# Methods for a species-specific genome-scale metabolic model designed for eukaryotes and applied to the Ascophyllum nodosum macroalga

Pauline Hamon-Giraud, Benoît Bergk Pinto, Jeanne Got, Coralie Rousseau, Erwan Corre, Simon Dittami, Gabriel Markov, Anne Siegel

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## To cite this version:

Pauline Hamon-Giraud, Benoît Bergk Pinto, Jeanne Got, Coralie Rousseau, Erwan Corre, et al. Methods for a species-specific genome-scale metabolic model designed for eukaryotes and applied to the Ascophyllum nodosum macroalga. JOBIM 2025 - Journées Ouvertes en Biologie, Informatique et Mathématiques, Jul 2025, Bordeaux, France. pp.109-117. hal-05374724

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# Methods for a species-specific genome-scale metabolic model designed for eukaryotes and applied to the *Ascophyllum nodosum macroalga*

Pauline HAMON-GIRAUD<fnref n="1" />, Benoît BERGK PINTO<fnref n="2" />, Jeanne GOT<fnref n="1" />, Coralie ROUSSEAU<fnref n="2" />, François THOMAS<fnref n="2" />, Erwan CORRE<fnref n="3" />, Simon DITTAMI<fnref n="2" />, Gabriel MARKOV<fnref n="2" /> and Anne SIEGEL<fnref n="1" />

Corresponding author: pauline.hamon-giraud@irisa.fr

**Keywords** Genome-scale metabolic models reconstruction, Biomass function, Pathway analysis, Microbiota, *Ascophyllum nodosum*

**Abstract** Genome-scale metabolic models (GEMs) are essential tools for studying metabolism, either for comparative analyses or to investigate interactions between organisms. However, genome annotation, biomass formulation, and network gap-filling are key steps in constructing a relevant GEM and ensuring the biosynthesis of specialized metabolites. We present a pipeline to integrate extensive biological knowledge (genomes of closely related species, metabolic profiling studies, potential interactions with microbiota) about an eukaryotic organism in order to generate high quality GEMs. To manage genome annotation limitations, the pipeline relies on a GEM reconstruction tool that propagates annotations across closely related species through the identification of orthologous genes. It also pays particular attention to biomass formulation, using a set of metabolomic studies to create a consensus biomass composition that seeks to closely reflect biological reality, such as incorporating specialized metabolites and their precursors. The gap-filling stage of the pipeline uses a semi-automated curation process for added reactions, taking into account the presence of orthologous genes, occurrence in phylogenetically related species and potential interactions with the organism's microbiota. The final GEM applied to the brown alga *Ascophyllum nodosum* comprises 3,536 metabolites and 3,072 biochemical reactions, predicting the synthesis of 1,023 compounds from 38 seawater-derived metabolites. Almost all reactions (99.98%) are linked to an enzyme supported in the algal genome. This refined model provides a framework for studying host-microbiota metabolic complementarity. This pipeline offers a scalable and robust method for reconstructing high-quality GEMs in emerging eukaryotic model organisms, improving metabolic network accuracy and expanding our understanding of species-specific metabolism. It also sheds light on the various level of knowledge related to the synthesis pathways of the biomass, paving the way to future studies to be undergone.

<footnote n="1">¹ Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France</footnote>

<footnote n="2">² Sorbonne Université, CNRS, Laboratoire de Biologie Intégrative des Modèles Marins, LBI2M, F-29680 Roscoff, France</footnote>

<footnote n="3">³ Sorbonne Université, CNRS, ABiMS bioinformatic platform, F-29680 Roscoff, France</footnote>
## Introduction

Genome-scale metabolic models (GEMs) are powerful tools used in various metabolic studies, including predicting the production of target metabolites, assessing the metabolic variability of an organism under various conditions, identifying enzyme functions, and modeling interactions between multiple cells or organisms [1]. However, their automatic reconstruction faces challenges due to various sources of uncertainty, such as the accuracy of genome annotation, biomass formulation, and the network gap filling procedure [2]. Moreover, taking into account the biosynthesis of specialized metabolites is a key challenge in improving the quality of a species-specific GEM [3]. To address those limitations, we propose a semi-automatic pipeline designed to optimize GEMs reconstruction of an eukaryotic organism by integrating as much as possible the available biological knowledge from several sources: reactions databases, metabolomic data or literature-based biomass data, annotated genomes from phylogenetically close species and associated microbiome MAGs or metabarcoding data. This approach emphasizes explainability, ensuring that all elements integrated into the network can be traced back to their sources, enabling an assessment of their relevance.

