# Generation of metabolomic-informed models of metabolism in complex microbial communities

Coralie Muller, Sylvain Prigent, Clémence Frioux

► To cite this version:

Coralie Muller, Sylvain Prigent, Clémence Frioux. Generation of metabolomic-informed models of metabolism in complex microbial communities. Journées scientifiques Agroécologie et Numérique 2025, Jan 2025, Dijon, France. . hal-04919231

HAL Id: hal-04919231

https://inria.hal.science/hal-04919231v1

Submitted on 29 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished 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.

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

Distributed under a Creative Commons Attribution 4.0 International License
# Generation of metabolomic-informed models of metabolism in complex microbial communities

coralie.muller@inria.fr

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

* These authors contributed equally to the work

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

2. Univ. Bordeaux, INRAE, UMR1332 BFP, 33140 Villenave d'Ornon, France

Keywords: Systems biology -- Ecology -- Metabolic modelling -- Heterogeneous data

## A routine analysis...

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

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

## but incomplete!

Genome Scale Metabolic network (GSMN) a solid tool ...but with loopholes [2]

* Experimental studies are costly.

* Adaptability (custom parameters).

* Facility of reproducibility.

* Serve as a proxy for experimental design.

Primary metabolism

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

* Generalist: built with generic database (Kegg, Metacyc ...) main pathways.

* Biased towards model organisms.

* Misses important functions related to the secondary metabolism [2]

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

Secondary metabolism
(Specialized pathways: antibiotic, color, odour, taste, protection)

## Objectives

To better understand how plants recruit microbes, it is essential to develop a comprehensive description of secondary metabolism, which is currently lacking.

Build high quality models of microbial metabolism could be a key point in development of methods for simulating and describing secondary metabolic pathways.

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

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

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

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

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

Provide a methodological approach that fosters using metabolomic to capture the set of functions that are really active.

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

### Genome Mining

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

> ATGGTA_

> ATGAT_

> ATGAT_

> ATGGCC_

### Genome sequencing

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

Improvement of models by genomics [3,4] and metabolomics [4,5] mining

### Creation of new high-quality models

New formalism for the representation of secondary metabolism
Integration of the new formalism in existing metabolic network

Complete and constrain metabolic networks with identified metabolites

### Mass spectrometry

### Annotation profiles

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

### Metabolome Mining

### Metabolic Network

### Metabolic Modelling

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

## Fitting step

To identify new metabolites potentially linked to secondary metabolism, we carry out a fitting step. This compares the metabolites in the metabolomics data with those in the metabolic network. *Metabolites that are present in the metabolomics data and absent from the networks are kept.*

The main challenge in this step is the heterogeneity of unique identifiers (IDs) for each metabolite across the various databases used to construct the metabolic networks. To address this, we will build a datatable that integrates information from multiple databases, enabling rapid and automated ID conversion.

## A real complexity

There is a real disparity between known and lesser-known species in the identification of metabolites due to the fact that they are less studied and their data is poorly referenced in databases.

Furthermore, finding metabolomic data for certain species can be a real challenge, in addition to the fact that there are many unidentified metabolites, as is the case here with *Penicillium polonicum*.

### 1. Build a datatable conversion file

Construction of a datatable enabling rapid and automatic conversion of IDs between the main databases (Metacyc [10], Bigg [12], ...) for the reconstruction of metabolic networks.

### 2. Fit annotation profiles vs metabolic networks

Identify shared and unique metabolites between metabolomics data and a metabolic network for a given species.

### METACYC

### conversion datatable

<table><thead><tr><th>UNIQUE-ID</th><th>CHEBI</th><th>ADD</th><th>COMPLEMENT</th><th>SEED</th><th>BIGG</th></tr></thead><tbody><tr><td>ANTOMATHENIA<br>CPO 12826<br>ANTOMATHENIA<br>GLY</td><td>29032<br>G2501<br>...</td><td>...</td><td>panthoetan acid<br>...</td><td>cdp00064<br>cdp00393<br>...</td><td>...</td></tr><tr><td>ANTOMATHENIA<br>GLY</td><td>57305</td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>

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

<table><thead><tr><th>UNIQUE-ID</th><th>CHEBI</th><th>ADD</th><th>COMPLEMENT</th><th>SEED</th><th>BIGG</th></tr></thead><tbody><tr><td>ANTOMATHENIA<br>CPO 12826<br>ANTOMATHENIA<br>GLY</td><td>panthoetan acid<br>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>ANTOMATHENIA<br>GLY</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>PROTOGENE<br>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr><tr><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td><td>...</td></tr></tbody></table>

manually curated complementary file

### Annotation profiles

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

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

### Metabolic networks

species id "M.CPO-12826" name "Folate"
species id "M.GLY" name "Glycine"

### fitting dat

<table><thead><tr><th>CHEBI</th><th>Match<br>COMMON NAME</th><th>Unmatch</th></tr></thead><tbody><tr><td>CPO-12826</td><td>62509</td><td>glycine</td></tr><tr><td>...</td><td>...</td><td>chrysogine<br>unidentified IB</td></tr></tbody></table>

### Note

The absence of a metabolite in a metabolic network while it is present in the metabolomics data is a clue to define and explore secondary metabolism.

