# Sub optimal 15N metabolic labelling in plant: A new way to perform large scale protein turnovers determination using the peptides isotopic distribution.

Willy Vincent Bienvenut, Olivier Langella, Thierry Balliau, Filippo Rusconi, Marlène Davanture, Melisande Blein-Nicolas

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Willy Vincent Bienvenut, Olivier Langella, Thierry Balliau, Filippo Rusconi, Marlène Davanture, et al. Sub optimal 15N metabolic labelling in plant: A new way to perform large scale protein turnovers determination using the peptides isotopic distribution. ProteoVilamoura, Spanish, French and Portuguese Proteomics Societies, May 2022, VilaMoura, Portugal. **hal-03815633**

HAL Id: hal-03815633

https://hal.science/hal-03815633

Submitted on 21 Oct 2022

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Public Domain
# Sub optimal ¹⁵N metabolic labelling in plant:

A new way to perform large scale protein turnovers determination using the peptides isotopic distribution

Willy Bienvenut, IDEEV, GQE, Gif/Yvette, France
# Proteostasis and large scale protein turnover rate determination

<figure><img src="image_2.png" /></figure>

Dissmeyer *et al*. *Trends Biochem. Sci.* 2019

of protein Half-life and Turnover rate
Influence directly the Protein abundance
# What about phototrophic organisms

Not possible to use
heavy-labelled amino acid

<figure><img src="image_3.png" /></figure>

Water, CO₂, fertilizers and trace elements...

¹²C → ¹³C (CO₂)

¹⁴N → ¹⁵N (NH₄NO₃)

Peptide: TFSQFGDVIDSK (2+)

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

Huttlin et al. *Mol. Cell. Proteomics* 2017
# Li et al. approach, Plant Cell 2017; 29:207-228

<figure><img src="image_6.png" /></figure>

21 days old non-labelled Arabidopsis plants

<figure><img src="image_7.png" /></figure>

Determination of the
FCP coefficient

(Fold Change in Protein)

A

Leaf 3

Signal Intensity

T1

<figure><img src="image_8.png" /></figure>

T3

<figure><img src="image_9.png" /></figure>

Leaf 5

Signal Intensity

TFQGPPHGIQVER
LPF 9.6%
Enrichment 46.8%

TFQGPPHGIQVER
LPF 34.2%
Enrichment 69.0%

TFQGPPHGIQVER
LPF 42.4%
Enrichment 70.8%

Leaf 7

Signal Intensity

TFQGPPHGIQVER
LPF 21.3%
Enrichment 59.5%

TFQGPPHGIQVER
LPF 68.3%
Enrichment 87.3%

TFQGPPHGIQVER
LPF 70.5%
Enrichment 87.2%

B

T1

<figure><img src="image_10.png" /></figure>

T3

<figure><img src="image_11.png" /></figure>

Determination of the LPF

(Labelled Protein Fraction)
# K<sub>D</sub> and K<sub>S</sub> calculation

<table><thead><tr><th></th><th></th><th>RBCL<br>(TFQGPPHGIQVER)</th><th>THI1<br>(MGPTFAMMISGQK)</th></tr></thead><tbody><tr><td rowspan="2">Fold Change in Protein<br>Abundance (FCP)</td><td>T0</td><td>1</td><td>1</td></tr><tr><td>T3</td><td>1.07</td><td>1.07</td></tr><tr><td rowspan="2">Labelled Protein Abundance<br>(LPF)</td><td>T0</td><td>0</td><td>0</td></tr><tr><td>T3</td><td>14.3%</td><td>96.3%</td></tr><tr><td colspan="4">K<sub>D</sub> = - (ln FCP / t) (1 - LPF)</td></tr><tr><td colspan="4">- (ln 1.07 / 3 d) (1 - 14.3%) = 0.04 d<sup>-1</sup></td></tr><tr><td colspan="4">- (ln 1.07 / 3 d) (1 - 96.3%) = 1.61 d<sup>-1</sup></td></tr><tr><td colspan="4">K<sub>S</sub> = (FCP / (1 - e<sup>-K<sub>D</sub>t</sup>)) · K<sub>D</sub></td></tr><tr><td colspan="4">1.07 / (1 - e<sup>-0.043</sup>) · 0.04 = 0.07 d<sup>-1</sup></td></tr><tr><td colspan="4">1.07 / (1 - e<sup>-1.613</sup>) · 1.61 = 1.73 d<sup>-1</sup></td></tr></tbody></table>

- Millar et al. method

- High data-processing

- Scripts not available

- ...

- Alternative approach... ?

- Adapted to our local tools

- Adaptable to field investigations

- Low cost

- ...
# Large scale protein turnover measurement...

