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Stefanie Wienkoop – Plant Systems Interaction

Comprehensive Protein turnover analysis of a partial 15N stable isotope metabolic labelling experiment In Planta RT: 19.6 - 90.0 SM: 15G 100

56.8

NL: 6.22E6 TIC F: + p SRM ms2 [email protected] [ 200.65-923.75] MS Contol_standard_8 00fmol4

56.6

NL: 6.58E4 TIC F: + p SRM ms2 [email protected] [ 200.65-916.75] MS Contol_standard_8 00fmol4

90 80

Relative Abundance

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RT: 19.6 - 90.0 SM: 15G NL: 6.73E7 TIC F: + p SRM ms2 [email protected] [ 142.65-568.55] MS Contol_standard_8 00fmol4

38.5

100 90 80

Relative Abundance

70 60 50 40 30 20 10 0 100

NL: 6.74E4

38.8

TIC F: + p SRM ms2 [email protected] [ 142.65-561.55] MS Contol_standard_8 00fmol4

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Stefanie Wienkoop http://homepage.univie.ac.at/stefanie.wienkoop//

Chicago, Proteomics-2014

90

Stefanie Wienkoop

Molecular Systems Biology and The Molecular Plant Phenotype

GenotypePhenotype Relationship

Genotype (genome sequence) : metabolic and regulatory reconstruction of the whole species Environmental Perturbation Molecular Phenotype

Analytics

in vivo Dynamics

Chicago, Proteomics-2014

Phenomics

Metabolomics

Proteomics

Genomics

Integration of the data, biostatistics and modelling

Stefanie Wienkoop

Proteomics Toolbox Analyses

Proteomics Profiling analyses (2.1)

Comparative analyses (2.2)

Localisation studies organelle/membrane enrichment

Unbiased relative quantification (2.2.1)

Database dependent protein ID

Improved genome annotation (2.3) Public Sources

Genomic database

Chicago, Proteomics-2014

Database independent peptide ID (MAPA)

Metabolomics - Proteomics data integration and transformsation (2.4)

Data mining

Wienkoop et al. JProt (2010)

Targeted absolute quantification (2.2.2)

Interpretation and visualisation (HIC, PCA, ICA)

Modelling

Biomarker discovery

Stefanie Wienkoop

Proteomics Toolbox Analyses

Proteomics Profiling analyses (2.1)

Comparative analyses (2.2)

Mass Western Localisation studies

ProMEX

organelle/membrane enrichment

Selpex & CamelCropper

ProtMAX

Metabolomics - Proteomics data integration and transformsation (2.4)

Improved genome annotation (2.3) Public Sources

Genomic database

Chicago, Proteomics-2014

Targeted absolute quantification (2.2.2)

Turnover & Database dependent Degradation Database independent protein ID peptide ID (MAPA)

Data mining

Wienkoop et al. JProt (2010)

Unbiased relative quantification (2.2.1)

Interpretation and visualisation (HIC, PCA, ICA)

Modelling

Biomarker discovery

Stefanie Wienkoop

FWF Project P23441-B20

SILIP- Drought Recovery Experiment

Medicago truncatula

Experimental Setup Drought Recovery (96 h) Experiment Re-Watering 15N (2.5 mM NH4 NO3)

In Planta Gel-free Shotgun-LC/MS/MS

5 TP

6 rep.

C vs D

2h 24 h

7 week old plants

48 h

10 days of drought stress

72 h

non-symbiotic (nodule free)

96 h Same set: 14N

Chicago, Proteomics-2014

(2.5 mM NH4 NO3)

Stefanie Wienkoop

Drought -> Drought Recovery drought

stomatal conductance

gs [mmol H2O m-2S-1]

500

drought recovery

water re-supply

400 300 200 C

100 0

DR 0

2

48

0

water potential (predawn)

ᴪw [MPa]

-0.5 -1

-1.5 -2 hours of drought recovery

Chicago, Proteomics-2014

96

shoot

NSAF

705

LC-Orbitrap MS/MS

root 708

454 251 456 Total: 1161

Stefanie Wienkoop

HIC: Protein and Metabolite Cluster Analysis Significant changed only (DR/C)!

