# When chemistry meets taxonomy: Studying glycolipidic chemomarkers in pelagic Sargassum spp. (Phaeophyceae) using molecular networking

Charlotte Nirma, Valérie Stiger-pouvreau, Marceau Levasseur, David

Touboul, Solène Connan, Sylvain Petek

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Charlotte Nirma, Valérie Stiger-pouvreau, Marceau Levasseur, David Touboul, Solène Connan, et al.. When chemistry meets taxonomy: Studying glycolipidic chemomarkers in pelagic Sargassum spp. (Phaeophyceae) using molecular networking. Journal of Phycology, In press, 10.1111/jpy.70116. hal-05426569

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RESEARCH ARTICLE

# When chemistry meets taxonomy: Studying glycolipidic chemomarkers in pelagic *Sargassum* spp. (Phaeophyceae) using molecular networking

Charlotte Nirma<fnref n="1" />

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

Valérie Stiger-Pouvreau<fnref n="1" />

Marceau Levasseur<fnref n="2" />

David Touboul<fnref n="2" />,<fnref n="3" />

Solène Connan<fnref n="1" />

Sylvain Petek<fnref n="1" />

**Correspondence**

Charlotte Nirma, Univ Brest, IRD, CNRS, Ifremer, LEMAR, IUEM, Plouzané F-29280, France.

Email: charlotte.nirma@univ-ubs.fr

**Funding information**

Centre National de la Recherche Scientifique, Grant/Award Number: 801Prime program; Agence Nationale de la Recherche, Grant/Award Number: ANR-19-SARG-0004 and ANR-19-SARG-0008

Editor: C. Amsler

**Abstract**

To chemically differentiate the three pelagic *Sargassum* morphotypes co-occurring in floating rafts and drifting across the Atlantic Ocean before stranding on West African, Caribbean, and Atlantic Mexican coastlines, we conducted an investigation of their metabolomic profiles. Hydroethanolic extracts from open-sea raft specimens were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS<fnref n="2" />), and the resulting spectra were processed through feature-based molecular networking with MetGem software. Several glycolipids were putatively identified through spectral matching and manual annotation, predominantly associated with *S. natans* var. *wingei*, *S. natans* var. *natans*, and *S. fluitans* var. *fluitans*. These findings were corroborated by statistical analyses of ¹H NMR spectral fingerprints. This study represents a chemotaxonomic assessment of pelagic *Sargassum* utilizing molecular networking, demonstrating its efficient utility for putative chemomarker identification. We further discuss the taxonomic status of the three varieties in light of our chemical data, along with observed physiological distinctions among the morphotypes.

**KEYWORDS**

chemomarkers, glycolipids, molecular network, *Sargassum*

INTRODUCTION

Pelagic *Sargassum* species (*Sargassaceae*, Phaeophyceae) are brown macroalgae living in the open sea without any benthic attachment phase and without sexual reproduction (Stiger-Pouvreau et al., 2023).

Their presence in the open sea, in the form of floating rafts of different sizes (Ody et al., 2019), constitutes a complex habitat that shelters a rich and diverse biodiversity (Huffard et al., 2014; Michotey et al., 2020; van Tussenbroek et al., 2024). Initially, these algae were observed in the original Sargasso Sea; however, since

**Abbreviations:** ¹H NMR, proton nuclear magnetic resonance; ADAP, Automated Data Analysis Pipeline; Da, Dalton; DDA, Data-Dependent Acquisition; DGDG, digalactosyldiacylglyceride; GASB, Great Atlantic *Sargassum* Belt; GNPS, Global Natural Product Social Molecular Networking; HR-MAS NMR, high-resolution magic angle spinning nuclear magnetic resonance; ISDB, In Silico DataBase; LC-MS<fnref n="2" />, liquid chromatography coupled to tandem mass spectrometry; LPC, lysophosphatidylcholine; LPE, lysophosphatidylethanolamine; MGDG, monogalactosyldiacylglycerides; MN, molecular network; PCA, principal component analysis; PLS-DA, Partial Least Squares Discriminant Analysis; RCO, acylium ion; SQDG, sulphoquinovosyldiacylglycerides; t-SNE, t-distributed Stochastic Neighbor Embedding; VIP, variable importance in projection.

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Journal of Phycology* published by Wiley Periodicals LLC on behalf of Phycological Society of America.

<footnote n="1">¹Univ Brest, IRD, CNRS, Ifremer, LEMAR, IUEM, Plouzané, France</footnote>

<footnote n="2">²CNRS, Institut de Chimie Des Substances Naturelles (ICSN), UPR 2301, Université Paris-Saclay, Avenue de la Terrasse, Gif-sur-Yvette, France</footnote>

<footnote n="3">³Laboratoire de Chimie Moléculaire (LCM), CNRS UMR 9168, École Polytechnique, Institut Polytechnique de Paris, Route de Saclay, CEDEX, Palaiseau, France</footnote>
2011, new developments have been observed in the north of Brazil and in the Gulf of Mexico, leading to a massive increase in the biomass of the algae and its expansion into the tropical North Atlantic Ocean and to the creation of new Sargasso seas, notably the Great Atlantic *Sargassum* Belt (GASB; Gower et al., 2013; Wang et al., 2019). The result is a proliferation of rafts drifting with the winds and currents. But the consequences of this proliferation are not limited to the open sea. As they approach the coast, the rafts strand and the algae are degraded by bacteria, producing hydrogen sulfide and causing serious health problems for local populations (de Széchy et al., 2012; Resiere et al., 2020; Smetacek & Zingone, 2013). These mass strandings, mainly on the coasts of the Caribbean islands, the Atlantic Mexican coastline, and West Africa, are causing social and economic damage. The ecological impact is difficult to quantify, but these groundings have catastrophic consequences for benthic fauna and flora (van Tussenbroek et al., 2017).