This pipeline was developed within the study of the macroalga *Ascophyllum nodosum*, a brown alga abundant along the coasts of Brittany and of industrial interest due to its biostimulant properties [4]. Our method to reconstruct the GEM first involved the use of AuCoMe [5], a bioinformatics tool that propagated functional annotations across a corpus of 62 closely related brown algae and outgroup of other stramenopiles species[6]. Next, we identified a set of 75 weighted compounds supported by a set of literature studies that were ponderated to define a generic algal biomass. To ensure network functionality, we implemented both manual and automated curation procedures, leveraging a discrete dynamical framework gap-filling tool (Meneco) and integrating data from both the associated microbiota [7] and closely related brown algae [6].

The final network resulting from the pipeline includes 3,536 metabolites and 3,072 biochemical reactions. It predicts the synthesis of 1,023 compounds by metabolic pathways initiated from 38 metabolites reflecting seawater composition, almost each (99.98%) reaction catalyzed by an enzyme supported by a corresponding genetic sequence in the genome. All 75 biomass compounds have a possible biosynthesis pathway described. The resulting GEM includes specific features that enable studies on metabolic complementarity between the algal host and its associated microbiota while minimizing false positives related to reactions potentially originating from microbial catalytic activity.

## Methods

**Genomic and metagenomic data.** The method used WGS data related to *A. nodosum* obtained in the Phaeoexplorer [6] project, sequenced with both Illumina and Nanopore technologies. To improve annotations, additional data from 61 stramenopile genomes, including 45 brown algae, were integrated using AuCoMe v0.5.1 [5], an automated tool designed to build GEMs while handling annotation heterogeneity. To predict metabolic pathway completions in *A. nodosum*, potentially arising from holobiont complementarity, microbiota data were integrated from various sources. These includes bacterial and fungal 16S metabarcoding data [7], fungal LSU and SSU data [8], and bacterial and fungal WGS datasets [9,10,11,12].
**GEMs reconstruction tools.** We used AuCoMe [5] v0.5.1 to enrich poorly annotated species by leveraging expertly annotated model species. Computations were performed on the GenOuest cluster (https://www.genouest.org/) (22 CPUs, 200GB RAM) using "filtering" option for orthology. To interpret metabarcoding and taxonomic data into GEMs, the EsMeCaTa [13] v0.2.12 tool (Estimating Metabolic Capabilities from Taxonomic Affiliations) was employed to infer functional annotations and protein sequences based on Uniprot knowledge. The microbiota GEMs were then produced using a command-line parallelized version PathwayTools (Mpwt python package) [14] from EsMeCaTa outputs and WGS data.

**Discrete-based simulation of the production of biomass compounds and gap-filling.** Simulations of producible metabolites, i.e., the metabolic modeling, were performed with Mene tools v3.3.0 [15]. The tool requires a list of available nutrient compounds, referred to as seeds, which initialize the inference of other reachable, i.e., producible, metabolites in the network. This step is referred to as network expansion, the formalism used in the dynamical system is the one of metabolic modeling associated with a Boolean semantic. It is used in the Meneco tool [16], which, in the genome-resolved approach, takes into account the added-value brought by a set of additional reactions, therefore suggesting the producibility of new metabolites resulting from adding reactions to the GEM.

In our case study, we used the full Metacyc database as a set of reactions. This set was furthermore enriched with artificial reactions linking all compounds to their higher-level (more generic) compounds class. The 26,866 reactions added with this approach made it possible to connect pathways described with various levels of precision in the Metacyc database.

