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\DOI{10.5802/crchim.276}
\datereceived{2023-09-05}
\daterevised{2023-11-22}
\dateaccepted{2023-11-23}
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\dateposted{2024-03-22}
\begin{document}

\begin{noXML}

%\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

\CDRsetmeta{articletype}{research-article}

\title{Global metabolome changes induced by environmentally relevant
conditions in a marine-sourced \textit{Penicillium restrictum}}

\alttitle{Alt\'{e}rations globales du m\'{e}tabolome chez un
\textit{Penicillium restrictum} d'origine marine induites en
conditions de culture environnementales}

\author{\firstname{Van-Tuyen} \lastname{Le}}
\address{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\address{Phu Tho College of Pharmacy, 290000 Phu Tho, Vietnam}
\address{Biotechnology Division, Vietnam-Korea Institute of Science and Technology, Hoa~Lac High-tech Park, Thach That province, Ha Noi, Vietnam}
\email[V.-T. Le]{lvtuyen@most.gov.vn}

\author{\firstname{Samuel} \lastname{Bertrand}\CDRorcid{0000-0002-5934-7012}\IsCorresp}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[S. Bertrand]{samuel.bertrand@univ-nantes.fr}
\curraddr[S. Bertrand]{UFR des Sciences Pharmaceutiques et Biologiques,
Nantes Universit\'{e}, 9 rue bias BP 61112, 44035 Nantes cedex 1,
France}

\author{\firstname{Marion} \lastname{Brandolini-Bunlon}\CDRorcid{0000-0003-0131-8216}}
\address{Universit\'{e} Clermont Auvergne, INRAE, UNH, Plateforme d'Exploration du M\'{e}tabolisme, MetaboHUB Clermont, 63000 Clermont-Ferrand, France}
\email[M. Brandolini-Bunlon]{marion.brandolini-bunlon@inrae.fr}

\author{\break\firstname{Emmanuel} \lastname{Gentil}\CDRorcid{0000-0001-8108-3693}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[E. Gentil]{emmanuel.gentil@univ-nantes.fr}

\author{\firstname{Thibaut} \lastname{Robiou du Pont}\CDRorcid{0009-0005-7121-7711}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[T. Robiou du Pont]{thibaut.robiou@univ-nantes.fr}

\author{\firstname{Vony} \lastname{Rabesaotra}\CDRorcid{0000-0002-2670-3754}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[V. Rabesaotra]{vony.rabesaotra@univ-nantes.fr}

\author{\firstname{Ga\"{e}tane} \lastname{Wielgosz-Collin}\CDRorcid{0000-0001-7355-8193}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[G. Wielgosz-Collin]{wielgosz-collin@univ-nantes.fr}

\author{\firstname{Aur\'{e}lie} \lastname{Mossion}\CDRorcid{0000-0002-8391-8938}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[A. Mossion]{aurelie.mossion@univ-nantes.fr}

\author{\firstname{Olivier} \lastname{Grovel}\CDRorcid{0000-0003-3165-4771}}
\addressSameAs{1}{Nantes Universit\'{e}, Institut des Substances et Organismes
de la Mer, ISOMER, UR~2160, F-44000 Nantes, France}
\email[O. Grovel]{olivier.grovel@univ-nantes.fr}

\thanks{Vietnam International Education Development (VIED) program.}

\begin{abstract}
Marine fungi have been found in all habitats and are able to adapt to
their environmental niche conditions. In this study, a combination of
liquid chromatography coupled to high-resolution mass spectrometry
(LC-HRMS) and gas chromatography coupled to mass spectrometry (GC-MS)
analytical approaches was used to analyse the metabolic changes of a
marine-sourced \textit{Penicillium restrictum} strain isolated from a
marine shellfish area. The \textit{P.~restrictum} MMS417 strain was
grown on seven different media including an ecological one with two
different water sources (synthetic seawater and distilled water),
conditions following the One-Strain-Many-Compounds (OSMAC) approach.
Extracts of all media were analysed by LC-HRMS (lipids and specialised
metabolites profiling) and GC-MS (fatty acids profiling). Acquired data
were analysed using a multiblock strategy to highlight metabolic
modification in regard to water conditions and to environmentally
relevant conditions (mussel-based culture medium). This revealed that
fatty acid composition of lipids was the most altered part of the
explored metabolisms whether looking to the effect of water or of
environmentally relevant conditions. In particular, data showed that
\textit{P.~restrictum} MMS417 is able to produce lipids that include
fatty acids usually produced by the mussel itself. This study also
provides insight into the \textit{P.~restrictum} adaptation to marine
salinity through fatty acids alteration and shows that lipid metabolism
is far more altered in an OSMAC approach than the specialized
metabolism. This study finally highlights the need for using
environment-mimicking culture conditions to reveal the metabolic
potentialities of marine microbes.
\end{abstract}

\begin{altabstract}
Les champignons marins sont pr\'{e}sents dans tous les habitats et sont
capables de s'adapter aux conditions de leur niche environnementale.
Dans cette \'{e}tude, une combinaison d'approches analytiques LC-HRMS
et GC-MS a \'{e}t\'{e} utilis\'{e}e pour analyser l'ensemble des
changements m\'{e}taboliques intervenant chez une souche de
{\textit{Penicillium restrictum}} {d'origine marine lorsqu'elle est
cultiv\'{e}e en conditions s'approchant de son environnement d'origine.
La souche} {\textit{P.~restrictum}} {MMS417 a \'{e}t\'{e} cultiv\'{e}e
dans des conditions conformes \`{a} l'approche OSMAC~: sept milieux
diff\'{e}rents, dont un milieu \'{e}cologique, avec deux sources d'eau
diff\'{e}rentes (eau de mer synth\'{e}tique et eau distill\'{e}e). Les
extraits de l'ensemble des milieux ont \'{e}t\'{e} analys\'{e}s par
LC-HRMS (profilage des lipides et des m\'{e}tabolites
sp\'{e}cialis\'{e}s) et GC-MS (profilage des acides gras). Les
donn\'{e}es obtenues ont \'{e}t\'{e} analys\'{e}es \`{a} l'aide d'une
strat\'{e}gie multiblocks afin de mettre en \'{e}vidence les
modifications m\'{e}taboliques li\'{e} \`{a} l'eau utilis\'{e}e et aux
conditions environnementales (milieu de culture \`{a} base de moules).
Ceci a r\'{e}v\'{e}l\'{e} que la composition en acides gras des lipides
\'{e}tait la partie la plus alt\'{e}r\'{e}e du m\'{e}tabolisme
explor\'{e}, que ce soit par rapport \`{a} l'effet de l'eau ou \`{a} la
pr\'{e}sence d'extrait de moule. En particulier, les donn\'{e}es ont
montr\'{e} que} {\textit{P.~restrictum}} {MMS417 est capable de
produire des lipides qui comprennent des acides gras habituellement
produits par la moule elle-m\^{e}me. Cette \'{e}tude donne
\'{e}galement un aper\c{c}u de l'adaptation de} {\textit{P.
restrictum}} \`{a} la salinit\'{e} marine par l'alt\'{e}ration des
acides gras et montre que les m\'{e}tabolismes lipidiques sont beaucoup
plus alt\'{e}r\'{e}s dans une approche OSMAC que le m\'{e}tabolisme
sp\'{e}cialis\'{e}. Cette \'{e}tude met enfin en \'{e}vidence la
n\'{e}cessit\'{e} d'utiliser des conditions de culture imitant
l'environnement pour r\'{e}v\'{e}ler les potentialit\'{e}s
m\'{e}taboliques des microorganismes marins.
\end{altabstract}


\keywords{\kwd{Marine-derived fungi}
\kwd{Lipids}
\kwd{One-strain-many-compounds (OSMAC)}
\kwd{Ecological media}
\kwd{Metabolome}
\kwd{Multiblock}}

