# Metagenome-scale metabolic modelling for the characterization of cross-feeding interactions in freshwater cyanobacteria-associated microbial communities

Juliette Audemard, Nicolas Creusot, Julie Leloup, Charlotte Duval, Sébastien Halary, Jeanne Got, B Marie, Gabriel V. Markov, Binta Diémé, Clémence Frioux

► To cite this version:

Juliette Audemard, Nicolas Creusot, Julie Leloup, Charlotte Duval, Sébastien Halary, et al. Metagenome-scale metabolic modelling for the characterization of cross-feeding interactions in freshwater cyanobacteria-associated microbial communities. JOBIM 2025 - Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2025, Bordeaux, France. hal-05281552

HAL Id: hal-05281552

https://inria.hal.science/hal-05281552v1

Submitted on 24 Sep 2025

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<figure><img src="image_1.png" /></figure>
# COMIC Project

## Metagenome-scale metabolic modelling for the characterization of cross-feeding interactions in freshwater cyanobacteria-associated microbial communities

### Juliette Audemard

Pleiade, Inria de l'Université de Bordeaux

**Research Supervisor**

Clémence FRIOUX Inria de l'Université de Bordeaux

**COMIC Project Leaders**

Binta DIÉMÉ Université Clermont Auvergne, INRAE, UNH, PFEM, MetaboHUB

Nicolas CREUSOT INRAE, UR1454 EABX, Bordeaux Metabolome, MetaboHUB
► **Freshwater cyanobacterial blooms**

**Cyanobacterial Blooms, or harmful algal blooms**

Rapid growth of cyanobacterial communities (i.e. *Microcystis*)

Driven by rising temperature and nutrients loads

* Toxin release

* Ecosystem disruption

* Public health risk

* Economic burden costs (tourism, agri-food)

<figure><img src="image_2.png" /><figcaption>Lake Erie, US, NOOA GERL 2019</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Lake Perjalpine, Switzerland, Monchamp et al. 2016</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Lake Chao, China, Chen et al. 2016.</figcaption></figure>
► **Freshwater cyanobacterial blooms**

**Cyanobacterial Blooms, or harmful algal blooms**

Rapid growth of cyanobacterial communities (i.e. *Microcystis*)

Driven by rising temperature and nutrients loads

* Toxin release

* Ecosystem disruption

* Public health risk

* Economic burden costs (tourism, agri-food)

<figure><img src="image_5.png" /><figcaption>Lake Erie, US, NOOA GERL 2019</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Lake Perjalpine, Switzerland, Monchamp et al. 2016</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Lake Chao, China, Chen et al. 2016.</figcaption></figure>

most pervasive cyanobacteria

**Microcystis**

<figure><img src="image_8.png" /><figcaption>Museum National d'Histoire Naturelle</figcaption></figure>
► **The phycosphere: host of microbial interactions**

**Phycosphere:**

**Microbial environment of the cyanobacteria**

**They are suspected to host:**

* Nutrients cycling

* Toxin modulation

* Metabolites exchanges, cross-feeding

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

A BLOOM
PHYCOSPHERE
<figure><img src="image_10.png" /></figure>

# The phycosphere: host of microbial interactions

## Phycosphere:

### Microbial environment of the cyanobacteria

### They are suspected to host:

* Nutrients cycling

* Toxin modulation

* Metabolites exchanges, cross-feeding

*Roseomonas sp. and Microcystis*
*Pérez-Carrascal et al, 2021*

Phylo-symbiosis

*Rhizobium sp. and Microcystis*
*Kim et al, 2019*

Growth

*Aeromonas veronii and Microcystis*
*Wang et al, 2016*

Mucilage formation

> Most phycosphere studies focus either
> on marine blooms or only on taxonomy

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

A BLOOM
PHYCOSPHERE
<figure><img src="image_12.png" /></figure>

# The phycosphere: host of microbial interactions

## Phycosphere:

### Microbial environment of the cyanobacteria

### They are suspected to host:

* Nutrients cycling

* Toxin modulation

* Metabolites exchanges, cross-feeding

> Underlying mechanisms of cross-feeding have yet to be clearly understood in freshwater blooms

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

A BLOOM
PHYCOSPHERE
► **COMIC-Project** | From a bloom to a systems biology approach

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

2021

Blooms sample in
Lake Cergy Pontoise

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

12 *Microcystis* phycospheres

BG11 culture medium
25°C - 16h light / 8h dark
► **COMIC-Project** | From a bloom to a systems biology approach