*Sargassum* species in the Sargasso Sea have been studied for decades, but recent research has focused on understanding this new proliferation phenomenon and the origin of the species involved. The study of drifting rafts has revealed the presence of three varieties corresponding to three morphotypes belonging to two pelagic species: *Sargassum natans* var. *wingei* (morphotype SN8), *S. natans* var. *natans* (morphotype SN1), and *S. fluitans* var. *fluitans* (morphotype SF1; Schell et al., 2015; Dibner et al., 2022; Siuda et al., 2024). The three morphotypes can be distinguished morphologically (Kergosien et al., 2024; Siuda et al., 2024), and under the same environmental conditions, they exhibit different physiology such as growth rate (Changeux et al., 2023; Magaña-Gallegos, García-Sánchez, et al., 2023; Magaña-Gallegos, Villegas-Muñoz, et al., 2023). However, it is still difficult to differentiate them at the molecular level (Amaral-Zettler et al., 2017; González-Nieto et al., 2020; Sissini et al., 2017; Siuda et al., 2024). A recent morphometric and chemical analysis distinguished the three *Sargassum* varieties by examining fatty acid ratios but without identifying a chemomarker to distinguish the two varieties of *S. natans* (Kergosien et al., 2024). Thus, the isolation of a chemomarker specific to one of the varieties of *S. natans* could lead us to conduct a taxonomic revision within the floating varieties of pelagic *Sargassum*. Various analytical techniques can be used for this purpose, including liquid chromatography (LC) or gas chromatography (GC), mass spectrometry (MS), and nuclear magnetic resonance spectroscopy (NMR). These techniques have made it possible, used separately or in combination, to identify metabolomic variations in different species of the same genus. A study of four species of the brown macroalgal genus *Lobophora* using LC-MS, GC-MS, and NMR spectroscopy identified polyunsaturated alcohols as chemomarkers (Gaubert et al., 2019). In the Sargassaceae family,

studies have been carried out to identify chemomarkers enabling morphological classification to be revised. In the *Cystoseira/Gongolaria/Ericaria* complex, a combined LC-MS¹ and HR-MAS NMR analysis was used to differentiate five species encountered in Brittany, France (Jégou et al., 2010, 2012). Interestingly, cyclized meroditerpenes were isolated as chemomarkers of *Gongolaria nodicaulis*, which were not identified in the other species of the complex. Always within the Sargassaceae family, turbinaric acid, a lipidic chemomarker, had also been identified in *Turbinaria conoides*, and never in *T. ornata*, by coupling GC-MS and HR-MAS NMR techniques and using a large geographical sampling of both species (Le Lann et al., 2008, 2014). Chemotaxonomic studies can also highlight spatiotemporal variations in the algal metabolome, as in the case of the brown Sargassaceae macroalga *Bifurcaria bifurcata* (Le Lann et al., 2014), which presented diterpene variations with hydrodynamical conditions and seasons, and also in *S. muticum*, which presented different metabolomic fingerprints in relation to a latitudinal gradient, from Portugal to Norway (Tanniou et al., 2015). Metabolomic analysis of pelagic *Sargassum* has been used for the discrimination of benthic species by GC-MS and infrared spectroscopy (Rosado-Espinosa et al., 2020), ¹H NMR, and HR-MAS NMR analysis, to differentiate between pelagic morphotypes (Hernández-Bolio et al., 2021; Kergosien et al., 2024). However, to date, no chemomarker has been identified in these pelagic *Sargassum* varieties.

The present study is based on the assumption that the three morphotypes living together in rafts have different chemical signatures and can be differentiated using metabolomic fingerprints. Pelagic *Sargassum* samples were collected in Guadeloupe, a region impacted by massive strandings. Samples were then analyzed by LC-MS²; tandem mass spectra were used to generate feature-based molecular networks (MNs). Molecular networks allow spectral similarities to be represented as a 2-D graph. If two molecules have similar fragmentation spectra (including neutral loss and diagnostic fragments), we can hypothesize that they have similar structures and therefore belong to the same molecular family. It is therefore possible to identify clusters of structurally similar molecules.

For over a decade, the use of molecular networks for the study and in silico annotation of MS² spectra has represented a major advance in the analysis of complex mixtures, enabling large data sets to be visualized on a spectral similarity map. As fragmentation spectra provide structural information, it is possible to compare experimental spectra with spectral databases to identify compounds or at least their analogs. Numerous libraries are available and can be queried for network annotation: Global Natural Product Social Molecular Networking (GNPS; Wang et al., 2016) or In Silico Database (ISDB; Allard et al., 2016) or those available on MS-Dial (Tsugawa et al., 2020). Molecular networks
voltage: 500 V, fragmentor voltage: 130 V, skimmer voltage: 45 V, and Octopole 1 RF voltage: 750 V. The use of a calibration solution, containing two internal reference masses (purine, C₅H₄N₄, m/z 121.0509, and HP-921 hexakis-1H,1H,3H-tetrafluoropentoxy-phosphazene, C₁₈H₁₈O₆N₃P₃F₂₄, m/z 922.0098), routinely resulted in mass accuracy below 3 ppm. Data-dependent MS² events were acquired for the five most intense ions detected by full-scan MS, in the m/z 100-1700 range, above an absolute threshold of 1000 counts. Selected precursor ions were fragmented with a fixed collision energy of 20 eV and with an isolation window of 1.3 amu. The mass range of the precursor and fragment ions was set to m/z 100-1700 and 50-1700, respectively.

