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\DOI{10.5802/crchim.262}
\datereceived{2023-06-23}
\daterevised{2023-09-08}
\dateaccepted{2023-09-14}
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\dateposted{2024-01-11}
\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{Studying chemical signals in plant--soil mesofauna interactions}

\alttitle{Un dispositif exp\'{e}rimental original pour r\'{e}colter les signaux chimiques du sol issus des interactions entre racines et faune du sol}

\author{\firstname{Victoria} \lastname{Bohm}}
\address{Univ Rouen Normandie, Normandie Univ, FR3038, SMS, UR 3233,
F-76000 Rouen, France}

\author{\firstname{Thomas} \lastname{Michel}\CDRorcid{0000-0002-2420-9875}}
\address{Universit\'{e} C\^{o}te d'Azur, Institut de Chimie de Nice,
CNRS UMR 7272, F-06108 Nice, France}

\author{\firstname{Estelle} \lastname{Forey}\CDRorcid{0000-0001-6082-3023}}
\address{Univ Rouen Normandie, INRAE, ECODIV USC 1499, F-76000 Rouen,
France}

\author{\firstname{Val\'{e}rie} \lastname{Agasse}\CDRorcid{0000-0001-8747-5954}}
\addressSameAs{1}{Univ Rouen Normandie, Normandie Univ, FR3038, SMS, UR
3233, F-76000 Rouen, France}

\author{\firstname{Pascal} \lastname{Cardinael}\CDRorcid{0000-0001-8828-4527}}
\addressSameAs{1}{Univ Rouen Normandie, Normandie Univ, FR3038, SMS, UR
3233, F-76000 Rouen, France}

\author{\firstname{Matthieu} \lastname{Chauvat}\CDRorcid{0000-0002-4831-5904}\IsCorresp}
\addressSameAs{3}{Univ Rouen Normandie, INRAE, ECODIV USC 1499, F-76000
Rouen, France}
\email[M. Chauvat]{matthieu.chauvat@univ-rouen.fr}

\keywords{\kwd{UHPLC-HRMS}\kwd{Biotic
interactions}\kwd{Exudates}\kwd{Springtails}\kwd{Metabolomics}} 

\altkeywords{\kwd{UHPLC-HRMS}\kwd{Interactions
biotiques}\kwd{Exsudats}\kwd{Collemboles}\kwd{M\'{e}tabolomique}}

\thanks{This study was financially supported by a ``Collaboration Grant
2020'' delivered to EF and TM by the GDR 3658 MediatEC CNRS. VB is
supported by a doctoral grant from the Universit\'{e} de Rouen
Normandie and the EDNC 508.}

\shortrunauthors

\begin{abstract} 
Discrimination of compounds produced from plant roots is a challenging
purpose, especially in presence of the soil matrix and the associated
fauna. In this study, we aimed to test a collection method of chemical
signals released by living plants and soil fauna in interaction in a
soil \mbox{matrix}. Microcosms, with stopcock, were filled with soil,
and four treatments were designed based on the presence/absence of an
annual herbaceous plant, \textit{Poa annua} and of a soil Collembola
species, \textit{\mbox{Folsomia} candida}. Soil leachates were
collected with a percolated solution and analyzed by a fast analytical
method combining analytical separation of compounds through ultra high
performance liquid chromatography with high resolution mass
spectrometry (UHPLC-HRMS). The experimental design appears to be
suitable for leachates sampling. Detected chemicals clearly
discriminated the treatments, demonstrating how biotic interactions
belowground between a plant and a Collembola species may change soil
chemical signals.
{\vspace*{-2pt}}
\end{abstract}

