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\DOI{10.5802/crchim.248}
\datereceived{2023-03-31}
\daterevised{2023-06-23}
\datererevised{2023-07-20}
\dateaccepted{2023-07-21}
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\xmorerows{1}{Molecular families} &amp; \xmorerows{1}{Compound name} &amp; 
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&amp; \multicolumn{2}{c}{Peak area} \\\cline{4-5}
&amp; &amp;  &amp; \parbox[t]{2.5cm}{\centering{Dried fruit}\break  {(\%)}} &amp; 
\parbox[t]{2.5cm}{\centering {Fruit hydrolate} {(\%)}}\vspace*{2pt} \\
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\dateposted{2023-11-21}
\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

\title{Composition of antifungal volatile organic compounds in
\protect\textit{Sextonia rubra} fruit by molecular networks}

\alttitle{Annotations des compos\'es organiques volatils antifongiques de
fruits de Sextonia rubra par r\'eseaux mol\'eculaires}

\author{\firstname{Marceau} \lastname{Levasseur}\CDRorcid{0000-0002-2735-5501}}
\address{CNRS, Institut de Chimie des Substances Naturelles (ICSN),
UPR2301, Universit\'{e} Paris-Saclay, Avenue de la Terrasse, 91~198
Gif-sur-Yvette, France}
\email[M. Levasseur]{marceau.levasseur@cnrs.fr}

\author{\firstname{Cyrille} \lastname{Santerre}\CDRorcid{0000-0002-8106-7287}}
\address{CEA, CNRS, Inserm, Laboratoire d'Imagerie Biom\'{e}dicale
Multimodale Paris Saclay (BioMaps), Service Hospitalier
Fr\'{e}d\'{e}ric Joliot, Universit\'{e} Paris-Saclay, 4 place du
g\'{e}n\'{e}ral Leclerc, 91~401 Orsay, France}
\address{Institut Sup\'{e}rieur International Parfum Cosm\'{e}tique Ar\^{o}mes,
Plate-forme scientifique, ISIPCA, 34-36 rue du parc de Clagny, 78~000
Versailles, France}
\email[C. Santerre]{csanterre@isipca-lafabrique.fr}

\author{\firstname{Juliette} \lastname{Segret}\CDRorcid{0009-0002-9357-6924}}
\addressSameAs{1}{CNRS, Institut de Chimie des Substances Naturelles
(ICSN), UPR2301, Universit\'{e} Paris-Saclay, Avenue de la Terrasse,
91~198 Gif-sur-Yvette, France}
\email[J. Segret]{juliette.segret@cnrs.fr}

\author{\firstname{Nicolas} \lastname{Elie}\CDRorcid{0000-0002-8733-0971}}
\addressSameAs{1}{CNRS, Institut de Chimie des Substances Naturelles
(ICSN), UPR2301, Universit\'{e} Paris-Saclay, Avenue de la Terrasse,
91~198 Gif-sur-Yvette, France}
\email[N. Elie]{nicolas.elie@cnrs.fr}

\author{\firstname{Christophe} \lastname{Genty}}
\address{CNRS, Laboratoire de Chimie Mol\'{e}culaire (LCM), UMR 9168,
\'{E}cole Polytechnique, Institut Polytechnique de Paris, Route de
Saclay, 91~128 Palaiseau, France}
\email[C. Genty]{christophe.genty@polytechnique.ed}

\author{\firstname{Yannick} \lastname{Estevez}}
\address{CNRS, UMR EcoFoG, AgroParisTech, CIRAD, INRAE, Universit\'{e}
des Antilles, Universit\'{e} de Guyane, 97~300 Cayenne, France}
\email[Y. Estevez]{yannick.estevez@cnrs.fr}

\author{\firstname{Nadine} \lastname{Amusant}}
\address{CIRAD, UMR EcoFoG, AgroParisTech, CNRS, INRAE, Universit\'{e}
des Antilles, Universit\'{e} de Guyane, 97~300 Cayenne, France}
\email[N. Amusant]{nadine.amusant@cirad.fr}

\author{\firstname{Emeline} \lastname{Hou\"{e}l}\CDRorcid{0000-0003-4888-2332}}
\addressSameAs{5}{CNRS, UMR EcoFoG, AgroParisTech, CIRAD, INRAE, Universit\'{e}
des Antilles, Universit\'{e} de Guyane, 97~300 Cayenne, France}
\address{Sorbonne Universit\'{e}, CNRS, Laboratoire de Biodiversit\'{e}
et Biotechnologies Microbiennes, LBBM, Observatoire Oc\'{e}anologique,
66 650 Banyuls-sur-Mer, France}
\email[E. Hou\"{e}l]{emeline.houel@cnrs.fr}

\author{\firstname{V\'{e}ronique} \lastname{Eparvier}\CDRorcid{0000-0002-2954-0866}\IsCorresp}
\addressSameAs{1}{CNRS, Institut de Chimie des Substances Naturelles
(ICSN), UPR2301, Universit\'{e} Paris-Saclay, Avenue de la Terrasse,
91~198 Gif-sur-Yvette, France}
\email[V. Eparvier]{veronique.eparvier@cnrs.fr}

\author{\firstname{David} \lastname{Touboul}\CDRorcid{0000-0003-2751-774X}\IsCorresp}
\addressSameAs{1}{CNRS, Institut de Chimie des Substances Naturelles
(ICSN), UPR2301, Universit\'{e} Paris-Saclay, Avenue de la Terrasse,
91~198 Gif-sur-Yvette, France}
\addressSameAs{4}{CNRS, Laboratoire de Chimie Mol\'{e}culaire (LCM),
UMR 9168, \'{E}cole Polytechnique, Institut Polytechnique de Paris,
Route de Saclay, 91~128 Palaiseau, France}
\email[D. Touboul]{david.touboul@cnrs.fr}

\shortrunauthors

\keywords{\kwd{\textit{Sextonia rubra}}
\kwd{Fruit}
\kwd{Hydrolate}
\kwd{VOC}
\kwd{Molecular network}
\kwd{Antifungal}}

\altkeywords{\kwd{Sextonia rubra}
\kwd{Fruit}
\kwd{Hydrolat}
\kwd{COV}
\kwd{R\'eseaux mol\'eculaires}
\kwd{Antifongique}}