\altkeywords{\kwd{Champignons d'origine marine}
\kwd{Lipides}
\kwd{M\'{e}tabolome}
\kwd{Milieu de culture \'{e}cologique}
\kwd{Multiblock}
\kwd{OSMAC}}

\shortrunauthors

\maketitle

{\vspace*{1pc}}

\twocolumngrid

\end{noXML}

\section{Introduction}\label{sec1}

Marine fungi have been found in all habitats and make a significant
contribution to the marine environment~\cite{1}. They can be found as
saprotrophs, parasites, or symbionts (epiphytic and endophytic) of
marine macroorganisms, as a consequence of the evolution of fungal cell
biology and feeding strategies~\cite{2,3}. In the dynamic marine
natural environments, fungi must be able to adapt to the change from
the local environment to survive and colonize a niche. This could be
considered as a physiological adaptation to their environment or as a
response to abiotic stresses (rapid modification of
environment)~\cite{4,5}. Furthermore, in ecological niches fungi are
also in competition or interactions with other micro- and/or
macro-organisms, such as bacteria~\cite{6,7}, microalgae~\cite{8},
seaweeds or macroalgae~\cite{9}, sponges~\cite{10}, or
molluscs~\cite{11,12,13}. In a natural product perspective, biotic or
abiotic stresses are responsible for activating specialised metabolite
biosynthetic gene clusters (BGCs) yielding the production of
stress-induced or activated specific compounds.

Considering marine-sourced fungi, salinity represents one of the main
environmental factors to be considered to achieve one step further in
understanding adaptation to marine environment. As one well documented
effect in yeasts, salinity affects the membrane lipid composition of
fungi, and consequently its fluidity, in particular through
modifications in the type of fatty acyl chains, sterol content, and the
nature of the polar phospholipids (PLs)
headgroups~\cite{14,15,16,17,18,19,20}. However, up to now no study
trying to show an overall effect on the global metabolic content has
been initiated in fungi. A second factor to be considered for the
understanding of metabolome expression and regulation in marine fungi
inhabiting holobionts is the influence of their microenvironment within
their host. In this way, it has been previously shown that fungi
cultured on host-derived media can produce specialised metabolites
which were not observed in other conditions~\cite{21,22,23,24}. It can
therefore be hypothesised that these compounds are involved in
homeostasis or defence within their holobiont of origin.

However, studying these effects in fungi remains quite
challenging~\cite{25}. The culture-based strategies are usually the
most employed for chemical diversity exploration, such as
One-Strain-Many-Compounds (OSMAC)~\cite{26} or microbial
cocultivation~\cite{27}. These strategies allow to reduce the
significant discrepancy between the actual number of chemically
characterised compounds obtained from microorganisms and the number of
bioinformatically characterised BGCs~\cite{28,29}. The OSMAC approach
is now largely used to explore the effect of various factors on fungal
specialised metabolisms~\cite{28}, such as temperature~\cite{30},
carbon source~\cite{30,31,32}, and culture medium~\cite{33}.

Recent trends in metabolomics tend to perform a global analysis at
multiple layers from core metabolome to lipids and specialised
metabolome~\cite{34}. This reflects the ability that has emerged in
recent decades to characterise the chemical composition of microbial
extracts using different high-resolution strategies, such as gas
chromatography coupled to mass spectrometry (GC-MS), liquid
chromatography coupled to mass spectrometry (LC-MS), and/or nuclear
magnetic resonance (NMR)~\cite{35,36}. It is also the consequence of
the use of chemometrics pipelines that are able to correctly fuse
complex data from various instrumentations using the so-called
``statistical multiblock approach''~\cite{37,38,39}. Thus, in the goal of
studying fungal metabolic adaptation to specific environmental
conditions, it is now possible to acquire high-resolution data using
different methods and then to analyse them together to get a global
overview~\cite{40,41}.

The present study propose to explore the metabolic adaptation of the
mussel-derived fungus \mbox{\textit{Penicillium restrictum}} MMS417 isolated
from mussel flesh within shellfish farming area~\cite{42} to both
salinity and environmentally relevant culture conditions. Such a
strategy, using host-derived culture conditions, is yet poorly applied
to microorganisms' growth for fungal metabolism exploration, as such
microorganisms are nearly exclusively grown in standard laboratory
conditions. This is expected to be a simple way of mimicking the
original ecological niche, to describe in situ fungal metabolism
behaviour. Thus, an OSMAC strategy was used to explore the metabolome
expression in response to various culture conditions. In this way, six
traditional culture media~\cite{33} were used along with a mussel
flesh-based medium~\cite{22}. The latter culture medium may be
considered as an environmentally relevant---or 
ecological---medium~\cite{23, 24, 43}. In addition, each medium was
studied as a marine-mimicking medium using synthetic seawater (SSW) or
as an artificial laboratory medium using distilled water (DW). Thus,
all together, the \mbox{\textit{P.~restrictum}} MMS417 metabolism
corresponding to those 14 culture conditions was explored using
specialised metabolites~\cite{44} and lipid~\cite{31,45} profiling by
LC-HRMS and fatty acid lipid content profiling by GC-MS~\cite{46}. The
multiblock analysis of the acquired data aimed to highlight specific
metabolic changes in the \textit{P.~restrictum} MMS417 strain according
to the use of SSW and of mussel extract in the culture medium.
\vspace*{-3pt}

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

\subsection{Fungal material}\label{sec21}

The studied strain, \textit{P.~restrictum} MMS417, was
isolated from a blue mussel \textit{Mytilus edulis} sampled in January
1997 at Port Giraud on the Loire estuary in France~\cite{42}. The
strain is preserved at the laboratory ISOMer UR2160, Nantes
Universit\'{e}, France. The strain has been identified after sequencing
of the ${\upbeta}$-tubulin gene; the internal transcribed spacer (ITS)
and regions of the rDNA have been identified after nucleotide BLAST
search (GenBank accession number for MMS417:KU720404, KU720398).
\vspace*{-5pt}

\subsection{Culture media preparation}\label{sec22}

Fungal solid cultures were performed in Petri dishes (10~cm diameter)
containing 15~mL of agar-based medium in 14 different media. Medium
\mbox{composition} was as follow: DCA (Dextrose 40~g/L, enzymatic digest of
Casein 10~g/L, Agar 15~g/L, Difco), MEA (Malt Extract 20~g/L, peptone
1~g/L, glucose 20~g/L, Agar 20~g/L), PDA (Potato extract 4~g/L,
Dextrose 20~g/L, $\mathrm{ZnSO}_{4}{\cdot}7\mathrm{H}_{2}\mathrm{O}$
0.01~g/L, $\mathrm{CuSO}_{4}{\cdot}5\mathrm{H}_{2}\mathrm{O}$ 0.005~g/L, Agar
15~g/L), YES (Yeast Extract 20~g/L, Sucrose 150~g/L, agar 20~g/L), MES
(Mussel Extract 20~g/L, Sucrose 150~g/L, agar 20~g/L), CYA (Czapek
concentrate 10~mL, Yeast extract 5~g/L, K$_{2}$HPO$_{4}$ 1~g/L, sucrose
30~g/L, Agar 15~g/L), Czapek concentrate (NaNO$_{3}$ 3~g/L, KCl
0.5~g/L, $\mathrm{MgSO}_{4}{\cdot}7\mathrm{H}_{2}\mathrm{O}$ 0.5~g/L,
$\mathrm{FeSO}_{4}{\cdot}7\mathrm{H}_{2}\mathrm{O}$ 0.01~g/L,
$\mathrm{ZnSO}_{4}{\cdot}7\mathrm{H}_{2}\mathrm{O}$ 0.01~g/L,
$\mathrm{CuSO}_{4}{\cdot}5\mathrm{H}_{2}\mathrm{O}$ 0.05~g/L), KMS
($\mathrm{MgSO}_{4}{\cdot}7\mathrm{H}_{2}\mathrm{O}$ 2.4~g/L,
NH$_{4}$NO$_{3}$ 2.4~g/L, Tris (tampon) 1.21~g/L, Agar 20~g/L). Details
for the preparation of the mussel-derived medium (MES) were presented
in a previous study~\cite{22}. Two osmotic conditions were prepared on
the basis of the presence or absence of synthetic seawater (36~g/L of
salinity---Coral Reef{\textregistered} salts).
\vspace*{-5pt}