\begin{altabstract} 
La discrimination des compos\'{e}s produits par les racines des plantes
est un d\'{e}fi, surtout en pr\'{e}sence de la matrice du sol et de la
faune associ\'{e}e. Dans cette \'{e}tude, nous avons cherch\'{e} \`{a}
tester une m\'{e}thode de collecte des signaux chimiques \'{e}mis par
les plantes vivantes en interaction avec de la faune du sol dans une
matrice de sol. Des microcosmes, munis d'un robinet d'arr\^{e}t, ont
\'{e}t\'{e} remplis de sol, et quatre traitements ont \'{e}t\'{e}
con\c{c}us en fonction de la pr\'{e}sence ou absence d'une plante
herbac\'{e}e annuelle, \textit{Poa annua}, et d'une esp\`{e}ce de
collembole du sol, \textit{Folsomia candida}. Les lixiviats du sol ont
\'{e}t\'{e} collect\'{e}s par percolation d'une solution et
analys\'{e}s par une m\'{e}thode analytique rapide combinant la
s\'{e}paration analytique des compos\'{e}s et la spectrom\'{e}trie de
masse \`{a} haute r\'{e}solution (UHPLC-HRMS). La conception
exp\'{e}rimentale semble convenir \`{a} l'\'{e}chantillonnage des
lixiviats. Les traitements sont nettement s\'{e}par\'{e}s au regard des
produits chimiques d\'{e}tect\'{e}s, d\'{e}montrant comment les
interactions biotiques souterraines entre une plante et une esp\`{e}ce
de collembole peuvent modifier les signaux chimiques du sol.
\end{altabstract} 

\maketitle

\vspace*{10pt}
\twocolumngrid

\end{noXML}

\section{Introduction}

{\vspace*{-3pt}}

In Ecology, the soil matrix is still considered as a ``black box''
regarding biochemical processes (e.g. root exudation, enzymatic
activities) and ecological processes (e.g. species interactions,
decomposition) occurring belowground~\cite{1,2}. Living organisms
within the soil compartment can reach impressive numbers with for
example millions of bacteria, or hundreds of tiny microarthropods (i.e.
arthropods under 2~mm like Collembola or Acari) in a handful of soil
coming from the vicinity of plant roots (i.e. the rhizosphere). The
expression of biological activities performed by these organisms and by
the interactions between them generate a huge diversity of chemicals
released in the soil~\cite{3,4}. This is especially true for the plant
roots or mycorrhiza associated to roots exudating chemicals (exudates)
to the belowground compartment. Plants, through their roots, are known
to exudate primary or secondary metabolites, either soluble or
volatile, ranging from low (${&lt;}$1000~Da; e.g., organic acids, phenols,
vitamins) to high molecular weight (e.g., glycomolecules, proteins, or
nucleic acids)~\cite{5,6,7}. These compounds can generate interactive
feedback loops between the roots and the other soil organisms that
regulate numerous soil processes like aggregation of soil particles
with soil nutrients, water availability, soil detoxification, structure
and function of soil food web as well as nutrient cycling and thereby
CO$_{2}$ release by the soil~\cite{8,9}.

Different chromatographic methods were developed to analyze metabolites
(metabolomics) presenting a large range of polarity. Ion chromatography
(IC) was dedicated to the analysis of ionic or ionizable compounds. For
volatile compounds, gas chromatography (GC) is the most used technique.
Nevertheless, HPLC appeared to be the most suitable technique to
analyze a large range of polarities. Moreover, coupling with mass
spectrometry becomes very useful, due to the complexity of these
matrices and the need for structural identification~\cite{7,10,11,12}.

Sensitivity and separation capacities of the analytical methods
achieved in the last decade, enable a better screening of the diversity
of the chemical signals released by plants or microbiome~\cite{13}.
However, most of these studies focused on root exudates obtained from
plants grown hydroponically or on gelose with a nutritive
solution~\cite{14,15,16,17,18}, meaning roots lacked living substrate
as is the soil. On the other hand, several studies performed in
microcosms tested how the presence of soil fauna may contribute to
mineralization but mainly focused on analyzing C, N, or P content of
the leachates~\cite{19,20,21,22}. For example, those studies showed
that presence of soil detritivores increases the amount of nitrate in
the soil and the metabolic activities of soil microbes. However, how
the presence of soil fauna in interaction with plant roots may modify
the chemical signals of the soil, and not only the cycling of major
elements, is still poorly documented. In the last decades however,
development of chemical analyses like metabolomics for
example~\cite{12} offers the opportunity to more accurately determine
the soil signals in terms of molecules. Accessing chemical compounds in
a real soil matrix with both the presence of roots and soil fauna is
still a methodological \mbox{challenge}.\looseness=-1

