# Predictive metabolomic to decipher plant eco-evolutive tendencies

Cathleen Mirande-Ney, Santiago Trueba, Yves Gibon, Sylvain Prigent

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Cathleen Mirande-Ney, Santiago Trueba, Yves Gibon, Sylvain Prigent. Predictive metabolomic to decipher plant eco-evolutive tendencies. Journées Ouvertes Biologie, Informatique, Mathématiques (JOBIM2025), Jul 2025, Bordeaux, France. hal-05166151

HAL Id: hal-05166151

https://hal.inrae.fr/hal-05166151v1

Submitted on 16 Jul 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Predictive metabolomic to decipher plant eco-evolutive tendencies

Cathleen MIRANDE-NEY<fnref n="1" />, Santiago TRUEBA<fnref n="2" />, Yves GIBON<fnref n="1" />,<fnref n="3" /> and Sylvain PRIGENT<fnref n="1" />,<fnref n="3" />

cathleen.mirande-ney@inrae.fr

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## Introduction

* Metabolomics serve as a powerful tool for understanding the biochemical diversity of plants and its relationship with ecological characteristics.

* Plant metabolic signatures reflect their adaptive strategies, which influence their functional groups, growth forms, and phenological patterns.

* Technological advances now allow large-scale metabolomic data collection.

* Machine learning provides effective tools to identify patterns and key variables within complex metabolomic datasets.

**How do predictive metabolomic analyses improve our understanding of plant metabolism and its role in ecological and evolutionary trends?**

## Methodology

### 74 plants leaf sampling

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### Untargeted Metabolomics analyses

* High-throughput robotized extraction

* UHPLC-MS analysis

ESI-

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Orbitrap elite – Thermo Scientific

### Machine learning

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## Results

### Predictive metabolomic

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<table><thead><tr><th>Reference</th><th>angiosperm</th><th>fern</th><th>gymnosperm</th></tr></thead><tbody><tr><td>angiosperm</td><td>1600</td><td>35</td><td>51</td></tr><tr><td>fern</td><td>0</td><td>90</td><td>0</td></tr><tr><td>gymnosperm</td><td>0</td><td>0</td><td>99</td></tr></tbody></table>

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<table><thead><tr><th>Reference</th><th>herb</th><th>shrub</th><th>tree</th></tr></thead><tbody><tr><td>herb</td><td>945</td><td>49</td><td>15</td></tr><tr><td>shrub</td><td>0</td><td>50</td><td>45</td></tr><tr><td>tree</td><td>5</td><td>126</td><td>640</td></tr></tbody></table>

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<table><thead><tr><th>Reference</th><th>annual</th><th>deciduous</th><th>evergreen</th><th>perennial</th></tr></thead><tbody><tr><td>annual</td><td>315</td><td>0</td><td>5</td><td>92</td></tr><tr><td>deciduous</td><td>0</td><td>431</td><td>107</td><td>49</td></tr><tr><td>evergreen</td><td>0</td><td>30</td><td>300</td><td>25</td></tr><tr><td>perennial</td><td>110</td><td>39</td><td>13</td><td>359</td></tr></tbody></table>

x random accuracy

### Metabolomic characterization of plant growth form

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## Conclusion and Perspectives

* Metabolomics fingerprint predicts plant functional groups, growth forms, and phenology with high accuracy for all models tested.

* Herbaceous plants are characterized by primary metabolites, in particular with leaves showing higher levels of TCA cycle intermediates (malate, fumarate, succinate), pyrimidine nucleoside compounds, and amino acids like threonine, all supporting growth.

* Trees are characterized by leaves with higher levels of carbohydrates and secondary metabolites, revealing a conservative and structural metabolic strategy promoting defense.

* Shrub metabolism is a mix of herbs and trees metabolisms.

* Metabolomic profiling, along with machine learning, can effectively predict ecological and evolutionary trends in plants.

* In addition, it opens the possibility to predict key physiological leaf traits, such as minimum leaf conductance ($g_{min}$), linking metabolism to water-use efficiency and stress tolerance. This approach provides a new framework for investigating plant adaptation and ecological strategies.

## Acknowledgements

This study received financial support from Bordeaux University, GPR Bordeaux Plant Sciences.

<footnote n="1">¹ Univ. Bordeaux, INRAE, UMR1332 BFP, 33882 Villenave d'Ornon, France</footnote>

<footnote n="2">² AMAP, Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, 34398 France</footnote>

<footnote n="3">³ Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140 Villenave d'Ornon, France</footnote>