1000

1500

2000

2500

3000

1D

LC

-MS/

MS

60

min

2D

LC

-MS/

MS

20

min

2D

LC

-MS/

MS

60

min

1.9x

2.9x

Added value 2D LC-MS/MS

1D RP LC-MS/MS versus 2D RPxRP LC-MS/MS: HEK293 cell line


Added value 2D LC-MS/MS

692

1769

0

200

400

600

800

1000

1200

1400

1600

1800

2000

1D LC-MS/MS 2D LC-MS/MSId

en

tifi

ed

pro

tein

s

2.6x

1264

505

187


1D LC-MS/MS 60min gradient

1D RP LC-MS/MS versus 2D RPxRP LC-MS/MS: Fat biopt sample


Example tissue profiling project Protein expression (positive controls)

GO Protein distributions

Cellular compartments

LFQ scatter plot Biological replicates

y= 0.9834x + 130390 R2=0.9842

Q: downstream effects of transgene? Hippocampus tissue of Transgenic mice 4 Conditions: WT, TG, WT treated, TG treated with drug 5 Biological replicates; 2D LC-MS/MS analysis (20 fractions, 1 hour gradient) Label-Free Quantitation (LFQ – MaxQuant) • LC-MS/MS analyses: 400 •MS spectra: 1.937.394 •MS/MS spectra: 2.323.458 •Detected isotope patterns: 66.602.271 • Isotope patterns sequenced: 1.295.489 • Average absolute mass deviation: 1,38 ppm • 1,3 Terrabyte data

PCA analysis – loading plot


•Matched MS/MS spectra to peptides: 500.317 • Identified proteins: 3.187 •Quantified proteins: 2.365 (≥2 peptides/protein) •Differential proteins: 276 (p<0.05) • Average CV < 21%* * Combining biological and technical reproducability

Transgene

Downstream

Proteins SDS-PAGE 9 Gel slices 9 in-gel digests

Ph

ase

1

Ph

ase

2

9 Samples RP pH2.7 LC-MS/MS

Data processing Statistical analysis

400

600

800

1000

1200

m/z

20 30 40 50 Time [min]

Gel enhanced LC-MS/MS workflow

Trypsin


Example of cellular proteome profiling project

Q: downstream miRNA effects on proteome? A375 melanoma cell line miRNA treated versus control 3 Biological replicates GeLC-MS/MS analysis (5 slices, 1 hour LC gradients) Label-Free Quantitation (LFQ – MaxQuant) • Identified proteins: 1.932 • Quantified proteins: 1.379 (≥2 peptides/protein) • Differential proteins: 337 (p<0.05) / 151 (p<0.01) • Good reproducibility (average CV < 20%)* • Data analysis: 70% overlap LC-MS/MS and RNA-Seq data * Combination biological and technical reproducability

PCA loading plot

Chromatogram and ion map of a gel fraction

Collaboration with Radboudumc, InteRNA, TNO (DTL hotel project)

Already suspected outlier

Conclusions

Example of cellular proteome profiling project

Results

Samples

Up regulated

Down regulated

Differential analysis

-10

-5

0

5

10 ∞

∞

178 Differentially expressed proteins

Results

Gene ontology: cellular localization

• 3,824 identified proteins (98.7% cell specific) • 2,550 quantified proteins (≥ 2 peptides/protein) • 178 differential proteins due to treatment:

• 138 proteins upregulated • 40 proteins downregulated

• Good basis for follow-up pharmaco-proteomics

Q: how does proteome cell line x look like? Q: First look at effect treatment on proteome (feasibility) → GeLC-MS/MS approach