## Results

**A pipeline for the construction and curation of eukaryotic genome-scale metabolic network from multiple eukaryotic genomes and associated microbiomes.** The pipeline starts with AuCoMe, which generates draft GEMs with PathwayTools [17] v26.0 which are then refined through orthologous gene propagation and metabolic pathway completion with spontaneous reactions. The final dataset comprises 62 GEMs, averaging 2,986 reactions (2,902 for *A. nodosum*), with a standard deviation of 335. In contrast, the draft GEMs before orthology-based enrichment contained an average of 2,200 reactions, with a standard deviation of 508 (Fig. 1A).

For the microbiota, a total of 449 GEMs were reconstructed. From unique taxonomic assignments of metabarcoding data [7], 316 bacterial and 13 fungal networks were generated. An additional 15 fungal networks were obtained from taxonomic assignments from endophytic fungi isolated strains [8]. Finally, 86 bacterial and 22 fungal networks were reconstructed from the WGS data [9,10,11,12] (Fig. 1B).

To ensure the quality of the algal metabolic network, it is crucial to assess whether its topology enables the biosynthesis of key biomass compounds from the available environmental nutrients. This requires defining both the algal growth medium and a comprehensive biomass function that here accounts for a wide range of molecules measured in the species, including both small molecules and more complex macromolecules characteristic of its composition. In this study, since *A. nodosum* is not cultivated but rather sampled from the seashore, the composition of seawater was used as the reference growth medium. The biomass function was specifically formulated based on metabolomic data
from the literature (see below) to ensure the inclusion of a broad spectrum of known compounds. It
was then mapped to the MetaCyc database [18,19] (Fig 1C).

As an initial step in functional gap-filling, the defined growth environment (seawater) was used as
the input set of compounds (seeds), while the biomass function represented the target metabolites
for the discrete-dynamical gap-filling tool Meneco [16] (Fig 1 GF0). This approach first identified com-
pounds that remained non-producible even after integrating all reactions from the MetaCyc database
into the *A. nodosum* GEM reconstructed with AuCoMe. In this case, 6 out of the 75 biomass target
compounds were predicted to be unproducible. Their production was made possible by the addition
1 reaction from BIGG [20] intializing the L-carnitine biosynthesis pathway, 1 from KEGG [21] for the
biosynthesis of linolenic acid, and 1 from Rhea [22] for the biosynthesis of 2-hydroxymyristate. For
cellulose, the polymer chain extension reaction was already present, a chain initialization reaction
consuming UDP-alpha-D-glucose and producing UDP and cellulose was created. Additional 76 re-
actions were added for the transport of the 38 seeds compounds within the cell compartments, 1
defining the biomass function and 1 for the biomass export (Fig 1D).

A second functional gap-filling step was performed to assess the number of biomass compounds that could be synthesized through reaction chains in the GEM (31 out of 75 target compounds) and to identify additional reactions from the MetaCyc v26.0 database [18,19] required to maximize the producibility of the biomass function. This process resulted in the prediction of 116 necessary reactions. In order to evaluate and filter the reactions predicted to have an added value over the production of the biomass function, several steps were performed before adding them to the network. First (Fig 1 GF1), a proteome-based gap-filling step checks for orthologs (e-value≤1e-10) between UniProt protein sequences associated with MetaCyc reactions proposed and the algal proteome (via BlastP) and genome (via TBlastN). Reactions with confirmed orthologs are integrated in the network indicating the corresponding gene(s) in metadata. In our case study, 39 were included due to their potential orthologous associated proteins with *A. nodosum* proteome (identified via BlastP). Second (Fig 1 GF2), a phylogenetic gap-filling step adds candidate reactions if they are present in the GEMs of one of the 45 closely related species (brown algae); the species is indicated in the GEM metadata for the sake of tracability. Third (Fig 1 GF3), a microbiota gap-filling step adds candidate reactions found in algal microbiota GEMs and flags them as potential microbial sources. In our case-study, 20 reactions were added because they were found in brown algae and/or in the *A. nodosum* microbiota, five of them being exclusive to the microbiota. The last black-box gap-filling step (Fig 1 GF4) consists in incorporating reactions to complete the GEM without specific explanatory link to available genomes. In our case-study, this corresponded to 29 reactions. All these steps are integrated in the MeneVal pipeline which automates AuCome and Meneco executions and input files creation. The method iteratively re-executes Meneco between each validation and reaction addition step to refine the set of candidate reactions, minimizing reactions functional redundancy for limiting unnecessary reactions addition (Fig 1E).

