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\DOI{10.5802/crchim.250} 
\datereceived{2023-04-18}
\daterevised{2023-06-28}
\dateaccepted{2023-07-24}
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\begin{document}

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%\makeatletter
%\def\TITREspecial{\relax}
%\def\cdr@specialtitle@english{Chemical Ecology -- Chemical Mediation in the Environment}
%\def\cdr@specialtitle@french{\'Ecologie chimique -- M\'ediation chimique dans l'environnement}
%\makeatother

\title{Effects of \protect\textit{Lagarosiphon major} extracts on the
metabolome and photosynthesis of \protect\textit{Microcystis aeruginosa}}

\alttitle{Effet des extraits de \protect\textit{Lagarosiphon major} sur le
m\'{e}tabolisme et la  photosynth\`{e}se de \protect\textit{Microcystis
aeruginosa}}

\author{\firstname{Juliette} \lastname{Tison-Rosebery}\CDRorcid{0000-0003-1090-7020}\IsCorresp}
\address{INRAE, UR EABX, F-33612 Cestas, France}
\email[J. Tison-Rosebery]{juliette.rosebery@inrae.fr}

\author{\firstname{Sylvia} \lastname{Moreira}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[S. Moreira]{sylvia.moreira@inrae.fr}

\author{\firstname{M\'{e}lissa} \lastname{Eon}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[M. Eon]{melissa.eon@inrae.fr}

\author{\firstname{Lucie} \lastname{Campillo}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[L. Campillo]{lucie.c.campillo@gmail.com}

\author{\firstname{Samuel} \lastname{Maubert}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[S. Maubert]{Samuel-Maubert@outlook.fr}

\author{\firstname{Carmen} \lastname{Malepeyre}}
\address{Centre de Valorisation des Agro-ressources, F-19100 Brive-la-Gaillarde, France}
\email[C. Malepeyre]{c.malepeyre@cva-ctt19.fr}

\author{\firstname{Julien} \lastname{C\'{e}lerier}}
\addressSameAs{2}{CVA, F-19100 Brive-la-Gaillarde, France}
\email[J. C\'{e}lerier]{j.celerier@cva-ctt19.fr}

\author{\firstname{Vincent} \lastname{Sol}\CDRorcid{0000-0003-0175-0156}}
\address{Universit\'{e} de Limoges, LABCIS, F-87025 Limoges, France}
\email[V. Sol]{vincent.sol@unilim.fr}

\author{\firstname{Jacky} \lastname{Vedrenne}\CDRorcid{0000-0001-9313-4283}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[J. Vedrenne]{jacky.vedrenne@inrae.fr}

\author{\firstname{Vincent} \lastname{Bertrin}\CDRorcid{0000-0002-3888-8282}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\email[V. Bertrin]{vincent.bertrin@inrae.fr}

\author{\firstname{Nicolas} \lastname{Creusot}\CDRorcid{0000-0002-4240-1529}}
\addressSameAs{1}{INRAE, UR EABX, F-33612 Cestas, France}
\address{Plateforme Bordeaux M\'{e}tabolome, France}
\email[N. Creusot]{nicolas.creusot@inrae.fr}

\shortrunauthors

\keywords{\kwd{\textit{Lagarosiphon major}}
\kwd{\textit{M. aeruginosa}}
\kwd{Bioactive compounds}
\kwd{Metabolomics}
\kwd{Photosynthesis}}

\altkeywords{\kwd{\textit{Lagarosiphon major}}
\kwd{\textit{M. aeruginosa}}
\kwd{Mol\'{e}cules bioactives}
\kwd{M\'{e}tabolomique}
\kwd{Photosynth\`{e}se}}

\begin{abstract}
Over the last decades, Aquitaine lakes have been exposed to growing
biomasses of invasive macrophytes, in particular \textit{Lagarosiphon
major}, and to frequent blooms of the cyanobacteria \textit{Microcystis
aeruginosa}. Our aim was to explore the potential role of \textit{L.
major} in the regulation of such blooms, through allelopathic
interactions. Experiments consisted of exponential growth phase cells
incubated for 96 h in \textit{L. major} extracts at increasing
concentrations. Our approach was based on untargeted metabolomics and
photosynthetic activity measurements. 

We demonstrated the anticyanobacterial properties of \textit{L. major}
extracts. In particular, we reported that \textit{L. major} extracts
inhibited \textit{M. aeruginosa} photosynthesis, and affected the
production of various metabolite classes and pathways mainly involved
in the secondary metabolism of the cyanobacteria. 

We further provided clear evidence about the existence of metabolite
classes in \textit{L. major} that may be involved in allelopathic
interactions with \textit{M. aeruginosa}, such as caffeic acid. 
\end{abstract}

\begin{altabstract}
Au cours des derni\`{e}res d\'{e}cennies, les lacs aquitains ont
\'{e}t\'{e} expos\'{e}s \`{a} un d\'{e}veloppement croissant de
macrophytes invasives, en particulier \textit{Lagarosiphon major}, et
\`{a} de fr\'{e}quentes prolif\'{e}rations de cyanobact\'{e}ries 
(\textit{Microcystis aeruginosa}). Notre objectif dans cette \'{e}tude
est d'explorer le r\^{o}le potentiel de \textit{L. major} dans la
r\'{e}gulation de ces prolif\'{e}rations, via la synth\`{e}se de
compos\'{e}s bioactifs. Les exp\'{e}riences ont consist\'{e} \`{a}
incuber pendant 96 h des cultures de cyanobact\'{e}ries en phase de
croissance exponentielle avec des extraits de \textit{L. major} \`{a}
diff\'{e}rentes concentrations. Notre approche se base sur la
combinaison de m\'{e}tabolomique non cibl\'{e}e et de mesures
d'activit\'{e} photosynth\'{e}tique. 

Nos r\'{e}sultats mettent en \'{e}vidence les propri\'{e}t\'{e}s
anti-cyanobact\'{e}ries des extraits de \textit{L. major}. En
particulier, nous d\'{e}montrons que les extraits de \textit{L. major}
inhibent la photosynth\`{e}se de \textit{M. aeruginosa} et affectent la
production de diverses classes de metabolites, ainsi que certains
pathways impliqu\'{e}s dans le m\'{e}tabolisme secondaire des
cyanobact\'{e}ries. 

Enfin, nous apportons la preuve de de l'existence chez \textit{L.
major} de classes de m\'{e}tabolites qui pourraient \^{e}tre
impliqu\'{e}s dans des interactionsde type all\'{e}lopathique avec
\textit{M. aeruginosa}.
\end{altabstract}

\maketitle

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\section{Introduction}\label{sec1}
In a global change context, the invasiveness of some macrophyte
species now causes acute environmental problems in lakes around the 
world~\cite{Koyamaetal2014}. These species disrupt benthic and pelagic
nutrient cycles by producing large amounts of 
biomass~\cite{Urbanetal2009}, and participate in local biodiversity 
decline~\cite{Mollotetal2017}. Thousands of tons of these plants must
be removed from lakes each year, with heavy harvesting costs. 