\subsection{Fermentation and extraction for OSMAC approach}\label{sec23}
\vspace*{-3pt}

Fungal cultures were carried out in four replicates for each medium.
Petri dishes were inoculated with stock cultures of the strain stored
at 20~\textdegree C and transplanted on MES medium 10~days before
inoculation. Cultures were incubated at 27~\textdegree C for 10~days
under natural light. Extraction of metabolites was performed on
standardised plugs sampled in three distinct places of each culture (at
the point of central impact, on the outskirts of the colony, in the
periphery but in contact with an adjacent colony). Fungal biomass and
agar layer were then extracted together to obtain both intra- and
extra-cellular metabolites. Samples were gathered and extracted twice
with 1.5~mL of CH$_{2}$Cl$_{2}$/EtOAc 1:1 (v/v) for each culture,
sonicated for 30~min and filtered on regenerated cellulose
$0.45~\upmu\mathrm{m}$ filters (Sartorius) before drying. Media without
fungi were also extracted following the same protocol and used as
controls (blanks samples).
\vspace*{-3pt}

\subsection{HPLC-MS analyses of specialised metabolites}\label{sec24}
\vspace*{-3pt}

High-performance liquid chromatography coupled to ultraviolet
absorption and electrospray ionisation---in negative and positive 
mode---high-resolution mass spectrometry
(\mbox{HPLC-UV-($+/-$)}-HRESI-MS) analyses were performed on a UFLC-MS (IT-TOF)
Shimadzu instrument using a Kinetex C18 column ($2.6~\upmu\mathrm{m}$,
$2.1\times 100$~mm, Phenomenex) and following previously described
conditions~\cite{44}. Samples ($5~\upmu\mathrm{L}$) were injected at a
concentration of 1~mg/mL in MeOH. A mixture of all extracts at the same
concentration was also prepared as a quality control (QC) sample. All
samples were analysed randomly, and MeOH blanks and QC samples were
regularly injected during the analysis sequence~\cite{47}.
\vspace*{-3pt}

\subsection{HPLC-MS analyses of lipids}\label{sec25}

HPLC-($+/-$)-HRESI-MS analyses were also performed on a UFLC-MS
(IT-TOF) Shimadzu instrument using a Kinetex C18 column
($2.6~\upmu\mathrm{m}$, $2.1\times150$~mm, Phenomenex). The
conditions were previously described by~\cite{31}. Samples
(2~$\upmu\mathrm{L}$) at a concentration of 1~mg/mL in isopropanol were
injected. As for the analyses of specialised metabolites, QC samples
were prepared, and all samples were injected randomly, and isopropanol
blanks and QC samples were regularly injected during the analysis
sequence~\cite{47}.
\vspace*{-3pt}

\subsection{Processing of HPLC-MS data}\label{sec26}

The HPLC-($+/-$)-HRESI-MS chromatogram raw data files were converted to
*.netCDF files using LCMS solution (version 3.60---Shimadzu). Automatic
peak-picking on the *.netCDF files was achieved using MZmine
2~\cite{48} with appropriate parameters (Table~S1). All data files
including media controls and MeOH blanks were examined to determine a
minimum noise level threshold.

The results were exported as a *.csv file containing all peaks observed
and referenced by their mass to charge ratio (${m/z}$) and retention
times ($t_{R}$) together with their respective peak areas in each
sample. The generated matrix was cleaned by removing all peaks
abundantly found in blank samples (culture media extracts) and
technical blank samples (MeOH injections).
\vspace*{-3pt}

\subsection{Preparation of fatty acid methyl esters (FAMEs) and
\textit{N}-acyl pyrrolidides (NAPs) for GC-MS analyses}\label{sec27}

FAMEs were prepared by submitting the crude extracts to
transesterification during 5~h at 80~\textdegree C under reflux with
methanolic hydrogen chloride 3~N/MeOH/CHCl$_{3}$ (5:3:1 v/v/v). 
N-acyl pyrrolidides
were prepared by direct treatment of FAMEs with pyrrolidine/acetic acid
(5:1 v/v) for 60 min at 85~\textdegree C under reflux.
\vspace*{-3pt}

\subsection{GC-MS analyses}\label{sec28}

FAMEs were analysed using a GC-MS instrument (Hewlett Packard HP
6890-GC System, Agilent Technologies, Santa Clara, CA, USA) linked to a
mass detector (HP 6890-E.I. 70~eV) equipped with a
SLB-$5^{\mathrm{TM}}$ column 
($60~\mathrm{m}\times 0.25~\mathrm{mm}\times 0.25~\upmu\mathrm{m}$).
The carrier gas was helium at a flow rate of 1~mL/min. The temperatures
of the injector and detector were set at 250~\textdegree C and
280~\textdegree C, respectively. One microliter was injected in
splitless mode. The column temperature was maintained at
170~\textdegree C for 4~min and increased to 300~\textdegree C at
3~\textdegree C/min for FAME analyses, and at 200~\textdegree C for
4~min and increased by 3~\textdegree C/min up to 310~\textdegree C for
NAP ones. The solvent delay was 9~min.
\vspace*{-3pt}

\subsection{Processing of GC-MS data}\label{sec29}
\vspace*{-3pt}

The GC-MS chromatogram raw data files were manually inspected to
characterise all FAs detected in the chemical profiles using GC-MS
solution (Hewlett Packard 1989--1997, G1701BA version B.00.00). Complete
identification of the peaks was performed by interpretation of the MS
spectrum obtained from the NAPs. In parallel, peak integration was
performed on the FAME profiles manually, and peak were aligned based on
former identification to provide the GC-MS data matrix. The generated
matrix was cleaned by removing all peaks abundantly found in blank
samples (culture media extracts) and technical blank samples.\looseness=-1
\vspace*{-3pt}

\begin{figure*}
\includegraphics{fig01}
\vspace*{-6pt}
\caption{\label{fig1}Experimental design used to assess the effect of
seawater and environmentally relevant culture conditions on the
metabolome and lipidome of \textit{Penicillium restrictum} MMS417. SSW:
synthetic seawater, DW: distilled water, MES: mussel-derived culture
medium, PI: positive ionisation, NI: negative ionisation, NAP:
${N}$-Acyl Pyrrolidides.\vspace*{-7pt}}
\end{figure*}

\subsection{Statistical analysis}\label{sec210}
\vspace*{-3pt}

All data analyses were performed using R 4.0.0 (CRAN). Data from
HPLC-HRESI-MS profilings were subjected to QC correction using the
statTarget package~\cite{49} using the following parameters: Frule~0,
QCspan~0.75, degree~2, imputeM~``KNN'' and coCV~100. The corrected peak
areas were further corrected to reflect each absolute compound amount
in the fungus by multiplying it by the corresponding extract's weight.
A similar correction was applied to GC-MS peak area. All data analyses
were performed using Unit-Variance (UV) scaling. The PCA was performed
using the ROPLS package~\cite{50}. The ComDim data analysis~\cite{51}
was performed using the R function ``accps()'' available from 
Tchandao Mangamana~\cite{52}. The MBPLS-DA was achieved using the
packMBPLSDA package~\cite{38} and the variables were selected based on
VIPc (cumulated variable importance in the projection) values.
\vspace*{-3pt}

