SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

The Radboud Centre for Proteomics, Glycomics & Metabolomics: Translating Research to Biomarkers to Diagnostics

Prof Alain van Gool

Head Radboud Center for Proteomics, Glycomics and Metabolomics Coordinator Radboud Technology Centers

Head Biomarkers in Personalized Healthcare

Science Meets Business event

Novio Tech Campus Nijmegen 28th Nov 2013

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

• Patient care • Research • Education

• 11.000 colleagues • 50 departments • 3.000 students • 1.000 beds (ambition to close 500 by improving

healthcare) • First academic centre outside US to fully implement EPIC

Translational medicine @ Radboudumc

Genetics

Bioinformatics Preclinical

pharmacology

Clinical trials

Flow cytometry

Cleanrooms

Neuroscience unit

Robotic operations

Preclinical Imaging

Microscopy

Malaria lab Biobank

Big Data

Radboudumc Technology Centres

Proteomics Metabolomics

Glycomics

Radboudumc Technology

Centers

Eg. Next Generation Life Sciences Maximize synergy within Radboudumc and with external partners / organisations

Alain van Gool Otto Boerman

Radboud Proteomics Center

Radboud Metabolomics Group

Radboud Glycomics Facility

Research Biomarkers Diagnostics

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

Radboud Centre for Proteomics, Glycomics & Metabolomics

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


• 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

Proteomics

• Proteome profiling - Differential protein expression - Protein complex composition - Labelfree - Labeled (SILAC, SPITC/PIC)

- Protein correlation profiling

• Protein identification - Purified proteins - Complex mixtures

• Protein characterization - Phosphorylation - Ubiquitinylation - Acetylation/Methylation

- Glycosylation

• Peptide/protein quantitation - Relative quantitation

- Absolute quantitation

Whole proteome analysis De novo protein identification

Protein complex isolation and characterization

Proteomics 2009 Nature 2010 EMBO Journal 2010 Nature 2011 Analytical Chemistry 2011 Expert Reviews Proteomics 2012

• Bottom-up proteomics (shotgun)

• Protein identification • Differential protein expression profiling Established (>300 projects done)

• Targeted proteomics

• Absolute/relative quantitation Emerging (5 projects ongoing)

• Top-down proteomics

• Intact protein characterization • Differential PTM analysis New

Proteomics approaches

Applications of bottom-up proteomics

• Differential protein expression in:

• Health/disease • Time • Before/after treatment

• Protein-protein interactions:

• Protein correlation profiling

• (Tandem) affinity purification

Information is obtained on peptide level, deduce protein effects

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

• In total 3,824 proteins were identified in either sample (98.7% cell specific)

• A total of 2,550 proteins was quantified and used for differential analysis

• 178 proteins were differentially expressed due to treatment: • 138 proteins upregulated • 40 proteins downregulated

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

Hierarchical clustering

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

What subcomplexes in mitochondrial proteome? • HEK293 cells • Isolation native mitochondrial protein

complexes • GeLC-MS using blue native gel electrophoresis

and nLC-LTQ-FT MS • Mascot protein identification • IDEAL-Q protein quantitation • Hierarchical clustering based on co-migration

Applications of targeted proteomics

(Absolute) quantitation of targets for: • Biomarkers

• Diagnostic test • Specific for specific protein variants (splice, PTM, etc)

• Quantitative analysis of specific pathways

• Metabolic pathways • Signalling cascades

• Quality control • Large scale targeted proteomics

• Comparable approach as DNA/RNA microarrays • Complete proteome SRM assays for different organisms

Schubert OT, et al. Cell Host Microbe. 2013: 13(5):602-12 The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacteriumtuberculosis

Research

Diagnostics

http://www.ncbi.nlm.nih.gov/pubmed/23684311





Method of the year 2012

Targeted Proteomics: focus on peptides of interest

Protein A Protein A isoform Protein B

Targeted proteomics: SRM assay development

Pro’s • Selective • Quantitative • Reproducible • Quite sensitive Con’s • Assay development • Low resolution MS

Etc …

Examplë: SRM output data Measurement of a peptide in complex matrix (tissue homogenate)

Use of heavy labeled standard • Confirmation of peak • Used for accurate (absolute) quantitation

MAB ESI - MS Intact MAB spectrum

Compound Spectra

147916.0294

148062.0367

148224.0781

148387.2015

148550.0889

148713.2075

+MS, 0.985-10.524min, Smoothed (0.07,6,SG), Baseline subtracted(0.80), Deconvoluted (MaxEnt, 2673.57-3122.37, *1.75, 10000)

0

2000

4000

6000

8000

Intens.

147250 147500 147750 148000 148250 148500 148750 149000 149250 149500 m/z

Applications top-down proteomics

Analysis of intact proteins by ESI-Q-tof MS

On protein level: • Analysis post-translational modifications / protein processing • Protein complex composition and dynamics • Biotech and biomedical research (and diagnostics?)

http://www.google.nl/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=NyRafGcsme2SmM&tbnid=YWTmu8HarTLqNM:&ved=0CAUQjRw&url=http://www.advion.com/products/triversa-nanomate/&ei=IQl6Use9DuiQ0AX8r4HQCg&bvm=bv.55980276,d.d2k&psig=AFQjCNH5wHXHOLNRkVJk0I09eWo499yKHQ&ust=1383815822924235

Analysis of intact Trastuzumab by top-down proteomics

Multiple charged ion

Single charged ion = intact protein

Analysis:

- Single proteins OK

- Protein (sub)complexes ?

Quantitative analysis of intact protein isoforms - N/C-terminal truncations - Splice variants - Post-translational modifications

(glycosylation, phosphorylation, etc)

148 kDa!