Invasive macrophytes show high growth rates as well as a very efficient
capacity for vegetative propagation and 
dispersal~\cite{MartinCoetzee2014}, giving them clear advantages over
other hydrophytes. These plants are also known to affect phytoplankton
biomass by altering light regimes and nutrient 
concentrations~\cite{VanDonkvandeBund2002}. In addition, allelopathy
seems to be a particularly effective mechanism to control 
the development of primary producers. 
Indeed, recently, an increasing number of
studies have stated that invasive macrophytes synthesize bioactive
compounds able to allelopathically affect epiphytic  communities
(bacteria, microalgae, and microfauna), phytoplankton, other plants,
and herbivores to ensure their competitive 
success~\cite{Shaoetal2005,Mohamed2017}. Allelochemicals produced
by aquatic vascular plants could even be more effective against
phytoplankton compared to competition for light and 
nutrients~\cite{VanDonkvandeBund2002}. In particular, phenols include
several toxic molecules such as the family of 
tannins~\cite{Gross2003,BarbehennConstabel2011,Santonjaetal2018}.
According  to~\cite{HiltGross2008}, cyanobacteria are the most
sensitive algal group to bioactive compounds from macrophytes. Recent
studies acknowledge this observation stating that prokaryotic algae are
more sensitive to allelochemicals compared to eukaryotic 
algae~\cite{Zhuetal2021}.

In a context of increasing eutrophication of aquatic ecosystems, the
genera of cyanobacteria that form harmful blooms are causing growing
environmental and health  hazards~\cite{Sukeniketal2015} due to the
production of cyanotoxins. Different approaches based on synthetic
chemicals like copper sulfate have been developed to control such 
blooms~\cite{Greenfieldetal2014}, however, to reduce collateral damage
to ecosystems, environmentally friendly approaches are now needed. In
this context, allelopathically active compounds from aquatic vascular
plants may offer an interesting option for effective biocontrol of
cyanobacteria growth. For instance, different physiological and
biochemical processes in cyanobacteria are affected by macrophyte 
extracts~\cite{Fink2007,Lietal2023}: disturbance of the membrane
integrity, oxidative stress, inhibition of extracellular enzyme
activity, inhibition of photosynthesis, and respiration.

The techniques used to study allelopathy between cyanobacteria and
macrophytes are diverse. Most often, cyanobacteria are cultured in a
mineral medium, and macrophyte extracts are added at different
concentrations around 1~g/L with exposure times varying between 3 and
10  days~\cite{Gross2003,Mohamed2017,HuHong2008}. Macrophytes
and cyanobacteria can sometimes be 
co-cultured~\cite{HuHong2008,Zhangetal2014a,
Nakaietal1999}.
Cyanobacteria 
growth is usually monitored to account for the potential
impact of macrophyte extracts. Direct methods such as determination
of cell number, dry weight, or 
biomass~\cite{Krugeretal2012,IslamBeardall2017}, are frequently used,
as are indirect methods: optical  density~\cite{Bouchammaetal2004},
pigment  concentrations~\cite{Chenetal2016}. Cyanobacteria's
photosynthetic capacity is also commonly  monitored
\cite{Wuetal2013},
as well as their toxicity by enzyme-linked immunosorbent 
assays~\cite{Chenetal2017,Geadaetal2017}. 

Despite their relevance, these methods show limitations in terms of
precocity and precision of the response measured to different
concentrations of bioactive  molecules
\cite{Zhangetal2014b}.
To tackle
this issue, it appears in recent literature that metabolomic approaches
may show a higher sensitivity in identifying allelopathic interactions,
particularly between cyanobacteria and 
macrophytes
\cite{Lietal2010, Lietal2023}. Metabolomics make it possible to
investigate tenuous and early molecular responses as they precede
changes in metabolism and  growth
\cite{Segersetal2019}.
Emerging in
the 1990s from technical advances in analytical chemistry, metabolomics
is defined as the study of all the metabolites of an  organism (i.e.
metabolome) at a given time and in a given 
context \cite{Fiehn2002, CochereauJunot2013}.
As metabolites are small
organic molecules involved in metabolic processes, the metabolome
represents what is produced from the functional expression of genes
under specific environmental  conditions (i.e. molecular 
phenotype)
\cite{Ferreiraetal2021}.
Globally, primary metabolites are
involved in growth or reproduction, whereas secondary  (or specialized)
metabolites are involved in allelopathic 
interactions~\cite{ErbKliebenstein2020}. Different techniques are used to
implement metabolomic analyses such as nuclear magnetic spectrometry
(NMR) or high-resolution mass spectrometry (HRMS) coupled with
separation  techniques such as liquid chromatography (LC) or 
gas  chromatography (GC). Regarding, allelopathic
interactions in aquatic
ecosystems, the untargeted approach based on LC-HRMS/MS is \mbox{currently}
the most widespread method since it both allows to determine the
molecular/biochemical response to the stress and to identify the
secondary metabolites  (bioactive compounds) potentially 
involved
\cite{Breitlingetal2006,
Lietal2023}. To this end, such a
method is often combined with bioguided fractionation in order to
direct the identification toward the active 
metabolites~\cite{Vieiraetal2016}. \looseness=-1

The Aquitaine coastline lakes share an exceptional natural and cultural
heritage, including many endemic species of plants and animals, all of
great ecological value. However, over the last decades, these lakes have
been exposed to growing biomasses of invasive macrophytes, in
particular \textit{Lagarosiphon  major} (\textit{L. major}), and to
frequent cyanobacteria  blooms (\textit{Microcystis aeruginosa}). Both
affect crucial ecological functions and disturb human health and 
activities~\cite{VillamagnaMurphy2010}, with potential consequences on
the regional economy. To date, the potential presence of natural
bioactive compounds in \textit{L. major} remains unknown. In this
context, considering the phytomass this species produces in our region,
we planned to explore in \textit{L. major} the potential existence of
bioactive compounds able to control cyanobacterial blooms. The
detection of such bioactive compounds may thus constitute a promising
and environmentally friendly solution to solve the twofold problem that
Aquitaine lakes must face: how to control \textit{Microcystis 
aeruginosa} (\textit{M. aeruginosa}) blooms and how to valorize the
\textit{L. major} biomasses harvested each year? To this end,
\textit{M. aeruginosa} cultures were exposed to increasing
concentrations of \textit{L. major} extracts under controlled conditions.
The potential responses of cyanobacteria were characterized by a
combined approach based on untargeted metabolomics and photosynthetic
activity measurements.

\section{Material and methods}\label{sec2}

\subsection{Reagents}\label{sec2.1}
Absolute ethanol for macrophyte extraction was purchased from Xilab. All
solvents used for  metabolomics (i.e. metabolome extraction and
UPLC-TOF analyses) were purchased from Biosolve (Dieuze, France). This
included methanol (MeOH, UPLC grade),  methylbutylether (MTBE, UPLC
grade), ultrapure water (UPW, UHPLC grade),  acetonitrile (ACN, UHPLC
grade), isopropanol (ISO, UHPLC grade), and formic  acid (purity: 99\%,
Biosolve) (see Supplementary Material for accurate composition of the
eluent). 

\subsection{Sampling and extraction of macrophytes}\label{sec2.2} 
Plants (\textit{Lagarosiphon major}) were collected in spring 2018 in
Parentis Lake (southwest France). Leaves and stems were washed to
remove benthic invertebrates and biofilm, then dried at  70~\textdegree
C until a stable weight was reached. Dried plants were ground to  10~mm
(RETSCH SM 300).

Extraction was realized in a thermoregulated reactor with 66\% ethanol
as solvent in distilled water and 10\% (w/v) total solid. The mixture
was agitated for four hours at  40~\textdegree C. The liquid extract
and the solid residue were separated under pressure through a 
$40~\upmu\mathrm{m}$ filter. The liquid extract was clarified through a
$0.4~\upmu\mathrm{m}$ filter and concentrated 3 times under 
vacuum, to reach  $300~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ (dried plants
equivalent) as a final concentration in distilled water.