<figure><img src="image_15.png" /><figcaption>Fig: Comparison number of fit, unfit and unidentified between two species: one well-known *Escherichia coli* [2] and one lesser-known *Penicillium polonicum* [5]</figcaption></figure>

## Take-home message

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

Secondary metabolism represents specific pathways which are of great interest in many fields, but which are difficult to explore in models.

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

Improving the quality of metabolic networks is essential if we want to be able to simulate secondary metabolism pathways.

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

Real complexity in identifying secondary metabolites due to their absence in generalist databases and in lesser-known organisms.

## Perspectives

### Genome Mining

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

Use specific databases and methods to identify BGCs of interest in genomes.

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

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

Create a formalism to standardise BGC information and integrate it into an existing metabolic network.

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

### Metabolome Mining

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

Developing a strategy to identify unknown metabolites in metabolomics data by gaining insights from metabolic networks.

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

Further identification of secondary metabolites present in the metabolomic data and their pathways to integrate them to the metabolic network.

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

## References

[1] Cichon, S., et al. *Mitocondria Bacteriochloroplastica*, V. Nedjaj, C.G., Lerouze, M., Müller, C., Sherman, D.J., Hückeborn, F., Labiche, S., and Fricson, E. (2002). Community-driven models of metagenomes: Atticating metabolic modeling and metaproteomics sequencing. *Microb. Biotechnol.*

[2] Tyre, K. et al. (2020). Plant metabolome analysis based on metadata modeling of microbial secondary metabolism. Current status and outlook. *PLoS Comput. Biol.*

[3] Pujol, P., et al., et al. (2019). Construction of 20 Plant Metabolome-cale Metabolic Models Shown Shared on Their Secondary Metabolites. *Biochemistry and Homeostasis* (2019).

[4] Hain, J.B., et al., et al. (2013). Mining metadata and metabolic data matrix in extreme environments: a roadmap for harmonizing the power of metadata and metabolic data. *Adv. Biochem. Res. 2*, (2013).

[5] Hain, J.B., et al., et al. (2000). An Open Source Software for Refining Metabolic Networks and Investigating Complex Networks. *Activity Using Empirical Metabolism Data*. *Analytical Chemistry*, (2004).

[6] Hain, J.B., Sh, S., Kourat, S.A., Medrano, M.H., and Weber, F. (2020). The aMSDB2 database version 2 increased tandem-coverage and more query features for modular proteins. *Nature Methods* 17, 40-45.

[7] Flavin, A., Hart, E., Compagn, C., Chevral, M., Dufresne, Y., Metelak, J., Jouries, P., Mahat, C., Leard, F., Lefèvre, L., et al. (2020). Sequence update of the core-essential peptide resources. *Nucleic Acids Res.* 48, 45.

[8] Pacheco, V., Castell, C., Domínguez, J., Périgue, L., Parada, S., Llobet, C., Rives, S., Berthier, S., Berton, E., Bic, R., et al. (2020). Pradecain: a multi-platform digital infrastructure for integrable metabolite spectral data and metatotal data. *Metabolomics*

[9] Cichon, S., et al. (2019). Metabolic network generation and analysis of 20000 metabolites. *Nature Methods* 16, 1001-1005.

[10] Henry, C.S., Dehloph, M., Rind, A.B., Pyrykane, S.M., Linney, B., and R.I. Stevens. High Throughput Generation and Optimization of Genome-scale Metabolic Models. *Nature Biotechnology* (2018).

[11] Kingza, Z.A., Lins, D.R., Schreiber, M., Miller, D., Fendriksen, J., Lerman, A.J., Hsueh, A., Ploen, B.D., and Lewis, N.E. BigGI: A platform for integrating standard data, and sharing genome-scale models (2010). *Nature Acad. Research

[12] Lengyel, M., Facke, K., Künz, M., et al. (2018). Systematic identification of metabolites controlling protein expression in E. coli. *Nat Commun.* 9, 4030.

inria

INRAE

université
de BORDEAUX

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

RÉGION
Nouvelle-
Aquitaine

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

RÉPUBLIQUE
FRANÇAISE

Liberté
Égalité
Fraternité

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

PROGRAMME DE RECHERCHE

AGROÉCOLOGIE
ET NUMÉRIQUE