* Proposed alternative

* Low labelling rate (5-6% vs. >70-90% ¹⁵N)

* Advantage :

* Peptide isotopic distribution is moderately modified!!!

* Heavy & light isotopomers overlay

* Minor sample complexity increase

* Bottlenecks...

* Intensive post-acquisition data processing:

* ¹⁵N labelling yield # peptide isotopic distribution

* Protein amount variation (label free quantitation)

* Processing based on our open sources softwares

* X!Tandem Pipeline, MassChroQ, MCQR (R package),...

* Additional R and Python processing scripts under development...
# The isotopic distribution world...

20201115_Taufkneiz_90_114_179503-18008_NT_03-05-04.03_AV_S1_NL_9.46E7
T: FTMS + g NG1 Fuel Box ms (350.000-1400.000)

<figure><img src="image_12.png" /></figure>

Peptide experimental isotopic
distribution

Peptide:
LAFYDYIGNNPAK
z = 2+
m/z = 743.3733 Th

<figure><img src="image_13.png" /></figure>

$$ \text{Isotope Fitting Score (IFS)} = -\log(1-\text{Corr.}) $$

<figure><img src="image_14.png" /></figure>

Peptide simulated isotopic
distribution

<figure><img src="image_15.png" /></figure>

$$ \text{Isotopic profile} = f(\% ^{15}\text{N}) $$
# Standard samples preparation and proteins ID

<figure><img src="image_16.png" /></figure>
# Isotopic distribution metabolic
# labelling kinetics

Peptide sequence: AGVKEYKLTYYTPEYETK

20200817_WEB_10CH-Rubisco-01 #8891-9077 RT: 40.8041.03 AV: 18 NL: 6.26E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_17.png" /></figure>

t₀ Ctrl

20200817_WEB_11-19Niss-Rubisco-03 #8032-6203 RT: 41.1041.44 AV: 39 NL: 2.71E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_18.png" /></figure>

t₁ (1 day)

20200817_WEB_12-19Niss-Rubisco-02 #8014-6079 RT: 40.9141.13 AV: 24 NL: 3.51E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_19.png" /></figure>

t₂ (3 days)

20200817_WEB_14-CH-Rubisco-01 #8033-9400 RT: 40.7941.15 AV: 26 NL: 3.42E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_20.png" /></figure>

t₄ Ctrl (10 days)

20200817_WEB_13-19Niss-Rubisco-02 #8006-9117 RT: 40.8041.25 AV: 42 NL: 6.88E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_21.png" /></figure>

t₃ (6 days)

20200817_WEB_14-19Niss-Rubisco-02 #8025-9207 RT: 41.0341.34 AV: 33 NL: 2.29E5
T: FTMS + p-NB Full lockms [360.0000-1400.0000]

<figure><img src="image_22.png" /></figure>

t₄ (10 days)
<figure><img src="image_23.png" /></figure>

Génétique
Quantitative
et Évolution
Le Moulon

<figure><img src="image_24.png" /></figure>

# Post Acquisition Data Processing

<figure><img src="image_25.png" /></figure>

Raw files

<figure><img src="image_26.png" /></figure>

X!Tandem search
results

<figure><img src="image_27.png" /></figure>

Protein
turnover
calculation
# Post Acquisition Data Processing

<figure><img src="image_28.png" /></figure>
# Post Acquisition Data Processing

<figure><img src="image_29.png" /></figure>

Filtering raw data
# A view Exp vs Theo Ri...

<figure><img src="image_30.png" /></figure>

## Pre-processing

Removing outliers: $R_{i \text{ Exp}}(\text{pepID}) < R_{i \text{ Theo}}(\text{pepID}) * 1.2 \text{ at } 6\% \text{ }^{15}\text{N}$

<figure><img src="image_31.png" /></figure>
# Filtering data

## 2 levels filters

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /><figcaption>Inter-Spectrum Correlation</figcaption></figure>

<caption>Peptide Ri distribution</caption>
# Isotopic ratios and profile filtering

## 2 levels filters

### Raw ratios

<figure><img src="image_34.png" /></figure>

$R^2 = 0.80$

$IFS = 0.71$

### Peptide(Ri) dist.

<figure><img src="image_35.png" /></figure>

$R^2 = 0.92$

$IFS = 1.08$

### Spectrum² Corr.

<figure><img src="image_36.png" /></figure>

$R^2 = 0.96$

$IFS = 1.37$

Single peptide Ratios

<figure><img src="image_37.png" /></figure>

<Ri> - 2 SD < Ri < <Ri> + 2 SD

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

MS Corr. > 0.9
# Additional filters ...