<figure><img src="image_16.png" /></figure>

2021

Blooms sample in
Lake Cergy Pontoise

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

12 *Microcystis* phycospheres

BG11 culture medium
25°C - 16h light / 8h dark

Untargeted meta-metabolomics

* From monophasic and biphasic extractions

* UPLC-HRMS / MS Analysis

* Maximization of annotations through different annotations workflows

<figure><img src="image_18.png" /><figcaption>Monophasic
extraction</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>Biphasic
extraction</figcaption></figure>
► **COMIC-Project** | From a bloom to a systems biology approach

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

2021

Blooms sample in
Lake Cergy Pontoise

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

12 *Microcystis* phycosphere

BG11 culture medium
25°C - 16h light / 8h dark

Untargeted meta-metabolomics

* From monophasic and biphasic extractions

* UPLC-HRMS / MS Analysis

* Maximization of annotations through different annotations workflows

HiFi Metagenomics

* Long-reads sequencing of the 12 phycospheres

* Input to metabolic models
## Methods | GSMN reconstruction and metabolic modeling

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

### Metagenomes-Assembled Genomes

* Unknown protein 23M

ACTGGAATCCCTGAACGACTTGGTTTCAAAGTCAAAGTCGTCACAGTCTGAGTCGCATCAGCAGTTGCAATCC

* Unknown protein 255

ACTGGAATCCCTGAGATTCGCTGCCATCGACGTGGCCGTACGTCCAGCTTCACGACTTGTTTCAAAGTCAAAGTCGTCACAGTCTGAGTCGC

<figure><img src="image_23.png" /></figure>

### Functional Annotation

protein_567 exonuclease activity 3.6.4.12 ko K16899

protein_566 ribulose-phosphate 3-epimerase 5.1.3.1 ko K01783

protein_568 efflux transmembrane transporter - ko K01783

protein_569 pyridoxal 5'-phosphate salvage 1.1.1.31 ku K00020

### PacBio long-reads sequencing

### Assembly

**MetaMDBG** Benoit et al, 2024

### Binning

**ngmlr** Sedlazeck et al, 2018

### Quality assessment

**Check2M** Chklovski et al, 2023

### Functional Annotation

**EggnogMapper** Huerta-Cepas et al, 2019
## Methods | GSMN reconstruction and metabolic modeling

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

### Metagenomes-Assembled Genomes

* Unknown protein 234

ACTGGAATCCCTGAACGACTTGGTTTCAAAGTCAAAGTCGTCACAGTCTGAGTCGTCATCAGGTTGCAATCC

* Unknown protein 255

ACTGGAATCCCTGAGATTCGCTGCCAATCGACGTCGCGTACAGTGCCACTTCACGACTTGTTTCAAAGTCAAAGTCGTCACAGTCTGAGTCGTC

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

### Functional Annotation

protein_567 exonuclease activity 3.6.4.12 ko K16899

protein_566 ribulose-phosphate 3-epimerase 5.1.3.1 ko K01783

protein_568 efflux transmembrane transporter - ko K01783

protein_569 pyridoxal 5-phosphate salvage 1.1.1.31 ku K00020

### GSMN automatic reconstruction

**Metage2Metabo** Frioux et al, 2020

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

### Pathway Tools

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

**METACYC**

<figure><img src="image_28.png" /></figure>

### Fluxer, E Coli metabolic network

Hari et al, 2020

### Genome-scale metabolic network (GSMN)

#### Network of all reactions inferred from the genome
## Methods | GSMN reconstruction and metabolic modeling

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

Metagenomes-Assembled Genomes

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

Functional Annotation

protein_567 exoglucosidase 3.6.4.12 ko:K10699
protein_566 ribulose-phosphate 3-epimerase 5.1.3.1 ko:K01783
protein_568 efflux transmembrane transporter - ko:K01783
protein_563 pyridoxal 5-phosphate kinase 1.1.1.31 ku:K00020

Logical metabolic modeling

Metage2Metabo Frioux et al, 2020

MisCoTo Frioux et al, 2018

GSMN

<figure><img src="image_31.png" /></figure>

Fluxer, E Coli metabolic network

Hari et al, 2020

+ environmental
nutrients

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

metabolomic
annotations

Individual metabolic potential

<figure><img src="image_33.png" /></figure>

Community metabolic potential

<figure><img src="image_34.png" /></figure>
► **Results** | Metagenomes-Assembled Genomes (MAGs)

