# Generation of metabolomic-informed models of metabolism for microbial communities

Coralie Muller, Sylvain Prigent, Clémence Frioux

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Coralie Muller, Sylvain Prigent, Clémence Frioux. Generation of metabolomic-informed models of metabolism for microbial communities. Metaboday 2025, Sep 2025, Talence (33), France. . hal-05304536

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Submitted on 8 Oct 2025

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# Generation of metabolic-informed models of metabolism for microbial communities

Coralie Muller¹,², Sylvain Prigent*²,³, Clémence Frioux*¹

1. Inria, Univ. Bordeaux, INRAE, F-33400 Talence, France

2. Univ. Bordeaux, INRAE, UMR 1332 BFP, F-33140 Villenave d'Ornon, France

3. Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140 Villenave d'Ornon, France

Keywords: Systems biology -- Microbial ecology -- Metabolic modelling -- Heterogeneous data integration

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coralie.muller@inria.fr

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## A routine analysis...

One purpose of metabolic modelling [1] is to decipher the functions and interactions occurring in microbial communities, especially in complex environments.

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## but incomplete!

Metabolic modelling is a useful approach:

* Experimental studies are costly.

* Adaptability (custom parameters).

* Reproducibility.

* Proxy for experimental design.

But with *loopholes* because of:

Genome Scale
Metabolic Networks
(GSMNs)

* Inadequate application of modelling methods

* Generalist: built with generic database (Kegg, Metacyc, ...) primary metabolism.

* Biased toward model organisms.

* Miss important functions related to the secondary metabolism[2].

## Objectives

The incomplete description of secondary metabolism (SM) in metabolic networks limits the possibility of simulations to better understand how plants recruit microbes.

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Primary metabolism
(Growth, cell division, respiration, reproduction)

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Secondary metabolism
(Specialised pathways: antibiotic, colour, odour, protection)

The development of methods enabling a comprehensive description of SM in order to build high-quality models of microbial metabolism has become a priority.

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Define a formalism for the representation of SM obtained from genome mining.

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Combine metabolic modelling and metabolic profile annotation in a virtuous cycle.

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## Improvement by genome and metabolome mining

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> AF_A1_1| gene = SARDH
ATCATGTAGAACTGATAACT
TTGGTGCTGGTGTAATTACG
> AF_A1_2|gene = ATP5F1B
ATCGTGTGTAGAACTTGTAA
TTGGTGTGTAGAACTGATAC

Genome sequencies

anti
SMASH
DB

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Biosynthetic Genes Clusters (BGCs)
identification [6,7]

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Genome Mining

Identification of previously uncharacterised
natural product biosynthetic gene clusters (BGCs).

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### Case study - 9 Penicillium strains

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### Roadmap from RoqD exploration

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Mapping: absence of Roquefortine C in GSMNs

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Incomplete annotation in GSMN reconstruction
Substitution by a similar but complete EC number in the annotation

<table><thead><tr><td>Enzyme</td><td>RoqD</td><td>geranyl diphosphate synthase</td></tr></thead><tbody><tr><td>EC number</td><td>EC:2.5.1.-</td><td>EC:2.5.1.1</td></tr></tbody></table>

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## A methodological roadmap

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## Take-home message & Perspectives

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* Combining omics data to create better models for simulating secondary metabolism pathways is necessary but faces a number of obstacles.

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Design method by implementing integration methods for each case highlighted in the roadmap

* The creation of the roadmap allowed us to identify development needs and to formulate ideas on how to improve the automated generation of GSMNs.

## References

[1] Cerk, K., et al. « Community-scale Models of Microbiomes: Articulating Metabolic Modelling and Metagenome Sequencing » . Microbial Biotechnology (2024)

[2] Qiu, S., et al. « Flux Balance Analysis-Based Metabolic Modeling of Microbial Secondary Metabolism: Current Status and Outlook » PLOS Computational Biology (2023)

[3] Prigent, S., et al. « Reconstruction of 24 Penicillium Genome-scale Metabolic Models Shows Diversity Based on Their Secondary Metabolism » . Biotechnology and Bioengineering (2018)

[4] Han, J., et al. « Mining Microbial and Metabolic Dark Matter in Extreme Environments: A Roadmap for Harnessing the Power of Multi-Omics Data » . Advanced Biotechnology (2024)

[5] Mandwal, A., et al. « MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data » . Analytical Chemistry (2024)

[6] Blin, K., et al. « antiSMASH 7.0: New and Improved Predictions for Detection, Regulation, Chemical Structures and Visualisation » . Nucleic Acids Research (2023)

[7] Flissi, A., et al. « Norine: Update of the Nonribosomal Peptide Resource » . Nucleic Acids Research (2019)

[8] Paulhe, N., et al. « PeakForest: A Multi-Platform Digital Infrastructure for Interoperable Metabolite Spectral Data and Metadata Management » . Metabolomics (2022)

[9] Cao, L., et al. « MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules » . Nature Communications (2021)

[10] Capsi, R., et al. « The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases » . Nucleic Acids Research (2014)

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<footnote>* These authors contributed equally to the work</footnote>