\subsection{Cyanobacteria culture}\label{sec2.3}
\textit{L. major} extracts were tested for anticyanobacterial activity
using toxic \textit{M. aeruginosa} non-axenic strains obtained from
the Museum National d'Histoire
Naturelle de Paris (France)
(\textit{Microcystis aeruginosa}
MNHN-PMC-2010-679). 

Prior to exposure to \textit{L. major} extract, \textit{M. aeruginosa}
strains were mass-cultured in 100~ml glass flasks in a sterilized BG-11
medium (2\% of concentrated Sigma-Aldrich Cyanobacteria BG-11
freshwater solution 50x in deionized water). Cultures were grown at 
25~\textdegree C, 
$18~\upmu\mathrm{mol}{\cdot}\mathrm{s}^{-1}{\cdot}\mathrm{m}^{-2}$ PAR
and with a 16~h: 8~h light:dark photoperiod. 

\subsection{Design of the experiment}\label{sec2.4}
The inoculum of strains in experiments consisted of exponential growth
phase cells at a density of 
$3\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1}$. Twenty-five ml
were inoculated in 100~ml glass flasks and incubated for 96~h with 
25~ml of \textit{L. major} extracts according to the following
concentration range (dry weight): 
$10~\mathrm{g}{\cdot}\mathrm{l}^{-1}$, 
$3.3~\mathrm{g}{\cdot}\mathrm{l}^{-1}$, 
$1.1~\mathrm{g}{\cdot}\mathrm{l}^{-1}$, 
$0.4~\mathrm{g}{\cdot}\mathrm{l}^{-1}$, 
$0.1~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ and a control group with 25 ml
of BG11 with the same content of distilled water (3.3\%). All 
experiments were carried out in four replicates. Flasks were covered
with a gas-permeable foam stopper to avoid contamination and placed in
the same conditions of light and temperature as above. Every day flasks
were manually shaken and positions were randomly changed to ensure the
same incubation conditions for each. Cell densities were determined for
each condition after 96 h of\break exposure.

In order to focus on the response directly attributable to
cyanobacteria, excluding any artifact due to \textit{L. major}
extracts, we tracked the temporal changes of the latter over 96 h. This
step was then implemented to characterize the photosynthetic activity
and the metabolites contained in macrophyte extracts as a function
of time. The experiment was carried out in four replicates under the same
conditions as above, considering only two concentrations (3.3 and 
$0.4~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ of extract). 

\subsection{Chlorophyll {\mbox{\rm a}} content, photosynthetic\newline efficiency
measurements, and associated\newline statistics}\label{sec2.5}
Light adaptation of the strains was carried out for 15 min at 
25~\textdegree C prior to the measurements that were all performed
within  2~h. Total chlorophyll $a$ (Chl $a$) related to cyanobacteria
was determined by multi-wavelength pulse-amplitude-modulated
fluorometry on a Phyto-PAM  system~\cite{Schreiber2004} immediately
after inoculation (T0) and after 24, 48, 72, and 96~h of exposition.
The automatic Zero-offset function (Zoff) previously suppressed the
potential background signal, measuring the filtrate of a blank sample
(BG11). Temperature  (25~\textdegree C) and distance between the
light-emitting diode and samples (8~mm) were kept constant for all 
measurements. Photosynthetic efficiency (yield) was estimated using the
saturation pulse method to determine photosystem II (PSII) quantum
yield (photosynthetic efficiency, Yeff) at 24~h, 48~h, 72~h, and 96~h.
Effective PSII quantum yield is defined as a measure of the
photosynthetic efficiency of the  community~\cite{Gentyetal1989}. 

Single-factor ANOVA statistical tests were performed using RStudio
version 4.0.3 to account for potential significant differences between
photosynthetic activities at different extract concentrations after
96~h. A post hoc Tuckey test was performed at 96~h to compare modalities
two by two. 

\subsection{Untargeted metabolomics}\label{sec2.6}

\subsubsection{Sampling and sample preparation} 
After four days of exposure, 40~mL of each replicate were quenched in
liquid nitrogen and stored at ${-}80~\text{\textdegree}\mathrm{C}$. The
samples were slowly defrosted at  4~\textdegree C overnight prior to
their  centrifugation (15 min, 3500 RPM) on ice, allowing to separate
the  exo-metabolome (i.e. culture medium) and the endo-metabolome
(cyanobacteria). Both were stored at 
${-}80~\text{\textdegree}\mathrm{C}$ prior to freeze drying and
further storage at ${-}80~\text{\textdegree}\mathrm{C}$. The extraction
was performed only on the endometabolome samples through a biphasic
extraction adapted  from Giavalisco \etal~\cite{Giavaliscoetal2011} and
Mazzella \etal~\cite{Mazzellaetal2023} (brief description in 
Supplementary Material, Section~1.1). 

\subsubsection{UPLC-HRMS/MS analyses and data processing} 
As a first step, all extracts (i.e. cyanobacteria endometabolome and
macrophyte extracts) were analyzed on a UPLC-ToF  system
(Ultra-Performance Liquid  Chromatography---Time of Flight Mass
Spectrometry, Xevo G2-S ToF, Waters). Following the injection of 
$10~\upmu\mathrm{L}$, the chromatographic separation was performed at 
40~\textdegree C on an HSS-T3  column ($150\times 2.1~\mathrm{mm}\times
1.7~\upmu$, Waters) connected to an HSS-T3 security guard cartridge
$(10\times 2.1~\mathrm{mm})$ by using two different gradients according
to the analysis of the hydrophilic or the lipophilic fraction,
respectively  (Supplementary Table~S1). HRMS data acquisition was
performed in MSe centroid mode after electrospray ionization operated
in positive (ESI${+}$) or negative (ESI${-}$) mode  (Supplementary
Table~S2). Briefly, MSe mode consisted in the acquisition of all ion
fragmentations including full scans (50--1500 $m/z$) at both low (6~eV)
and high (10--40~eV ramp) collision energy with a scan time of 0.2~s.
Lockspray  solution (Leucine Enkephalin, Waters) was infused every 60~s
at  $10~\upmu\mathrm{L}/\mathrm{min}$ in parallel with the sample to
correct mass drift during the  acquisition (Lockmass: 556.2771 in
ESI${+}$ and 554.2615 in ESI${-}$). Data processing (i.e. preprocessing and
further filtration) is described in Supplementary 
Material\unskip\break (Section~1.2.3).

Since the reliability of MSe mass spectra with available MS2 libraries
was  low (see below), a limited number of relevant samples (i.e.
extracts of cyanobacteria from only two conditions: control and exposed
to 10~g/L) were re-injected on UPLC-Qexactive+  instrument
(ThermoScientific) in order to acquire actual MS2 allowing to improve
the annotation of metabolites and associated pathways. The
chromatographic conditions were similar to those of UPLC-TOF 
(Supplementary Table~S1). The mass spectrometer was operated in DDA
top5 acquisition mode consisting of a full scan (75--1500 Da) followed
by MS2 acquisition of the five most abundant peaks for each scan.
Source and acquisition parameters are detailed in Supplementary
Material (Supplementary Table~S3). Data were directly processed
with \href{http://prime.psc.riken.jp/compms/msdial/main.html}{MS-DIAL}.