To overcome this issue, this study focused on the development of an
original sample collection method of chemical signals and their
analysis by UHPLC-HRMS. This is a fast analytical method combining
analytical separation of compounds with high resolution mass
spectrometry which enables molecular mass assessment with high
precision. This method is well known to analyze compounds in complex
matrices~\cite{23}.

The signals obtained could help assess chemical signal modifications
following biotic interactions belowground (a plant and a group of soil
fauna). In this study we used \textit{Folsomia candida}, a species
belonging to Collembola, a dominant soil fauna group~\cite{24} known to
regulate the growth and phenology of plants~\cite{25} in association
with \textit{Poa annua}, a common worldwide-distributed grass
species~\cite{26,27}. By adding or not individuals of
\textit{F.~candida} in microcosms with plants, we wanted to test
whether their presence would modify the chemical signals of the soil
matrix.

\section{Materials and methods} \label{sec2}
\subsection{Experimental design and microcosm setup} \label{sec2.1}

To collect soil signals obtained by the interaction between plants and
Collembola, an innovative plant microcosm was designed to sample soil
leachates. First, a neutral material such as glass was required in
order to avoid exogen pollution or degradation. Secondly, soil quantity
had to be adapted to allow submersion by a solvent and its fast
sampling to avoid release of degradation compounds and stress compounds
in the presence of organic solvents. To meet these specifications, 20
homemade microcosms (\mbox{Figure}~\ref{fig1}) were manufactured from
classical borosilicate beakers (total volume 200~mL, diameter 6~cm,
height 9.5~cm) equipped with PTFE stopcock purchased from VWR
(Fontenay-sous-Bois, France).

\begin{figure*}
\includegraphics{fig01}
{\vspace*{-.15pc}}
\caption{\label{fig1}Pictures of the microcosms of the respective four
treatments.}
{\vspace*{-.1pc}}
\end{figure*}

The experiment was set up in May 2020 with different treatments
corresponding to two factors with two levels: presence or absence of a
plant (\textit{Poa annua}) and presence or absence of a collembolan
species (\textit{Folsomia candida}). The following treatments were
established:
\begin{itemize}
\item Soil without plant and Collembola: ``Control''
\item Soil with plant but without Collembola: ``P''
\item Soil without plant but with Collembola: ``C''
\item Soil with plant and with Collembola: ``PC''.
\end{itemize}

Each treatment was replicated 5 times in microcosms filled with soil
(160~mL of soil) prior to the establishment of the treatment. The soil
was a rendosol (Organic Matter content 6.17\%; pH~7.79) collected from
a chalk grassland in Normandy, France. A part of the soil was
autoclaved with two cycles of 105~{\textdegree}C at 48~h intervals to
eliminate the original fauna and microflora. This sterilized soil was
used as a substrate for all microcosms. From the remaining part of the
collected soil, microorganisms (fungi and bacteria) were extracted in a
suspension by first stirring during 1~h 500~g of fresh sieved (2~mm)
soil in 2~L of physiological water (0.85\% NaCl), according to the
protocol described by Eisenhauer {\etal}~\cite{28}. The suspension was
then filtered through a filter paper (Whatmann N{\textdegree}5)
purchased from VWR (Fontenay-sous-Bois, France) and a volume of 37~mL
of microbial filtrate (i.e. microbial suspension) was inoculated into
each of the 20 microcosms. In order to establish the microbial
community, the microcosms were then incubated at a temperature of
25~{\textdegree}C for 10~days (D10). In parallel, seedlings of
\textit{Poa annua} (4~cm height) obtained from a single individual were
transferred to half of the microcosms (one seedling per microcosm). A
total of 20 individuals (only adults, based on animal size) of a
typical Collembola, \textit{Folsomia candida}, reared in the
laboratory, was introduced in each of the 5 microcosms of the
treatments~C and~PC. All the microcosms were then incubated in a
climate chamber (temperature: 20~{\textdegree}C, daily light/dark 16~h/8~h)
with soil moisture maintained at 60\% of the soil water holding
capacity during the experiment. The experiment was stopped after 4
weeks (D38).\looseness=-1