\thanks{This work has received financial support from the CNRS through
the 80 $|$ Prime program}

\begin{abstract}
\textit{Sextonia rubra} {is a tropical tree exploited for its durable
wood.} {Hydrodistillation of its fruit was
performed for the first time and the resulting hydrolate was} {analyzed}
{by headspace{-}solid phase
microextraction{-}gas chromatography{-}mass spectrometry} (HS-SPME-GC-MS)
{together} {with the dried fruit.} {Data analysis was performed by
conventional methods and by molecular networks using MetGem software.
Our methodology proved to be efficient to annotate the compounds 
 detected complementary to the use of MassHunter Unknown Analysis.}
 
{Hydrolate and dried fruit both
displayed a rich and diverse HS-SPME-GC-MS sesquiterpenic profile,
alongside with moderate antimicrobial activity towards the filamentous
fungus} {\textit{Tricophyton rubrum}}
{for the hydrolate.}
\end{abstract}

\begin{altabstract}
\textit{Sextonia rubra} est un arbre tropical exploit\'e pour son bois
durable. Pour la premi\`ere fois, une hydrodistillation de ses fruits a
\'et\'e r\'ealis\'e. L'hydrolat r\'esultant et les fruits s\'ech\'es ont \'et\'e analys\'es
en espace de t\^ete-microextraction en phase solide-chromatographie en
phase gazeuse-spectrom\'etrie de masse (ET-MEPS-CG-SM). Le traitement des
donn\'ees a \'et\'e r\'ealis\'e par des m\'ethodes conventionnelles et \`a laide de
r\'eseaux mol\'eculaires en utilisant le logiciel MetGem. Notre
m\'ethodologie sest av\'er\'ee pertinente pour annoter les compos\'es d\'etect\'es
et ce, de mani\`ere compl\'ementaire \`a l'utilisation de MassHunter Unknown
Analysis. L'hydrolat et les fruits s\'ech\'es poss\`edent tous deux des
profils riches et divers majoritairement repr\'esent\'e par des
sesquiterp\`enes. L'hydrolat poss\`ede une activit\'e antimicrobienne mod\'er\'ee
contre le champignon filamenteux \textit{Tricophyton rubrum}.
\end{altabstract}

\maketitle

\vspace*{10pt}
\twocolumngrid

\end{noXML}

\section{Introduction}\label{sec1}


\textit{Sextonia rubra} (Mez.) van der Werff (Lauraceae) is a tropical
tree endemic to the Guiana Shield and the Brazilian Amazonia \cite{1}.
It is known for its heartwood natural durability and its use as a
building material \cite{2}. The natural durability of its wood comes
from two main {extractives,}
rubrynolide and rubrenolide, both of which
have antifungal and insecticidal activity \cite{3,4,5,6}.\looseness=-1 

Although this species has been known since 1889, its fruit had never
been studied in terms of chemical composition. Moreover, no report of
any use is described in the literature.

Fruit generally contains specialized metabolites involved in the
attraction of pollinators or the deterrence of pathogens/predators,
thus involved in their dispersal and defense\cite{7,8,9,10}.
It should be noted that the pollinators of \textit{S. rubra} flowers
are still unknown \cite{11}. Specialized fruit metabolites belong to
various molecular families \cite{8,12}. These are of high
interest from economic and health points of view due to the antioxidant or
antimicrobial activities of fruit essential oils and hydrolates
\cite{13,14}. 

The characterization of the volatile organic compounds (VOCs) is
routinely performed by gas chromatography (GC) coupled to a mass
spectrometer (MS) or a flame ionization detector (FID). The data generated
can be compared to those present in databases such as the National
Institute of Standards and Technology (NIST) or Wiley's SpectraBase
\cite{15}. More recently, we introduced a workflow for GC-EI-MS
data analysis that includes the visualization of the spectra collection
as a molecular network \cite{16,17}. This method allows the fast
and reliable classification of the 
{EI}{-}MS (electron ionization) data leading to accurate
molecule class identification together with home-made databases. We
converted the NIST and Wiley spectral libraries in order to make them
interpretable by the MetGem software. Headspace{-}solid{-}phase
microextraction{-}GC{-}MS (HS-SPME-GC-MS) \cite{18} and molecular
networking analysis were combined to identify the VOCs diversity of
\textit{S. rubra} dried fruit and its hydrolate. The biological
activity of the hydrolate against 3 human pathogens, namely
\textit{Candida albicans}, methicillin{-}resistant
\textit{Staphylococcus aureus} and \textit{Tricophyton rubrum}, was
tested. Finally, its cytotoxic activity was studied with human MRC-5
cell line.


{\vspace*{-2pt}}

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

\subsection{Plant material}\label{sec21}

{\vspace*{-2pt}}


During a sampling campaign in French Guiana in October 2020, a mature
tree, belonging to the species \textit{Sextonia rubra}, was sampled
(5{\textdegree}16$'$846$''$~N~52{\textdegree}54$'$938$''$~W, near the
Paracou research station, between Kourou and Sinnamary, in a typical
\textit{terra firme} forest area; Permit: ABSCH-CNA-FR-240495-4). Its
fruit, probably immature (see Figure~S4 in Supplementary Data), was
collected and then stored at ${+}4$~{\textdegree}C in the field and
then at ${-}80$~{\textdegree}C pending analysis. Finally, the pieces of
fruit were dried at room temperature (20{--}25~{\textdegree}C) due to
their small volume  (${&lt;}$2~cm$^{3}$) and then finely ground in a
mortar.\looseness=-1

{\vspace*{-2pt}}

\subsection{Hydrodistillation}\label{sec22}

{\vspace*{-2pt}}

A weight of 50.13 g of dried fruit was introduced into a flask with a
volumetric capacity of 1~L into which 500~mL H$_2$O
Milli{-}Q$^{\text{\textregistered}}$ was introduced. The flask was connected to a
Clevenger apparatus, covered with aluminum foil, and heated to
80--90~{\textdegree}C (see Figure~S1 in SI). The
coolant for the VOC condensation was a mixture H$_2$O/EtOH (30\%/70\%,
v/v) cooled to 0~{\textdegree}C. The first condensate was obtained after
2~h of heating and collected up to 5~h to obtain a total volume of
18~mL of hydrolate. Hydrodistillation was repeated twice.