Based on this gap-filling workflow, the initial number of 116 candidate reactions to be added to the
GEM was reduced to 88, among with 49 are associated with protein sequences linked to the initial
genome, and 29 reactions corresponds to biological processes for which biological evidences have
to be found with specific methods.
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Complete pipeline for the construction and curation of our eukaryotic GEM from multiple eukaryotic genomes and associated microbiomes.</figcaption></figure>
**Creation of tailor-made, species-specific algal biomass** Biomass composition of *A. nodosum* was established by aggregating data from multiple sources to form a consensus. Compound measurements were primarily drawn from chemical quantification studies covering diverse conditions such as seasonality and geographic location [23,24,25] and included data from Russia's White Sea and from Bodø, Northern Norway. Additional references included core metabolism compounds from *Ectocarpus* sp. 7 [26] and more specific metabolites such as fucoidans [27,28,29], phlorotannins [30], and fucoxanthin [31]. Instead of focusing on specific conditions, the quantification was designed to capture general trends in the relative proportions of different compound classes, ensuring compatibility with potential future quantitative analyses. Values were estimated using averages and proportionality ratios. When specific measurements were available, averages were calculated, and quantities in µmol/g dry weight were extrapolated based on the *Ectocarpus* sp. 7 20 proteinogenic amino acids ratios [26]. When only class-level data was available, the proportions of sub-compounds were estimated based on reported ratios. Regarding proteinogenic amino acids, for example, quantities were extrapolated based on the ratios observed in *Ectocarpus* sp. 7. When no such data were available, we assumed an equal distribution across all compounds of the class.

The resulting biomass consists of a total of 75 compounds (Fig. 2), including 2.21% of all the 20 proteinogenic amino acids, whose synthesis ensures enzymatic production autonomy. It also contains a set of 12 carbohydrates, including 6 monosaccharides (Glucose, Galactose, Xylose, Mannose, Fucose, Xylonate) and 2 more complex polysaccharides (alginate and cellulose), which make up approximately 55% of the biomass. Additionally, a set of 12 fatty acids accounts for 5% of the composition. Regarding the production of metabolites more specific to brown algae, GDP-L-fucose is included as a precursor of fucoidan [28], along with a set of monosaccharides serving as its structural components. UDP-glucose was also introduced as it a precursor in cellulose biosynthesis. For phlorotannins, only the phloroglucinol precursor [30] was incorporated, as it has a well-documented biosynthetic pathway. This final biomass function retained only major compounds, prioritizing those with known biosynthetic pathways described in databases or supported by literature.

**Analysing algal biomass according to the pathways information of the predicted algal and holobiont GEMs.**

Fig. 3 classifies the different compounds of the biomass according to the sources needed in the GSM reconstructon process needed to predict their production. After the initial reconstruction of the *A. nodosum* GEM using AuCoMe and before gap-filling, the model could produce 30 out of the 75 biomass compounds, primarily canonical amino acids (16 out of 20) and, more broadly, carboxylates (28 out of 30). Following gap-filling, including the addition of 4 manually curated reactions, 39 reactions from genomic validation, 15 from phylogenetic validation, and five from microbiota validation, 20 additional biomass compounds became producible, with a predominance of fatty acids, carotenoids, and more generally, carboxylates. The final set of 29 reactions added to enable the synthesis of the remaining 25 biomass compounds introduced a more diverse range of metabolites, with a higher proportion of sugars, amines, chlorophyll derivatives, and vitamins.