Cluster: 28S mt-Ribosome

Cluster: 39S mt-Ribosome

Cluster: F1F0 ATP synthase

Cluster: cytochrome b-c1 complex

Cluster: NADH dehydrogenase & TCP1

Cluster: trifunctional enzyme & isocitrate dehydrogenase

Cluster: cytochrome C oxidase & mt-Ribosomal subcomplex

Example of complexome analysis project Bottom up proteomics

Collaboration with NCMD, Bob Lightowlers

Q: What subcomplexes in mitochondrial proteome? HEK293 Mitochondrial fraction 2 BN gel lanes (4-12% AA & 5-15% AA) 24 gel slices per gel lane • Migration profiles for 953 proteins • Unambiguous ID of 24 known complexes • Validation of 8 implied interactors of the mt-Ribosome • 11 novel putative interactors of the mt-Ribosome

Hierarchical clustering

Fit-for-purpose sample preparation

MARS-14 depletion

GeLC-MS/MS 1D LC-MS/MS 2D LC-MS/MS GeLC-MS/MS 1D LC-MS/MS 2D LC-MS/MS

A B C D E F

Human CSF


Q: Changes in exosome proteome related to clinical phenotype?

Samples: - urine exosomes from patients with rejection after renal transplantation

- 4 subject groups (CTRL, REJ, CMV, BK)

Approach: - Gel enhanced 1D LC-MS/MS analysis (9 fractions)

- Per subject group: 2 different pools of multiple patients

- 2 separate experiments (LTQ FT Ultra & MaXis 4G)

Results: - Robust sample preparation is crucial

- In total 521 proteins identified

- Exosome enrichment confirmed by gene ontology classification (Cellular Components)

Collaboration with Department of Urology

Example of urine exosome analysis project Bottom up proteomics

Anammox batch reactor

PCA analysis – loading plot

2D Hierarchical Clustering

Q: optimal growth conditions? Anammox bacterium 3 Different growth conditions 4 Technical replicates 1D LC-MS/MS analysis (1 hour gradient) Label-Free Quantitation (LFQ – MaxQuant) • Identified proteins: 270 • Differential proteins: 75 • Excellent reproducibility (average CV < 6.5%)

LFQ scatter plot technical replicates

y= 1.0141x + 1250.7 R2=0.9991

Example of biotechnology project Bottom up proteomics

Collaboration with Boran Kartal/Mike Jetten (FNWI RU)

Q: Effect of two bacterial growth conditions? Desulfobacillus bacterium 2 Different growth conditions; 2 Biological replicates GeLC-MS/MS analysis (9 slices, 1 hour gradient) Label-Free Quantitation (LFQ – MaxQuant) • Identified proteins: 1.228 • Quantified proteins: 950 • Differential proteins: 245 (p<0.05) / 109 (p<0.01) • Excellent reproducibility (average CV < 10%)*

* Biological replicates: technical reproducability likely better

Protein expression example

Example of biotechnology project

LFQ scatter plot Biological replicates

y= 1.0167x - 49244 R2=0.998

PCA loading plot

PC1 (72.9%)

PC

2 (

14

.7%

)

Collaboration with external client




Top down proteomics

Targeted proteomics






• Intact protein analysis • Post-translational modification • Analysis of low to medium

complexity samples

Top down proteomics

LC-MS Ion map of protein complex with MS spectrum of one subunit

Deconvoluted protein spectrum

Instruments:

Applications

• Characterization of intact

proteins:

• Post-translational

modifications

• Protein processing

• Splice variants

• Protein complex analysis

• Composition

• Complex-specific subunit

variants

• Quality control of biotech

products

Top down proteomics

Quantitative analysis of intact protein isoforms

Collaboration with Floris van Delft (Synnafix)

Complexes Native Electrophoresis

60 Gel slices 60 in-gel digests

Ph

ase

1

Ph

ase

2

60 Samples RP pH2.7 LC-MS/MS

Data processing Complexome

Profile

400

600

800

1000

1200

m/z

20 30 40 50 Time [min]

Bottom-up Complexome Profiling workflow

Trypsin

Complexes Native Electrophoresis

Gel slice of interest

Protein extraction, reduction and SPE

Ph

ase

1

Ph

ase

2

Protein sample

LC-MS/MS with fraction collection

Data processing Top-Down profiling

Top-Down Complexome Profiling workflow

Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

15

24

23

11 128 10 16 17 2618

20919

22211413

12 13 14 15 16 17 18 19 20 Time [min]

0.0

0.5

1.0

1.5

2.0

2.5

7x10

Intens.