{\vspace*{-2pt}}

\subsection{HS-SPME-GC-MS analyses}\label{sec23} 

{\vspace*{-2pt}}

A volume of 5~mL of
hydrolate or 5~g of dried fruit was introduced into a headspace vial
(HS), the liquid/gaseous phase equilibration took place for more than
24 h, and then exposed to a conditioned (260~{\textdegree}C for 20~min)
solid{-}phase micro{-}extraction (SPME) fiber Divinylbenzene Carbon
Wide Range Polydimethylsiloxane (DVB/C{-}WR/PDMS),
80~${\upmu}$m, Agilent Technologies, Waldbronn, Germany) for
5~min without heating.
The split ratio was 100:1 for the hydrolate
sample and splitless for the dried fruit sample. Following this step,
the compounds retained by the fiber were desorbed by introducing the
fiber into the GC injection port at 250~{\textdegree}C for 3~min and
data acquisition was simultaneous with desorption. The analyses were
performed on an Agilent 7890B{-}GC (Agilent Technologies, Waldbronn,
Germany) coupled to an Agilent 7000D triple{-}Quadrupole{-}GC (Agilent
Technologies, Waldbronn, Germany). Volatile compounds were separated on
a HP{-}5ms column ($30~\mathrm{m}\times 0.25~\mathrm{mm} \times0.25~{\upmu}$m, Agilent
Technologies, Waldbronn, Germany) subjected to the following
temperature gradient: initial temperature ${+}$50~{\textdegree}C, ramped
at ${+}$20~{\textdegree}C${\cdot}$min$^{-1}$ to 150~{\textdegree}C, then
ramped at $+$10~{\textdegree}C${\cdot}$min$^{-1}$ to
${+}$210~{\textdegree}C, finally ramped at
${+}$20~{\textdegree}C${\cdot}$min$^{-1}$ to ${+}$320~{\textdegree}C. The
chromatographic conditions were optimized to separate the molecular
diversity of each molecular family. Helium was the carrier gas at a
flow rate of 1.2~mL${\cdot}$min$^{-1}$. Detection parameters were set 
as follows:  {electron energy} 70~eV, ion source
temperature ${+}$150~{\textdegree}C, MS quadrupole temperature
${+}230$~{\textdegree}C, mass scanning range \textit{m/z} 50--500,
scanning rate: 3.5~scans${\cdot}$s$^{-1}$. 
\vspace*{-.4pc}

\subsection{Data processing}\label{sec24}
\vspace*{-.2pc}

Chemical diversity analysis of fruit VOCs was performed on MassHunter
Unknown Analysis (Agilent, B.09.00), using the coupled spectral
libraries from Wiley (2011) and NIST (2017). Default parameters were
retained for the analyses with the exception of annotation assignment
where only those with a score above 80\% were considered. The retention
indexes were determined by querying databases. In parallel, molecular
networks of the HS-SPME-GC-MS data were created \cite{16}. The raw
data in .d were converted to .mzML using the Peak Picking function of
MSConvert (v. 3.0.20344), a module of the Proteowizard package
\cite{19}, the CWT algorithm with ``SNR $=$ 0'', and ``Peak Spacing $=$ 0''.
The .mzML files were then imported into MZmine (v. 2.53)
\cite{20}. Only the ions detected above 500~arbitrary unit (a.u.)
of intensity were kept in the analyses: ``Noise level $=$ 500''. A
chromatogram construction step according to the ADAP algorithm was
performed with the following parameters: ``minimum group size $=$
3~scans'', ``group intensity threshold $=$ 500'', ``minimum 
highest  intensity
$=$ 500'', ``{\textit{m/z}} {tolerance $=$ 0.3~or 900~ppm''. Then
chromatogram deconvolution was performed
according to the following parameters: ``S/N
threshold $=$ 7'', ``minimum feature height $=$ 500'', ``coefficient area
threshold $=$ 50'', ``peak duration range $=$ 0.01--0.6~min'' and ``retention
time (RT) wavelet range $=$ 0.01--0.06''. A spectral
deconvolution was \mbox{performed} using Multivariate Curve Resolution
algorithm with ``deconvolution window width $=$ 0.2~min'', ``RT tolerance $=$
0.05'' and ``minimum number of peaks $=$ 1''. A list of peaks was generated
by alignment using the ``Join Aligner'' function with ``}{\textit{m/z}}
{tolerance $=$ 0.3 or 900 ppm'', ``weight for} {\textit{m/z}} $=$ 80'', ``RT
tolerance $=$ 0.1~min'' and ``Weight for RT $=$ 20''. The data were exported
in .mgf and the metadata\break in .csv.

\subsection{Molecular network by MetGem}\label{sec25}

{Molecular networks were created using MetGem software 
{(}v. 1.3.6 -- 20220401 -- Nightly Cl builds:\break 
\href{https://github.com/metgem/metgem/releases/tag/nightly}{https://github.com/metgem/metgem/releases/tag/\ubreak nightly}).
The parameters for the cosine score calculation were: ``}{\textit{m/z}}
{$=$ 0.3'', ``minimum matched peaks $=$ 3'' (MMP) and the option
``treat as MS1 data'' was used. t-SNE representation must be performed
with MMP ${\geq}$ 3 for electroionization analyses. The parameters to
create the t-SNE representation were: ``cosine score(s) above: 0.6'',
``number of iterations $=$ 10,000'', ``learning rate $=$ 10'',
``perplexity $=$ 6'' and ``early exaggeration $=$ 12''. Annotation was
performed by querying the coupled spectral libraries from Wiley (2011)
and NIST (2017). The MMP used to match the experimental spectra to
those in the databases was 5, although many peaks are present in the
spectra obtained by electroionization. This parameter can be modified
after an initial analysis according to the molecular families studied.
The data in 
{native} 
format were converted by the software Lib2NIST
Library Conversion Tool v. 1.0.6.3 into .msp, a format interpretable by
MetGem. Then the .msp was modified by replacing the ``Names'' in
``NAMES'' and the ``Exact Mass: x'' in ``Exact Mass: 0'' so that no
correspondence of} {\textit{m/z}} of parent ions is established between
the databases and the data resulting from the analyses in
HS-SPME-GC-MS.\looseness=-1