## Molecular network processing

The MS² data files were converted from Agilent Technologies data format to mzXML format using MSConvert software from the ProteoWizard package. Data were then preprocessed using MZMine v. 2.53 (Pluskal et al., 2010) based on the methodology previously described by Olivon et al. (2017). Mass detection was performed by maintaining the noise level at 1000 for MS level 1 and at 5 for MS level 2. Automated Data Analysis Pipeline (ADAP) chromatogram builder (Myers et al., 2017) was used with a minimum scan group size of 3, a group intensity threshold of 1000, a highest minimum intensity of 1000, and an m/z tolerance of 0.008 or 20 ppm. Deconvolution was performed using the ADAP wavelet algorithm with a median m/z center calculation, an m/z range for MS² of 0.05 Da, a retention time (RT) range for MS² of 0.5 min, an S/N threshold of 10, a minimum feature height of 1000, a coefficient of 10 for a peak duration range between 0.01 and 1.5 min, and a RT wavelet range of 0.00-0.04 min. Isotopes were grouped using the isotopic peaks grouper algorithm with an m/z tolerance of 0.008 or 20 ppm, a RT tolerance of 0.2 min, and a maximum charge of 2, using the most intense mode for the representative isotope. Peak alignment was performed using the join aligner module with an m/z tolerance of 0.008 or 20 ppm, a weight for m/z of 80%, a RT tolerance of 0.1 min, and a weight for RT of 20%. The MGF files of spectral data and their corresponding CSV metadata files were exported using the Export for GNPS option.

## MetGem parameters

Cosine scores and molecular networks were calculated using MetGem 1.3.6 software (Olivon et al., 2018). The MS² spectra were window-filtered

by selecting only the six highest peaks in the ±50 Da window across the entire spectrum. The data were filtered by removing all peaks in the ±17 Da range around the precursor m/z. The m/z tolerance windows used to find matching peaks were set at 0.04, and cosine scores were considered for spectra sharing at least two matching peaks. The minimum cosine score to link two nodes was set at 0.60. For standard searches in MetGem's associated databases (GNPS, MS-Dial, and ISDB), the parameters were as follows: m/z tolerance = 0.2 Th (Thomson); minimum matched peaks = 2; minimum intensity = 0%; parent m/z tolerance = 17 Th; minimum cosine score = 0.60. For the search for analogs, the same parameters were used and the m/z tolerance for the precursor ion was set at 100.

## Proton nuclear magnetic resonance analysis

Dry acetone and dichloromethane crude extracts were dissolved in 0.6 mL deuterated chloroform (CDCl₃). The ¹H NMR spectra were recorded on a Bruker Avance 400 MHz spectrometer in the UBO Shared Service facility (Brest, France). Acquisitions were recorded using the standard pulse sequences available in Bruker software (Bruker, France), with 32 scans. Spectra were processed using MestReNova software.

## Statistical analysis

The ¹H NMR spectra were processed with MestReNova software. Spectra were automatically Fourier-transformed, and baselines were corrected using Whittaker's smoother correction, then normalized. MestreNova's features were used to bin the spectra with a width of 0.001 ppm. The bin ranged from 0 to 10 ppm. The spectra were normalized to the largest peak. After stacking the spectra, the data set was exported in .txt format as a matrix and processed in a spreadsheet. The regions of δ 7.18-7.32 ppm corresponding to the CDCl₃ signal, as well as the region of δ 5.25-5.35 ppm corresponding to traces of residual dichloromethane solvent (only for dichloromethane extracts), were removed to eliminate the effects of these abundant signals in the multivariate analysis. The resulting data matrices were submitted to MetaboAnalyst 6.0 software for statistical analysis. Principal component analyses (PCAs) and partial least squares discriminant analysis (PLS-DA) were performed with sum normalization and without scaling data on the two matrices obtained for acetone and dichloromethane extracts.
<figure><img src="image_3.png" /><figcaption>FIGURE 1 Molecular network of annotated metabolites from the H₂O/EtOH extract of SN8, SN1, and SF1 by LC-HRMS² analysis (107 nodes belonging to six clusters represented). Cluster annotation was performed using MetGem databases. The pie charts in the nodes represent abundance, and the colors correspond to the three morphotypes or analytical blank (red: SN8, blue: SN1, green: SF1, and gray: Analytical blank).</figcaption></figure>

# RESULTS

## Analysis of hydroethanolic extracts

To identify chemomarkers capable of discriminating among the three morphotypes of pelagic *Sargassum*, hydroalcoholic extracts were analyzed using reverse-phase liquid chromatography coupled with electrospray ionization high-resolution tandem mass spectrometry (RPLC-ESI-HRMS²) in Data-Dependent Acquisition (DDA) mode. The resulting spectra were preprocessed using MZMine 2.53. Data-Dependent Acquisition mode enabled the fragmentation of precursor ions in a complex sample and providing structural information about the molecules. Structurally related molecules tend to exhibit similar MS² fragmentation patterns, allowing for the construction of feature-based molecular networks through spectral alignment and comparison.

Feature-based molecular networks obtained using MetGem software revealed 2128 nodes, each corresponding to an individual molecular feature (precursor ion with its MS² spectrum at a defined retention time). Among these, 1347 nodes were grouped into 56 clusters, while 781 remained as singletons. Cluster organization was calculated according to the spectral similarity of the fragmentation patterns, using a cosine score between 0 (no similarity) and 1 (perfect match). These molecular networks were generated using a cosine score of 0.60. Nodes were color-coded with the SN8 morphotype in red, SN1 in blue, and SF1 in green. An entirely blue node corresponded to a molecule identified only in the SN1 extract. Pie charts represented the proportion of a molecule in the different morphotypes/varieties. The experimental spectra were

then compared with those in the public databases available on MetGem. When the experimental spectra matched the libraries, the nodes were annotated. For the annotations shown in Figure 1, the GNPS (Wang et al., 2016), MS-Dial (Tsugawa et al., 2020), and ISDB (Allard et al., 2016) databases were queried.