Analysis of a 40-subunit protein complex

Mitochondrial complex I of Y. lipolytica

• Problem: 3D structures of modelled subunits do not fit within measured structure by electron miscroscopy

• Hypothesis: Unknown N-terminal and/or C-terminal processing

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

• Established subunits: 40 • Subunits encoded by mitochondrial DNA: 7 • Subunits encoded by nuclear DNA: 33 • Structural elucidation in progress

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

Fully characterized N7BM subunit

Mass error: 0.0145 Da (0.9 ppm)

Characterized protein form

• N-terminus processing: Methionine truncation • C-terminus processing: None • Additional PTMs: Protein N-terminal acetylation (S2)

16.062 m/z Da


• Glycomics


• Metabolomics





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

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

Whole serum glycomics

B4GalT1

Example: glycoproteomics in rare diseases

33

• 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

{Dirk Lefeber et al,

NEJM 2013}

Dietary intervention

Incomplete glycosylation Complete glycosylation

ChipCube-LC- Q-tof MS

• Outcome 1: Explanation of disease

• Outcome 2: Dietary intervention as succesful personalized therapy

• Outcome 3: Glycoprofile transferrin applied as diagnostic test (MS-based)

• Genetic defect in glycosylation enzyme identified via exome sequencing


• 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


• 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

42

The innovation gap in biomarker research & development

Some numbers

Data obtained from Thomson Reuters Integrity Biomarker Module (April 2013)

Alzheimer’s Disease

Chronic Obstructive Pulmonary Disease

Type II Diabetes Mellitis

Eg Biomarkers in time: Prostate cancer May 2011: 2,231 biomarkers Nov 2012: 6,562 biomarkers Oct 2013: 8,358 biomarkers

EU: CE marking

USA: LDT, 510(k), PMA

43

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

Contact information

• Proteomics

• Glycomics

• Metabolomics

• Biomarkers

Visiting address: Radboud umc, route 774/830

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

Back-ups

Personalized Healthcare @ Radboudumc

People are different Stratification by multilevel diagnosis

+ Patient’s preference of treatment

Exchange experiences in care communities

Select personalized therapy

Issue 2:

The big current bottleneck in Next Generation Life Sciences:

49

(Big) data

Knowledge

Understanding

Decision

Action

Translation is key !

Experimental setup

ESI spectrum of 6+ charged subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

6+

'1679.35506+

'1682.19056+

'1684.85616+

'1686.01806+

'1688.51476+

'1690.928612+

'1692.67456+

+MS, 56.8-58.7min #3408-3522

0

1

2

3

4

5

4x10

Intens.

1677.5 1680.0 1682.5 1685.0 1687.5 1690.0 1692.5 1695.0 1697.5 m/z

6+

1.682 m/z Da

Deconvoluted spectrum of 1 subunit Survey View

500

1000

1500

2000

2500

m/z

10 20 30 40 50 60 70 Time [min]

'10069.0770Mr

'10087.0920Mr

'10103.0766Mr

'10110.0557Mr

'10125.0318Mr

'10132.0368Mr

'10141.0021Mr

'10149.0079Mr

+MS, 56.8-58.7min, Baseline subtracted(0.80), Deconvoluted (MaxEnt, 503.09-2244.16, *0.063125, 50000)

0

2

4

6

8

4x10

Intens.

10070 10080 10090 10100 10110 10120 10130 10140 10150 m/z

10.088 m/z Da

Small to large intact subunits in a single analysis

9 kDa subunit (deconvoluted)

75 kDa subunit (deconvoluted) 49 kDa subunit (deconvoluted)

'9603.9448Mr

'9617.9600Mr

'9631.9697Mr

'9644.9081Mr

'9654.9367Mr

'9669.9202Mr

'9685.8928Mr

+MS, 51.9-52.6min, Deconvoluted (MaxEnt, 503.09-2410.26, *0.10625, 50000)

0.0

0.5

1.0

1.5

5x10

Intens.

9550 9600 9650 9700 9750 m/z

49989.6584

+MS, 54.6-56.9min, Smoothed (0.07,3,SG), Deconvoluted (MaxEnt, 498.39-2528.81, *0.664063, 8000)

2

4

6

8

4x10

Intens.

49400 49600 49800 50000 50200 50400 50600 m/z

74340.9883

75196.3196

76237.1362


0

1

2

3

4

5

6

4x10

Intens.

73500 74000 74500 75000 75500 76000 76500 77000 77500 m/z

20 kDa subunit (deconvoluted)

'20707.5208Mr

'20725.4879Mr

'20744.4732Mr

'20755.4811Mr '20763.4648

Mr

'20781.4432Mr


0.0

0.2

0.4

0.6

0.8

1.0

5x10

Intens.

20680 20700 20720 20740 20760 20780 20800 m/z

Top down / bottom up analysis of NUMM 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

Deconvoluted and simulated spectra Compound Spectra

'13107.3636Mr +MS, 14.5-15.6min, Deconvoluted (MaxEnt, 566.30-2196.57, *0.063125, 50000)

15128.45671+C₆₆₃H₁₀₂₈N₁₉₂O₂₀₃S₆, , 15119.4339

13114.37681+ C₅₇₄H₈₈₁N₁₆₆O₁₇₈S₅, , 13107.3587

0.0

0.5

1.0

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2.0

2.5

5x10

Intens.

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5x10

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5x10

13000 13250 13500 13750 14000 14250 14500 14750 15000 m/z

Measured spectrum

Simulated spectrum - unprocessed form (database entry)

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

Overlay of deconvoluted and simulated spectra NUMM subunit

Mass error: 0.0049 Da (0.4 ppm)

13.114 m/z Da

SMB 28112013 Alain van Gool - Technologiecentra Radboudumc

Technology

Transcript of SMB 28112013 Alain van Gool - Technologiecentra Radboudumc