\subsection{Antimicrobial assays}\label{sec26}

The hydrolate antimicrobial activity was determined against the
following pathogenic strains: {\textit{Candida albicans}} {ATCC10231,
methicillin-resistant} {\textit{Staphylococcus aureus}} {(MRSA)
ATCC33591,} {\textit{Tricophyton rubrum}} {SNB-TR1. A negative control
without microorganism was performed. Fungal and bacterial cultures are
performed with Roswell Park Memorial Institute ({RPMI})
and Mueller Hinton media respectively. The
concentration of essential oil (EO) in the hydrolate was unknown,
hence we performed dilutions of the hydrolate in RPMI or
Mueller--Hinton culture medium} \cite{21,22}. Eight dilutions
were performed in duplicate for each microorganism with  the following
hydrolate: culture medium ratios: 100:0, 80:20, 60:40, 20:80, 10:90,
5:95, and 2.5:97.5, in other words
dilutions to 0\%, 20\%, 40\%, 80\%, 90\%, 97.5\%. Negative controls
with sterile H$_2$O Milli-Q$^{\text{\textregistered}}$ followed the same
relative concentrations as the tests. The positive controls were
 performed using
antibiotics solubilized in dimethyl sulfoxide (DMSO, Sigma-Aldrich,
Saint-Quentin-Fallavier, France) to obtain the following mass
concentrations: vancomycin at 0.64~mg${\cdot}$mL$^{-1}$ against MRSA
and fluconazole at 1.28~mg${\cdot}$mL$^{-1}$ {against} {\textit{C.
albicans}} {and} {\textit{T. rubrum}} {before cascade dilutions. The
reading of the activity tests against MRSA was performed after 24~h,
3~days for} {\textit{C. albicans}} {and 5~days for} {\textit{T.
rubrum}}. Each test was performed in duplicate and was repeated twice.


\subsection{Cytotoxic assays}\label{sec27}

The MRC-5 cell line derived from normal lung tissue was obtained from
the American Type Culture Collection (Rockville, MD, USA), and was
cultured according to the supplier's instructions. Human MRC-5 cells
were grown in DMEM supplemented with 10\% fetal calf serum (FCS) and
1\% glutamine. The cell line was maintained at 37~{\textdegree}C in a
humidified atmosphere containing 5\% CO$_2$. Cell viability was
determined by a luminescent assay according to the manufacturer's
instructions (Promega, Madison, WI, USA). For IC$_{50}$ determination,
the cells were seeded in 96-well plates ($3\times 10^{3}$ cells/well)
containing 100~${\upmu}$L of growth medium. After 24~h, the
cells were treated with the hydrolate at 3 different relative
concentrations, {i.e.}, 10\%, 5\% and 1\%. Each concentration was
obtained from the stock solution. Experiments were performed in
triplicate. After 72~h of incubation, 100~${\upmu}$L of
CellTiter Glo reagent was added for 15~min before recording the
luminescence with the spectrophotometric plate reader PolarStar Omega
(BMG LabTech). The dose-response curves were plotted with Graph Prism
software, and the IC$_{50}$ values were calculated from the polynomial
curves using the Graph Prism software.

\section{Results}\label{sec3}

\subsection{Characterization of the volatile chemical diversity of
\textit{S. rubra} hydrolate and dried fruit by
conventional methods}\label{sec31}


The dried fruit was hydrodistilled to obtain a hydrolate. It smells
like cut grass and to a lesser extent like mango. VOCs
with a cut grass smell are mainly C$_{6}$ then C$_{7}$ and C$_{9}$
oxygenated molecules such as n{-}hexanol, n{-}hexanal, n-heptenal,
n-nonanol, n-nonanal, n-nonanone and other saturated or unsaturated
alcohols and aldehydes \cite{23,24}. Among these compounds, n-hexanal, 
{2-hexenal,} heptanal and nonanal were detected but at low
relative intensities ($&lt;$1\% for each). Some characteristic molecules
of the mango fruit odor are also detected in the \textit{S. rubra}
fruit hydrolate, such as n{-}hexanal, 2{-}hexenal, heptanal,
${\upalpha}${-}pinene, ${\upbeta}${-}pinene, limonene, nonanal and
${\upalpha}${-}humulene. However, certain major mango VOCs such as
{${\updelta}$}-3-carene, terpinolene and myrcene were not detected (see
Table~\ref{tab1} and Cuevas{-}Glory \etal \cite{25}).