**Discussion**

**Tailor-made biomass function to refine network qualitative analyses.** The biomass function built in this study is based on estimated quantities derived from a consensus of multiple studies. The
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Ascophyllum nodosum biomass compounds approximated percentage quantities according to MetaCyc ontology compounds hierarchical classification.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Classification of biomass compounds producible from seawater components. (GF=Gap-Filling). **A.** (blue) 30 compounds producible before gap-filling – Biosynthetic reactions linked to algal genes. **B.** (red) 20 additional compounds producible after adding 24 candidate gap-filling reactions – Supported by genomic information. **C.** (yellow) 25 remaining biomass compounds producible after adding the final 29 candidate reactions – Completing the gap-filling process.</figcaption></figure>
latter enabled the application of advanced gap-filling methods, revealing genomic information that is present or missing for the biosynthesis of a wide range of characteristic compounds. It allows for a more in-depth qualitative refinement of the GEM compared to biomass formulations previously applied in quantitative analyses applied to brown algae like *Ectocarpus* sp. 7 [26] and *Saccharina japonica* [32]. This approach still retains the weighted compounds necessary for such analyzes assuming the addition of RNA, DNA, and the required biosynthetic energy (ATP) [33,34]. However, these values should not be considered fixed or universal, as significant fluctuations have been reported throughout the algal life cycle, influenced by factors such as seasonality [23], temperature, and geographic location. Additionally, it is important to note that the described compounds vary in complexity, ranging from small molecules to macromolecules. As a result, the reported quantities may not always be mutually exclusive, as some compounds could be structural components of others. To take into account such variability, MDF approaches integrating ranges of metabolite concentrations [35] may be closer to the biological reality, and are indeed used for plant genome-scale modeling [36].

**A Gap-filling approach to maximize the number of reactions linked to genomic information and promote explicability** While the acceptable level of uncertainty in introducing reactions through gap-filling remains debated [2], the iterative reconstruction method presented here helps minimize false-positive reactions while assigning metadata to support hypotheses on their biological relevance. Notably, 39 reactions were linked to genes present in the *A. nodosum* genome but missed during automated gene prediction. An additional 15 reactions, found in other brown algal genomes, may also exist in misassembled regions of the *A. nodosum* genome. Meanwhile, five reactions originating from associated microbiota offer a limited yet valuable set of candidates for further study of potential symbiotic interactions at the holobiont level. In addition, the number of reactions added without a clear origin was reduced from 116 to 29. Despite these advantages, certain limitations remain. For validation via BlastP, not all reactions in databases are linked to protein sequences, preventing comprehensive testing. Additionally, matches within closely related enzyme families do not guarantee identical metabolic functions. Alternative approaches, such as AlphaFold, could provide deeper insights by analyzing structural-functional similarities rather than relying solely on sequence alignments.

**Database knowledge dependency** A limitation of GEM reconstruction based on genomic data is its reliance on existing database knowledge associating functional annotations with genomic sequences, which primarily covers well-characterized core metabolism and lacks specificity. When applied to understudied species like brown algae, these automated methods fail to capture a significant part of their unique metabolic features. This highlights a major challenge: the need to explore the "omics dark matter" [37,38]. Developing alternative methodologies capable of suggesting biosynthetic pathways for compounds without known biosynthesis pathways, particularly in poorly studied species or metabolites, will be essential to further enhance metabolic network reconstruction.

## Availability and Implementation

The network reconstructed with AuCoMe before gap-filling has been made accessible via Wiki on the Phaeoexplorer project website (https://phaeoexplorer.sb-roscoff.fr/metabolic_networks/). However, the final network after gap-filling is not yet available. The code for the MeneVal pipeline is available on GitHub (https://github.com/AuReMe/MeneVal).
## Acknowledgements

Our thanks go to the Phaeoexplorer project for providing the genomic data, to the members of the ANR Seabioz project for data sharing and fruitful discussions, and to the Genouest platform for providing the computational resources.

## Funding information

The work was carried out as part of the Seabioz ANR program (ANR-20-CE43-0013), and has received financial support from the Centre National de la Recherche Scientifique (CNRS-MITI Algometabionte).

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