\subsubsection{Chemometric analyses and aggregated\newline metabolomic 
dose--responses}
The metabolomic datasets were analyzed using the statistical
analysis tool from
\href{https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml}{MetaboAnalyst 5.0} 
and
\href{https://lbbe.univ-lyon1.fr/fr/dromics}{DROMICS}~\cite{Larrasetal2018}. 

Prior to the chemometric analyses in MetaboAnalyst, data were processed
using the interquartile range (IQR) in order to keep only the 5000 more
significant features. Then, data were normalized (sample's median),
transformed (cube root of variable values), and scaled (variable value
pareto) to obtain homoscedasticity. For the endometabolome datasets,
unsupervised (PCA, HCA) and supervised (PLS-DA) multivariate analyses
were implemented to get a comprehensive picture of the metabolomic
response and to test if the metabolomic fingerprints differed between
the tested concentrations. Such analyses were performed for all the
datasets, individually. 

To take advantage of the serial dilution-based screening strategy, 
DROMICS~\cite{Larrasetal2018} was used in order to provide aggregated
metabolomic dose--responses, as the empirical cumulative distribution
frequency (ECDF) of the benchmark dose 1SD (BMD\tsub{1\mathrm{SD}}) of
each  feature (see Supplementary Section~1.2.3). To do so, the
entire dataset (i.e. without IQR filtration) was first normalized,
transformed, and scaled. In DROMICS, the features were first filtered
according to their  responsiveness (i.e. Quadratic trend test, FDR $=$
0.01) and then categorized according to their  trends (increase,
decrease, U-shape, Bell-shape) with increasing concentration of the
extract  (see  Supplementary Section~1.2.3). The responsive
features fitting with the proposed quadratic models were selected for
annotation.

\subsubsection{Metabolite annotation}
Metabolite annotation was performed using MS-DIAL combined with
MSClean-R, allowing the multiplexing of positive and negative data and
the clustering of adducts, the use of an 
{in-house}
MS2  library (kind gift from G. Marty), and the further implementation of  software
(\href{http://prime.psc.riken.jp/compms/msfinder/main.html}{MS-Finder},
\href{https://bio.informatik.uni-jena.de/2022/05/sirius-5-is-released/}{SIRIUS 5})
for {in silico} machine learning and molecular networking 
(Supplementary Table~S6 and  Supplementary Figure~S1 and~S2). To this
end, only a part of the HRMS-TOF raw data was processed including those
from the cyanobacteria endometabolome (control and at 3.3~g/L
conditions) and the macrophyte extract (3.3~g/L). 

Briefly, the datasets were processed with the suspect list from DROMICS
and an 
{in-house}
database covering central metabolism. For both
processes, the signals were cleaned using MS-CleanR and further
annotated using MS-Finder and SIRIUS. In both softwares, several 
databases (\href{https://pubchem.ncbi.nlm.nih.gov/}{Puchem},
\href{https://coconut.naturalproducts.net/}{COCONUT},
\href{https://hmdb.ca/}{HMDB},
\href{https://www.ebi.ac.uk/chebi/}{CheBi,}
\href{https://biocyc.org/}{Biocyc}, etc{\ldots}) were explored from
{in silico} fragmentation and machine-learning, based on the
simplified molecular-input line-entry system (SMILES). In particular,
Sirius was used to take advantage of its machine-learning algorithm to
compare our experimental MS2 fragments with the predicted ones. 

Since the match between TOF spectral data and the 
{in-house}
library was low (data not shown), some samples were analyzed on an
UPLC-Qex+ to obtain more accurate MS2, allowing to improve the number of
matches with MS2 spectra from experimental acquisition or {in
silico} and machine learning tools. These HRMS data were directly
processed using MS-DIAL combined with MS-cleanR, MS-Finder, and SIRIUS.
Details on processing parameters are provided in Supplementary
Material  (Supplementary Table~S5,  Supplementary Figures~S1 and~S2). 

In the best case, the metabolites were putatively annotated at 
Level 2a (i.e. probable structure due to library MS2 spectrum match)
while most of them were at L3 (tentative candidates related to match
{in silico} or machine learning based fragmentation) according to
the confidence scale  of Schymanski  \etal~\cite{Schymanskietal2014}.
Also, SIRIUS allowed annotation at the pathway and superclass
levels based on a neural network  algorithm (Natural Products
Classifier, \cite{Kimetal2021}. 
The putative annotations
of all the  datasets (Hydro ESI${+}/{-}$ from both LC-TOF/LC-Qex, Lipo
ESI${+}/{-}$ from LC-TOF) were finally merged in a single sheet and further
filtered to obtain a list of putative metabolites, pathways and
superclasses ($\Delta$ppm $<$ 10; ZODIAC score  $>$ 0.4; Single
putative SMILE from SIRIUS). 

\subsection{Chemical characterization of \textit{L. major} raw
extracts}\label{sec2.7} 
UPLC-HRMS/MS analyses (TOF and QExactive+) were performed on macrophyte
extracts as described above  (i.e. only the hydrophilic gradient). The
dataset (ESI${+}/{-}$) was processed directly in MS-Dial combined with
MS-Clean-R, MS-Finder, and SIRIUS as described above for the
endometabolome. 

\section{Results}\label{sec3} 

\subsection{Photosynthetic activity of \textit{M. aeruginosa}\newline
strains}\label{sec3.1}
The average Chl $a$ contents in controls were about 
$900~\upmu\mathrm{g}/\mathrm{l}$. No clear impact of extracts was
observed on Chl $a$ whatever the concentration 
considered\unskip\break 
(Figure~\ref{fig1}).

\begin{figure*}
\includegraphics{fig01}
\caption{\label{fig1}Impact of \textit{L. major} extracts on \textit{M.
aeruginosa} strains: Chl $a$ contents
ns: non significant difference with the control;  *: significant
difference $(p<0.05)$.}
{\vspace*{3pt}}
\end{figure*}

Yield values for controls were about 0.40. The photosynthetic yield
value of \textit{M. aeruginosa} was significantly reduced with the
10~g/l extract (Figure~\ref{fig2}). 

\begin{figure*}
\includegraphics{fig02}
\caption{\label{fig2}Impact of \textit{L. major} extracts on 
\textit{M. aeruginosa} strains: photosynthetic yield values.}
\end{figure*}

After 96~h of exposure, cell densities for the different conditions
were not significantly different from the initial one
$(3\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1})$: 
$2.7\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1}$ for 
$10~\mathrm{g}{\cdot}\mathrm{l}^{-1}$; 
$2.8\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1}$ for 
$3.3~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ and 
$1.1~\mathrm{g}{\cdot}\mathrm{l}^{-1}$; 
$2.7\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1}$ for 
$0.4~\mathrm{g}{\cdot}\mathrm{l}^{-1}$; 
$2.9\times10^{-5}~\mathrm{cells}{\cdot}\mathrm{ml}^{-1}$ for 
$0.1~\mathrm{g}{\cdot}\mathrm{l}^{-1}$.\looseness=-1

\subsection{Metabolomic response of \textit{Microcystis\newline aeruginosa}
exposed to \textit{Lagarosiphon major}\newline extracts}\label{sec3.2}