\subsection{Annotation of LC-HRMS data}\label{sec211}
\vspace*{-3pt}

The selected features based on VIPc were putatively identified
considering their accurate molecular masses. In details, measured
accurate masses were used for compounds' molecular formula
determination considering a 6~ppm error, which was compared with the
information available on the dictionary of natural products (DNP)
database. Ultraviolet absorbance (UV) spectra were used to suggest
secondary metabolite classes.
\vspace*{-3pt}

\section{Results}\label{sec3}

To assess the impact of the modification of culture conditions on the
metabolism of \textit{P.~restrictum}, 14 \mbox{culture} media were used in an
OSMAC experiment. The fungus was grown for 10 days using seven solid
culture media (CYA, DCA, PDA, YES, MEA, KMS and MES), supplemented or
not with synthetic seawater, prior to extraction. Tracking a global
effect of the replacement of distilled water (DW) by synthetic seawater
(SSW) requires to analysing all metabolic changes in the culture
conditions, having in mind to get as much information as necessary on
the fungal metabolism. Thus, after extraction of the culture medium and
the fungal biomass together, all extracts ($n=56$) were profiled,
focusing on specialised metabolites (SM), lipids, and fatty acids (FAs)
(Figure~\ref{fig1}). Two different LC-HRMS protocols were used for profiling
first specialised metabolism~\cite{44} and then lipids~\cite{31}. In
parallel, an aliquot of each extract was subjected to
transesterification to obtain the full FA composition of lipids.
\vspace*{-6pt}

\subsection{General profiling}\label{sec31}
\vspace*{-2pt}

\begin{figure*}[t!]
\includegraphics{fig02}
\caption{\label{fig2}LC-HRMS (base peak chromatograms) and GC-MS (total
ionic current chromatograms) profiles of the extracts from
\textit{Penicillium restrictum} MMS417 in all 14 cultures conditions:
CYA, DCA, PDA, YES, MEA, KMS, and MES using either distilled water 
(DW) or synthetic seawater (SSW). The red
line corresponds to the positive ionisation mode and blue line to the
negative mode.}
\end{figure*}

As a first step, a simple LC- and GC-MS-based metabolomics analysis was
performed to compare the metabolic profiles of all culture \mbox{conditions.}
{Figure~\ref{fig2}} provides an overview of all acquired \mbox{metabolic} profiles (LC-MS
and GC-MS). This gave a snapshot of the chemical composition of the
strain when grown on different media, from which it can be easily
visualised that the chromatogram profiles showed variations according
to culture conditions.


As a general observation on the specialised metabolite profiles in the
positive ionisation mode, the majority of the peaks appeared at the end
of the chromatograms (18--25~min). Interestingly, there were
some particular peaks observed mostly in the profiles corresponding to
MES-SSW extracts in comparison to the other media (5--10~min). In the
negative ionisation mode, many peaks appeared only in the SSW profiles
in comparison to those from the DW cultures (except CYA-DW). This was
particularly observed in the case of PDA-SSW and DCA-SSW extracts.\looseness=-1

A similar observation was achieved on lipidome. In the lipid profiles
(Figure~\ref{fig2}), the most polar lipids (i.e. FAs) along with specialised
metabolites eluted at the beginning of the chromatogram (approx.
\mbox{2--18~min}), PLs and glycolipids (GLs) between 18 and 35~min,
and finally neutral lipids (NLs) (mostly triglycerides) between 38 and
45~min. Profiles obtained from both KMS and CYA media appeared clearly
different with almost no PLs, GLs nor NLs observed. Contrarily, all
lipid categories were observed in profiles from MES and YES media. In
addition to these general lipidome composition observations, FA
compositions of lipids were also characterized, leading to the
observation that lipid composition was stable qualitatively except for
GC-MS profiles obtained from MEA-DW and MES-SSW extracts, which showed
a strongly different composition in comparison to others. In this way,
\textit{P.~restrictum} MMS417 contained FAs of carbon chain lengths
ranging from C12 to C26. The most common and abundant FAs were 16:0,
18:0, 18:1, and 18:2 which, taken altogether, often represent more than
90\% of the total FA content of fungi~\cite{53,54,55}. These four FAs were
present in all extracts in this study. Minor FAs (12:0, 14:0, 15:0,
16:1, 17:0, 17:1, 19:1, 19:0, etc.) were also detected, but represented
less than 10\% of the total FA content. However, the relative
concentration of each individual FA ranged from less than 1\% of the
total acid FA content to over 47\%, showing a high variability
depending on the culture medium.

To further analyse the profiling data, multivariate unsupervised data
analyses were performed \mbox{using} principal component analyses (PCA) on the
following data from all samples: LC-HRMS profiles in positive
ionisation mode (883 features), LC-HRMS metabolome profiles in negative
ionisation mode (544 features), LC-HRMS lipidome profiles in positive
ionisation mode (1748 features), LC-HRMS lipidome profiles in negative
ionisation mode (10535 features), and GC-MS FA composition (34
features). In all PCAs (Figure~\ref{fig3}), the first axis represented 24\%
(LC-HRMS metabolome profiles in positive ionisation) to 61\% (LC-HRMS
lipidome profiles in negative ionisation) of the variability, and a
moderate clustering according to culture medium could be observed using
the first two components (too many culture conditions were explored to
expect clear clustering). All specialised metabolome and lipidome
analyses revealed some discrimination of the MES and YES extracts
(independently of the presence of SSW), and FA profiling also
highlighted clear differences for these two culture conditions. In
addition, the second axis of the lipidome data in positive ionisation
highlighted a KMS culture medium\break specificity.

\begin{figure*}[t]
{\vspace*{-4pt}}
\includegraphics{fig03}
{\vspace*{-4pt}}
\caption{\label{fig3}PCA score plot (Unit Variance---UV---scaling) of
all acquired data (LC-HRMS and GC-MS) on extracts obtained from the 14
culture media (4 replicates of each culture media: CYA, DCA, PDA, YES,
MEA, KMS, and MES using either DW or SSW).
Triangles and circles correspond to
distilled water (DW) and synthetic seawater (SSW), respectively.
CYA, DCA, PDA, YES, MEA, KMS, MES correspond to used culture media.}
{\vspace*{-4pt}}
\end{figure*}

However, the ability to conclude on a global culture medium effect
using such a high amount of data was impaired by the difficulties to
use at the same level the different data acquired on the extracts.
Therefore, it could be interesting to analyse all data simultaneously.
In such cases, a so-called multiblock data analysis can be
performed~\cite{34}.

Multiblock data analyses correspond to strategies allowing the
co-analysis of different datasets acquired from the same samples, one
block corresponding to one analytical method in our study. One major
difficulty that multiblock strategies encounter is the block size
differences effect. In our study, five blocks of data were acquired,
having a high variation in data size. To overcome the block size
differences effect, the unsupervised multivariate ComDim strategy (also
called Common Components and Specific Weights Analysis---CCSWA)~\cite{39} 
was used to analyse the data. This multiblock strategy
is still very rarely used to explore metabolomic data, particularly in
the natural products field~\cite{41}. However, in addition to perform a
simultaneous analysis of all data, it provides also information about
the importance of each data block.

\begin{figure*}
\vspace*{-3pt}
\includegraphics{fig04}
\vspace*{-2pt}
\caption{\label{fig4}\fontsize{9.9}{11.5}\selectfont
Multiblock unsupervised data analysis using ComDim
(extract weight correction and Unit-Variance---UV---scaling) of all
acquired data (LC-HRMS and GC-MS). (A) Score plots of dimensions 1 and
2 and (B) Score plots of dimensions 1 and 3; (C) Block influence 
(salience) on the separation of dimension 1 and 2 and (D) Block
influence (salience)  on the separation of dimensions 1 and 3. SSW:
synthetic seawater, DW: distilled water; CYA, DCA, PDA, YES, MEA, KMS,
MES correspond to used culture media; FA: fatty acid profiles by GC-MS;
Lipid: Lipid profiles either in the negative (NEG) or positive (POS)
ionisation; Met.Spe.: specialised metabolites profiles either in the
negative (NEG) or positive (POS) ionisation.}
\vspace*{-2pt}
\end{figure*}

The ComDim score plot (Figure~\ref{fig4}A,B) allowed visualising in an
unsupervised way all the data self-structure. As observed in the
individual data set (Figure~\ref{fig3}), the MES and YES culture media (using
both DW and SSW) corresponded to the first dimension, which accounts
for 59\% of the total variance of the data. This first dimension (Dim1)
was mostly derived from the lipidome in the negative \mbox{ionisation} data
block as observed in Figure~\ref{fig4}C. The second dimension (Dim2), which
accounted for 8\% of the total variance, highlighted the MES specific
effect. Interestingly, in addition to lipidome specificity, FA
composition exhibited a high specificity in the MES medium. Exploring
further the dimension of the ComDim analysis (Figure~\ref{fig4}B) highlighted
the tendency for culture media to cluster. However, no SSW cluster were
observed, suggesting the lack of a global salinity effect.