Ph

ase

3

Integrated Complexome Profiling workflow

Protein fractions of interest

Peptides RP pH2.7 LC-MS/MS

Peptide MS2 level Data

nESI-MS/MS Protein MS2

level data

Characterized proteoform

Tryp

sin

Example of complexome analysis Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

'1009.716810+

'1121.79549+

'1261.89388+

'1442.02087+ '1682.1905

6+

'2018.42955+

+MS, 56.8-58.7min #3408-3522

0

1

2

3

4

5

4x10

Intens.

1000 1200 1400 1600 1800 2000 2200 m/z

5+

6+

7+

8+

9+

10+

5+

6+ 7+

8+

9+

10+

1.682 m/z Da

Q: Composition of mitochondrial complex 1?

• Y. lipolytica complex 1 as a model for human

• 42 established subunits (7 mtDNA, 35 nDNA)

•Unknown mature subunit forms

•Unknown and dynamic post-translational modifications

• Study: Combine Top-Down and Bottom-Up characterization of all subunits

Collaboration with Ulrich Brandt

Top down proteomics

Experimental setup

LC-MS ion map of 40-subunit protein complex Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

ESI spectrum of 1 subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

'1009.716810+

'1121.79549+

'1261.89388+

'1442.02087+ '1682.1905

6+

'2018.42955+

+MS, 56.8-58.7min #3408-3522

0

1

2

3

4

5

4x10

Intens.

1000 1200 1400 1600 1800 2000 2200 m/z

5+

6+

7+

8+

9+

10+

5+

6+ 7+

8+

9+

10+

1.682 m/z Da

Top down / bottom up analysis of subunit protein (13,2 kDa)

Top-Down LC-MS/MS (ETD)

Top-Down NSI-MS/MS (ETD)

Bottom-Up LC-MS/MS (CID & ETD)

Matched peptide sequences in red, amino acids matched as ETD fragment ions are marked yellow (only for Top-Down data)

Hypothesized protein form

• N-terminus processing: Targeting sequence cleavage at S18 • C-terminus processing: None • Additional PTMs: None

Top down proteomics

Overlay deconvoluted experimental and simulated spectra

'10923.3198Mr

'10947.2792Mr

'10961.2630Mr

CI filtered Captive 3ul 05FA_Tray02-E1_01_1071.d: +MS, 11.2-12.1min, Deconvoluted (MaxEnt, 503.10-2187.28, *0.063125, 50000)

CIfilteredCaptive₃ul₀₅FA_Tray₀₂-E₁₀1₁071.d:C₄₈₀H₇₄₃N₁₃₉O₁₅₂S₄, , 11014.3560

CIfilteredCaptive₃ul₀₅FA_Tray₀₂-E₁₀1₁071.d:C₄₇₇H₇₃₄N₁₃₈O₁₅₂S₃, , 10923.3105

0.0

0.2

0.4

0.6

0.8

1.0

6x10

Intens.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

6x10

0.0

0.2

0.4

0.6

0.8

1.0

1.2

6x10

10920 10940 10960 10980 11000 11020 m/z

Measured spectrum

Simulated spectrum - unprocessed form (database entry)

Simulated spectrum - hypothesized form (according to MS/MS results)

Top down proteomics

Characterization of complex subunits

Q: Composition of mitochondrial complex 1?