\subsection{Sample collection and preparation} \label{sec2.2}

To collect soil chemical signals/metabolites, a volume of 100~mL of a
methanol/ultrapure water (75/25,~v/v) mixture was introduced on the top
of each microcosm. Methanol was purchased from Carlo Erba (Val de
Reuil, France) and ultrapure water was obtained from a Milli-Q water
purification system from Millipore Ltd. (Bedford, MA, USA). The
solution was percolated through the microcosm in less than 3~min and
then, was collected using the stopcock. Around 80~mL were thus obtained
from each microcosm. The sampled solution was then evaporated and
concentrated to 20~mL using a SP Genevac EZ-2 Envi (Genevac Ltd,
Ipswich, UK) prior to UHPLC-HRMS analyses.

\subsection{UHPLC-HRMS method} \label{sec2.3}

The metabolite profiles were acquired on a UHPLC instrument (Dionex
Ultimate 3000, Thermo Scientific, Waltham, MA, USA) equipped with a RS
pump ({\textcopyright}~RS Components Ltd), an autosampler, and a
thermostatically controlled column oven, and coupled to a Photodiode
Array Detector (PDA), as well as an \mbox{accurate} quadrupole Time of
Flight (qToF) mass spectrometer equipped with an Electrospray
Ionisation (ESI) source (Impact~II, Bruker
Daltonics$^{\text{\textregistered}}$, Billerica, MA, USA). The
separation was carried out using a Luna$^{\text{\textregistered}}$
Omega Polar C18 column (2.1 ${\times}$ 100~mm, 1.6~${\upmu}$m,
Phenomenex, Torrance, CA, USA). Gradient elution was performed using
ultrapure water (Cogolin reagent FMP, Fluka) (A) and acetonitrile
(Cogolin reagent FMP) (B), both acidified with 0.1\% formic acid as
follows: 5\% B during 2~min, then 40\% B for 6~min followed by a 4-min
linear gradient up to 100\%, and finally 2~min at 100\% in isocratic
mode. The analysis was followed by a 4-min equilibration time in
initial conditions for the next round of analysis. Flow rate was
0.5~mL/min, temperature 40~{\textdegree}C, injection volume
2~${\upmu}$L. Blank injections correspond to injection of pure
methanol.

MS detection was performed in negative and positive ionization modes.
MS parameters were set as follows: nebulizer gas N$_{2}$ at 3.5~bar,
N$_{2}$ dry gas at $12~\text{L}{\cdot}\text{min}^{-1}$, capillary
temperature at 200~{\textdegree}C, and voltage at 3000~V in negative
mode and at ${-}$2500~V in positive mode. Analyses were recorded at an
acquisition frequency of 4~Hz, and the mass range was set from $m/z$ 50
to 1200~uma. Mass spectrometer calibration was performed systematically
with a mixture of formate and acetate solution composed of 25~mL of
ultrapure water, 25~mL of propan-2-ol (Sigma Aldrich, Darmstadt,
Germany), 0.5~mL of NaOH, 25~${\upmu}$L of formic acid, and
75~${\upmu}$L of acetic acid. The MS-based metabolomic data were
calibrated automatically by internal calibration with a solution of
formate and acetate before exporting the data to netCDF files (acquired
in centroid mode) using Bruker Compass DataAnalysis~4.3 software.