\subsection{Effect of synthetic seawater on the metabolome
expression}\label{sec32}

To investigate more in depth the effect of seawater on fungal
metabolism (i.e. SSW extracts vs DW extracts), data were analysed in a
supervised way \mbox{using} MBPLS-DA. Optimisation of the number of PLS
components indicated that only the first 2 should be kept to avoid
overfitting~\cite{38}. The corresponding cross-validated error rate of
classification was \mbox{$1.8\% \pm 1.8\%$.} The resulting score plot
(Figure~\ref{fig5}A) showed a \mbox{significant} separation between medium
based on DW and SSW. Exploration of the importance of each block in the
separation (Figure~\ref{fig5}B) indicated that all blocks impacted
similarly the~separation.\linebreak However, the most important block in the
separation remained the lipid profiling acquired in negative ionisation
and the less important block was the FA composition.

\begin{figure}
{\vspace*{2pt}}
\includegraphics{fig05}
{\vspace*{2pt}}
\caption{\label{fig5}Multiblock supervised data analysis using MBPLS-DA
(extract weight correction and Unit Variance---UV---scaling)  of all
acquired data (LC-HRMS and GC-MS) to discriminate distilled water (DW)
and synthetic seawater (SSW). (A) Score Plot; (B) Block importances in
the projection. FA: fatty acid profiles by GC-MS; L: Lipid profiles
either in the negative (NI) or positive (PI) ionisation; SM:
specialised metabolites profiles either in the negative (NI) or
positive (PI) ionisation; CYA, DCA, PDA, YES, MEA, KMS, MES correspond
to used culture media.}
{\vspace*{2pt}}
\end{figure}

\break
Based on VIPc values, features of interest were selected
(Table~\ref{tab1}). Among the Top 30 features selected, besides FAs, 16
\mbox{features} corresponded to ions detected by specialised metabolites
profiling. The feature corresponding to
${m/z}~427.3208~[\mathrm{M}+\mathrm{H}]^{+}$ eluted at 21.88~min was
annotated as dankasterone B
($\mathrm{C}_{28}\mathrm{H}_{42}\mathrm{O}_{3}$, calculated
${m/z}~427.3207$, ${\Updelta}$ppm~0.23), a fungal
steroid~\cite{56,57,58}. The feature
${m/z}~482.2927~[\mathrm{M}+\mathrm{H}]^{+}$ eluted at 21.68~min was
annotated as \mbox{isomers} of cytochalasin B$_{5}$ 
($\mathrm{C}_{29}\mathrm{H}_{39}\mathrm{NO}_{5}$, calculated
${m/z}$ 482.2901, ${\Updelta}$ppm~5.39), an hybride PKS-NRPS macrolide
previously isolated from a marine-derived fungus \textit{Phoma}
sp.~\cite{59}. Some unidentified metabolites at
${m/z}~209.0781~[\mathrm{M}+\mathrm{H}]^{+}$,
524.3151 $[\mathrm{M}+\mathrm{H}]^{+}$,
363.0273 $[\mathrm{M}-\mathrm{H}]^{-}$,
214.1452 $[\mathrm{M}-\mathrm{H}]^{-}$,
429.287  $[\mathrm{M}-\mathrm{H}]^{-}$,
437.2544 $[\mathrm{M}-\mathrm{H}]^{-}$,
386.2595 $[\mathrm{M}-\mathrm{H}]^{-}$,
484.3054 $[\mathrm{M}+\mathrm{H}]^{+}$,
232.6385 $[\mathrm{M}+\mathrm{H}]^{+}$ and two isomers at 
${m/z}$ 744.5735 $[\mathrm{M}+\mathrm{H}]^{+}$,
744.5733 $[\mathrm{M}+\mathrm{H}]^{+}$ did not find any hit from fungal
natural products databases.


\begin{table*}
\caption{\label{tab1}Most altered features (Top30) regarding 
distilled water (DW) and synthetic seawater (SSW)
differences highlighted by MBPLS-DA (Figure~\ref{fig6}) and selected
based on cumulated variable importance in the projection (VIPc)
{\vspace*{2pt}}}
\tabcolsep=6pt
\begin{tabular}{ccccc}
\thead
\parbox[t]{2cm}{\centering Features of interest$^{\mathrm{a}}$} &amp; Block &amp; 
\parbox[t]{1.3cm}{\centering VIPc value} &amp; 
\parbox[t]{2cm}{\centering Overexpression group} &amp; 
 Annotation$^{\mathrm{b}}$ \vspace*{3pt}\\
\endthead
\textit{br}-18:0 &amp; FA &amp; 0.0124 &amp; SSW &amp; Branched saturated fatty acid with 18 carbons \\
\textit{iso}-16:0 &amp; FA &amp; 0.0123 &amp; SSW &amp; 14-methylpentadecanoic acid \\
18:0 DMA$^{\mathrm{c}}$ &amp; FA &amp; 0.0106 &amp; SSW &amp; Dimethyl acetal stearic acid \\
12:0 &amp; FA &amp; 0.0085 &amp; SSW &amp; Lauric acid \\
14:0 &amp; FA &amp; 0.0084 &amp; SSW &amp; Myristic acid \\
19:1 &amp; FA &amp; 0.0065 &amp; DW &amp; Nonadecenoic acid \\
14:1 &amp; FA &amp; 0.0050 &amp; DW &amp; Myristoleic acid \\
17:0 DMA$^{\mathrm{c}}$ &amp; FA &amp; 0.0050 &amp; DW &amp; Dimethyl acetal margaric acid \\
\textit{cj}-18:2 &amp; FA &amp; 0.0050 &amp; DW &amp; Octadecadienoic acid \\
26:0 &amp; FA &amp; 0.0045 &amp; SSW &amp; Cerotic acid \\
7.40neg209.0781 &amp; SM NI &amp; 0.0029 &amp; SSW &amp; Unidentified metabolite \\
4,8,12-TM 13:0$^{\mathrm{d}}$ &amp; FA &amp; 0.0027 &amp; SSW &amp; 4,8,12-trimethyl tridecanoic acid \\
19:0 &amp; FA &amp; 0.0022 &amp; DW &amp; Nonadecanoic acid \\
23.86pos524.3151 &amp; SM PI &amp; 0.0021 &amp; SSW &amp; Unidentified metabolite \\
11.21neg363.0273 &amp; SM NI &amp; 0.0021 &amp; SSW &amp; Unidentified metabolite \\
10.04neg214.1452 &amp; SM NI &amp; 0.0020 &amp; DW &amp;  Unidentified metabolite \\
19.59neg429.2867 &amp; SM NI &amp; 0.0020 &amp; SSW &amp; Unidentified metabolite \\
21.13neg437.2544 &amp; SM NI &amp; 0.0020 &amp; DW &amp;  Unidentified metabolite \\
14.08neg386.2595 &amp; SM NI &amp; 0.0020 &amp; DW &amp;  Unidentified metabolite \\
21.88pos427.3208 &amp; SM PI &amp; 0.0020 &amp; SSW &amp; Dankasterone B~\cite{56, 58} \\
21.68pos482.2927 &amp; SM PI &amp; 0.0019 &amp; SSW &amp; Cytochalasin B5~\cite{59} \\
20:5 &amp; FA &amp; 0.0019 &amp; SSW &amp; Eicosapentaenoic acid \\
1.21pos255.0626 &amp; SM PI &amp; 0.0019 &amp; DW &amp;   Unidentified metabolite \\
19.41pos484.3054 &amp; SM PI &amp; 0.0019 &amp; SSW &amp; Unidentified metabolite \\
1.06neg295.0643 &amp; SM NI &amp; 0.0018 &amp; DW &amp;   Unidentified metabolite \\
19.68pos232.6385 &amp; SM PI &amp; 0.0018 &amp; SSW &amp; Unidentified metabolite \\
23.13pos744.5735 &amp; SM PI &amp; 0.0018 &amp; DW &amp;  Unidentified metabolite \\
23.45pos744.5733 &amp; SM PI &amp; 0.0017 &amp; DW &amp;  Unidentified metabolite \\
19.62pos467.3157 &amp; SM PI &amp; 0.0017 &amp; SSW &amp; Unidentified metabolite \\
20:0 &amp; FA &amp; 0.0017 &amp; DW &amp; Arachidic acid 
\botline
\end{tabular}
\tabnote{$^{\mathrm{a}}$ In GC-MS, data features were indicated based
on their abbreviated names; for LC-HRMS features, they were indicated
by RT, ionisation mode, and ${m/z}$ value; $^{\mathrm{b}}$ for GC-MS,
annotation is not putative, in contrary to LC-MS annotation;
$^{\mathrm{c}}$ DMA: dimethyl acetal; $^{\mathrm{d}}$ TM: trimethyl.
{\vspace*{2pt}}}
\end{table*}