Among the compounds annotated through spectral similarity within the databases, the majority were lipid compounds (phospholipids, galactolipids, etc.). Comparison with the databases also revealed the presence of carnitines and xanthines (data not processed in this study). A closer look at lipids revealed that they were predominantly identified in SN1 (blue nodes) and SF1 (green nodes). Since the seaweeds were collected at the same time and in the same station, and the extracts from each morphotype were prepared and analyzed under the same conditions, we can hypothesize that these lipid compounds are less diverse in SN8. This molecular family would then be discriminating in the differentiation of the pelagic *Sargassum* varieties. This hypothesis was supported by previous results obtained by Kergosien et al. (2024) identifying fatty acid ratios as a possibility/ability to discriminate morphotypes.

We, therefore, focused on discriminating molecules, namely glycolipids. Three types of glycolipids were putatively identified, and most of them are synthesized by algae: monogalactosyldiacylglycerides (MGDGs), digalactosyldiacylglycerides (DGDGs), and sulphoquinovosyldiacylglycerides (SQDGs). Table 1 lists the precursor and fragment ions of MGDG, DGDG, and SQDG annotated in this study by comparison of their fragmentation spectra with spectral databases and by annotation propagation and manual dereplication of the spectra (manual annotation in molecular networking refers to
<table><caption>TABLE 1 Major ions and their putative annotations of compounds detected in pelagic *Sargassum* hydroethanolic extracts.</caption><thead><tr><th rowspan="2">Class</th><th rowspan="2">n</th><th rowspan="2">Precursor ion<br>[M + NH<sub>4</sub><sup>+</sup> ± SD<sup>a</sup> (m/z)</th><th rowspan="2">RT ± SD<sup>a</sup><br>(min)</th><th rowspan="2">MS/MS<br>(m/z)</th><th rowspan="2">Adduct formula</th><th rowspan="2">Theoretical<br>m/z</th><th rowspan="2">Δ m/z ppm</th><th rowspan="2">Annotated fatty<br>acids</th><th colspan="2">Occurrence in<br>varieties</th><th rowspan="2">Potential<br>chemomarker<br>in varieties</th></tr><tr><th>SN8</th><th>SN1</th><th>SF1</th></tr></thead><tbody><tr><td rowspan="7">MGDG</td><td>1</td><td>740.5301</td><td>21.2</td><td>561; 543;<br>313; 305</td><td>C<sub>41</sub>H<sub>74</sub>NO<sub>10</sub></td><td>740.5313</td><td>1.6</td><td>16.0/16.4</td><td>x</td><td>x</td><td>SF1</td></tr><tr><td>2</td><td>742.5454</td><td>21.8</td><td>563; 545;<br>313; 307</td><td>C<sub>41</sub>H<sub>76</sub>NO<sub>10</sub></td><td>742.5469</td><td>2.0</td><td>16.0/16.3</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>3</td><td>744.5606</td><td>22.0</td><td>547; 337;<br>313; 309</td><td>C<sub>41</sub>H<sub>78</sub>NO<sub>10</sub></td><td>744.5626</td><td>2.6</td><td>16.0/16.2</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>4</td><td>770.5961</td><td>22.1</td><td>591; 573;<br>335; 313</td><td>C<sub>43</sub>H<sub>80</sub>NO<sub>10</sub></td><td>770.5782</td><td>-23.2</td><td>16.0/18.3</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>5</td><td>772.5917 ± 0.0013</td><td>22.7 ± 0.0</td><td>575; 337;<br>313</td><td>C<sub>43</sub>H<sub>82</sub>NO<sub>10</sub></td><td>772.5939</td><td>2.8</td><td>16.0/18.2</td><td>x</td><td>x</td><td>No</td></tr><tr><td>6</td><td>790.5622</td><td>20.6</td><td>611; 593;<br>335; 333</td><td>C<sub>45</sub>H<sub>76</sub>NO<sub>10</sub></td><td>790.5469</td><td>-19.3</td><td>18.3/18.4</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>7</td><td>792.5609</td><td>21.0</td><td>613; 585;<br>335</td><td>C<sub>45</sub>H<sub>78</sub>NO<sub>10</sub></td><td>792.5626</td><td>2.1</td><td>18.3/18.3</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td rowspan="5">DGDG</td><td>8</td><td>902.5859</td><td>20.0</td><td>561; 543;<br>313; 305</td><td>C<sub>47</sub>H<sub>84</sub>NO<sub>15</sub></td><td>902.5841</td><td>-2.0</td><td>16.0/16.4</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>9</td><td>904.597</td><td>20.2</td><td>563; 545;<br>527; 313;<br>307</td><td>C<sub>47</sub>H<sub>86</sub>NO<sub>15</sub></td><td>904.5998</td><td>3.0</td><td>16.0/16.3</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td>10</td><td>930.6124</td><td>20.4</td><td>589; 571;<br>335; 311</td><td>C<sub>49</sub>H<sub>88</sub>NO<sub>15</sub></td><td>930.6154</td><td>3.2</td><td>16.1/18.3</td><td>x</td><td>x</td><td>SN8</td></tr><tr><td>11</td><td>932.6293 ± 0.0004</td><td>21.2 ± 0.0</td><td>573; 335;<br>313</td><td>C<sub>49</sub>H<sub>90</sub>NO<sub>15</sub></td><td>932.6311</td><td>1.9</td><td>16.0/18.3</td><td>x</td><td>x</td><td>SN8</td></tr><tr><td>12</td><td>810.5391</td><td>25.4</td><td>549; 537;<br>313; 311</td><td>C<sub>41</sub>H<sub>80</sub>NO<sub>12</sub>S</td><td>810.5401</td><td>1.3</td><td>16.0/16.1</td><td>x</td><td>x</td><td>SN1</td></tr><tr><td rowspan="4">SQDG</td><td>13</td><td>812.5578</td><td>25.4</td><td>551; 539;<br>465; 331;<br>313</td><td>C<sub>41</sub>H<sub>82</sub>NO<sub>12</sub>S</td><td>812.5558</td><td>-2.5</td><td>16.0/16.0</td><td>x</td><td>x</td><td>SF1</td></tr><tr><td>14</td><td>834.5379</td><td>25.2</td><td>591; 573;<br>465; 335;<br>313</td><td>C<sub>43</sub>H<sub>80</sub>NO<sub>12</sub>S</td><td>834.5401</td><td>2.7</td><td>16.0/18.3</td><td>x</td><td>x</td><td>SF1</td></tr><tr><td>15</td><td>838.5708</td><td>25.4</td><td>577; 339;<br>313</td><td>C<sub>43</sub>H<sub>84</sub>NO<sub>12</sub>S</td><td>838.5714</td><td>0.7</td><td>16.0/18.1</td><td>x</td><td>x</td><td>SF1</td></tr></tbody></table>