\begin{table*} %tab1
\caption{\label{tab1}Analysis of the main VOCs detected by
HS-SPME-GC-MS}
\tabcolsep=2pt
\begin{tabular}{cccccc}
\thead
\xmorerows{1}{Molecular families} &amp; \xmorerows{1}{Compound name} &amp; 
\morerows{1}{\parbox[t]{2.5cm}{\centering Retention index (HP$_5$-ms)}}
&amp; \multicolumn{2}{c}{Peak area} \\\cline{4-5}
&amp; &amp;  &amp; \parbox[t]{1.5cm}{\centering{Dried fruit}  {(\%)}} &amp; 
\parbox[t]{2cm}{\centering {Fruit hydrolate} {(\%)}} \\
\endthead
Acetate &amp; Phenacylidene diacetate &amp; ND &amp; -- &amp; 4.2 \\
Aldehyde &amp; n-Hexanal &amp; 800 &amp; -- &amp; 0.9 \\
Aldehyde &amp; 2-Hexenal &amp; 853 &amp; -- &amp; 0.3 \\
Aldehyde &amp; Heptanal &amp; 901 &amp; -- &amp; 0.4 \\
Aldehyde &amp; Nonanal &amp; 1105 &amp; -- &amp; 0.1\vspace*{5pt} \\
Alkane &amp; 1,1-Dimethyl-cyclopentane &amp; ND &amp; -- &amp; 0.1\vspace*{5pt} \\
Alkene &amp; Dehydro-ar-ionene &amp; 1355 &amp; 0.3 &amp; --\vspace*{5pt} \\
Amino acid &amp; Anthranillic acid &amp; ND &amp; -- &amp; 0.2 \vspace*{5pt}\\
Ester derivatives &amp; 4-Methylbenzoic acid ethenyl ester &amp; ND &amp; -- &amp; 0.4 \vspace*{5pt}\\
Indene &amp; 1-Ethyl-trans-3-n-heptyl-2,3-dihydroindene &amp; ND &amp; -- &amp; 0.8 \vspace*{5pt}\\
Ketone &amp; Sulcatone &amp; 987 &amp; -- &amp; 0.5\vspace*{5pt} \\
MH &amp; ${\upalpha}$-Pinene &amp; 939 &amp; 26.8 &amp; 1.6 \\
MH &amp; Camphene &amp; 955 &amp; 1.9 &amp; -- \\
MH &amp; ${\upbeta}$-Sabinene &amp; 961 &amp; 10.3 &amp; -- \\
MH &amp; ${\upbeta}$-Pinene &amp; 964 &amp; -- &amp; 0.6 \\
MH &amp; \textit{o}-Cymene &amp; 1026 &amp; 0.6 &amp; 0.4 \\
MH &amp; 1,4-Dimethyl-4-vinyl-1-cyclohexene &amp; ND &amp; -- &amp; 0.6 \\
MH &amp; Camphene &amp; 954 &amp; -- &amp; 0.3 \\
MH &amp; \textit{(R)}-Limonene &amp; 1030 &amp; 0.6 &amp; -- \vspace*{5pt}\\
OM &amp; Linalool &amp; 1215 &amp; 0.5 &amp; -- \\
OM &amp; Eucalyptol &amp; 1028 &amp; 2.8 &amp; 13.5 \\
OM &amp; ${\upalpha}$-Phenetylethanol &amp; 1061 &amp; -- &amp; 0.4 \\
OM &amp; ${\upalpha}$-Campholenal &amp; 1125 &amp; -- &amp; 0.1 \\
OM &amp; Fenchol &amp; 1126 &amp; -- &amp; 0.9 \\
OM &amp; Dehydroborneol &amp; ND &amp; -- &amp; 0.9 \\
OM &amp; 4-Acetyl-1-methylcyclohexene &amp; 1131 &amp; -- &amp; 0.6 \\
OM &amp; \textit{(1R)}-(\textit{2R})-Nopinone &amp; 1142 &amp; -- &amp; 0.7 \\
OM &amp; ${\upalpha}$-Thujol &amp; ND &amp; -- &amp; 0.3 \\
OM &amp; Pinocarvone &amp; 1164 &amp; -- &amp; 0.3 \\
OM &amp; Borneol &amp; 1165 &amp; -- &amp; 1.8 \\
OM &amp; Terpinen-4-ol &amp; 1178 &amp; -- &amp; 0.4 \\
OM &amp; \textit{(S)}-${\upbeta}$-Fenchol &amp; 1112 &amp; -- &amp; 0.7 \\
OM &amp; \textit{(S)}-Myrtenal &amp; 1196 &amp; -- &amp; 0.4 \\
OM &amp; Cuminal &amp; 1226 &amp; -- &amp; 0.1\vspace*{5pt} \\\tabularBreak{1}
SH &amp; Cadalene &amp; 1656 &amp; -- &amp; 0.3 \\
SH &amp; ${\upalpha}$-Cubebene &amp; 1351 &amp; 0.5 &amp; 0.2 \\
SH &amp; Clovene &amp; 1396 &amp; -- &amp; 0.2 \\
SH &amp; 7-epi-${\upalpha}$-Cadinene &amp; 1490 &amp; 0.4 &amp; -- \\
SH &amp; ${\upalpha}$-Copaene &amp; 1376 &amp; 14.4 &amp; 7.3 \\
SH &amp; ${\upbeta}$-Copaene &amp; 1430 &amp; 0.4 &amp; 0.2 \\
SH &amp; \textit{(R)}-Sativene &amp; 1396 &amp; -- &amp; 0.1 \\
SH &amp; cis-${\upalpha}$-Bergamotene &amp; 1433 &amp; 2.4 &amp; 2.2 \\
SH &amp; ${\upalpha}$-Santalene &amp; 1424 &amp; 1 &amp; 0.9 \\
SH &amp; ${\upbeta}$-Caryophyllene &amp; 1427 &amp; 6.3 &amp; 7.4 \\
SH &amp; cis-${\upalpha}$-Bergamotene &amp; 1433 &amp; 3.1 &amp; 3 \\
SH &amp; cis-${\upbeta}$-Farnesene &amp; 1439 &amp; 2.2 &amp; 2.8 \\
SH &amp; ${\upgamma}$-Cadinene &amp; 1449 &amp; -- &amp; 0.2 \\
SH &amp; ${\upalpha}$-Humulene &amp; 1456 &amp; 1.4 &amp; 2.6 \\
SH &amp; ${\upbeta}$-Gurjenene &amp; 1439 &amp; 0.1 &amp; 0.2 \\
SH &amp; ${\upgamma}$-Muurolene &amp; 1477 &amp; 2.1 &amp; 3.7 \\
SH &amp; Cuparene &amp; 1502 &amp; -- &amp; 2.6 \\
SH &amp; \textit{(S)}-Germacrene D &amp; 1503 &amp; 8.9 &amp; 3.7 \\
SH &amp; ${\upbeta}$-Selinene &amp; 1509 &amp; 0.6 &amp; 1.7 \\
SH &amp; ${\upalpha}$-Muurolene &amp; 1517 &amp; 2.3 &amp; 4.3 \\
SH &amp; ${\upalpha}$-Bisabolene &amp; 1522 &amp; 1.7 &amp; 3.4 \\
SH &amp; ${\upgamma}$-Cadinene &amp; 1534 &amp; 1.3 &amp; 2.4 \\
SH &amp; ${\updelta}$-Cadinene &amp; 1541 &amp; 2.8 &amp; 5.8 \\
SH &amp; ${\upalpha}$-Calamenene &amp; 1527 &amp; 2.7 &amp; 7.4 \\
SH &amp; Isoledene &amp; 1373 &amp; -- &amp; 0.8 \\
SH &amp; ${\upalpha}$-Dehydro-ar-himachalene &amp; 1523 &amp; -- &amp; 0.2 \\
SH &amp; ${\upalpha}$-Calacorene &amp; 1543 &amp; 0.3 &amp; 2 \\
SH &amp; ${\upalpha}$-Corocalene &amp; 1629 &amp; -- &amp; 0.2 \vspace*{5pt}\\
OS &amp; Guaijol-acetate &amp; 1727 &amp; -- &amp; 0.2 \\
OS &amp; ${\upgamma}$-Amorphene &amp; 1496 &amp; 1 &amp; -- \\
OS &amp; ${\upbeta}$-Caryophyllene epoxide &amp; 1578 &amp; -- &amp; 0.8 \\
OS &amp; Caryophylenol &amp; 1568 &amp; -- &amp; 0.3 \\
OS &amp; Isoshyobunone &amp; ND &amp; -- &amp; 0.1 \\
OS &amp; \textit{(S)}-Cubenol &amp; 1636 &amp; -- &amp; 0.2 \\
Unidentified &amp; Unidentified &amp; ND &amp; -- &amp; 0.1 
\botline
\end{tabular}
\tabnote{Theoretical retention indices are determined by NIST,
Wiley and The Pherobase databases. Relative peak areas were calculated
using MassHunter. MH: Monoterpene hydrocarbons, OM: Oxygenated
monoterpenes, SH: Sesquiterpene hydrocarbons, OS: Oxygenated
sesquiterpenes.}
\end{table*}