•Predicted: 42 subunits (7 mtDNA, 35 nDNA)

•Detected: 240 protein subunit isoforms

(truncations, PTMs)

•Straight but time-consuming path to subunit characterization

Top down proteomics

Intact complexome analysis from tissue biopsies

Pilot study:

• Native tissue biopsies

• Isolate membrane complexes

• Separate and isolate complexes using Blue Native gels

• LC-MS/MS analysis

• Data analysis

Tissue 1 (n=3)

Tissue 2 (n=3)

Subunit

Subunit – tissue 1

Subunit – tissue 2

• Identified protein sequence of subunit • Deduce simulated sequences from database • Determine fit with experimental data

Top down proteomics

Example of diagnostic top-down proteomics

• 12 families with liver disease and dilated cardiomyopathy (5-20 years)

• Initial clinical assessment didn’t yield clear cause of symptoms

• Specific sugar loss of serum transferrin identified via glycoproteomics

ChipCube-LC- Q-tof MS

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test

• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing

{Tegtmeyer et al, NEJM 370;6: 533 (2014)}

Genomics Glycomics Metabolomics

Top down proteomics

By Monique van Scherpenzeel, Dirk Lefeber



Top down proteomics

Targeted proteomics






• Peptide-based • Sensitive quantitative analysis • Suitable for very complex

samples

Targeted proteomics

Nature Methods: Method of the year 2012

protein expression data

Data Analysis

Protein A isoform 1 Protein A isoform 2 Protein B

Applications

(Absolute) quantitation of protein biomarkers:

• Biomarker research: Quantitative analysis of specific set of proteins

• Biomarker validation: Validation and prioritization of selected biomarkers

• Diagnostics: Analysis of qualified biomarkers

Targeted proteomics

Research Diagnostics

Instruments:

Biomarker innovation gap

• Imbalance between biomarker discovery, validation and application

• Many more biomarkers discovered than available as diagnostic test

50

Selection of biomarkers

Single Reaction Monitoring workflow P

has

e 1

Selection of optimal peptides

• Unique • Best detectable in LC-MS

Optimize detection by selecting optimal transitions

Ph

ase

2

Proteins Peptides Data Analysis RP pH2.7 LC-MS/MS

Trypsin

Isotope labeled

standards

Isotope labeled

standards

Targeted proteomics

LCM-proteomics workflow

Laser Captue Microdissection Sa

mp

le p

rep

arat

ion

Proteins

Trypsin

Peptides 9 µm tissue

sections

LC-M

S/M

S

Data Analysis Targeted SRM Data Analysis

CONTROLS

CONDITION 1

CONDITION 2

1D LC-MS/MS

Biomarker Discovery Biomarker Validation

Targeted proteomics

• Proteomics • Bottom-up (shot-gun) proteomics • Targeted proteomics • Top-down proteomics

• Glycomics

• Glycan profiling • (Targeted) Glycoproteomics

• Metabolomics

• Untargeted metabolomics • Targeted metabolite profiling



Key experts: Jolein Gloerich Hans Wessels Alain van Gool Monique Scherpenzeel Dirk Lefeber Leo Kluijtmans Ron Wevers

Source: Allison Doerr, Nature Methods 9,36 (2012)

Glycomics

Glycosylation markers in human medicin

• Biomarker for disease and therapy monitoring: rheumatoid arthritis,

oncology, hepatitis • MUC2 glycosylation in colon carinoma • Human blood groups (A, B, O, AB)

• CDTect (Carbohydrate-Deficient transferrin) • Infectious diseases • IgA nephropathy

1% of genes directly involved in glycosylation About 50% of proteins is glycosylated

IgA

Glycosylation types

• N-glycosylation

• Asparagin linked • 8 - 20 saccharides

• O-glycosylation

• Serine/Threonine linked • <10 sacchariden

• Glycosaminoglycans

• 100-200 disaccharide units • Agrin, Perlecan, Syndecan, Glypican

• Glycolipids

Diagnostics Research

Urinary glycan profiling

Serum glycan profiling

O-glycan profiling

PNGaseF chip

Chemical biology

Glycopeptide profiling

glycolipid profiling

Whole protein glycoprofiling

Nucleotide-sugars

Glycomics approaches

Glycomics application areas

• Mechanisms of glycosylation disorders

Linking genes to glycomics profiles

Understanding neuromuscular pathophysiology

• Glycomics Technology Platform Services

Functional foods

Glycan tracers

Biomarkers

Glycomics

Intact glycoprotein

Free glycans

Glycopeptides 500

750

1000

1250

1500

1750

m/z

10 15 20 25 30 35 40 Time [min]

