rzubarev pathway analysis march 2011

8/3/2019 RZubarev Pathway Analysis March 2011

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Roman Zubarev

[email protected]

Physiological Chemistry I,Department for Medical Biochemistry & Biophysics,

Karolinska Institutet, Stockholm

Pathway Analysis:Google for Proteomics?..


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J. David Sweatt,Artist and Scientist

Cellular complexity

Eukaryotic Cell


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ReductionistMolecular Biology: Pathway Biology:

Experiment and Analysis in Proteomics

Experiment: large-scaleAnalysis: few individual molecules?


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Top-down vs Bottom-upfor protein ID and characterization

Top-down Bottom-up

Intact protein

MW

Dissociation

Fragments

Fragments

Dissociation

MW of peptides

Enzymatic digest

MS

MSn

MS2

MS

MS2

MSn

CombinedTop-down/Bottom-up

Intact protein

Fragments

Dissociation

MW of peptides

Enzymatic digest

MS

MS

MS2

MSn

Low throughput High throughput, butinformation lost!

Not yetHigh throughput


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Protein Identification byTandem Mass Spectrometry

ILNKPEDETHLEAQPTDASAQFIRNLQISNEDLSKEPSISREDLISKEQIVIRSSRQPQSQNPK

LPLSILKEKHLRNATLGSEETTEHTPSDASTT

EGKLMELGHKIMRNLENTVKETIKYLKSLFSHAFEVVKT

Protein sequence

EDLISKEQIVIR

LPLSILKNLENTVK

LMELGHKQPQSQNPKNLQISNEDLSK

SLFSHAFEVVKNATLGSEETTEHTPSDASTTEGK

ILNKPEDETHLEAQPTDASAQFIR

Enzymatic

digest

Tryptic peptides

NLENTVK

Tryptic peptide

MS/MS

N L E N T V K

Fragmentation

232.17346.22

388.20444.28

484.33511.37555.40

623.45666.44

712.52

Fragment

masses YourPeptide/protein

is this:

Score = 77

Molecular mass: 817.44


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Mass Accuracy

1 ppm = 1 part per million


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Deep vs Top Proteomics

2002 2003 2004 2005 2006 2007 2008 20090

75

25

50

%o

fproteome

coverage


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Protein Identification, Quantification

Top proteome : 1500-3000 proteins, 5000-9000 peptidesNo protein separationNo peptide separation (on-line reverse-phase LC only)Single LC/MS experiment, 0.5-2.0 h long


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Deciphering signaling pathwaysMS Interactions

Pathway

Database

What is Pathway Analysis ?

Databasefilling

Fundamentalstudies

Functional Pathway Proteomics

Interpretation A:


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Use of known signaling mechanismsto identify activated pathwaysMS

PathwayDatabase

Using database toidentifyevents

Analytical Pathway Proteomics

What is Pathway Analysis ?

Molecular Diagnostic of Cells

GOOGLE for Proteomics

Application

research

Interpretation B:


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Google for Proteomics*

*with apologies to Google Corp.


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Google for Proteomics*

Load yourproteomics data here

*with apologies to Google Corp.

Click here


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Analytical Pathway Biology

PathwaySearchEngine

Up- and Down-Regulated(Activated)

Pathways/Key NodesWeight Factors

FullProteomics

Data:

Sample

Control

Zubarev, R. A.; Nielsen, M. L.; Savitski, M. M.; Kel-Margoulis, O.; Wingender, E.; Kel, A.Identification of dominant signaling pathways from proteomics expression data,

J. Proteomics, 2008, 1, 89-96.


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Control Cells

Sample Cells

Pathway Analysis Workflow


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SEQUENCE

RT

MASCOT RTAPEX INTENSITYMASCOT

INTENSITY

FULLINT MZMASCOT MZQUANTI IPI MASCOTSCOR E PROTE INSCORE

K.LVTDLTK.

V28.747.738

28.844.61

8 2.626.406.738

43.460.716.000.00

0 395.239.288395.238.556 IPI00022434 49.000.000 357.350.006

K.YLYEIAR.

R31.408.630

31.538.35

5 740.226.3187.096.348.500.000 464.250.610464.251.129 IPI00022434 51.000.000 357.350.006

K.CCTESLV

NR.R26.978.939

27.209.02

4 8.670.519.000.000

19.580.344.000.00

0 569.752.808569.753.174 IPI00022434 66.000.000 357.350.006

K.YICENQD

SISSK.L27.003.353

27.505.02

2 8.670.519.000.000

134.643.984.000.0

00 722.324.402722.323.059 IPI00022434 40.000.000 357.350.006

K.VPQVSTP

TLVEVSR.N32.628.799

32.766.89

1 8.670.519.000.000 81.970.281.250 756.424.805756.427.673 IPI00022434 74.000.000 357.350.006

K.KVPQVST

PTLVEVSR.