* **12** circularized *Microcystis* genomes of 12 distinct strains

* **97** High quality MAGs of phycosphere associated bacteria
  out of 183 bins, with 62 uni-contigs MAGs

Each phycosphere

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

**1 strain of *Microcystis***
**1787 (±11) reactions on average**

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

**5-12 distinct associated bacteria**
**8 on average**
**2010 (±257) reactions on average**

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

**1 Pseudogenome**
An assemblage of unbinned contigs, medium, low-quality MAGs
**2935 (±835) reactions on average**

<figure><img src="image_38.png" /></figure>
<figure><img src="image_39.png" /></figure>

## ► Results | Microcystis GSMNs and Genospecies

* Clustering build on GSMNs reactions content distances matrix

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

Consistency between metabolic networks content and genospecies

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

Tanglegram of Microcystis ANI phylogenetic tree on the left, and of the hierarchical clustering of GSMN's reactions presence matrix on the right. Hierarchical clustering was conducted using Jaccard distance and complete-linkage clustering. Equal branches between the left and the right tree are the same color
<figure><img src="image_42.png" /></figure>

## ► Results | Microcystis GSMNs and Genospecies

* Clustering build on GSMNs reactions content distances matrix

<figure><img src="image_43.png" /></figure>

Consistency between metabolic networks content and genospecies

**GSMNs reconstruction strategy**
**conveys metabolism specificity of the strains**

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

Tanglegram of Microcystis ANI phylogenetic tree on the left, and of the hierarchical clustering of GSMN's reactions presence matrix on the right. Hierarchical clustering was conducted using Jaccard distance and complete-linkage clustering. Equal branches between the left and the right tree are the same color
<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

# Results | Secondary metabolites biosynthesis

* Antismash annotation of biosynthetic genes cluster (BGCs)

* Metabolomic-based annotation of phycospheres intracellular metabolites

*Microcystis* secondary metabolites BGCs are mostly consistent with their genospecies

Metabolomics identify most secondary metabolites

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

On the left, the phylogenetic tree of Microcystis twelve strains. On the right, a matrix with the secondary metabolites biosynthetic genes cluster (BGC) presence by genomes according to AntiSmash analysis, and metabolites presence in the phycosphere according to metabolomics (LC-MS, Liquid Chromatography coupled to Mass Spectrometry).
<figure><img src="image_48.png" /></figure>

## ► Results | Secondary metabolites biosynthesis

* Antismash annotation of biosynthetic genes cluster (BGCs)

* Metabolomic-based annotation of phycospheres intracellular metabolites

*Microcystis* secondary metabolites BGCs are mostly consistent with their genospecies

Metabolomics identify most secondary metabolites

**Secondary metabolites are mostly absent from GSMNs**

**Most non-ribosomal syntheses are concerned**

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

On the left, the phylogenetic tree of Microcystis twelve strains. On the right, a matrix with the secondary metabolites biosynthetic genes cluster (BGC) presence by genomes according to AntiSmash analysis, and metabolites presence in the phycosphere according to metabolomics (LC-MS, Liquid Chromatography coupled to Mass Spectrometry).
## Results | Secondary metabolites biosynthesis

* Antismash annotation of biosynthetic genes cluster (BGCs)

* Metabolomics annotation of phycospheres intracellular metabolites

### Microcystis secondary metabolites with their genus species

### Metabolomics identify most secondary metabolites

### Secondary metabolites are mostly absent from GSMNs

### Most non-ribosomal synthesis are concerned

## How can we handle missing secondary metabolites pathways ?

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

On the left, the phylogenetic tree of Microcystis twelve strains. On the right, a matrix with the secondary metabolites biosynthetic genes cluster (BGC) presence by genomes according to AntiSmash analysis, and metabolites presence in the phycosphere according to metabolomics (LC-MS, Liquid Chromatography coupled to Mass Spectrometry).
<figure><img src="image_51.png" /></figure>

► **Results** | Integration of MC-LR toxin biosynthesis in GSMNs

* Curation of a biosynthetic pathway absent from databases (*MetaCyc*, *KEGG*, *Rhéa*) through literature mining

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

Curated biosynthesis pathway of Microcystin-LR.

2-OxoGlu, 2-oxoglutarate; Adda, (25,35,85,9S)-3-amino-9-methoxy-2,6,8-trimethyl-10-phenyl-4,6-decienoic, AST: aspartate transaminase; CS, citramalate synthase; D-MeAsp, D-methylaspartate; IPMI, isopropylmalate isomerase acid.
<figure><img src="image_53.png" /></figure>

## ► Results | Integration of MC-LR toxin biosynthesis in GSMNs

Integrated a key metabolite in GSMNs
hepatotoxin microcystin-LR

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

Curated biosynthesis pathway of Microcystin-LR.