\begin{figure*}
{\vspace*{3pt}}
\includegraphics{Figure1}
{\vspace*{-8pt}}
\caption{\label{fig1}Chromatograms of the VOCs of dried fruit
(upper part) and fruit hydrolate (lower part) by HS-SPME-GC-MS. The
chromatogram is colored according to the different molecular families
detected and annotated.}
{\vspace*{-5pt}}
\end{figure*}

VOCs diversity in dried fruit (Figure~\ref{fig1}) 
is mainly represented by sesquiterpene
hydrocarbons (18 detected compounds---69.2\%), monoterpene
hydrocarbons (4 detected compounds---15.4\%), oxygenated
monoterpenes (2 compounds detected---7.7\%), oxygenated
sesquiterpenes (1 compound detected---3.8\%) and one alkene (1
compound detected---3.8\%). The relative peak areas of the main
compounds correspond to {${\upalpha}$}-pinene (26.8\%---monoterpene),
{${\upalpha}$}-copaene (14.4\%---sesquiterpene),
{${\upbeta}$}-sabinene (10.3\%---monoterpene), (-)---germacrene D
(8.4\%{---}sesquiterpene), and {${\upbeta}$}{-}caryophyllene 
(6.3\%---sesquiterpene) (Table~\ref{tab1}). The odors of these compounds are
described as grassy and/or woody. VOCs diversity in fruit hydrolate 
(Figure~\ref{fig1}) is
mainly composed of sesquiterpene hydrocarbons (25 detected 
compounds---43.1\%), oxygenated monoterpenes (14 detected 
compounds---24.1\%), highly volatile families (10 detected 
compounds---17.2\%), oxygenated sesquiterpenes (5 detected 
compounds---8.6\%) and monoterpene hydrocarbons (4 detected 
compounds---6.9\%). The relative areas of the main compounds correspond to
eucalyptol (13.5\%{---}monoterpene), {${\upalpha}$}-calamenene 
(7.4\%---sesquiterpene), {${\upbeta}$}{-}caryophyllene (7.4\%{---}sesquiterpene),
{${\upalpha}$}{-}copaene (7.3\%{---}sesquiterpene) and
{${\updelta}$}{-}cadinene (5.8\%{---}sesquiterpene) (Table~\ref{tab1}).
The odors of these compounds are described as grassy and/or woody.\looseness=-1

If we compare the sums of the peak relative areas corresponding to the
different molecular families between the two samples analyzed, the
hydrodistillation process seems to favor the extraction of small highly
volatile molecules (${+}$13.4\%), oxygenated monoterpenes (${+}$16.4\%) and
oxygenated sesquiterpenes (${+}$4.8\%). On the other hand, the relative
abundance of monoterpene hydrocarbons and sesquiterpene hydrocarbons
decreased by 8.5\% and 26.1\%, respectively, in comparison with the
dried fruit (see Table~S1 in Supplementary Data).

\subsection{Characterization of the chemical diversity of 
$\mathrm{S.~rubra}$ 
hydrolate and dried fruit by molecular networks}\label{sec32}

The resulting data can also be used to create molecular networks by
pre{-}processing the data with MZmine v. 2.53 and MetGem v. 1.4.1
\cite{17,20}. Molecular \mbox{networks} allow comparison of
chemical diversity between different complex samples. Their principle
is based on the assumption that structurally close molecules share
similar fragmentation patterns. Thus, it is possible to visualize
clusters grouping structurally similar molecules and to annotate them
by correspondence with spectral banks \cite{26}. Two tools, among
others, are currently available for generating molecular networks from
mass spectrometry data: GNPS and MetGem \cite{17,27}. In contrast
to GNPS, MetGem is a software package offering the user a certain
modularity, {i.e.} real-time parameter modification, generation of
several molecular networks, and the ability to generate a t-SNE
representation. This algorithm represents the chemical diversity of one
or more samples in a 2-dimensional space, where the distances between
nodes are the corollary of the spectral similarity between the studied
molecules. This representation can be used to determine whether there
are structural similarities between different clusters of the same
molecular network \cite{17,28}. MetGem is able to reprocess MS1
data from both MALDI{-}ToF and GC{-}MS analyses \cite{16,29}.
However, GC{-}MS databases are not open{-}source, {e.g.,}
NIST, Wiley\,\ldots\ It is however possible to query them through MetGem after
their conversion into a dedicated format.