PGM1 profile

CID fragmentation spectrum

Example: Intact glycoprotein biomarker

• 12 families with liver disease and dilated cardiomyopathy (5-20 years)

• Initial clinical assessment didn’t yield clear cause of symptoms

• Specific sugar loss of serum transferrin identified via glycoproteomics

ChipCube-LC- Q-tof MS

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin developed and applied as diagnostic test

• Genetic defect in glycosylation enzyme (PGM1) identified via exome sequencing

{Tegtmeyer et al, NEJM 370;6: 533 (2014)}

Genomics Glycomics Metabolomics

60

Example: Glycopeptide profiling

• Optimized procedure using simple sample prep of plasma • Detection of ~12.000 unique deconvoluted monoisotopic masses per

single analysis (> 50% are glycopeptides)

500

1000

1500

2000

m/z

5 10 15 20 25 30 35 40 Time [min]

Proof of principle study:

Monique van Scherpenzeel, Dirk Lefeber, Hans Wessels, Alain van Gool Translational Metabolic Laboratory, Radboudumc, unpublished data

Example: Glycan analysis by nanoChip-QTOF MS

• High-resolution glycoprofiling

• Microfluidic chip system results in simplified operating conditions, increased reproducibility and robustness

• CHIP formats: C18, Carbograph, C8, HILIC, phosphopeptides, PNGaseF

Bio-informatics : • Coupling with public glyco-databases • Annotation of glycan linkages

Glycan profiling in serum

B4GalT1


• Glycomics


• Metabolomics





Metabolomics approaches

Diagnostics • Organic acids • Amino acids • Purines&Pyrimidines • Monosaccharides/Polyols • Carnitine(-esters) • Sterols

Research • Assay development for specific

metabolites or metabolite classes • Untargeted metabolite profiling • Metabolite biomarker identification

Equipment • GC • 2 GC-MS • 3 LC-MS/MS • 2 amino acid analysers • HPLC

Example: targeted diagnostics in metabolic disease

Amino acids Amino acid analyser

Carnitine-ester profile LC-MS/MS

Purines & pyrimidines - HPLC & LC-MS/MS

Organic acids GC-MS

DIAGNOSIS OF INBORN ERROR OF METABOLISM

Example: untargeted metabolomics to diagnose individual patients

Human plasma

20 controls vs 1 patient

Agilent QTOF MS-data

- Reverse phase liquid chromatography - Positive mode - Features

•Accurate mass (165.07898) • Retention time • Intensity

XCMS Alignment Peak comparison > 10000 Features

Chemometric pipeline • T-test • PCA • P95

Metabolite identification Online database HMDB

phenylalanine

Integrated databases

A blind study

Plasma sample choice : Dr. C.D.G Huigen

Analytical chemistry : E. van der Heeft

Chemometrics : Dr. U.F.H. Engelke

Diagnosis : Prof. dr. R.A. Wevers;

Dr. L.A.J. Kluijtmans

Test 10 samples from 10 patients with 5 different

Inborn Error of Metabolism’s

21 controls

The blind study

MSUD (2) → leucine, isoleucine, valine, 3-methyl-2-oxovaleric acid

Aminoacylase I deficiency (2) → N-acetylglutamine, N-acetylglutamic acid, N-acetylalanine, N-acetylserine, N-acetylasparagine, N-acetylglycine

Prolinemia type II (2) → proline, 1-pyrroline-5-carboxylic acid

Hyperlysinemia (2) → pipecolic acid, lysine, homoarginine, homocitrulline

3-Hydroxy-3-methylglutaryl-CoA lyase deficiency (2) → 3-methylglutaryl-carnitine, 3-methylglutaconic acid, 3-hydroxy-2-methylbutanoic acid, 3-hydroxy-3-methylglutaric acid

Diagnostic metabolites found in blood plasma

• Correct diagnosis in all 10 patients

• Five different IEM’s identified by

differential metabolites

• The approach works!!!

