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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
70 60 50 40 30 20 10 0 100 90 80 70 60 50 40 30 20 10 0 20
25
30
35
40
45
50
55 Time (min)
60
65
70
75
80
85
90
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
90 80 70 60 50 40 30 20 10 0 20
25
30
35
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55 Time (min)
60
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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
D C 2
24
48
D
D
72 96 h
C
C 2
24
48
72 96 h
2
24
48
72 96 h
*
* D
D
*
C
*
C
* 2
D C
*
* D
D C 2
24
48
72 96 h
24
C
*
2
24
48
72 96 h
n.d.
48
72 96 h
shoot *
* D C
2 24 96 h
48
72
2 24 96 h
48
72
D C
* D
D = drought-recovery
C
*
C = control
* = display for marked AA
* D C 2 24 96 h
48
72 2 24 96 h
n.d.
48
D
D
C
C
72
2 24 96 h
48
72
*
*
D
D
C
*
C
* 2 24 96 h
D C
*
*
*
D D C 2 24 96 h
48
72
D
C
*
48
C 2 24 96 h
48
72 2 24 96 h
48
72
72
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