{\def\ffigcaption{\label{fig2}}
\xsubfig[t!]{1}{}
\figcnt
\begin{figure*}[p]
\includegraphics{fig02}
\xcaption{\label{fig2a}\ffigcaption}{t-SNE representation of some of the chemical
diversity of the VOCs in dried fruit and hydrolate. Cluster A contains
monoterpene hydrocarbons, cluster B contains oxygenated monoterpenes
and cluster C contains sesquiterpenoids, grouping sesquiterpene
hydrocarbons and oxygenated sesquiterpenes. The color code shows the
main molecular families. The {green} nodes are the ions detected in the
hydrolate and the {red} nodes correspond to the ions detected in the
dried fruit. The total molecular network contains 1088 nodes, 482 of
which are shown in this figure, {i.e.,} those corresponding
to VOC. 199 nodes are singletons and 407 correspond to polysiloxane
derivatives (shown in Figure~S1). Pie-charts correspond to the peak
areas of each detected ion using MZmine. Node size is defined by
the maximum area for a peak corresponding to an ion detected in both
samples.}
\end{figure*}}

Figure~\ref{fig2} is a portion of the t{-}SNE representation of the
ions detected in HS{-}SPME{-}GC{-}MS of the dried fruit and hydrolate.
Three clusters are \mbox{distinguishable,} with cluster A grouping
monoterpene hydrocarbons. Nodes in this cluster share the following
fragments \textit{m/z} 93.1, 91.1, 79.1, 77.0. Cluster B includes
oxygenated monoterpenes and monooxygenated molecules with a number of
carbon atoms lower than 10 ($n_{C}&lt;10$). The nodes  
\mbox{constituting} \mbox{cluster}
B, and being annotated as oxygenated monoterpenes, share the following
\mbox{common} fragments \textit{m/z} 93.1, 81.1, 79.1, 69.1, 67.0. However,
common higher molecular weight fragments are shared, partially, between
oxygenated monoterpenes and oxygenated molecules with $n_{C}&lt;10$.
Cluster C groups sesquiterpene hydrocarbons and oxygenated
sesquiterpenes. The nodes corresponding to sesquiterpene hydrocarbons
share the following common fragments \textit{m/z} 204.2, 189.2, 161.2,
105.1, 93.1, 91.1 and 79.1. The nodes corresponding to aldehydes,
ketones, esters are scattered in the network. Moreover, polysiloxane
impurities were detected and annotated but are not represented in
Figure~\ref{fig2} (see Figure~S1 for more information). These compounds
probably come from the SPME fiber used which is composed of
polydimethylsiloxane (DVB/C-WR/PDMS).

The clustering performed by MetGem according to the fragmentation
patterns of the molecules is specific to each molecular family, {i.e.}
monoterpene hydrocarbons, oxygenated monoterpenes and sesquiterpenoids
are distinguished (Figure~\ref{fig2}). On the other hand, compounds
belonging to highly volatile families as well as oxygenated molecules
with $n_{C}&lt;10$ are dispersed in the network (Figure~\ref{fig2}). The
MetGem annotations are consistent with those performed by the
MassHunter Unknown Analysis (Agilent, B.09.00) algorithm. This
observation is only valid for ions with a high relative intensity
(relative area $&gt;$ 0.2\%).
Indeed, when the intensity is
below this threshold, the cosine scores decrease (see Figure~S3
in Supplementary Data). This results in uncertain matches with the
spectral banks, so it remains and will remain necessary to check the
data manually. Moreover, this methodology was not able to annotate the
ion detected at Retention Time (RT) $=$ 13.73 min (Table~\ref{tab1}). The use of
MetGem therefore complements the MassHunter software, as it facilitates
the visualization of data obtained from GC-MS analyses. Indeed, it is
possible to apply additional filters to this visualization (retention
time, common fragments\,\ldots). In addition, a large number of samples can
be analyzed simultaneously to determine whether they share a similar
chemical diversity. However, many nodes are annotated as contaminants,
in particular polysiloxanes, but belong to clusters not shown in
Figure~\ref{fig2} (Figure~S1), thereby not interfering with data
analysis since the respective surface area of each of  these compounds
was not taken into account in  the analysis.\looseness=-1

The exploration and annotation of the chemical diversity of VOC is now
possible through the use of numerous libraries. It is possible to
annotate quickly and with high confidence the identity of the ions
present in a molecular network without using retention indices
\cite{27,30}. This methodology has already been successfully used to
search for compounds with larvicidal activity against \textit{Aedes
aegypti} \cite{31}.

\subsection{Comparison of VOCs composition between different organs of\ 
\,$\mathrm{S.~rubra}$}\label{sec33}

To our knowledge, this is the first time that the volatile chemical
composition of the \textit{Sextonia rubra} fruit has been studied. On
the other hand, a previous study carried out on essential oils (EO) of
\textit{S. rubra} leaves led to the identification of 44.6\%
monoterpenes, 3.2\% oxygenated monoterpenes and 47.7\%
sesquiterpenoids. The EO of branches was made up of 1.2\% monoterpenes,
66.8\% sesquiterpenes and 26.7\% sesquiterpenoids (relative areas)
\cite{32}. The monoterpenes common to the hydrolate, dried fruit,
branches and leaves of \textit{S. rubra} are {${\upalpha}$}{-}pinene and
camphene \cite{32}. Sesquiterpenes common to the hydrolate, dried
fruit, branches and leaves are {${\upalpha}$}{-}copaene,
{${\upbeta}$}{-}caryophyllene, {${\upalpha}$}{-}muurolene,
{${\updelta}$}{-}cadinene and {${\upalpha}$}{-}calamenene \cite{32}. In
general, the molecular family most represented in the EO or hydrolates
of the different tissues of \textit{S. rubra} corresponds mainly to the
sesquiterpenes with the exception of leaves. The relative VOC
compositions are similar between fruit and leaves but differ widely
with that of the branches. Alc\^{a}ntara \etal\ obtained
extraction yields of EO from \textit{S. rubra} fresh leaves and
branches of 0.14\% and 0.01\%, respectively. The hydrodistillation
carried out on \textit{S. rubra} dried fruit resulted in a hydrolate
with an extraction yield of 36\%.