N30.977.934

31.125.68

9 8.670.519.000.000 497.071.281.250 547.317.688547.317.871 IPI00022434 78.000.000 357.350.006

ProteinIDandabundance

SEQUENCE

RT


INTENSITY

FULLINT MZMASCOT MZQUANTI IPI MASCOTSCORE PROTEINSCORE

K.LVTDLTK.

V28.747.738

28.844.61

8 2.626.406.738

43.460.716.000.00

0 395.239.288395.238.556 IPI00022434 49.000.000 357.350.006

K.YLYEIAR.

R31.408.630

31.538.35

5 740.226.3187.096.348.500.000 464.250.610464.251.129 IPI00022434 51.000.000 357.350.006

K.CCTESLV

NR.R26.978.939

27.209.02

4 8.670.519.000.000

19.580.344.000.00

0 569.752.808569.753.174 IPI00022434 66.000.000 357.350.006

K.YICENQD

SISSK.L27.003.353

27.505.02

2 8.670.519.000.000

134.643.984.000.0

00 722.324.402722.323.059 IPI00022434 40.000.000 357.350.006

K.VPQVSTP

TLVEVSR.N32.628.799

32.766.89

1 8.670.519.000.000 81.970.281.250 756.424.805756.427.673 IPI00022434 74.000.000 357.350.006

K.KVPQVST

PTLVEVSR.

N30.977.934

31.125.68

9 8.670.519.000.000 497.071.281.250 547.317.688547.317.871 IPI00022434 78.000.000 357.350.006

SEQUENCE

RT


INTENSITY

FULLINT MZMASCOT MZQ UANTI IPI MASCOTSCORE PROTEINSCORE

K.LVTDLTK.

V28.747.738

28.844.61

8 2.626.406.738

43.460.716.000.00

0 395.239.288395.238.556 IPI00022434 49.000.000 357.350.006

K.YLYEIAR.

R31.408.630

31.538.35

5 740.226.3187.096.348.500.000 464.250.610464.251.129 IPI00022434 51.000.000 357.350.006

K.CCTESLV

NR.R26.978.939

27.209.02

4 8.670.519.000.000

19.580.344.000.00

0 569.752.808569.753.174 IPI00022434 66.000.000 357.350.006

K.YICENQD

SISSK.L27.003.353

27.505.02

2 8.670.519.000.000

134.643.984.000.0

00 722.324.402722.323.059 IPI00022434 40.000.000 357.350.006

K.VPQVSTP

TLVEVSR.N32.628.799

32.766.89

1 8.670.519.000.000 81.970.281.250 756.424.805756.427.673 IPI00022434 74.000.000 357.350.006K.KVPQVST

PTLVEVSR.

N30.977.934

31.125.68

9 8.670.519.000.000 497.071.281.250 547.317.688547.317.871 IPI00022434 78.000.000 357.350.006

IPI# ProteinAbundanceSample

1 23 45678910111213


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Receptor

Signal molecule

Adaptors

Kinases

Transcription factors

mRNA

Proteome

BioBase, Germany


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ProteinsObserved

Stimulus

KeyNode-Mediated Analysis: Upstream

KeyNode1 3050

KeyNode2 2987KeyNode3 2073

KeyNodeN 25

Score

Pathway score:

(keynode score)


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Zubarev, R. A.; Nielsen, M. L.; Savitski, M. M.; Kel-Margoulis, O.; Wingender, E.; Kel, A.Identification of dominant signaling pathways from proteomics expression data,

J. Proteomics, 2008, 1, 89-96.

Quantitative Pathway Analysis

Pathway Search Engine proof of principle

0 0.1 0.2 0.3 0.4 0.5-0.5 -0.4 -0.3 -0.2 -0.1

Score = S(sample) - S(control), arb. units

Numberof

pathways

0

100

200

0

100

200

0

100

200

Stress

Stress

EGF

EGF

Sample - Control1

Sample - Control2

Control1 - Control2

EGFStress


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Quantitative Pathway Analysis

Pathway Search Engine first application

Sthl, S.; Fung, Y.M.E.; Adams, C. M.; Lengqvist, J.; Mrk, B.; Stenerlw, B.;Lewensohn, R.; Lehti, J.; Zubarev, R. A.; Viktorsson, K. Proteomics and Pathway AnalysisIdentifies JNK-signaling as Critical for High-LET Radiation-induced Apoptosis in Non-SmallLung Cancer Cells, Mol. Cell Proteomics, 2009, 8, 1117-1129.


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Kinase upstream of JNK

Tumor marker

Pathway Analysis: Validation of JNK++

Sthl S et al.,Mol. Cell Proteomics,2009, 8, 1117-1129.