\subsubsection{Metabolomic fingerprint and associated aggregated 
dose--response}
Results from exploratory PCA and HCA performed on each dataset (i.e.
Hydro-ESI${+}/{-}$, Lipo-ESI${+}/{-}$) revealed that the metabolomic
fingerprints differed between the different concentrations 
tested\unskip\break
(Supplementary Figure~S3). In particular, HCA and associated heatmaps
revealed how the \mbox{highest} \mbox{concentrations} (10 and 3.3~g/L), clustered
together, triggered a shift in the metabolome of \textit{Microcystis
aeruginosa} (Supplementary Figure~S3). Hence, HCA results revealed two
main metabolite clusters in all datasets: one with high intensities at
the highest concentrations and low intensities for the control, and
another cluster with opposite patterns. Further PLS-DA results
confirmed for all datasets the clear discrimination between the
different conditions tested, showing significant (high R$^{2}$ and
Q$^{2}$ values) and distinct distributions along component 1. This axis
explained 34.7\% (HydroESI${+}$) and 71.6\% (LipoESI${-}$) of the total
covariance  (Figure~\ref{fig3}). 

\begin{figure*}
{\vspace*{4pt}}
\includegraphics{fig03}
{\vspace*{4pt}}
\caption{\label{fig3}PLS-DA scores plots of the metabolome hydrophilic 
(a, ESI${+}$; b, ESI${-}$) and lipophilic fractions  (c, ESI${+}$; d, ESI${-}$)
of \textit{M. aeruginosa} strain.}
{\vspace*{4pt}}
\end{figure*}

The further implementation of DROMICS \mbox{allowed} us to categorize the
features according to their quadratic trends  (i.e. Increase, Decrease,
U-shape, Bell) and to provide an aggregated response (ECDF) according
to their sensitivity to the extract  (i.e. BMD\tsub{1\mathrm{SD}}),
revealing interesting patterns for each dataset  (Figure~\ref{fig4} and
Supplementary Figure~S4). First, the full aggregated ECDF showed that
50 to 75\% of the metabolome responded at concentrations higher than
1~g/L of 
\textit{Lagarosiphon major} extract  (Figure~\ref{fig4}).
Then, for all datasets, increasing and decreasing trends were the most
represented  (${>}$60\% of total signal), while increasing features
were more frequent than decreasing ones except for the lipophilic
ESI${-}$  (Supplementary Figure~S4). Nevertheless, the high number of
decreasing features for all the fractions  (938 for Hydro-ESI${+}$, 1597
for Hydro-ESI${-}$, 3410 for Lipo-ESI${+}$, 2493 for Lipo-ESI${-}$)
highlighted the potential inhibition of the biosynthesis of hundreds of
metabolites  (Supplementary Figure~S4). Nevertheless, it is important
to notice that most of these features are likely shared between
ESI${+}$/ESI${-}$. In the same way, the analytical redundancy between
various adducts corresponding to the same metabolites was not yet
filtered at this level. Thus, the high number of features following a
quadratic trend should be considered
carefully. 

\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Empirical cumulative distribution function (ECDF)
of the BMD\tsub{1\mathrm{SD}} values from the metabolome in the
hydrophilic (a, ESI${+}$; b, ESI${-}$) and lipophilic (c, ESI${+}$; d,
ESI${-}$) fractions of \textit{M. aeruginosa} strain. In the
distribution, one point is one feature. The redcolor means
upregulation, while in blue is downregulation.  The numbers in the top
left are the number of features for each conditions (i.e. the number of
point in the ECDF).}
\end{figure*}

\subsubsection{Candidate pathways and associated\newline metabolites} 
By processing both LC-TOF and LC-Qexactive HRMS data in MS-Dial
combined with MS-cleanR and further MS-Finder and SIRIUS annotation
tools, some putative metabolites and associated pathways have been
preliminary annotated. Overall, in case of a poor match between our
spectra and the 
{\mbox{in-house}}
library, MS-Finder and mostly Sirius
proposed a putative formula and structure  (Smile, InchI) for 588 signals
in the hydrophilic phase, and for 274 signals in the lipophilic phase
following a significant quadratic trend (Inc, Dec, U, Bell) or showing
significant up/down-regulation (see Supplementary\unskip\break
Table~S6 and S7). 

Figure~\ref{fig5} summarizes the contribution of these metabolites to
different NPC  pathways~\cite{Kimetal2021}. The most
represented pathways were the amino acids and peptides (37.1\%)
followed by the alkaloids (22\%), the fatty acids (21.2\%), and the
terpenoids (11.0\%). For these pathways, the proportion of various
trends was quite similar: almost 30\% of increasing, 30\% of
decreasing, and 30\% of metabolites rather following U-shaped or bell
trends. 

\begin{figure*}
\includegraphics{fig05}
\caption{\label{fig5}Distribution of the putative metabolites among the
NPC pathways in both hydrophilic and lipophilic phases and associated
trends according DROMICS. Inc $=$ increasing, Dec $=$ decreasing.}
\end{figure*}

Figure~\ref{fig6}a reports 
the distribution of BMD\tsub{1\mathrm{SD}}
according to the different pathways that revealed quite similar
sensitivity. Indeed, for all the pathways considered, most of the
putative metabolites responded significantly for exposure
concentrations below 1.25~g${\cdot}$l$^{-1}$ 
of macrophyte extract. Looking at the
fold change of the putative metabolites in these various pathways
showed some discrepancies  (Figure~\ref{fig6}b). Indeed, the classes of
polyketides and fatty acids were significantly downregulated (i.e.
median $<$ 0) in comparison to the other  classes (Kruskal--Wallis,
Dunn post hoc,  $p<0.01$). The others were mainly upregulated (median
$>$ 0) although many amino acids and peptides (12\% of the total) and
alkaloids (7.3\% of the total) were still significantly downregulated,
as reported in  Figure~\ref{fig5}. 

\begin{figure*}
\includegraphics{fig06}
\caption{\label{fig6}Distribution of BMD\tsub{1\mathrm{SD}} values (a)~and
significant fold change (b)~of putative metabolites from both
hydrophilic and lipophilic phases of \textit{M. Aeruginosa}
endometabolome according to NPC classes. \textit{BMD}\tsub{1\mathrm{SD}} are
expressed in g/L (a). Significant fold changes were from ANOVA $t$-test
($p$ value $<$ 0.01) between the control and 3.3~g/L 
conditions (b). In b, significative differences in the distribution of
log2fold between the classes are noted ``u'' and ``d'' according
to Kruskal--Wallis and post hoc Dunn's tests, respectively  $(p<0.01)$.}
\end{figure*}

At the superclass level, the oligopeptides and the small peptides were
the most abundant (26 and 17\% among the total annotated metabolites,
respectively)  (Supplementary Figure~S5). Concerning the small peptides
superclass, numerous features up/down regulated were attributed to the
di/tri-peptides class while two amino acids (i.e. L-methionine,
N-methylthreonine) and one alkaloid (tetrandrine) were putatively
annotated (L2a) according to experimental MS2 fragmentation spectra 
(Supplementary Table~S6). Other ``abundant'' superclasses significantly
up/dow-regulated were the sphingolipids (6\%), triterpenoids (5\%),
tryptophan alkaloids (5\%), glycerophospholipids (4\%), and
eicosanoids (4\%), mainly represented by the prostaglandins class 
(Supplementary Figure~S5 and~S6). Overall, our results revealed that
268 putatively annotated metabolites at L3 belonging to various
superclasses/classes from amino acids and peptides (125), fatty acids
(79), alkaloids (26), polyketides (15), terpenoids (10), carbohydrates
(8), shikimate and phenylpropanoids (2) pathways (NPC class probability
$>$ 0.5) followed a decreasing, U-shape or bell trends associated to
the exposure to increasing concentration of \textit{L. major} extract 
(Supplementary\break Table~S6). 