<footnote><sup>a</sup>For ions detected in more than one morphotype, the precursor ion *m/z* and the retention time are presented as mean ± standard deviation. Retention time's standard deviation for these compounds was smaller than 0.05min.</footnote>
the identification of fragment ions in MS² spectra by as-
signing them to specific structural features or neutral
losses, based on chemical knowledge and literature,
which complements automated database matching by
validating or refining candidate molecular identities).
These ions were identified as [M+NH₄]⁺ in the pos-
itive ion mode. The t-SNE projection of the MetGem
software (Figure S1) highlighted the presence of other
molecules structurally close to glycolipids identified in
other clusters spread across the network. However,
comparison with the databases did not allow us to an-
notate these compounds. Nevertheless, the proximity
of the t-SNEs suggests spectral similarity, facilitating
manual annotation of these compounds. All MS² spec-
tra of annotated compounds (1–15) have been featured
in Figures S2–S16, and spectral matching information
has been presented in Table S1.

These putative annotations were refined by studying
the detailed fragmentation of each species. The HRMS
spectra exhibited signal ion peaks characteristic of these
compounds, confirming the presence of these molecular
classes (Da Costa et al., 2016; Körber et al., 2023; Lopes
et al., 2019; Zianni et al., 2013). An example of a fragmen-
tation study for each glycolipid class is shown in Figure 2.
For MGDG, the MS² spectrum of compound 2 (m/z
742.5454, C₄₁H₇₆NO₁₀, Δm/z 2ppm, Figure 2a) showed
an ion at m/z 545.4545 assigned as [M+NH₄⁻·197]⁺,

resulting from the combined loss of NH₃ (–17Da) and a
hexose (–180Da) due to cleavage of the sugar bond near
the hemiacetal oxygen bond with proton transfer to give
a diacylglycerol structure. Neutral losses also provide in-
formation on chain length and number of unsaturations.
Thus, among the fatty acids identified, C16 chains without
unsaturation (–256.24Da) are frequently observed. The
composition of fatty acyl chains can be deduced by the
presence of product ions corresponding to each fatty acyl
group in the form of an acylium plus 74 ion (RCO+74).
The 74 amu adduct corresponds to the glycerol-derived
fragment C₃H₆O₂, which combines with the fatty acyl
chain acylium ion (RCO⁺) during fragmentation of glyco-
glycerolipids. In the case of the MGDG spectrum, RCO
ions were detected at m/z 307.2262 and 313.2739 corre-
sponding to C16:3 and C16:0, respectively.

Similarly, for DGDG, the MS² spectrum of the com-
pound 9 (m/z 904.5970, C₄₇H₈₆NO₁₅·Δm/z 3ppm,
Figure 2b) exhibited the loss of the carbohydrate frac-
tion (–[180 + 162] Da) combined with the loss of NH₃
(–17 Da), leading to the formation of the product ion
at m/z 545.4587, indicated as [M + NH₄⁻·359]⁺. These
neutral losses confirmed the molecular family. The ion
at m/z 563.4702 corresponded to the loss of the di-
galactosyl group. The [RCO + 74]⁺ ions were detected
at m/z 313.2758 and 307.2284, corresponding to C16:0
and C16:3, respectively.

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

m/z

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

m/z

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

m/z

FIGURE 2 LC-MS² spectra of the three types of glycolipids identified by spectral comparison, and main fragmentation. (a) compound 2 [M+NH₄]⁺ MGDG ion with *m/z* 742.5454, (b) compound 9 [M+NH₄]⁺ DGDG ion with *m/z* 904.5970, and (c) compound 12 [M+NH₄]⁺ SQDG ion with *m/z* 810.5391. (NL, Neutral loss).
<figure><img src="image_7.png" /><figcaption>FIGURE 3 Score plot of principal component analyses of ¹H NMR profiles of (a) acetone and (b) dichloromethane extracts from sargassum morphotypes.</figcaption></figure>

In the case of SQDGs (Figure 2c), a neutral loss of 261 Da from the $[M+NH_4]^+$ ion was observed for compound 12 at *m/z* 810.5391 ($C_{41}H_{80}NO_{12}S$, Δ*m/z* 1.3ppm) leading to the product ion at *m/z* 549.4882 corresponding to the loss of the sulfo-group and $NH_3$. The RCO ions of the carbon chains were then observed, visible at *m/z* 311.2572 and 313.2752, corresponding to C16:1 and C16:0 respectively.

It appeared that MGDGs and DGDGs were more represented in the SN1 morphotype, whereas SQDGs were mainly observed in the SF1 morphotype (Table 1). However, these glycerolipid classes were poorly represented (MGDG and DGDG) or even absent (SQDG) in the SN8 morphotype. Analysis of the data revealed several molecules of interest as specific chemomarkers. Their exclusive detection suggests that they can be used as distinctive markers to discriminate between varieties. In addition, several compounds were observed in two out of three varieties; this partial distribution profile also suggests that these molecules can be considered as chemomarkers of the variety in which they are absent. Thus, their absence becomes as informative as their presence, reinforcing their usefulness in chemotaxonomic approaches to discriminate between pelagic *Sargassum* varieties.