## Raw ratios

<figure><img src="image_40.png" /></figure>

8755972 MS²

R² = 0.069

IFS = 0.03

## Ri filters

<figure><img src="image_41.png" /></figure>

2842304 MS²

R² = 0.96

IFS = 1.37

## Favouring data redundancy

<figure><img src="image_42.png" /></figure>

2709529 MS²

R² = 0.96

IFS = 1.43

Redundant measurements
in multiples replicates (≥ 2 out of 4 MS run)
Minimum isotopes ratios (≥ 4 I)
Minimum number of spectra (≥ 5 MS²)
# Post Acquisition Data Processing

<figure><img src="image_43.png" /></figure>
# Post Acquisition Data Processing

## Highly redundant peptide isotopic profiles

* Multiple acquisitions (Triplicate at least)
* Multiple Rt (10-30 s peptide elution)
* Multiple z (2+,3+...)

$$ \log(R_{ijk}) = \text{isotope}_i + \text{MSrun}_j + \text{RT}_k + \varepsilon_{ijk} $$

* isotope_i: isotope i fixed effect

* MSrun_j: MS run j fixed effect

* RT_k: retention time random effect

* εijk: residual

<figure><img src="image_44.png" /></figure>

Ratio modelled by multiple regression

<figure><img src="image_45.png" /></figure>

Single isotopic distribution per non-redundant peptide
# Post Acquisition Data Processing

<figure><img src="image_46.png" /></figure>
# ¹⁵N rate: an iterative approach

<figure><img src="image_47.png" /></figure>

Ex.: Ctrl sample

IFS
Slope
Intercept

IFS Distribution

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<¹⁵N rate> = 0.37% ± 0.27% (n=7945)
# ¹⁵N rate evolution

¹⁵N Metabolic labelling

Ctrl t₀

<¹⁵N> = 0.37% ± 0.27%
n=7945

¹⁵N t₁

<¹⁵N> = 0.53% ± 0.55%
n=8690

¹⁵N t₂

<¹⁵N> = 1.35% ± 0.84%
n=7265

¹⁵N t₃

<¹⁵N> = 1.95% ± 0.95%
n=7712

¹⁵N t₄

<¹⁵N> = 2.06% ± 0.80%
n=7552

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>

<figure><img src="image_57.png" /></figure>

<figure><img src="image_58.png" /></figure>

<figure><img src="image_59.png" /></figure>

<figure><img src="image_60.png" /></figure>
“Gold standard” measurement

<figure><img src="image_61.png" /></figure>

<figure><img src="image_62.png" /></figure>
# Apparent <sup>15</sup>N rate to LPF

<figure><img src="image_63.png" /></figure>

$$ \text{LPF} = \frac{[\text{labelled}]}{([\text{initial}] + [\text{labelled}])} $$

$$ ([\text{initial}] + [\text{labelled}]) = 1 $$

$$ ^{15}\text{N}_{\text{App}} = \frac{(^{15}\text{N}_{\text{Nat}} \times [\text{initial}] + ^{15}\text{N}_{\text{Max}} \times [\text{labelled}])}{([\text{initial}] + [\text{labelled}])} $$

$$ \text{LPF} = \frac{(^{15}\text{N}_{\text{App}} - ^{15}\text{N}_{\text{Nat}})}{(^{15}\text{N}_{\text{Max}} - ^{15}\text{N}_{\text{Nat}}} $$

<figure><img src="image_64.png" /></figure>
# Post Acquisition Data Processing

<figure><img src="image_65.png" /></figure>
MCQR

* A set of objects and functions for R to handle MassChroQ results:

* Label free quantitation

* Developed for natural isotopic abundance!

* Requires some adaptations

* Isotope selection

* Fine tuning...
# To conclude...

* Low ¹⁵N metabolic labelling is compatible with:

* Peptide characterisation and protein identification

* Determination of the Protein Fold Change (MCQR)

* Data post acquisition processing provides:

* Reliable experimental isotopic distributions

* Evaluate the ¹⁵N labelling rates

* Estimation of the Labelled Protein Fraction (LPF)

* In progress:

* Improvement of the LPF accuracy

* Protein label free quantitation even with ¹⁵N metabolic labeling

* R script package under development
W6

Willy, 04/05/2022
# Acknowledgement's

Liam Le Goffic
Thomas Welfelé
Olivier Langella
Thierry Balliau
Filippo Rusconi,
Marlène Davanture,
Michel Zivy
Mélisande Blein-Nicolas

## Collaboration with

C. Masclau-Daubresse & A. Marmagne
(IJPB, Versailles)

S. Colombié & Y. Gibon
(BFP, Bordeaux)

<figure><img src="image_66.png" /></figure>