\subsection{Data analysis} \label{sec2.4}

All the converted analyses were processed by the XCMS
software~\cite{29} under R~software version~3.5.1, following steps to
generate the final data matrix: (1)~peak picking (peakwidth $=$ c
(2.20) ppm $=$ 2) without threshold prefilter; (2)~correction of
retention time (method $=$ obiwarp), (3)~grouping (bw $=$ 10, minfrac
$=$ 0.3, minsamp $=$ 1), (4)~fill peaks, and (5)~report and generate
the data matrix transferred to Excel software. To remove technical
variability, the matrix was then filtered according to blanks and pools
of three following steps using in-house R~scripts: (1)~\mbox{filtering}
the matrix using the Signal/Noise (S/N) ratio in order to remove peaks
observed in blanks (S/N set at~10), (2)~filtering the matrix using the
peaks coefficient of variation in order to remove peaks with variable
intensities in the pooled samples (threshold at~0.3), (3)~filtering the
matrix according to autocorrelation between the peaks.

To focus on chemical signals resulting from plants, either alone (P) or
in combination with Collembola (PC), signals of the Control treatment
(i.e. soil) were subtracted from both Plant (P) and Plant and
Collembola (PC) treatments. Then signals from the C treatment were
subtracted from PC. Orthogonal partial least squares discriminant
analysis (oPLS-DA) was performed on both resulting P and PC matrices to
determine whether a discrimination of plant-released-compounds was
possible based on the presence or absence of Collembola. Furthermore, a
Permanova test was used on both reshaped P and PC matrices to test for
statistical differences between chemicals \mbox{obtained}.

\section{Results and discussion} \label{sec3}

Combining both negative (ESI${-}$) and positive ionisations (ESI${+}$), a
total of 478 ions were detected. A mixture of equal volumes of P, C,
PC, and Control extracts called ``Pooled sample'' was injected to
evaluate if leachate compounds could be detected and was compared to
the blank injection. The resulting Base Peak Chromatograms (BPC) can be
divided into three parts (Figure~\ref{fig2}).

\begin{figure*}
\includegraphics{fig02}
\caption{\label{fig2}Base Peak Chromatograms (BPC) obtained for blank
(red dotted lines) and pooled sample (blue dotted lines). (a)~ESI${-}$;
(b)~ESI${+}$.}
{\vspace*{-.3pc}}
\end{figure*}

The first part with retention times between 0--5~min, corresponds to
polar entities, whereas the second and third parts, from 5--10~min and
10--15~min, respectively, refer to compounds with average and low
polarities. Ions produced in the source in negative mode result from
compounds with acidic properties and conversely, compounds with basic
properties are ionized in positive mode. There are some differences
regarding the retention time distribution between the three parts of
chromatograms. There is also inequality between chromatograms resulting
from each ionization mode. Indeed, negative ionization seems to be more
specific than positive ionization, as most compounds detected with
positive ionization mode were also found in the blank injection.

The first part of the chromatograms shows an elution band corresponding
to non-retained \mbox{compounds}, the most polar ones presenting no
retention by the stationary phase. ESI${+}$ chromatograms show more than
twice the intensity observed in ESI${-}$ chromatograms. This is not
surprising because the mobile phase used for the analyses is acidified.
The second part (retention time~$T$ of \mbox{5--10}~min) of the ESI${-}$
chromatogram shows no specific compounds whereas the ESI${+}$ one does,
corresponding to compounds with average polarities. Comparison with the
last chromatogram part \mbox{indicates} the presence of compounds with low
polarity, especially in ESI${-}$ mode.

\begin{figure*}
\includegraphics{fig03}
\caption{\label{fig3}oPLS-DA analysis results performed on two sets of
ions, one referring to metabolites found in Plant treatment (P) and the
second one in Plant ${+}$ Collembola treatment (PC). R$^{2}$X and
R$^{2}$Y represent the fraction of the variance of the $x$ and $y$
variable explained by the model, while Q$^{2}$ suggests the predictive
performance of the model. R$^{2}$X, R$^{2}$Y, and Q$^{2}$ vary between
0 and~1.}
\end{figure*}