The molecular network also showed a cluster of phospholipids that were mainly represented in the SN1 and SF1 morphotypes in different quantities (Figure 1). Comparison with the database proposed compounds identified from the phospholipid class: lysophosphatidylcholine (LPC) 16:0 (*m/z* 496.357 $[M+H]^+$, $C_{24}H_{50}NO_7P$), LPC 20:5 (*m/z* 542.3232 $[M+H]^+$, $C_{28}H_{48}NO_7P$), and LPC 22:6 (*m/z* 568.3398 $[M+H]^+$, $C_{30}H_{50}NO_7P$). Another cluster identified

a lysophosphatidylethanolamine (LPE) 18:1 (*m/z* 480.3087 $[M+H]^+$, $C_{23}H_{46}NO_7P$).

Preliminary results obtained from molecular networks of LC-MS² spectra showed that the compounds discriminating pelagic *Sargassum* morphotypes mainly belonged to the lipid class. Research was therefore focused on apolar extracts. With the aim of identifying other chemomarkers or confirming those identified by MS², acetone and dichloromethane extracts were made from the three *Sargassum* morphotypes to study their metabolic profiles by ¹H NMR technique. The ¹H NMR spectra obtained were aligned and split into bins using MestReNova software. The matrices obtained for the acetone and dichloromethane profiles were then analyzed in MetaboAnalyst 6.0. A PCA was performed to visualize metabolome variation as a function of morphotypes.

## Analysis of acetone and dichloromethane extracts

The PCAs obtained with the ¹H NMR profiles of the acetone and dichloromethane extracts are presented in Figure 3. The score plot (PC1 vs. PC2), with 64.3% of the variance explained for acetone extracts (Figure 3a) and 55.2% for dichloromethane extracts (Figure 3b), divided the samples into two groups. Whatever the extraction solvent, we could clearly see that the metabolic fingerprints of the SN1 and SF1 morphotypes were grouped together on the score plot graphs. The fingerprints obtained for morphotype SN8 were isolated in another group, which certainly indicates the presence of different metabolites.
<figure><img src="image_8.png" /><figcaption>FIGURE 4 Variable importance in projection scores of partial least squares discriminant analysis of ¹H NMR profiles of (a) acetone and (b) dichloromethane extracts from *Sargassum* morphotypes. The colored boxes on the right indicate the relative concentrations of corresponding metabolites in each group under study.</figcaption></figure>

Given the PCA results, which revealed that the samples were structured into distinct groups, a PLS-DA analysis was performed to identify the most discriminating variables (Figure S17). Analysis of the PLS-DA loading plots represents a factorial map of the variable's weights. The variables responsible for separating the groups were distributed on either side of the graph by analogy with the samples that over- or underexpress them, highlighting the discriminating variables. Variable importance in projection (VIP) values were calculated for each variable to indicate their contribution to the observed discrimination (Figure 4). In our study, these variables represented the discriminant bins or chemical shifts of possible chemomarkers between varieties. For example, for the acetone extract fingerprints (Figure 4a), the discriminant variables were signals δ 1.25 and 5.29. These signals could be representative of glycolipid compounds identified by LC-MS². The signal at 1.25ppm was not incompatible with an aliphatic chain chemical shift, whereas the signal at 5.29ppm could have been a proton of the asymmetric carbon of glycerol (regularly observed between 5.29–5.34 ppm in glycosylglycerolipids) or olefin protons carried by acyl chains, often observed in the unsaturated fatty acids (Buedenbender et al., 2020; Knaack et al., 2021; Plouguerné et al., 2010). The VIP of the dichloromethane extracts (Figure 4b) showed that the bins with the greatest influence on SN8 clustering were almost similar to those identified in the acetone extract. Among the most discriminating signals were those corresponding to chemical shifts under 1 ppm that should correspond to terminal methyl groups of the carbon chains. Signals were also present at δ 5.30–5.39 ppm (glycerol asymmetric carbon or olefinic hydrogens), 1.26 ppm (aliphatic chain), and 1.95 ppm (allylic CH₂). The signals at 2.34–2.37 ppm, which may have belonged to the

alpha-methyl protons of the carbonyl group, and the
signals at 1.53–1.56ppm (beta-CH₂) were also identi-
fied. In order to certify the chemical shift assignments
obtained for the NMR spectra discriminant variables,
it would be necessary to perform complementary 2-D
NMR HSQC spectra in order to show C-H correlations.
However, the chemical shifts observed for protons
seemed compatible with the identification of fatty acid
chains, which may belong to glycolipids. Although dif-
ferent extraction protocols and analytical techniques
were applied (LC-MS² for the hydroethanolic extracts
enriched in polar lipids and ¹H NMR analysis for the
acetone and dichloromethane extracts targeting apo-
lar metabolites), both approaches seemed to converge
on the same set of chemical markers. This apparent
overlap can be explained by the amphiphilic nature of
glycolipids, which are soluble in both partially polar and
apolar solvents and therefore detectable in both extract
types.

Statistical analysis showed that SN8 had a distinct
metabolic signature, whereas SN1 and SF1 shared
more similarities in their ¹H NMR profile. Moreover, ¹H
NMR and MS analyses seemed to indicate that identi-
fied chemomarkers in the lipid family may be related.