≥ 2 fold change p ≤ 0.05 n=6

Chicago, Proteomics-2014

Roots (278)

Shoots (174)

2 24 48 72 96

2 24 48 72 96

Proteins Metabolites

Stefanie Wienkoop

Protein and Metabolote Response Overview fold change (DR/C)

2

24

48

72

96

Roots

Proteins Metabolites

140 20

132 20

126 17

115 13

142 3

Shoots

Proteins Metabolites

43 21

50 13

60 7

47 10

51 0

Metabolites fully recover!

Chicago, Proteomics-2014

Stefanie Wienkoop

Correlation Network & Functional GO Analysis Biol. Process: TRANSLATION

2h

96 h

Chicago, Proteomics-2014

Stefanie Wienkoop

Regulatory Relevant Protein Correlation-Cluster 2h after Re-Watering Shoot

Root

Shoot

Root

Cluster K Cluster A

Cluster B

Cluster L

Group 3: Cluster C

Time point 1 down-regulation

Cluster D

Group 1:

Cluster H

Time point 1 up-regulation

Group 5: Time point 5 up- or down-regulation

Chicago, Proteomics-2014

Stefanie Wienkoop

Relative cluster proportion of stress recovery response proteins Shoots

Cluster A 2% Cluster L 13%

Cluster K 4%

Cluster B 11%

Cluster C 1% Cluster D 1% Cluster E 0%

Cluster A 5%

Cluster G 5%

Cluster L 13%

Cluster K 3%

Cluster B 20% Cluster J 9%

Cluster F 13%

Cluster J 14%

Cluster I 11%

Roots

Cluster C 10%

Cluster I 4% Cluster H 11%

Cluster H 25% Cluster G 5%

Chicago, Proteomics-2014

Cluster F 10%

Cluster D 5% Cluster E 5%

Stefanie Wienkoop

First Conclusions

• Translational Regulation within first 2 hours of rewatering.

• While physiological and metabolic levels fully recovered after 96 hours, protein regulation still in process

Chicago, Proteomics-2014

Stefanie Wienkoop

Why studying protein turnover?

1. Transcript and protein measurements are poorly correlated in experiments of abundance and stability when exposed to environmental perturbations. 2. Understanding the mechanisms controlling whole plant nitrogen-to-protein incorporation and protein-to-nitrogen breakdown will advance our knowledge of plant nitrogen acquisition and allocation.

Chicago, Proteomics-2014

Stefanie Wienkoop

How studying protein turnover?

1. 15N stable isotope metabolic labeling is one of the major approaches for Mass Spectrometry based proteomics. 2. Additionally to protein turnover, AA turnover can be monitored. 3. Protein turnover analysis involves the partial-labeling strategy.

Chicago, Proteomics-2014

Stefanie Wienkoop

Challenge 1

1. Partial metabolic labelling => high complexity! 2. Identification of all proteinogenic AAs => possible? 3. Absolute levels from all proteinogenic AAs => very difficult! 4. Absolute levels of all identified proteins => possible? 5. Proteotypic peptides (>2) for all proteins => possible?

Chicago, Proteomics-2014

root *

* D

n.a. 2

24

48

C

72 96 h 2

24

48

72 96 h

D C

* D

D = drought-recovery

C

*

C = control

*

* = display for marked AA

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48

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C 2

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48

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*

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n.d.

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72 96 h

shoot *

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2 24 96 h

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* D

D = drought-recovery

C

*

C = control

* = display for marked AA

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48

72 2 24 96 h

n.d.

48

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D

C

C

72

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GC-MS amalysis of 15N incorporation into Amino Acids No labelling RIA0 14N/15NNA

roots

C D

Chicago, Proteomics-2014

15N labelling RIA of 15N enrichment

Stefanie Wienkoop

Callenge 2

How to retrieve the relative isotope abundance (RIA) of partial metaboloc labelling from MS data?