• Validated method diagnostic SOP

• Planned for execution in line with genetics

2012

Patient Targeted

Metabolic

screen

Targeted

gene

analysis

Diagnosis

+ follow-up

2013 / 2014

Patient

Whole

exome

sequencing Targeted

confirmatory

metabolite +

enzyme

testing

Diagnosis

+ follow-up

Targeted assays vs holistic approach

Next

generation

metabolic

screening

Times are changing… (functional) genome analysis

Human samples

Plasma, CSF (urine) Controls vs. patient

QTOF Mass Spectrometry

- Reverse phase liquid chromatography - Positive and negative mode - Features

XCMS Alignment Peak comparison > 10,000 Features

Personalized metabolic diagnostics

Xanthine Uric acid

72

Full metabolite profile: Highly suspected of xanthinuria


• Glycomics


• Metabolomics





A problem in biomarker land

Imbalance between biomarker discovery and application.

• Gap 1: Strong focus on discovery of new biomarkers, few biomarkers progress beyond initial publication to multi-center clinical validation.

• Gap 2: Insufficient demonstrated added value of new clinical biomarker and limited development of a commercially viable diagnostic biomarker test.

Discovery Clinical validation/confirmation

Diagnostic test

Number of biomarkers

Gap 1

Gap 2

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The innovation gap in biomarker research & development

Some numbers

Data obtained from Thomson Reuters Integrity Biomarker Module

Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers 25 Sep 2014: 9,975 biomarkers with 31,403 biomarker uses

EU: CE marking

USA: LDT, 510(k), PMA

Shared biomarker research through open innovation

We need to set up a open innovation network to share biomarker knowledge and jointly develop and validate biomarkers (at level of NL and EU):

1. Assay development of (diagnostic) biomarkers

2. Clinical biomarker quantification/validation/confirmation

Shared knowledge,

technologies and objectives

Funding: NL – STW; EU - Horizon2020, IMI; Fast track pharma funds

Good example of multi-center biomarker validation

Biomarker Development Center (Netherlands)

STW perspectief grant

Biomarker Development Center

Public-private partnership 4 years

Project grant 4.3M Eur of which 2.2M government,

and 2.1M industry (0.9M cash/1.2M kind)

Close interactions with:

- Clinicians (biomarker application)

- Industry

- Patient stakeholder associations

Open Innovation Network !

http://www.erasmusmc.nl/index.htm

Biomarker Development Center (Netherlands)

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http://www.erasmusmc.nl/index.htm

healthy disease disease + treatment

Challenge: how to identify subpopulations in Personalized Healthcare?

healthy disease disease + treatment

• Biomarkers in populations often have a wide range • Within this range, subpopulations can behave quite differently • Chemometric methods dealing with multiple biomarker data points are needed

to reveal such individual differences and enable personalized medicine

(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)

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Approach Multiple

biomarker datapoints

Chemometrics Kernel transformation

Biosamples

Apply methods to identify subpopulations for Personalized Medicine

Urine NMR

81

(Source: Jasper Engel, Lionel Blanchet, Udo Engelke, Ron Wevers and Lutgarde Buydens)

Contact information

• Proteomics

• Glycomics

• Metabolomics

• Biomarkers

Visiting address: Radboud umc, route 774/830 https://www.radboudumc.nl/Research/ProteomicsMetabolomicsGlycomics/

http://laboratorymedicine.nl/104_theme_104_Translational-Metabolic-Laboratory.html

[email protected] [email protected] Alain.van [email protected] [email protected] [email protected] [email protected] [email protected] Alain.van [email protected] [email protected]

Health & Medicine

Overview Radboudumc Center for Proteomics, Glycomics and Metabolomics april 2015