2-OxoGlu, 2-oxoglutarate; Adda, (25,35,85,9S)-3-amino-9-methoxy-2,6,8-trimethyl-10-phenyl-4,6-decienoic, AST: aspartate transaminase; CS, citramalate synthase; D-MeAsp, D-methylaspartate; IPMI, isopropylmalate isomerase acid.
<figure><img src="image_55.png" /></figure>

1 strain of *Microcystis*

Each phycosphere

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

5-12 distinct MAGs of
associated bacteria

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

**1 Pseudogenome**
An assemblage of unbinned contigs and medium to low-quality MAGs
<figure><img src="image_58.png" /></figure>

1 strain of *Microcystis*

Each phycosphere

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

5-12 distinct MAGs of
associated bacteria

<figure><img src="image_60.png" /></figure>

**1 Pseudogenome**
An assemblage of unbinned contigs and medium to low-quality MAGs
► **Results** | Rarefaction curve of EC diversity

* Based on accumulation of nun-redundant EC number in metagenomes

No reached plateau

Union of phycosphere functions
is large

Small gain of EC diversity from
pseudogenomes

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

Rarefaction-curve like of ECs number annotated on phycosphere metagenomes. Rarefaction curves represents accumulation of nun-redundant EC numbers by number of sampled associated-bacteria, for 100 sampling. The starting point is Microcystis EC numbers. PGs are excluded from the sampling of phycosphere's individual curves
<figure><img src="image_62.png" /></figure>

# Results | Rarefaction curve of EC diversity

* Based on accumulation of nun-redundant EC number in metagenomes

No reached plateau

Union of phycosphere functions is large

Small gain of EC diversity from pseudogenomes

All members brings new functions to the phycosphere metabolic repertoire

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

Rarefaction-curve like of ECs number annotated on phycosphere metagenomes. Rarefaction curves represents accumulation of nun-redundant EC numbers by number of sampled associated-bacteria, for 100 sampling. The starting point is Microcystis EC numbers. PGs are excluded from the sampling of phycosphere's individual curves
<figure><img src="image_64.png" /></figure>

# Results | Phycosphere metabolome comparison

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

* PCA of metabolomics normalised signal intensity

Phycosphere metabolomes cluster its cyanobacteria genospecies

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

Principal components analysis (PCA) on metabolomics normalised signal intensity, for the twelve phycospheres triplicates.
<figure><img src="image_67.png" /></figure>

# Results | Phycosphere metabolome comparison

<figure><img src="image_68.png" /></figure>

* PCA of metabolomics normalised signal intensity

Phycosphere metabolomes cluster its cyanobacteria genospecies

Cyanobacteria shape the phycosphere metabolome

<figure><img src="image_69.png" /></figure>

Principal components analysis (PCA) on metabolomics normalised signal intensity, for the twelve phycospheres triplicates.
<figure><img src="image_70.png" /></figure>

# Results | Phycosphere metabolome comparison

<figure><img src="image_71.png" /></figure>

* PCA of metabolomics normalised signal intensity

Phycosphere metabolomes cluster its cyanobacteria genospecies

Cyanobacteria shape the phycosphere metabolome

Metabolomics reflects the dominant biomass of *Microcystis*

<figure><img src="image_72.png" /></figure>

Principal components analysis (PCA) on metabolomics normalised signal intensity, for the twelve phycospheres triplicates.
<figure><img src="image_73.png" /></figure>

# Results | Phycosphere metabolism comparison

<figure><img src="image_74.png" /><figcaption>Metabolomics signal

Shaped by the cyanobacteria</figcaption></figure>

<figure><img src="image_75.png" /><figcaption>Phycosphere GSMNs

No pattern - Unique phycosphere</figcaption></figure>
<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

# ► Results | Metabolic modelling and metabolic capabilities

<figure><img src="image_78.png" /></figure>

<figure><img src="image_79.png" /></figure>

## ○ individual reach

individual bacteria reached metabolites

<figure><img src="image_80.png" /></figure>

## ● community gain

metabolites produced only through exchanges with the community

<figure><img src="image_81.png" /></figure>

## ○ pseudogenome added value

metabolites reached through pseudogenomes interactions

<figure><img src="image_82.png" /></figure>

Heatmap of metabolites reach across each phycosphere metabolic capabilities modelling, grouped by genospecies (A, E, I, K, O, and X). "Individual reach", metabolites produced by at least one community member in the phycosphere; "Added value", metabolites reached only through metabolites exchanges; "not reached", metabolites not reached in modelling or absent from GSMNs
<figure><img src="image_83.png" /></figure>

