# Maximizing metabolome coverage and metabolism in Microcystis sp. :
A comparative evaluation of monophasic and biphasic extraction-based workflow

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BORDEAUX
METABOLOME

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METABOHUB

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UNIVERSITÉ
Clermont
Auvergne

Thomas Forjonel,¹,² Melissa Eon,¹,² Charlotte Duval,³ Sylvia Moreira,¹ Benjamin Marie,³ Binta Diémé,⁴ Nicolas Creusot¹,²

1 INRAE, UR1454 EABX, 2 Bordeaux Metabolome, MetaboHub, 3 Muséum National d'Histoire Naturelle, MCAM, Paris, France, 4 Université Clermont Auvergne

## Context

* Cyanobacterial blooms are a phenomenon of growing international concern, due to their potential ecological, human and economic impacts.

* Recently, some studies have highlighted the potential role of metabolic interactions between heterotrophic bacteria and cyanobacteria in the phycosphere in promoting cyanobacterial blooms.

* In this context, there is a growing need to comprehensively depict metabolome and metabolism of cyanobacteria and heterotrophic bacteria consortium.

* This study aims to explore various metabolomic workflows in order to provide a comprehensive picture of the chemical landscape and so the metabolism for twelve strains of *Microcystis* sp. To such an end, biphasic (MTBE/MeOH/H₂O leading to a hydrophilic and lipophilic extracts) and monophasic extraction (MeOH/H₂O leading to a single extract) coupled to UPLC-HRMS/MS analyses were compared

## Methodology

### Sampling and cultivation

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Fresh water Cyanobacteria :
*Microcystis* sp.
Twelve strains collected in October 2021

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### Sampling, quenching, freeze drying

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### Monophasic extraction

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MeOH/H₂O (1:5)
→ Ultrasonication

### Biphasic extraction

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Lipophilic phase
MTBE/MeOH (3:1)
Hydrophilic phase
H₂O/MeOH (3:1)
→ Grinding

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## Metabolomic workflow

### UPLC-HRMS/MS analysis

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UHPLC-Qq-TOF Compact, Bruker
ESI +/-, 50-1500 m/z, RT : 1-15min

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### Data processing

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MS-DIAL 5.5
+ FragHUB
(MS2 spectral matching)

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UPLC-Qex+
ESI +/-, 50-1500 m/z, RT : 1-20min

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### Data analysis & annotation

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Features profiling

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In silico annotation
(SIRIUS 6.1)

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NPClassifier

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## Results

### HCA

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## 1. Whole metabolome fingerprints

## Biphasic extraction

Clustering according to the genotype previously identified

A total of 6 genospecies (groups A-X) have been identified from the 12 strains of *microcystis* sp.

### HCA

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Log transformation, Pareto scaling
Top 7000 metabolites (t-test/Anova)

These results suggest that neither the extraction method nor the type of mass analyzer has a major impact on the clustering of metabolic profiles across *Microcystis* strains.

## 2. Chemical Landscape of putative annotated metabolites

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87 alkaloids; 511 Amino acids and peptides; 67 carbohydrates
118 Fatty acids; 47 Polyketides
37 Terpenoids; 5 shikimates & Phenyls propanoids

793 putative annotated metabolites
78 in L2 (spectral DB) + 715 L3 (structural DB)

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619 alkaloids; 2104 Amino acids and peptides; 259 carbohydrates
660 Fatty acids; 161 Polyketides
191 Terpenoids; 63 shikimates & Phenyls propanoids

3475 putative annotated metabolites
252 in L2 (spectral DB) + 3223 in L3 (structural DB)

Overall, the biphasic extraction led to a significantly higher number of putatively annotated metabolites (3475 vs 872), including a substantial increase across all major chemical classes, such as alkaloids, peptides, fatty acids, and terpenoids. Additionally, more high-confidence annotations (L2, spectral database) were obtained with the biphasic method (252 vs 78), highlighting its greater efficiency for metabolite detection and identification.

### Venn diagram
based on
(SMILES)

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✓ Only 134 annotated metabolites were shared between the two methods. Difference due to sonication (monophasic) vs grinding (biphasic extraction) ?

→ Overall strong influence of both extraction and analysis methods on the metabolite profiles obtained

→ Need for multiplexing to cover the broadest range of metabolites

## Major outcomes

* The clustering results revealed that neither the extraction method or the mass analyzer had a major influence on the overall structure of metabolic profiles across *Microcystis* strains.

* However, annotation results showed a strong disparity in the number and diversity of metabolites detected, with the biphasic workflow yielding a much broader chemical coverage.

* Consistently, the Venn diagram showed a surprisingly low overlap between the two workflows, with only 134 shared features out of 793 (monophasic) and 3475 (biphasic) metabolites.

* These results demonstrate that capturing the full chemical landscape of a biological sample remains challenging, and highlight the value of combining complementary extraction and analytical approaches to achieve a more comprehensive view of the metabolome.

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## References:

[1] Igwaran et al. (2024). Cyanobacteria Harmful Algae Blooms: Causes, Impacts, and Risk Management. https://doi.org/10.1007/s11270-023-06782-y

[2] Louati, I. et al. (2023). The phyto-bacterioplankton couple in a shallow freshwater ecosystem: Who leads the dance? Harmful Algae, 126, 102436.

https://doi.org/10.1016/j.hal.2023.102436

## Acknowledgment

The project COMIC is funded by the INRAE

metaprogram HOLOFLUX. Thanks to Bordeaux

Metabolome and XPO facilities ; MetaboHUB

(ANR-11-INBS-0010)

Looking for a thesis/job !

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