\subsection{Biomolecules in \textit{L. Major} extracts}\label{sec3.3}
UPLC-HRMS-analyses provided a detailed picture of the chemical
landscape (even if not totally holistic according to the ethanolic
extraction) of \textit{Lagarosiphon major} extracts. Hence, almost 2201
features were putatively annotated at the pathway level  (probability
$>$ 0.5,  Supplementary Table~S8). Most of these metabolites belonged
to the terpenoids (23\%), fatty acids (22\%), alkaloids (18\%), amino
acids and peptides (17\%) pathways  (Figure~\ref{fig7}). 

\begin{figure*}
\vspace*{-2pt}
\includegraphics{fig07}
\vspace*{-2pt}
\caption{\label{fig7}Distribution in the various NPC\_pathways of the
putative annotated metabolites in \textit{L. major} extract. (Zodiac
score $>$ 0.5, NPC probability $>$ 0.5,  $n=2179$).}
\vspace*{-10pt}
\end{figure*}

At the superclass level, 1538 features were putatively annotated
(probability $>$ 0.5,  Supplementary Table~S8). The small peptides
(12\%), the oligopeptides (8\%), the triterpenoids (7\%), the fatty
acids and conjugates (7\%), and the sesquiterpenoids, the tryptophan
alkaloids and the steroids (all at 5\%) were the more abundant while
the flavonoids represented less than 1\%  (Supplementary Figure~S7). At
the class level, the cyclic peptides (6\%), the tripeptides (5\%), the
amino acids (5\%), the dipeptides (4\%), the hydroxy-fatty acids (4\%)
and other octadecanoids (3\%) encompassed 27\% of the total annotated
metabolites  (Supplementary Figure~S8). The other main classes (i.e.
higher than 2\% of the total) were the cinnamic acids and derivatives,
the oleanane triterpenoids, the open-chain polyketides, the
prostaglandins, pyridine alkaloids, the glycerophosphoethannolamides,
the dicarboxylic acids, the phenolic acids, limonoids, the
n-acylamines and the unsaturated fatty acids, 
accounting for 23\% of the\break total. 

Among all the putatively annotated pathways according to
NPC-classifier
\cite{Kimetal2021}, a few features matched our
{in-house}
experimental MS2 library. Among others, p-coumarate,
caffeic acid, and saikosaponin A were putatively annotated at L2a
level  (Table~\ref{tab1},\break  Supplementary Figure~S9--S11). Thanks to
MS-Finder and SIRIUS, additional structures were found including
triterpene  saponins (e.g. spinasaponin A, acutoside A; lucyoside M), 
flavonoids (Quercetin 3-galactoside, selaginellin M, Luteolin
7-O-beta-D-diglucuronide) or phenolic  acids (vanillic acid, salicylic
acid glucoside) and phenylpropanoids (caffeic acid), all annotated at
the level 3  (Table~\ref{tab1}).

\onecolumngrid
\pagebreak

\begin{sidewaystable}
\caption{\label{tab1}List of putatively annotated metabolites in 
\textit{L. major} extracts}
\fontsize{5.5}{6.6}\selectfont
\tabcolsep=3pt
\begin{tabular}{ccccccccccccccccc}
\thead
$m/z$ & Formula & Rt & adduct & $\Delta m/z$ (Da) & 
\parbox[t]{.5cm}{\centering{Sirius score}} & 
\parbox[t]{.5cm}{\centering{Zodiac score}} & 
Putative name & InChIKey & 
\parbox[t]{1cm}{\centering{CSI.FingerID score}} & 
\parbox[t]{1cm}{\centering{Confidence score}} & xlogp & 
\parbox[t]{.8cm}{\centering{MS2 exp. match}} & 
\parbox[t]{1.8cm}{\centering{NPC.superclass}} & CHEBI & 
\parbox[t]{1cm}{\centering{PUBCHEM CID}} & 
\parbox[t]{1cm}{\centering{Confidence level}}
\vspace*{2pt}\\
\endthead 
465.1012 & 
C$_{21}$H$_{20}$O$_{12}$ & 
\08.993 & 
[M${+}$H]${+}$ & 
\mn0.002\0 & 
44.9 & 
1.0 & 
\parbox[t]{1.5cm}{\centering{Quercetin 3-galactoside}}\vspace*{2pt} & 
OVSQVDMCBVZWGM-DTGCRPNFSA-N & 
\0${-}$96.7 & 
0.2 & 
\mn0.4 & 
N & 
Flavonoids & 
67486 & 
5281643 & 
L3 \\ 
167.0334 & 
C$_8$H$_8$O$_4$ & 
\07.009 & 
[M${-}$H]${-}$ & 
\mn0.002\0 & 
\07.4 & 
1.0 & 
Vanillic acid & 
WKOLLVMJNQIZCI-UHFFFAOYSA-M & 
\0${-}$43.4 & 
0.1 & 
\mn0.6 & 
N & 
\parbox[t]{1.5cm}{\centering{Phenolic acids (C6-C1)}}\vspace*{2pt} & 
16632 & 
54675858 & 
L3\\ 
299.0764 & 
C$_{13}$H$_{16}$O$_8$ & 
\06.923 & 
[M${-}$H]${-}$ & 
\mn0.001\0 & 
29.2 & 
1.0 & 
\parbox[t]{1.5cm}{\centering{Salicylic acid glucoside}}\vspace*{2pt} & 
TZPBMNKOLMSJPF-TWEVDUBQSA-N & 
\0${-}$31.5 & 
0.1 & 
${-}$0.9 & 
N & 
\parbox[t]{1.5cm}{\centering{Phenolic acids (C6-C1)}} & 
87766 & 
151505 & 
L3 \\ 
637.1025 & 
C$_{27}$H$_{26}$O$_{18}$ & 
\08.536 & 
[M${-}$H]${-}$ & 
\mn0.002\0 & 
\04.7 & 
1.0 & 
\parbox[t]{1cm}{\centering{Luteolin 7-O-beta-D-diglucuronide}}\vspace*{2pt} & 
PBBVWJQPAZYQDB-DBFWEQBMSA-L & 
${-}$156.9 & 
0.2 & 
${-}$0.6 & 
N & 
\parbox[t]{1.5cm}{\centering{Phenolic acids (C6-C1)}} & 
57815 & 
46878427 & 
L3\\ 
165.0547 & 
C$_9$H$_8$O$_3$ & 
\03.561 & 
[M${+}$H]${+}$ & 
${-}$0.0005 & 
38.8 & 
1.0 & 
$p$-Coumaricacid & 
NGSWKAQJJWESNS-UTCJRWHESA-N & 
\0${-}$19.8 & 
0.2 & 
\mn1.4 & 
\textbf{Y} & 
\parbox[t]{1.7cm}{\centering{Phenylpropanoids (C6-C3)}}\vspace*{2pt} & 
17450 & 
1549106 & 
L2a\\ 
163.0391 & 
C$_9$H$_8$O$_4$ & 
10.04\0 & 
[M${-}$H$_2$O${+}$H]${+}$ & 
\mn0.0008 & 
39.4 & 
1.0 & 
Caffeate & 
QAIPRVGONGVQAS-DUXPYHPUSA-N & 
\0${-}$13.3 & 
0.5 & 
\mn0.9 & 
\textbf{Y} & 
\parbox[t]{1.7cm}{\centering{Phenylpropanoids (C6-C3)}}\vspace*{2pt} & 
36281 & 
689043 & 
L2a \\ 
779.459\0 & 
C$_{42}$H$_{68}$O$_{13}$ & 
13.882 & 
[M${-}$H]${-}$ & 
${-}$0.0003 & 
27.8 & 
1.0 & 
Acutoside A & 
LEQCLUFJRGKLOA-WLMDKFIXSA-N & 
${-}$243.2 & 
0.1 & 
\mn2.3 & 
N & 
Diterpenoids & 
65370 & 
21606142 & 
L3 \\ 
817.4351 & 
C$_{42}$H$_{66}$O$_{14}$ & 
12.567 & 
[M${+}$Na]${+}$ & 
${-}$0.001\0 & 
44.1 & 
1.0 & 
Spinasaponin A & 
BQPYEFAVIPEQIK-CGCSGISQSA-N & 
\0${-}$54.6 & 
0.2 & 
\mn3.0 & 
N & 
Triterpenoids & 
9228 & 
441951 & 
L3 \\ 
835.4466 & 
C$_{44}$H$_{68}$O$_{15}$ & 
12.962 & 
[M${-}$H]${-}$ & 
\mn0.002\0 & 
46.8 & 
1.0 & 
Lucyoside M & 
FNSXRAISABPGKU-UHFFFAOYSA-N & 
\0${-}$95.3 & 
0.3 & 
\mn2.7 & 
N & 
Triterpenoids & 
\textbf{176325} & 
131751578 & 
L3\\ 
779.4576 & 
C$_{42}$H$_{68}$O$_{13}$ & 
13.339 & 
[M${-}$H]${-}$ & 
\mn0.001\0 & 
10.1 & 
1.0 & 
Saikosaponina & 
KYWSCMDFVARMPN-MSSMMRRTSA-N & 
${-}$102.1 & 
0.2 & 
\mn2.5 & 
\textbf{Y} & 
Triterpenoids & 
\textbf{8996} & 
167928 & 
L2a \\ 
159.1019 & 
C$_8$H$_{16}$O$_3$ & 
12.036 & 
[M${-}$H]${-}$ & 
\mn0.002\0 & 
20.8 & 
1.0 & 
\parbox[t]{1.8cm}{\centering{3-Hydroxyoctanoic acid}}\vspace*{2pt} & 
NDPLAKGOSZHTPH-UHFFFAOYSA-N & 
\0${-}$10.9 & 
0.3 & 
\mn2.0 & 
N & 
\parbox[t]{1cm}{\centering{Fatty Acids and Conjugates}} & 
37098 & 
26613 & 
L3 \\ 
295.2283 & 
C$_{18}$H$_{32}$O$_3$ & 
14.367 & 
[M${-}$H]${-}$ & 
${-}$0.0004 & 
22.7 & 
1.0 & 
Coronaric acid & 
FBUKMFOXMZRGRB-YFHOEESVSA-N & 
\0\0${-}$5.7 & 
0.1 & 
\mn5.3 & 
N & 
Octadecanoids & 
34494 & 
6246154 & 
L3 \\ 
329.2338 & 
C$_{18}$H$_{34}$O$_5$ & 
11.434 & 
[M${-}$H]${-}$ & 
${-}$0.0004 & 
31.3 & 
1.0 & 
Pinellic acid & 
MDIUMSLCYIJBQC-MVFSOIOZSA-N & 
\0${-}$25.2 & 
0.1 & 
\mn3.1 & 
N & 
Octadecanoids & 
34506 & 
9858729 & 
L3 \\ 
291.1964 & 
C$_{18}$H$_{28}$O$_3$ & 
13.74\0 & 
[M${-}$H]${-}$ & 
\mn0.0002 & 
\05.1 & 
1.0 & 
Colnelenic acid & 
OYKAXBUWOIRLGF-VMBRNALUSA-N & 
\0${-}$27.8 & 
0.1 & 
\mn4.6 & 
N & 
Octadecanoids & 
60959 & 
6441679 & 
L3 \\ 
309.2077 & 
C$_{18}$H$_{30}$O$_4$ & 
13.996 & 
[M${-}$H]${-}$ & 
${-}$0.001\0 & 
24.7 & 
1.0 & 
Octyl docosanoate & 
MWSXIWIELAIOBE-UHFFFAOYSA-N & 
\0${-}$14.5 & 
0.1 & 
\mn4.7 & 
N & 
Octadecanoids & 
80081 & 
80081 & 
L3 
\botline
\end{tabular}
\end{sidewaystable}