► Results

| Metabolic modelling and metabolic capabilities

<figure><img src="image_84.png" /></figure>

○ **individual reach**
individual bacteria reached metabolites

* **community gain**
metabolites produced only through
exchanges with the community

● **pseudogenome added value**
metabolites reached through
pseudogenomes interactions

<figure><img src="image_85.png" /></figure>

The 12 *Microcystis* share common metabolic capabilities
consistent with their genospecies
<figure><img src="image_86.png" /></figure>

► Results

| Metabolic modelling and metabolic capabilities

<figure><img src="image_87.png" /></figure>

○ **individual reach**
individual bacteria reached metabolites

* **community gain**
metabolites produced only through
exchanges with the community

● **pseudogenome added value**
metabolites reached through pseudogenomes
interactions

<figure><img src="image_88.png" /></figure>

Phycosphere metabolic capabilities are unique
and are not consistent within genospecies
<figure><img src="image_89.png" /></figure>

# Results | Phycosphere-phycosphere interactions

* Phycosphere are merged and their interactions are modelled

<figure><img src="image_90.png" /></figure>

There are much more interactions between phycospheres if pseudogenomes are not included

<figure><img src="image_91.png" /></figure>

<figure><img src="image_92.png" /><figcaption>excluding pseudogenomes</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>including pseudogenomes</figcaption></figure>

Classification of metabolites produced solely through inter-phycosphere interactions. On the left, added-value of phycosphere-phycosphere interactions, including their PGs. On the right, added-value of phycosphere-phycosphere interactions, excluding their PGs. Metabolites classes are from NPclassifier
## ► Results | Phycosphere-phycosphere interactions

* Phycosphere are merged and their interactions are modelled

<figure><img src="image_94.png" /></figure>

There are much more interactions between phycospheres if pseudogenomes are not included

Keeping pseudogenomes leverages access to new metabolites for phycospheres

<figure><img src="image_95.png" /></figure>

Classification of metabolites produced solely through inter-phycosphere interactions. On the left, added-value of phycosphere-phycosphere interactions, including their PGs. On the right, added-value of phycosphere-phycosphere interactions, excluding their PGs. Metabolites classes are from NPclassifier
<figure><img src="image_96.png" /></figure>

Conclusion

* Genome-scale metabolic networks provide accurate representation of metabolism specificity

But severely lack secondary metabolites pathways

* Assessment of metabolic capabilities from a set environment of nutrients

* Phycosphere metabolome are shaped by dominant cyanobacteria and consistent with their genospecies

* GSMNs content analysis and metabolic capabilities shows unique phycosphere with singular cyanobacteria and associated bacteria reach
<figure><img src="image_97.png" /></figure>

► Perspectives

* Improve metabolomics and metabolic modelling integration

* Characterization of complementaries and ground for
interactions between associated bacteria and cyanobacteria
through essential metabolic cycle
*i.e. sulfur cycle, azote cycle*

* Dig into the clear discrepancies between metabolic capabilities of same genospecies cyanobacteria
# Acknowledgements

**Inria (Bordeaux)**
Clémence FRIOUX

**Inria (Rennes)**
Jeanne GOT

**INRAE (Clermont)**
Mohammed MOUFFOK
Marie LEFEBVRE
Binta DIÉMÉ

**INRAE (Bordeaux)**
Nicolas CREUSOT

**Museum National d'Histoire Naturelle (Paris)**
Charlotte DUVAL
Julie LELOUP
Benjamin MARIE
Sébastien HALLARY

**Station Biologique de Roscoff (Roscoff)**
Gabriel MARKOV

<figure><img src="image_98.png" /></figure>

<figure><img src="image_99.png" /></figure>

CNRS • SORBONNE UNIVERSITÉ

Station Biologique
de Roscoff

METAPROGRAMME

HOLOFLUX

INRAE

MUSÉUM

NAL HIST

NATURELLE

<figure><img src="image_100.png" /></figure>

## Related work from team members

- Coralie MULLER, integration of metabolomics in GSMNs

- Nicolas MAURICE, metagenomes assembly assessment

- Leonard BRINDEL, Metage2Metabo results visualisation