\subsection{VOCs in $\mathrm{S.~rubra}$ fruit: a perspective}\label{sec34}

Based on the original composition of the fruit hydrolate, we determined
its relative minimum inhibitory concentrations (RMIC) against three
human pathogens, {i.e.} \textit{Candida albicans}, MRSA and
\textit{Tricophyton rubrum} (Table~S2). The concentration of EO in the
hydrolate was unknown, hence, we performed dilutions of the hydrolate
in RPMI or Mueller--Hinton culture medium. It has been demonstrated that
EO rich in sesquiterpenes can exhibit moderate to high antifungal
activities \cite{33,34,35,36}. The growth of \textit{C.
albicans} and MRSA was not altered in the presence of the hydrolate,
regardless of the concentration used. On the other hand, when
\textit{T. rubrum} was exposed to relative concentrations greater than
or equal to 5\%, its growth was inhibited. This observation could be due
to the high proportion of {${\upalpha}$}-copaene in the hydrolate.
Indeed, Hou\"{e}l \etal~had determined a MIC of
{${\upalpha}$}{-}copaene at 4~${\upmu}$g${\cdot}$mL$^{-1}$ against
\textit{T. rubrum} \cite{34}. Moreover, cuminal, although detected
in low proportion (Table~\ref{tab1}), is responsible for a high activity
against dermatophyte such as \textit{T.~rubrum} \cite{37}. In view
of the mechanism of action of terpenes, the activity of the hydrolate
against \textit{T. rubrum} could be similar towards filamentous fungi
sharing a cell membrane close to that of \textit{T. rubrum}
\cite{38,39,40}. Despite the anti-fungal activity observed against
\textit{T. rubrum}, the hydrolate did not show any cytotoxic activity
against the MRC-5 cell line at relative concentrations of 10\%, 5\%,
1\% (Table~S3).

By comparing the current scientific literature with our study, the EOs
composition of \textit{S. rubra} (obtained from: bark, branches,
fruit, heartwood, leaves, seeds, stem and twigs) is dominated by
{${\upbeta}$}{-}caryophyllene and eucalyptol which are detected in the EO
and hydrolate prepared from different organs of \textit{S. rubra}
\cite{32,41}. Lauraceae's EO are characterized by a high abundance in
hydrocarbons and oxygenated sesquiterpenoids, which is consistent with
the relative intensities of the molecular families detected in the
fruit hydrolate (Table~S1). In the Lauraceae family, the main 
VOCs detected are {${\upbeta}$}-caryophyllene, germacrene D, bicyclogermacrene,
caryophyllene oxide, {${\upalpha}$}-bisabolol and byclogermacrenal. In
the two samples analyzed, {${\upbeta}$}-caryophyllene and germacrene D are
detected with relative intensities greater than 5\% \cite{42}.

{Moreover,} our study raises new questions on the ecological functions
of sesquiterpenes detected in \textit{S. rubra} fruit. Interestingly
some compounds exhibit insecticidal activity, such as
{${\upbeta}$}{-}caryophyllene ($&gt;$5\%) and
{${\upalpha}$}{-}humulene ($&gt;$1\%), against \textit{Aedes
aegypti}, and describing the spectrum of insecticidal activity of these
compounds would be of interest \cite{43,44}.\break The same applies to
${\upalpha}$-humulene ($&gt;$1\%) against \textit{Sitophilus
zeamais} and \textit{Tribolium castaneum} \cite{45}.
Additionally, {${\upalpha}$}{-}copaene (${&gt;}$5\%) and sulcatone
(0.5\%) could be an attractant {for} some arthropods
\cite{30,46,47}. ${\upbeta}$-caryophyllene (${&gt;}$5\%)
has also been identified for its role as an attractant {for}
\textit{Elsholtzia rugulosa}'s pollinators and for its ability to
inhibit the growth of pathogenic bacteria \cite{48,49}.
Eucalyptol, a major component of the VOCs of this fruit (2.8\% in
dried fruit, 13.5\% in hydrolate), has been reported to have
anti-fungal activity against phytopathogenic fungi \textit{Aspergillus
flavus}, \textit{Botrytis fabae} and \textit{Uromyces fabae}
\cite{50,51}. Although present in small quantities in the
hydrolate (0.2\%), anthranillic acid has also been reported to have
antifungal activity against \textit{Botrytis cinerea} \cite{52}.
Eventually, some terpenes are known to promote plant growth such as
{${\upbeta}$}{-}caryophyllene (${&gt;}$5\%) and
{${\upalpha}$}{-}pinene (26.8\% in dried fruit, 1.6\% in hydrolate)
\cite{46,53}. In view of the maturity of the fruit in comparison
with the literature, it is possible that it emits these two compounds
in order to promote their maturation \cite{1,54}. However, these
suppositions require a better understanding of the biology of
\textit{S. rubra}, including the identification of its pollinators and
pathogens to elucidate the role of these compounds in the ripening and
protection of\break its fruit. 

\section{Conclusions}\label{sec4}
In this work we identified 98.5\% of the number of peaks corresponding
to VOCs emanating from the \textit{S. rubra} dried fruit and hydrolate
by HS{-}SPME{-}GC{-}MS using the NIST and Wiley databases. The analysis
performed on MassHunter Unknown Analysis served as a guide to verify
the annotation results obtained by MetGem. The use of MetGem proves to
be a complementary methodology to the use of MassHunter, as numerous
filters can be used when processing data from a large number of
samples. In addition, MetGem can now be used to query databases
specific to GC-MS analyses. Finally, the hydrolate of this fruit has
a relative antifungal activity of 5\% against \textit{T. rubrum}. In
general, this study presents a protocol to exploit data from GC{-}MS
analyses on MetGem and query spectral banks such as NIST and Wiley
\mbox{participating} in the characterization of the chemical composition of
the Amazonian fruit diversity. Particular attention should be paid
to sequiterpenes in the future to characterize their ecological
functions following their synthesis by trees. This work is part of a
series of studies designed to characterize the chemical defenses of
\textit{S. rubra}.

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

\section*{Acknowledgments}
We thank the CIRAD team, Saint-Omer Cazal, Michel Baisie, Soepe Koese,
Alpha, and the INRAE tree climber Jocelyn Cazal for the pruning and
felling of the studied tree. 

\back{}

\section*{Supplementary data}

Supporting information for this article is available on the journal's
website under \printDOI\ or from the author.  
These are also available on the Zenodo.org under 
DOI:\href{https://doi.org/10.5281/zenodo.8167333}{10.5281/zenodo.8167333}.
Figure~S1: Total molecular network obtained by using MetGem software,
Table~S1: Summary table of the main VOCs in \textit{S. rubra} hydrolate
and essential oils, Figure~S2: Correlations graph, Table~S2: Summary
table of antimicrobial tests, Table~S3: Summary table of cytotoxicity
tests, Figure~S3: \textit{S. rubra} fruit photography.

\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crchim-248-suppl.pdf}}

\bibliographystyle{crchim}
\bibliography{crchim20230337}
\refinput{crchim20230337-reference.tex}

\end{document}
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