\twocolumngrid 

\section{Discussion and perspectives}\label{sec4}

\subsection{Molecular and physiological responses of 
\textit{M.~aeruginosa} to \textit{L. major} extracts}\label{sec4.1} 
In this study, we demonstrate the anticyanobacterial properties of
\textit{L. major} extracts. In particular, we report that \textit{L.
major} extract inhibits \textit{M. aeruginosa} photosynthesis, with a
clear drop in the cyanobacteria photosynthetic yield at a concentration
of  $10~\mathrm{g}{\cdot}\mathrm{l}^{-1}$. Photosynthesis is a key
physiological process targeted by allelopathic 
compounds~\cite{KornerNicklisch2002,Lietal2023}. Numerous studies
suggest that allelochemicals from macrophytes show photosynthesis
inhibitory properties by disrupting the electron transport chain, 
reducing pigment content, or modifying Chl $a$ 
fluorescence~\cite{Zhuetal2010}. If the active concentration of 
$10~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ may appear quite high, this is
consistent with literature that frequently reports the effects of
extracts from several grams per liter. For instance,  \textit{Chara
globularis} inhibits cyanobacteria growth at  
$2.5~\mathrm{g}{\cdot}\mathrm{l}^{-1}$~\cite{Nakaietal2008} and
\textit{Egeria densa} at  
$3~\mathrm{g}{\cdot}\mathrm{l}^{-1}$~\cite{Barbosaetal2022}.

The metabolome of \textit{M. aeruginosa} on the other hand changed from
the lowest tested concentration (0.1~mg/L). This study is the first to
characterize the aggregated dose--response of the metabolome from an
allelopathic interaction perspective. In particular, EDCF highlighted
that 25--50\% of the metabolome responded to concentrations lower than 
$1~\mathrm{g}{\cdot}\mathrm{l}^{-1}$ while the yield still did not
change. Thus, our results demonstrate, in the context of our study, the
earlier detection of the molecular responses regarding the
physiological ones, as recently reported in the 
literature~\cite{Creusotetal2022,Pino-Otinetal2019}. Such results
further underline the potential use of metabolomics as an early and 
sensitive (predictive) marker of the potential impairment of
physiological processes such as
photosynthesis. 

Moreover, this study highlights that the response of the \textit{M.
aeruginosa} endometabolome is not monotonic. Indeed, even if the
majority of the putative annotated metabolites followed increasing or
decreasing trends, many others followed a bell trend. If such
discrepancies have already been reported in microalgae or periphyton in
metabolomic and other omic\unskip\break 
approaches
\cite{Larrasetal2020,Lipsetal2022,Creusotetal2022}, this
study reports it regarding potential chemical interactions. Altogether,
such outcomes underline the need to improve the knowledge about single
metabolite or \mbox{pathway} dose--responses. Here, our data suggest that
various biosynthetic pathways in \textit{M. aeruginosa} might be
impaired by \textit{L. major} extracts  (e.g. amino acids and peptides,
alkaloids). Nevertheless, as discussed thereafter, the current
experimental design limited our ability to go further and provide
actual clues about the impact of \textit{L. major} extract on key
metabolic functions. 