DISCUSSION

The pelagic *Sargassum* varieties, responsible for mass
strandings in the tropical North Atlantic Ocean, are cur-
rently classified into three morphotypes belonging to
two species, *S. natans* and *S. fluitans*. Although mor-
phological criteria can be used to differentiate these
morphotypes (Kergosien et al., 2024; Siuda et al., 2024),
it is important to study their molecular composition in
order to identify or highlight one or more chemomarkers
that can be used to differentiate them. This study focused on the search for discriminant compounds in the metabolomic profiles of pelagic *Sargassum* extracts. Chemotaxonomic study of *Sargassum* using a molecular network approach represents a method of interest, as it enables medium-throughput analysis of metabolites. Furthermore, species discrimination can be performed without requiring prior knowledge of the genome, unlike genetic barcoding, which requires the design of specific probes.

Extracts from the three morphotypes were analyzed by LC-MS². This highly sensitive technique makes it possible to annotate minor compounds in the extract. The construction of molecular networks from the MS² spectra enabled us to quickly visualize the diversity of molecules present in the extracts studied. To generate these molecular networks, we used a spectral alignment algorithm based on MetGem software. Comparison with the spectral databases enabled us to putatively annotate compounds belonging to the glycolipid class, mainly represented in the SN1 and SF1 extracts. Seven MGDGs, four DGDGs, and four SQDGs were annotated by spectral comparison and proposed manual annotation. These compounds are present in the chloroplasts of eukaryotic algae (Hölzl & Dörmann, 2019). Monogalactosyldiacylglycerides and SQDGs are the most abundant lipids in the thylakoid membrane and play important roles in photosynthesis. These compounds are present in not only brown macroalgae (mainly in Fucales) but also in red and green algae (Plouguerné et al., 2014).

Among the 15 compounds putatively identified in this study, certain polar lipids, such as DGDG (16:1/18:3) for SN8, MGDG (16:0/16:3) for SN1, and SQDG (16:0/18:1) for SF1, stood out as exclusive and robust chemomarkers. Their unique presence in a single variety and absence in the others suggest a strong discriminatory value, essential for rapid and reliable identification. More interestingly, certain molecules shared by two varieties but absent in the third reinforced their role as indirect negative tracers. For example, the absence of DGDG (16:0/18:3) in SN8, although detected in SN1 and SF1, underlined its relevance for excluding this variety in comparative analyses.

Beyond these discriminating compounds, it is worth noting that a large fraction of metabolites overlapped across the three morphotypes. Although the present study focused primarily on exclusive or discriminant chemomarkers, shared metabolites may also carry valuable taxonomic and ecological information. They could represent conserved biochemical traits that reflect evolutionary constraints or common physiological requirements within pelagic *Sargassum*. A deeper investigation specifically targeting these overlapping compounds would be necessary to better evaluate their roles and potential significance in the chemotaxonomic interpretation.

The discriminating molecules putatively identified using feature-based molecular networks were essentially fatty acids. These results confirmed the results of a study by Kergosien et al. (2024), who reported that fatty acids constituted a family of discriminating molecules in the study of Caribbean pelagic *Sargassum* morphotypes. That study highlighted the quantification of certain compounds: palmitoleic acid (16:1n-7) for SN8, α-linoleic acid (18:3n-3) for SN1, and myristic acid (14:0) for SF1. A calculation of the ratios of those fatty acids was used to establish a dichotomous determination key for the different morphotypes (Kergosien et al., 2024).

The present study has highlighted a distinction in the metabolic profile of SN8 compared with SN1 and SF1. Although SN8 and SN1 have been described as belonging to the same species, *Sargassum natans* (Amaral-Zettler et al., 2017; Dibner et al., 2022), molecular analysis of lipidomic fingerprints appears to be more discriminating and distinguishes SN8.

This differentiation between SN8 and SN1 was already reported in a study of pelagic *Sargassum* stranded on the east coast of the Mexican Caribbean using NMR spectroscopy (Hernández-Bolio et al., 2021). In their study, the comparison of the NMR profiles of hydroethanolic extracts from the three morphotypes showed clear variation in the multivariate statistical analyses. The compounds responsible for this variation were identified as amino acids (glutamine and glutamate), myo-inositol, and trimethylamine. Although the families of molecules responsible for the variation were glycolipids in the present study on alive samples (collected in the open sea), our results confirmed that the SN8 morphotype has a different metabolic fingerprint from the two other morphotypes.

We can presume that the three pelagic morphotypes evolved together (sympatry) in *Sargassum* rafts drifting on the Atlantic Ocean, as they have been observed together in these rafts. However, we may wonder how to explain this metabolic variation in SN8. The main difference was observed in glycolipids from the thylakoids, and this could result in differences in the physiology (photosynthesis, growth) of the morphotype SN8 compared to the two other morphotypes. Indeed, SN8 is characterized by a slower growth and nutrient uptake than the two other morphotypes, as demonstrated by in situ experiments carried out in a Martinique Bay (Changeux et al., 2023) and ex situ experiments in Mexico (Magaña-Gallegos, García-Sánchez, et al., 2023; Magaña-Gallegos, Villegas-Muñoz, et al., 2023). This difference in physiology represents an adaptive strategy to nutrients, developed by SN8 but not shared by SN1 or SF1. However, in another in situ experiment carried out by the last authors (Magaña-Gallegos, García-Sánchez, et al., 2023), SN8 exhibited a higher growth rate compared to SF1, showing the physiological variations that could be observed
according to the environmental conditions (either in an enclosed bay in Martinique or near an open shore on the Mexican coastline). Finally, these physiological results have raised the question of whether SN8 and SN1 belong to the same species. With a simpler approach than genomic analysis, this study has demonstrated the applicability of a chemotaxonomic approach to pelagic *Sargassum* for discrimination based on distinctive metabolic profiles. Molecular network analysis was particularly relevant for this medium-throughput application, enabling the identification of chemomarkers, notably glycolipids. Complementary MS² and ¹H NMR analyses confirmed previously identified markers (fatty acid family), reinforcing the robustness and value of this strategy in studying the specific diversity of pelagic *Sargassum* species.