Chicago, Proteomics-2014

CamelCropper

David Lyon

FWF Project P23441-B20

Stefanie Wienkoop

CamelCropper for 15N partial labelling – protein turnover detection SelPEx- Selective Peptide Extraction

MS data upload into CamelCropper

List of identified peptides with high quality MS properties: - Robust and reproducible MS signals / peak shape - m/z ratio, RT and ID Castillecho et al. 2013 IN: Plant Proteomics Methods and Protocols. Ed. J.V. Jorrin Novo, S. Komatsu, S. Wienkoop, W. Weckwerth: Springer New York.

Chicago, Proteomics-2014

CamelCropper - Calculation of all possible m/z values of the 15N isotope pattern for each peptide. - Ancoring to the monoisotopic precursor (MIP0) - ID of the „Camel neck“ and „Camel back“ - Calculation of eg RIA [H]/ [H+L] for calculation protein synthesis and degradation

Stefanie Wienkoop

Workflow Upload of 1394 peptide sequences (430 proteins)

samples LC/MS

SelPEX

(AAseq, charge, RT)

RAW data mzML

search input (ID) picked data points peptide label ratios Chicago, Proteomics-2014

Stefanie Wienkoop

PCA Plot: RIA Intensities of Shoot Proteins

control

0

drought

Chicago, Proteomics-2014

24

48

72

96

Stefanie Wienkoop

Cluster Analysis: RIA ration DR vs. C

Chicago, Proteomics-2014

Stefanie Wienkoop

Cluster Analysis of PS: RIA ration DR vs. C

Chicago, Proteomics-2014

Stefanie Wienkoop

BoxPlot LSU

Chicago, Proteomics-2014

Stefanie Wienkoop

BoxPlot SSU

Chicago, Proteomics-2014

Stefanie Wienkoop

SSU

1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

RIA

LSU

C SSU

0

24

48

72

96

h of DR

DR SSU

LSU

0.6

0.6

0.5

0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

0 0

RSUs abundance did not significantly change during drought recovery!

24

48 72 Ho15N

96

0 0

24

48 HAS

RSUs RIAs did significantly increase during drought recovery!

=> Equal Synthesis and Degradation Rates! Chicago, Proteomics-2014

LSU

RIA

RIA

prot. abundance D/C

Protein Levels vs. Turnover

72

96

Stefanie Wienkoop

Protein Levels vs. Turnover RIA C Av. RSU synthesis rates per 24 h: Contol = 0.05 DR= 0.13

Av. RSU degradation rates per 24 h: Control = 0.06 DR= 0.12

SSU

LSU

0.6

0.6

0.5

0.5

0.4

0.4

0.3

LSU

RIA

RIA

SSU

D

0.3

0.2

0.2

0.1

0.1

0 0

24

48 72 Ho15N

96

0 0

24

48 HAS

RSUs RIAs did significantly increase during drought recovery! => RuBisCO turnover ~2 fold higher during drought recovery! Chicago, Proteomics-2014

72

96

Stefanie Wienkoop

Second Conclusion

1) AA turnover increased within first 24 h of drought recovery. -> AA levels increased within 48 h -> direct incorporation into proteins 2) A strong increase in protein turnover was observed along drought recovery (synthesis and degradation rates)

 variation in isotope composition can be caused by multiple assimilation events, organ specific loss of nitrogen, and resorption and reallocation of nitrogen.  Reduced N-uptake during drought caused decreased AA levels

Chicago, Proteomics-2014

Thanks to the Team!!!

Christiana Staudinger Vlora Mehmeti Wolfgang Hohenwarter Lena Fragner Luis Recuenco-Muñoz David Lyon Reinhard Turetschek MA Castillejo (Selpex) Wolfram Weckwerth and others

+ Green team!! [P23441-B20] [P24870-B22]

Getinet Desalegn Hans-Peter Kaul

Let Us Meet Again We welcome you all to our future conferences of OMICS Group International Please Visit:

www.omicsgroup.com www.conferenceseries.com www.proteomicsconference.com