The Top10 VIPc variables related to this discrimination were FAs even
if the FA block importance in the MBPLS-DA model was the smallest. This
reflects that a large number of lipids observed in negative mode
ionisation profiles are altered slightly but significantly. Concerning
lipids, it was observed that their FA composition in the extracts using
SSW culture media consisted mostly of saturated FAs (12:0, 14:0, 19:0,
20:0, 26:0), polyunsaturated FAs \mbox{(PUFAs)} (\textit{cj}-18:2, 20:5) and
branched FAs (BCFAs) \mbox{(\textit{br}-18:0,} 18:0-DMA, \textit{iso}-15:0,
4,8,12-TM 13:0), whereas FA composition in the extracts from DW
cultures consisted of mono-unsaturated FAs (14:1, 19:1), \mbox{BCFAs}
(17:0-DMA), PUFAs (\textit{cj}-18:2).

\subsection{Effect of mussel components on the metabolome
expression}\label{sec33}

As clearly observed in Figure~\ref{fig4}A, the composition of
\textit{P.~restrictum} MMS417 extract when grown on MES and YES medium
exhibited specific chemical patterns without considering DW vs SSW
effect. To observe such an effect on overall fungal metabolism, data
were analysed in a supervised way using MBPLS-DA. The analysis was
focused on \mbox{highlighting} differences between the MES culture medium in
comparison to all other media, disregarding DW and SSW. {Optimisation} of
the number of PLS {components} indicated that only the first 2 should be
kept, \mbox{allowing} us to obtain a classification error rate of
$5.56\% \,{\pm}\, 5.56\%$, and to avoid overfitting~\cite{38}. The
\mbox{resulting} score plot {(Figure~\ref{fig6}A)} showed a significant
separation between MES media and the others. Exploration of the
importance of each block in the separation (Figure~\ref{fig6}B)
indicated that the FA block was the most important one, all other
blocks being of similar importance.


\begin{figure}
\includegraphics{fig06}
{\vspace*{-2pt}}
\caption{\label{fig6}Multiblock supervised data analysis using MBPLS-DA
(extract weight correction and UV scaling) of all acquired data
(LC-HRMS and GC-MS) to discriminate the MES media from the other
culture conditions (CYA, DCA, PDA, YES, MEA, KMS).
(A) Score Plot; (B) Block importances in the
projection. FA: fatty acid profiles by GC-MS; L: Lipid profiles either
in the negative (NI) or positive (PI) ionisation; SM: specialised
metabolites profiles either in the negative (NI) or positive (PI)
ionisation.}
\end{figure}

Based on the cumulated variable importance in the projection (VIPc),
features of interest were \mbox{selected} (Table~\ref{tab2}). Interestingly,
the Top30 VIPc variables related to this discrimination were from the
FA profiles except one from specialized metabolism. The only feature
from another data block corresponded to the feature
${m/z}=415.2713~[\mathrm{M}-\mathrm{H}]^{-}$ eluted at 15.03~min and
annotated as the tricarboxylic FA agaric acid 
($\mathrm{C}_{22}\mathrm{H}_{40}\mathrm{O}_{7}$, calculated ${m/z}$
415.2697, ${\Updelta}$ppm~3.85). Interestingly, some of the FAs only
present in the extracts obtained using the MES media were BCFAs or
PUFAs such as 4,8,12-TM 13:0, \textit{iso}-18:0, 
\textit{anteiso}-18:0, 18:0-DMA, \textit{br}-18:0, 20:4, 20:5.


\begin{table*}
\caption{\label{tab2}Most altered features (Top30) regarding MES media
and all other culture conditions highlighted by MBPLS-DA
(Figure~\ref{fig6}) and selected based on cumulated variable importance
in the projection (VIPc)}
\tabcolsep=12pt
\begin{tabular}{ccccc}
\thead
Features$^{\mathrm{a}}$  &amp; {Block}  &amp; {VIPc}  &amp;
\parbox[t]{2.2cm}{\centering Overexpressed in MES}  &amp;
Annotation$^{\mathrm{b}}$\vspace*{2pt}\\
\endthead
{22:2}  &amp; FA &amp; 0.0504 &amp; Yes &amp; 8,13-docosenoic acid \\
4,8,12-TM 13:0$^{\mathrm{c}}$  &amp; FA &amp; 0.0494 &amp; Yes &amp; 4,8,12-trimethyltridecylic acid \\
{\textit{iso}-17:0}  &amp; FA &amp; 0.0493 &amp; Yes &amp; Isomargaric acid \\
{20:1}  &amp; FA &amp; 0.0491 &amp; Yes &amp; Eicosenoic acid \\
{\textit{anteiso}-17:0}  &amp; FA &amp; 0.0442 &amp; Yes &amp; Anteisomargaric acid \\
{20:2}  &amp; FA &amp; 0.0439 &amp; Yes &amp; 6,11-eicosenoic acid \\
{20:4}  &amp; FA &amp; 0.0439 &amp; Yes &amp; Eicosatetraenoic acid \\
{16:1}  &amp; FA &amp; 0.0423 &amp; Yes &amp; Palmitoleic acid \\
{20:5}  &amp; FA &amp; 0.0403 &amp; Yes &amp; Eicosapentaenoic \\
{\textit{iso}-16:0} &amp; FA &amp; 0.0304 &amp; Yes &amp; Isopalmitic acid \\
{\textit{iso}-18:0} &amp; FA &amp; 0.0287 &amp; Yes &amp; Isostearic acid \\
{15:0} &amp; FA &amp; 0.0284 &amp; Yes &amp;         Pentadecylic acid \\
{\textit{anteiso}-18:0} &amp; FA &amp; 0.0279 &amp; Yes &amp; Anteisostearic acid \\
18:0 DMA$^{\mathrm{d}}$ &amp; FA &amp; 0.0268 &amp; Yes &amp; Dimethyl acetal stearic acid \\
{\textit{br}-18:0} &amp; FA &amp; 0.0257 &amp; Yes &amp;      Branched octadecanoic acid \\
{16:0} &amp; FA &amp; 0.0218 &amp; Yes &amp;         Palmitic acid \\
{18:0} &amp; FA &amp; 0.0170 &amp; - &amp;           Stearic acid \\
{14:0} &amp; FA &amp; 0.0142 &amp; Yes &amp;         Myristic acid \\
{19:0} &amp; FA &amp; 0.0141 &amp; Yes &amp; Nonadecylic acid \\
{20:0} &amp; FA &amp; 0.0139 &amp; - &amp;   Arachidic acid \\
{17:0} &amp; FA &amp; 0.0108 &amp; - &amp;   Margaric acid \\
{18:1} &amp; FA &amp; 0.0106 &amp; - &amp;   Oleic acid \\
{22:0} &amp; FA &amp; 0.0088 &amp; - &amp;   Behenic acid \\
{12:0} &amp; FA &amp; 0.0087 &amp; Yes &amp; Lauric acid \\
{24:0} &amp; FA &amp; 0.0084 &amp; - &amp;   Lignoceric acid \\
{18:2} &amp; FA &amp; 0.0082 &amp; - &amp;   Linoleic acid \\
{17:1} &amp; FA &amp; 0.0043 &amp; - &amp; 9-heptadecenoic acid \\
{22:1} &amp; FA &amp; 0.0033 &amp; Yes &amp; Docosenoic acid \\
{19:1} &amp; FA &amp; 0.0031 &amp; - &amp; Nonadecenoic acid \\
{15.03neg415.2713}  &amp; SM NI &amp; 0.0012 &amp; Yes &amp; Agaric acid~\cite{60,61}
\botline
\end{tabular}
\tabnote{$^{\mathrm{a}}$ in GC-MS, data features were indicated based on their
abbreviated names; for LC-HRMS features, they were indicated by RT,
ionisation mode, and $m/z$ value; $^{\mathrm{b}}$ for GC-MS,
annotation is not putative, in contrary to LC-MS annotation; 
$^{\mathrm{c}}$ TM: trimethyl; $^{\mathrm{d}}$ DMA: dimethyl acetal.}
\end{table*}