\subsection{Uncertainties regarding the interpretation of metabolomic
data}\label{sec4.2}
First of all, as we subtracted from the cyanobacteria endometabolome the
signals from macrophyte extracts, it is likely that primary metabolites
occurring in both matrices have been removed from the dataset, limiting
the assessment of \textit{L. major} effects on \textit{M. aeruginosa}
metabolism. Conversely, the lipophilic phase signals of the
endometabolome were not filtered and could then be ``contaminated'' by
the macrophyte extract. Nevertheless, the hydrophilic extraction of
\textit{L. major} matrix logically limited the content of lipophilic
chemicals that could be further partitioned in the lipophilic phase of
\textit{M.~aeruginosa} endometabolome. Thus, even if it cannot be
excluded that increasing signals in the lipophilic phase were at least
partly due to \textit{L. major} extracts, many metabolite trends from
the lipophilic phase of \textit{M. aeruginosa} metabolome decreased or
followed a bell or U-shape with increasing concentration of \textit{L.
major} extract. 
\looseness=-1

Despite those limitations, our results reveal that 268 annotated
metabolites belonging to various classes, mainly from amino acids and
peptides, fatty acids, and alkaloids pathways (NPC class probability
${>}$ 0.5), follow such trends  (i.e. decreasing, U, bell).
Among amino acids and peptides, the most impacted metabolites were 
tripeptides and cyclic peptides which are both known as secondary
metabolites involved in allelopathic interactions  (e.g. antibacterial,
antifungal
activities)~\cite{Moore1996,Srivastavaetal2022,Singhetal2017}. For
example, gamma-glutamyl peptides---one of the tripeptides putatively
annotated in this study---are known to be involved in endogenous
processes dedicated to protecting cyanobacteria against environmental 
stressors (oxidative stress and xenobiotic
detoxification)
\cite{Jaiswaletal2022}.
Some other annotated
metabolites were identified as mycosporine-like amino acids, described
in the literature as {photoprotective} molecules~\cite{Singhetal2017}.
Among fatty acids, many putative metabolites were classified as
ceramides  (sphingolipids superclass) known for their role in
adaptation to biotic and abiotic stress in plants at both physiological
and structural  levels~\cite{Alietal2018,Michaelsonetal2016}. Others
were glycerophospholipids known as major constituents of the cellular
membrane, prostaglandins described as anti-inflammatory 
chemicals~\cite{DiCostanzoetal2019}, and unsaturated fatty acids
playing a role in membrane physiology, stress regulation, and with
bioactive properties (antibacterial and  antifungal)~\cite{HeDing2020}.
\looseness=-1

Altogether, our results show the clear modulation of various metabolite
classes and pathways mainly involved in the secondary metabolism of
\textit{M.~aeruginosa}, suggesting a potential impairment of defense
mechanisms against environmental predators and  competitors (fungi,
bacteria, plants). Further experiments are needed in order to 
(i)~determine if the central metabolism of \textit{M.~aeruginosa} is
also potentially impaired by \textit{L. major} metabolites and (ii)~to
identify these bioactive chemicals. To do so, bio-guided fractionation
of \textit{L. major} extract seems to be a promising approach that would
reduce its complexity, allowing to unravel the actual effect on
\textit{M.~aeruginosa} metabolome and to isolate the key metabolites
identified through cutting-edge structural elucidation 
techniques~\cite{Vieiraetal2016}. 

\subsection{Extract composition, in relation to the effect observed on
\textit{M. aeruginosa}}\label{sec4.3}
Allelopathic compounds with algicidal effects are classified into four
categories: polyphenols, alkaloids, fatty acids, and 
terpenoids~\cite{Zhuetal2021}. Among the 40 macrophyte species reported
in the literature,  Nezbrytska \etal~\cite{Nezbrytskaetal2022} listed
the most promising ones for biological control of cyanobacteria,
considering their ability to produce such allelochemicals. If
\textit{Nuphar lutea}, \textit{Myriophyllum spicatum}, 
\textit{Ceratophyllum demersum},
\textit{Phragmites communis}, or \textit{Elodea canadensis} are cited because
largely studied, to date \textit{Lagarosiphon major} remains absent
from this list, and from literature in general, because its chemical
composition is absolutely unknown.

Hence, to the best of our knowledge, this study reveals for the first
time anticyanobacterial properties of \textit{L. major} extracts,
containing metabolite classes with known allelopathic properties such
as alkaloids, fatty acids, terpenoids  (e.g. saponins), and polyphenols
(e.g. flavonoids) as revealed by the rough \mbox{screening} and further
HRMS analyses. Among putative annotated metabolites, the following may
be involved in allelopathic interactions between macrophytes and
cyanobacteria as recently reviewed by  Zhu \etal~\cite{Zhuetal2021} and
Mohamed~\cite{Mohamed2017}:  the phenylpropanoids p-coumarate, caffeic
acid; the polyphenols vanillic acid and salicylic acid glucoside,
luteolin 7-O-beta-D-diglucuronide; the flavonoid quercetin
3-galactoside; the triterpenoids saponines spinasaponin A, acutoside A,
lucyoside M and saikosaponina A; the fatty acids 3-hydroxyoctanoic
acid, coronaric acid, pinellic acid, conelenic acid and octyl
docosanoate. Among them, caffeic acid has been recently described
as highly active in the mg${\cdot}$l$^{-1}$
range concentration on \textit{M.
aeruginosa}, leading to growth impairment, reactive oxygen species
(ROS) production and modulation of various metabolic 
pathways~\cite{Lietal2023, Herreraetal2019}.  

Further investigations are still needed to confirm those metabolite
identities and their role in the potential impairment of metabolic
pathways associated with primary and secondary metabolisms. 

\section{Conclusions and perspectives}\label{sec5} 
To the best of our knowledge, this study is the first to reveal the
anticyanobacterial effect of \textit{L. major} extracts. This was
observed at both physiological (photosynthesis) and molecular
levels (metabolome). Despite uncertainties in the interpretation of the
metabolomic response, our results highlighted the potential impairment
of various metabolic pathways associated with the complex dose--response
of multiple classes of putative metabolites. 

The next steps will consist in (i)~isolating and identifying active
metabolites from \textit{L. major} extracts through the implementation
of a bio-fractionation strategy associated to a cutting-edge structural
elucidation based on the combined use of LC-HRMS and RMN; 
(ii)~confirming the biological activity of the pure compounds
identified against \textit{M. aeruginosa} metabolism, physiology, and
growth;  (iii)~measuring their concentrations in the environment; 
(iv)~characterizing such macrophyte-cyanobacteria interactions on the
field to get insight about actual allelopathic interactions between the
two species. 

\section*{Conflicts of interest}
The authors have no conflict of interest to declare.

\section*{Acknowledgments}
Support from INRAE and Carnot Eau \& Environnement is gratefully
acknowledged. The authors thank G. Marty for providing the {in-house}
MS2 library in the frame of MTH2.0 project (ANR 11-INBS-0010). 

This work was carried out with the support (equipment and personnel)
provided by the XPO scientific infrastructure (LIFE research
infrastructure - Living in Freshwater and Estuaries, INRAE) of EABX
research unit, and by the Bordeaux Metabolome scientific
insfrastructure (MetaboHUB research\break infrastructure).

\CDRGrant[ANR]{11-INBS-0010}

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\section*{Supplementary data}
Supporting information for this article is available on the journal's
website under \printDOI\ or from the author.  

\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crchim-250-suppl.pdf}}
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\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{Table-S7.xlsx}}
\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{Table-S8.xlsx}}

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