The chemotaxonomic approach used here involved extraction followed by LC-MS² analysis, which is considered rapid compared to the approach of isolating molecular markers, which requires DNA extraction and purification; PCR isolation of markers; and sequencing, which can take ~10 days. This molecular approach does not currently allow, with the markers used, a distinction between the two varieties SN8 and SN1, as shown by Amaral-Zettler et al. (2017) and Sissini et al. (2017), even if low divergence exists between both morphotypes of *Sargassum natans* (Siuda et al., 2024). In contrast, the workflow described in this study allowed the acquisition of chemical fingerprints within a much shorter time frame, while simultaneously providing functional insights into metabolite diversity. Therefore, this approach offers a valuable and time-efficient complement to traditional morphological and molecular methods.

The study of differences in morphotype profiles represents a challenge for understanding the origin, drift, and persistent development of the phenomenon of massive growth and stranding of *Sargassum* varieties along tropical Atlantic coasts.

## CONCLUSIONS

This metabolomic study of pelagic *Sargassum* sought to identify one or more chemomarkers to differentiate three morphotypes. To this end, LC-MS² analyses of open ocean *Sargassum* extracts were carried out, and the results were analyzed using molecular networks. This study revealed the presence of glycolipids in greater quantities in the SN1 and SF1 morphotypes. To confirm these preliminary results, ¹H NMR spectra of apolar *Sargassum* extracts were analyzed, and statistical tests also revealed a clustering of SN1 and SF1 morphotypes.

This study of pelagic *Sargassum* morphotypes using molecular networks with MetGem software has shown that MetGem software is a tool of choice for identifying

discriminating chemomarkers. It is now necessary to extend this study using a larger cohort of samples and to evaluate the potential of molecular networks, notably by working with other extraction solvent polarities and negative ionization. This study opens up exciting prospects for annotating the metabolome and genome of pelagic *Sargassum* species or, even, extending to the genus *Sargassum*. It also raises new questions about whether geographic variation or seasonal dynamics may drive the observed differences in lipid composition between morphotypes.

## AUTHOR CONTRIBUTIONS

**Charlotte Nirma:** Conceptualization (equal); formal analysis (lead); investigation (lead); writing – original draft (lead); writing – review and editing (equal).

**Valérie Stiger-Pouvreau:** Conceptualization (equal); funding acquisition (equal); writing – review and editing (equal). **Marceau Levasseur:** Investigation (supporting); resources (equal); writing – review and editing (equal). **David Touboul:** Investigation (supporting); resources (equal); writing – review and editing (equal).

**Solène Connan:** Conceptualization (equal); funding acquisition (equal); writing – review and editing (equal).

**Sylvain Petek:** Conceptualization (equal); funding acquisition (equal); writing – review and editing (equal).

## ACKNOWLEDGMENTS

The authors thank the laboratories LEMAR and ICSN for their help and all the technical facilities that enabled us to carry out this work.

## FUNDING INFORMATION

This work, part of the SARGASSUM ORIGINS and SAVE-C projects, was supported by grants from ANR under the call SARGASSUM Programme 2019 (Grants No. ANR-19-SARG-0004 and ANR-19-SARG-008, respectively). This work is part of C.N.'s Postdoctoral project. We acknowledge the Laboratory of Marine Sciences (LEMAR UMR 6539) set at IUEM (UBO) for their financial support. This work has received financial support from the CNRS through the 801Prime program for the PhD of M.L.

## ORCID

Charlotte Nirma https://orcid.org/0000-0002-3238-1616

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Figure S1.** t-SNE molecular network representation constructed on MS² homology. The pie charts in the nodes represent abundance, and the colors correspond to the three morphotypes (red: SN8, blue: SN1, green: SF1 and gray: blank). Annotated metabolites identified from MS² standard database query clusters are boxed in black.

**Figure S2.** MS² spectrum of MGDG 16:0/16:4 (1).

**Figure S3.** MS² spectrum of MGDG 16:0/16:3 (2).

**Figure S4.** MS² spectrum of MGDG 16:0/16:2 (3).

**Figure S5.** MS² spectrum of MGDG 16:0/18:3 (4).

**Figure S6.** MS² spectrum of MGDG 16:0/18:2 (5).

**Figure S7.** MS² spectrum of MGDG 18:3/18:4 (6).

**Figure S8.** MS² spectrum of MGDG 18:3/18:3 (7).

**Figure S9.** MS² spectrum of DGDG 16:0/16:4 (8).

**Figure S10.** MS² spectrum of DGDG 16:0/16:3 (9).

**Figure S11.** MS² spectrum of DGDG 16:1/18:3 (10).

**Figure S12.** MS² spectrum of DGDG 16:0/18:3 (11).

**Figure S13.** MS² spectrum of SQDG 16:0/16:1 (12).

**Figure S14.** MS² spectrum of SQDG 16:0/16:0 (13).
**Figure S15.** MS² spectrum of SQDG 16:0/18:3 (14).

**Figure S16.** MS² spectrum of SQDG 16:0/18:1 (15).

**Figure S17.** Score plot of partial least squares discriminant analysis of ¹H NMR profiles of (a) acetone and (b) dichloromethane extracts from *Sargassum* morphotypes. PLS-DA loading plot of (c) acetone and (d) dichloromethane extracts.

**Table S1.** Major ions and their putative annotations and spectral matching information of compounds detected in pelagic *Sargassum* hydroethanolic extracts.

**How to cite this article:** Nirma, C., Stiger-Pouvreau, V., Levasseur, M., Touboul, D., Connan, S., & Petek, S. (2025). When chemistry meets taxonomy: Studying glycolipidic chemomarkers in pelagic *Sargassum* spp. (Phaeophyceae) using molecular networking. *Journal of Phycology*, *00*, 1–14. https://doi.org/10.1111/jpy.70116