\section{Discussion}\label{sec4}

Several studies have previously demonstrated the high capacity of fungi
to adapt their metabolic processes to environmental variables such as
temperature and salinity conditions~\cite{16,62,63,64,65}. It was 
\mbox{proposed}
that fungi are able to reshape the molecular structure of their cell
membrane through modification of their lipid composition such as
modification of saturated, unsaturated, branched, or cyclic FA contents
in individual PLs~\cite{66,67}. In addition, it is a now well-known
fact that expression of biosynthetic gene clusters responsible for the
production of specialised metabolites is under the control of various
biotic or abiotic factors~\cite{28,68}. In this way, many natural
products have been discovered following culture screenings using the
so-called OSMAC approach~\cite{26,28}. In the present study, we
envisaged exploring the global metabolome variations induced by media
modifications by analysing both the lipidome including FA composition
of lipids, and the specialised metabolome of \textit{P.~restrictum}.
Unlike many studies, the choice of growing conditions was dictated by
ecological considerations to understand the influence of marine
salinity and the microenvironment of the original holobiont of the
fungal strain studied. This was achieved by using a medium based on
mussel flesh and reconstituted with seawater, a medium that tended to
come as close as possible---under laboratory conditions---to the
original conditions in which the strain was naturally growing.


The supervised multiblock data mining strategy revealed a clear effect
of addition of SSW in the culture medium in all acquired data
(specialised and lipidic profiles with 
the same statistical importance---Figure~\ref{fig6}B). 
However, this also highlighted that,
even if in all OSMAC evaluated conditions a salt attrition effect was
observed, it is not constant and specificities could be observed in
some culture media~\cite{69}. Thus, culturing the fungal strain on just
one medium usually could not highlight a general effect of
salinity \unskip\break \cite{25,70}. The most altered features
corresponded to FAs (Table~\ref{tab1}). Stahl and Klug reported that
in fungi the most common
and abundant FAs are 16:0, 18:0, 18:1, 18:2 that can represent up to
95\% of \mbox{total} FA content after analysing 100 strains of filamentous
fungi including oomycetes, zygomycetes, \mbox{ascomycetes} and
basidiomycetes~\cite{54}. Similar lipidic compositions were further
confirmed in previously published data~\cite{31,46,55,71,72,73}.
However, lipids in fungi can be extremely diverse and more unusual
compounds such as PUFAs 18:3, 20:4 or 22:6 can also been found to be
produced in some conditions~\cite{46,72,74,75}.


Various studies have reported the impact of osmotic stress or salinity
on membrane fluidity through modification of FA saturation. In fact,
modification of FA composition is essential for normal cell function in
response to salinity change, mainly by maintaining fluidity or inducing
rigidity of the cell membrane~\cite{76}. However, the observed effect
remains different according to the fungal strains studied. In the
literature, several studies in yeasts reported that an increase in
salinity enhanced the rate of FA unsaturation (18:1, 18:2)~\cite{20,62,67,77,78}. 
In the case of \textit{Debaryomyces hansenii}, such an
increase may appear only at a high level of NaCl~\cite{20}. In
contrary, in the case of the yeast \textit{Yarrowia lipolytica}, such a
stress did not induce significant changes in the unsaturation of
FAs~\cite{15}. Furthermore, in filamentous fungi such as \textit{P.
notatum}, \textit{P. chrysogenum} and \textit{Aspergillus flavus} the
presence of NaCl in culture media increased the abundance of saturated
FAs (16:0, 18:0), while significantly decreasing the unsaturated
ones~\cite{62}. One study, focused on a marine-derived
\textit{Epicoccum nigrum}, also highlighted changes in FA composition
with the observation of a lower proportion of the unsaturated 18:1,
18:2 as an adaptive response to salinity stress in acidic
conditions~\cite{77}, even if this observation was strongly influenced
by pH and temperature. In this way, our study was consistent with
literature, where FA composition was clearly altered by the culture
condition modifications evaluated~\cite{31,46,54,73}, where
differences were observed not only in the diversity and types of FAs,
but also in their relative concentrations. The most abundant FAs,
whatever the culture conditions, were found to be 16:0, 18:0, 18:1 and
18:2, representing more than 90\% of total FA content. The use of SSW
did not modify the production of these four main FAs, but it increased
the production of linear or branched minor even-chain length FAs,
which, except the 20:5, all corresponded to saturated FAs (12:0, 14:0,
\textit{br}-16:0, \textit{br}-18:0, 26:0). In comparison, FAs
overproduced in DW conditions corresponded to the unsaturated 19:1,
14:1 and \textit{cj}-18:2, together with the saturated 17:0 DMA, 19:0
and 20:0. Altogether, this study further highlights the complex
mechanisms involved in salinity adaptation of fungi.

In addition to FAs, the intensity of two features was highlighted as
significantly enhanced in the presence of SSW. They were putatively
identified as dankasterone B and one of the isomeric cytochalasin
B$_{5}$, IV or 21,22-dihydrocytochalasin B. Dankasterone B is a steroid
isolated from a marine-derived fungus \textit{Penicillium} sp. which
showed significant cytotoxic activity against HL-60, Hela, and K562
with IC$_{50}$ values of 3.25, 4.74, and $7.89~\upmu\mathrm{M}$,
respectively~\cite{57}. Cytochalasin B$_{5}$ and isomers are complex
phenylalanine-derived macrolides belonging to the cytochalasan family,
a class of fungal natural products produced by a large range of fungi
from various---including marine---ecological
niches. Cytochalasins all show various biological activities but are
considered mainly as cytostatic or cytotoxic compounds that can be
involved in chemical war against competitors~\cite{79}. However, most
of the features for which a significant overproduction was observed
under SSW conditions could not be annotated using the natural products
databases queried. This result further demonstrates that salinity can
induce specialised metabolism pathways and then the production of
potential bioactive metabolites that can occur in situ but remain
cryptic in artificial laboratory conditions~\cite{25,80,81,82,83,84}.