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DYNAMIC PROTEOMICS APPROACH

for drug target identification:by the speed of change (1 h), 10% selectionby the total change in 48 h, 10% selection

Overall: top 3% (35 proteins)


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Pathway Analysis of Dynamic Proteomics Data

Proteins frominput list

I) Protein mapping on Pathways


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Pathway Analysis of Dynamic Proteomics DataUpstream Search:

for Speed, 0-60 minfor Magnitude, 0-2800 min

Key Nodes

KN Scoring:S = (SA SB)*log2(SA/SB)

Top KN is selected: one for Speed, one for Magnitude


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Pathway Analysis of Dynamic Proteomics Data

Two top KNs

OverlappingMolecules

= Drug Target Candidates

Downstream KN search


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Rank, speed

Rank,magnitud

e

Overlap of downstream lists from Fgamma, c-FLIP(h):9 proteins, of which 2 from input list (known dynamics):

TOPI, (speed + magnitude)-rank 22826S proteasome, (speed+ magnitude)-rank 787

Identification of TOPI as thedrug targetfrom 812 proteins in the input list


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Rank, speed

Rank,magnitude

Overlap of downstream lists from Fgamma

, c-FLIP(h):4 proteins, none from the input list:

TOPI CKII

Two NR-related proteins

What if TOPI is removedfrom Input list?..


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What if otherproteins (besides TOPI) removed from Input list?

-20% random-50% random

-182 top scoring

TOPI + 8 other TOPI + 11 other TOPI + 342 other

Of which:52 from Input List

TOPI - #12Thus, Pathway Analysis is a powerful method

for Drug Target discovery by Dynamic Proteomics

D.M. Good and R.A. Zubarev, submitted


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Myeloid-derived suppressor cells (MDSC): accumulate in patientsand animals with cancer where they mediate systemic immune suppressionand obstruct immune-based cancer therapies.

MDSC suppress antitumorimmunity through a variety

of diverse mechanisms.

Suzanne Ostrand-Rosenberg and Pratima Sinha


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Chornoguz et al., Mol. Cell. Proteomics, in press.

Proteomic Pathway Analysis Reveals Inflammation IncreasesMyeloid-Derived Suppressor Cell Resistance to Apoptosis


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4T1 - spontaneously metastatic mammary carcinoma

4T1/IL-1 - transfected with the IL-1 gene (high levels ofIL-1 heighten inflammation in the tumor microenvironment)


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Caspase

Fas


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Fas agonist Jo2 mAb


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Conclusion: inflammation enhances MDSC accumulation by increasingMDSC resistance to Fas-mediated apoptosis.


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no. of keymolecules 1192

key molecules Rankslog(e-values) Random Ranks

log(e-values)

MO000018008 KOR 1 1 1 1 12.31 767 1190 1081 1107 -4.95MO000022259 RSK1 143 26 4 3 7.59 1051 706 323 1044

-1.45

Activated keynodes in Chronic Pain Mouse model: 4 + 4 mice

Downregulated keynodes in SNL versus Control Tissue samplesMO000017741 LAP 1091 1165 1188 1185 -7.19 114 262 510 951 1.06MO00003

5620 LAP1 1083 1154 1189 1188 -7.31 605 889 775 571 -0.71MO000000208 RIP 1047 1173 1191 1186 -7.63 264 285 1185 202 -0.43MO000017811 proCaspase-9 1178 1189 1186 1105 -7.70 578 210 1064 1142 -1.57MO000018276 proCaspase-10 1180 1186 1181 1163 -7.77 209 734 886 227 0.64

0

100

200

300

400

-8 -6 -4 -2 0 2 4 6 8 10 12

Sample

Random

log(e-values) Sample Random

-8 0 0-7 5 0

-6 0 1-5 5 1-4 22 8-3 67 25-2 92 73-1 113 1980 364 2971 227 2852 84 1733 91 1004 87 27

5 21 26 12 27 0 08 1 09 0 0

10 0 011 0 012 0 013 1 0

With G. Bakalkin, Uppsala


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Preliminary quantitative molecular model of chronic pain

VFth = Hap1 - KOR +0.33*(insulin:InsR) - 0.60*cyclin_E;

VFth - paw withdrawal thresholds ingmeasured by von Frey filaments

Hap1 - Huntingtin-associated protein-1, which degradation is stimulated by

insulin through ubiquitylation. Involved in trafficking of GABA-A receptor.KOR - kappa opioid receptor

-4.00E+06-2.00E+060.00E+002.00E+064.00E+066.00E+06

8.00E+061.00E+071.20E+071.40E+07

0 2 4 6 8 10 12 14 16VF test, day 10

Predic

tVF

test R=0.962

Predictive model: P = f(K1, K2, KN) = F(proteome)

P = disease progression


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Pathway Analysis provides activation levels of key nodes andsignaling pathways starting from expression proteomics dataThe final goal is:

- drug target discovery;- disease mechanism discovery;

- patient stratification;- predictive quantitative molecular model of a disease

Pathway Analysis findings need to be validated!

Take-home messages

rzubarev pathway analysis march 2011

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