\break
According to the oPLS-DA, the two different treatments, plant alone or
with Collembola (P versus PC), seem to be well distinguished according
to high values of both R$^{2}$Y and Q$^{2}$ (Figure~\ref{fig3}),
meaning that treatments induce different chemical signals. Furthermore,
both treatments were strongly different according to a Permanova test
(PC versus~P; \mbox{$F = 2.33$}; $p = 0.0084$). \textit{Folsomia
candida} seems to{\break} affect chemical signals released in the soil
following plant roots--Collembola interactions. We cannot exclude that
\textit{Folsomia candida}, the Collembola species used in our
experiment, may have fed upon roots during the experiment. Collembola
in general are known to switch diet and may behave like herbivores in
presence of plant roots~\cite{30}, surely modifying chemicals released
from plant roots. For example, root herbivory was shown to influence
soil nutrients~\cite{31,32}. On the other hand, we can neither exclude
that interactions between \textit{Poa annua} and \textit{F.~candida}
might also take place through indirect pathways, through changes in
microbial communities composition and structure as already
shown~\cite{33}.

A variable importance in projection (VIP) score plot is also presented
in Figure~\ref{fig4}.
This plot selects the ions which explain most differences between
treatments. In ordinate are presented these ions with the following
nomenclature: M for mass, followed by the corresponding $m/z$ ratio
followed by the retention time (T) in seconds. Thus, most of the ions
with the highest VIP scores were eluted in the dead time, which means
that most of the ions explaining differences between microcosms
are very polar\break entities.

\begin{figure}
\includegraphics{fig04}
{\vspace*{-.5pc}}
\caption{\label{fig4}VIP score plot resulting from the oPLS-DA
performed on two treatments, P (Plant) and PC (Plant ${+}$ Collembola).
This plot shows the 30 most important VIP scores.}
{\vspace*{-.5pc}}
\end{figure}

These ions have higher intensities in PC treatment than in P treatment.
Among the 30 top VIP score \mbox{values}, only three ions, with $m/z$
830, $m/z$ 773 and $m/z$ 784, respectively, were eluted in the third
part of the chromatogram. Only 6 out of the top 30 VIP ions exhibited a
higher intensity in P treatment than in PC treatment. Relating the
presence of Collembola to the lower intensity of these ions is not easy
but several hypotheses could be proposed: first, Collembola can
directly metabolized these ions for example by absorbing them through
their ventral tubes~\cite{34}. Secondly, \textit{F.~candida} can
indirectly impact the ions by regulating the microbial community
structure and functions as discussed previously~\cite{33,35}. Finally,
the presence of Collembola can also modify the plant gene expression
(RNA) and then the nature and intensity of metabolites exuded by the
roots~\cite{36,37,38}. In the same manner, the 24 ions showing higher
intensities in PC treatment than in P treatment could be attributed to
supplementary ions directly provided by the presence of fauna through
trophic interactions with the roots for example~\cite{30}, or
indirectly through modified soil microbial communities or root
exudates~\cite{33,35}.

\section{Conclusion} \label{sec4}

In this work, the experimental design developed as support for growing
plants, microcosms made with beakers equipped with stopcock,
facilitates the sampling and analysis of soil chemical compounds.
Indeed, different treatments were studied and differences in chemical
signals were detected. Such a method offers opportunities to explore
chemical signals induced by plant root--soil organism interactions in
complex soil matrices and also allows control of other parameters such
as for example soil properties and climatic variables. Further
investigations will be performed to identify metabolites collected.

\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}

The authors would like to thank Matthieu Normand and Evelyne Blanchard,
Ecodiv Lab, Universit\'{e} de Rouen, for their technical assistance. We
are grateful to Charles Lanel, glass-blower at the Universit\'{e} de
Rouen, for designing and manufacturing the microcosms. Also, authors
would like to thank St\'{e}phane Greff and the joint metabolomics
service (plateforme Mallabar) at IMBE (Aix-Marseille Universit\'{e}),
for their welcome and help.

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