Regarding the metabolic consequences of using mussel flesh in the
culture medium instead of yeast extract, a preliminary analysis of the
data as simple as the unsupervised PCA clearly pointed out an effect of
these environmentally relevant culture conditions. In addition, the use
of the multiblock approach (Figure~\ref{fig5}A) highlighted that each
evaluated part of the metabolism was impacted by the OSMAC strategy.
However, the most significantly altered part of the analysed metabolism
was the FA composition of lipids. This result was unexpected as it was
shown in a previous study focused on specialised metabolites that, when
cultured in the MES-SSW medium, \mbox{\textit{P.~restrictum}} MMS417
overproduced two classes of specialised metabolites, namely
pyran-2-ones and lactam macrolides~\cite{21}. It therefore appears that
the most important response was firstly the induction of lipid
metabolism pathways necessary for the physiological adaptation of the
fungus, before the \mbox{expression} of more complex biosynthetic mechanisms
of specialised metabolism.

As for salinity, mussel flesh had a minor effect on the production of
the four major FAs 16:0, 18:0, 18:1 and 18:2. On the contrary, we
observed drastic metabolic changes in the production of branched FAs
(4,8,12-trimethyl-13:0, \textit{iso}-17:0, \textit{anteiso}-17:0,
\textit{iso}-16:0, \textit{iso}-18:0, \textit{anteiso}-18:0, dimethyl
acetal 18:0 and \textit{br}-18:0) and long-chain FAs (20:1, 20:2, 20:4,
20:5, 22:1, 22:2) which are rather unusual in 
fungi~\cite{46,75,85,86,87,88,89}. Very interestingly, we observed under these conditions the
presence of seven FAs which have never been described in fungi
(4,8,12-trimethyl-13:0, \textit{iso}-18:0, \textit{anteiso}-18:0,
dimethyl acetal 18:0, \mbox{\textit{br}-18:0,} 20:4, 20:5) but which have been
previously reported to be produced by mussels~\cite{90,91} (signals
from blank mediums being removed in the data analysis process). This
observation may be explained by (1)~the presence of signal metabolites
in the mussel extract that initiates such unusual FA production, or
more likely by (2) the presence of their precursors in the mussel
extract that can be incorporated in fungal biosynthetic pipelines
leading to complex lipids such as triglycerides or PLs that include
these exogenous FAs. Such a production of natural products through
mixed biogenetic origins has been little demonstrated so far but it is
hypothetised for the biosynthesis of brominated alkaloids from sponge
and their endosymbiotic microorganisms~\cite{92}, for the
arginine--citrulline biosynthetic loop in the
\mbox{\textit{Amphimedon queenslandica}} holobiont~\cite{93} and for
the sponge/bacterium mixed origin of calyculin A~\cite{94}. In our
model, such a phenomenon would be particularly interesting to confirm
through targeted lipid purification or deep investigation of the two
protagnonists genomes. It is also noteworthy that the intensity of a
feature annotated as agaric acid was significantly enhanced by use of
the MES medium. This tricarboxylic FA, widespread in fungi~\cite{60,61}, 
is a competitive inhibitor of the adenine nucleotide translocase
known to strongly inhibit FA synthesis~\cite{60, 95}, alter sterol
synthesis~\cite{60} and reduce bacterial biofilm formation~\cite{96}.
Thus, it can be hypothesised that this compound could be involved in
the regulation of the mussel bacterial population by \textit{P.
restrictum} within the holobiont microenvironment. In addition, it
could be interesting to know if some other environmental stresses
(other osmotic stresses~\cite{25,97}, microbial coculture~\cite{27},
etc.) or the presence of other holobiont extracts within the medium
(from oysters, sponges~\cite{98}, seeweeds~\cite{99}, etc.) may \mbox{induces}
\mbox{similar} responses on the lipid metabolism in comparison to mussel
extract and/or salt presence.


In conclusion, this study engaged an ecologically-oriented OSMAC
approach to expand fungal chemical diversity using not only specialised
metabolite profiling but also lipid profiling and FA characterisation.
All those data were explored, for the first time in this context,
simultaneously using multiblock data mining strategies (COMDIM and
MBPLS-DA). This approach was proven to be efficacious as it allowed us
to show that under some environmentally relevant conditions not only
the specialised metabolism is strongly affected, but also the whole
cellular physiology of the fungus as revealed by lipid alteration.
Acquiring such a large quantity of chemical data on a single organism
provides valuable insight to explore the biochemical basis of fungi in
regard to interaction with the host after integration of complementary
OMICS data (transcriptomic, proteomics, etc.) along with genome-scale
metabolic modelling~\cite{100,101}.

This is in line with the observations made recently by Carroll et al.
on the importance of using culture conditions close to the environment
in which they naturally grow, when they observed that only 66\% of the
studies carried out on marine fungi in the last five years used
cultures including seawater~\cite{102}. Host-derived media appear
clearly more relevant than classical artificial culture
media~\cite{21,22,23,24}. Such culture condition may not be considered as
strictly environmentally relevant. However, to the best of our
knowledge, it remains currently one of the closest solutions to mimic
host-related conditions at the laboratory level. In the present study,
as we observed the presence of mussel FAs in the lipid composition of
the fungus, we can assume that such lipids of hybrid origin are
naturally produced within the bivalves, which could not have been
observed otherwise. Furthermore, compounds rare or unknown in fungi
were also observed, such as a number of FAs showing the lack of data on
the fungal lipidome. Even more than for natural products, research into
the chemistry of fungal lipids needs to be encouraged and \unskip\break continued.

Finally, it appears that the use of environmentally relevant conditions
for the chemical study of marine microorganisms is essential not only
for the \mbox{discovery} of new bioactive molecules~\cite{102}, but also for
understanding chemical interactions within marine microenvironments.

{\vspace*{-1pt}}

\section*{Declaration of interests}

The authors do not work for, advise, own shares in, or receive funds
from any organization that could \mbox{benefit} from this article, and have
declared no affiliations other than their research organizations.

{\vspace*{-1pt}}

\section*{Author contributions}

OG initiated the project. SB designed the
experimental works and carried out the multi-block analysis. 
MB-B performed the multi-block analysis validation.
AM and GW-C supervised
the GC-MS experiments and their analysis. VR performed the
GC-MS experiments. V-TL was involved in the collection of all
data analysis and performed the all data analysis. V-TL, SB and 
EG perform the LC-HRMS analysis. V-TL
and TRP performed fungal culture.  V-TL and
SB wrote the draft manuscript. All authors revised, read,
and approved the final version of the manuscript.

%\section*{Compliance with ethical standards}

{\vspace*{-1pt}}

\section*{Ethical approval}

This article does not contain any study with human and/or animal
participants performed by any of the authors.

{\vspace*{-1pt}}

\section*{Data availability}

Raw data of the LC-HRMS and GCMS data along extracted data matrices are
available at Recherche Data Gouv
\url{https://entrepot.recherche.data.gouv.fr/dataset.xhtml?persistentId=doi:10.57745/LYEUWW} 
(deposited on November 27 12:25:59 CET 2023).

{\vspace*{-1pt}}

\section*{Funding}
Authors are grateful to the Vietnam International Education Development
(VIED) program for PhD for financial support.

{\vspace*{-1pt}}

\section*{Acknowledgments}
This work is a part of the PhD thesis of the first author V-TL. 
Authors would like to thank the Corsaire-ThalassOMICS Metabolomics
Core Facility of Biogenouest, Nantes Universit\'{e} for supporting
LC-HRMS analyses.

\back{}

\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-276-suppl.pdf}}

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