Metabolomics (2011) 7:289–304
DOI 10.1007/s11306-010-0239-2
ORIGINAL ARTICLE
Metabolic fingerprinting as an indicator of biodiversity:
towards understanding inter-specific relationships
among Homoscleromorpha sponges
Julijana Ivanišević • Olivier P. Thomas •
Christophe Lejeusne • Pierre Chevaldonné
Thierry Pérez
•
Received: 12 May 2010 / Accepted: 3 September 2010 / Published online: 22 September 2010
Ó Springer Science+Business Media, LLC 2010
Abstract Sponges are an important source of secondary
metabolites showing a great diversity of structures and
biological activities. Secondary metabolites can display
specificity on different taxonomic levels, from species to
phylum, which can make them good taxonomic biomarkers.
However, the knowledge available on the metabolome of
non-model organisms is often poor. In this study, we demonstrate that sponge chemical diversity may be useful for
fundamental issues in systematics or evolutionary biology,
by using metabolic fingerprints as indicators of metabolomic
diversity in order to assess interspecific relationships. The
sponge clade Homoscleromorpha is particularly challenging
because its chemistry has been little studied and its phylogeny is still debated. Identification at species level is often
troublesome, especially for the highly diversified Oscarella
genus which lacks the fundamental characters of sponge
taxonomy. An HPLC–DAD–ELSD–MS metabolic fingerprinting approach was developed and applied to 10 Mediterranean Homoscleromorpha species as a rapid assessment
of their chemical diversity. A first validation of our approach
was to measure intraspecific variability, which was found
significantly lower than interspecific variability obtained
between two Oscarella sister-species. Interspecific relationships among Homoscleromorpha species were then
J. Ivanišević C. Lejeusne P. Chevaldonné T. Pérez (&)
Diversité, Evolution et Ecologie Fonctionnelle Marine, UMR
6540 CNRS, Centre d’Océanologie de Marseille, Université de
la Méditerranée, Rue de la Batterie des Lions, 13007 Marseille,
France
e-mail: [email protected]
J. Ivanišević O. P. Thomas C. Lejeusne
Laboratoire de Chimie des Molécules Bioactives et des Arômes,
UMR 6001 CNRS, Institut de Chimie de Nice, Université de
Nice-Sophia Antipolis, Parc Valrose, 06108 Nice, France
inferred from the alignment of their metabolic fingerprints.
The resulting classification is congruent with phylogenetic
trees obtained for a DNA marker (mitochondrial COI) and
demonstrates the existence of two distinct groups within
Homoscleromorpha. Metabolic fingerprinting proves a
useful complementary tool in sponge systematics. Our case
study calls for a revision of Homoscleromorpha with further
phylogenetic studies and identification of additional chemical synapomorphic characters.
Keywords Metabolic fingerprinting Porifera Homoscleromorpha Secondary metabolism Sponge systematics
1 Introduction
Occurring within cells, tissues or biofluids, low-molecularweight organic compounds constitute the secondary metabolome of an organism (Griffiths 2007; Nobeli and
Thornton 2006; Wolfender et al. 2009). Secondary
metabolites provide a great natural reservoir of structural
and functional chemical diversity. They have evolved as
products of natural selection, the interactions between the
organism and its environment being a major driving force
in structuring chemical diversity. On the other hand,
secondary metabolites have an important role in many
ecological processes that shape biodiversity (Paul et al. 2007;
Paul and Puglisi 2004; Rosenthal and Berenbaum 1992). Up
to now, exploring the biodiversity through its chemical
component have been mostly restricted to natural product
chemistry and the use of secondary metabolites diversity as a
source of novel and potentially active compounds. Several
attempts were undertaken to use these compounds in
chemotaxonomy as an alternative or complementary tool to
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elucidate classification patterns and to propose potential
synapomorphic chemical markers at different taxonomic
levels (Bergquist and Wells 1983; Dunstan et al. 2005;
Erpenbeck and van Soest 2005; Erpenbeck and van Soest
2007; Mooney et al. 2007; Van Soest and Braekman 1999).
A synapomorphy is defined as a derived trait shared by two
or more taxa, which is believed to reflect their common
ancestry. A strong limitation of this approach is that natural
product chemists mainly focus on the description of original
compounds whereas only reports of similar compounds in
distinct organisms could provide useful synapomorphic
chemical markers (Erpenbeck and van Soest 2007).
A more global metabolomics approach, called metabolic
fingerprinting can be used as a diagnostic tool to screen the
metabolic diversity of living systems (Ellis et al. 2007;
Fiehn 2002; Nobeli and Thornton 2006; Weckwerth and
Morgenthal 2005). The main objective of metabolic fingerprinting is to compare multiparametric patterns (or fingerprints) as dynamic metabolic phenotypes of a high
number of samples (Wolfender et al. 2009). This approach
is widely applied in phytochemistry and phytotherapy for
purposes such as quality control of plants, characterization
and classification of medicinal plants (Fu et al. 2009; Ge
et al. 2008; Han et al. 2008; Yang et al. 2007) and studies
of plant response to stress and plant-host interactions
(Allwood et al. 2008; Shulaev et al. 2008). Metabolomics
approaches prove useful to distinguish among individual
signals and thus might serve in biomarker discovery, for
the identification of new chemotaxonomic characters in
systematics, or as indicators of short-term environmental
changes in ecology studies (Fiehn 2002; Wolfender et al.
2009). Compared to an exhaustive metabolite analysis,
metabolic fingerprinting is a rapid, untargeted and highthroughput method, and it requires small amounts of biological material.
The secondary metabolome comprises a large diversity
of compounds exhibiting a wide range of structures and
polarities. No single analytical method encompasses the
chemical diversity of an entire metabolome (Wolfender
et al. 2009; Xie et al. 2008). Therefore, there is a need to
develop protocols providing a comprehensive and rapid
snapshot of the chemical composition of a given biological
system at a given time. Multiparallel analytical technique
is necessary and HPLC–MS, in comparison to NMR and
GC–MS, is the most versatile analytical tool combining
high sensitivity, resolution and detection of a large panel of
compound polarities (Wolfender et al. 2009; Xie et al.
2008). This kind of analytical method combined with
multivariate statistical data analysis has already been successfully used to identify, discriminate and classify samples of terrestrial plants (Xie et al. 2008; Yang et al. 2007)
but never to discriminate between marine species. A robust
clustering of NMR spectra from partially purified extracts
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was only once applied to select marine sponges that produce the same bioactive compound or family of compounds (Pierens et al. 2005).
In marine ecosystems, sponges represent one of the most
important sources of secondary metabolites, with a great
diversity of structures, biosynthetic pathways and biological activities. Being the sister group of the Eumetazoa,
Porifera are a key group in evolutionary biology especially
to better understand phylogenetic relationships at the basis
of the Metazoan tree (see for instance Ereskovsky et al.
2009a). Consequently, their systematics and phylogeny are
dynamic fields of research. Traditional sponge systematics
is based on morphological characters, particularly the size
and shape of skeleton elements, spicules and spongin
fibres, as well as supplementary histological and reproductive characters. Additional chemotaxonomic markers
such as biochemical compounds (e.g. sterols and fatty
acids) have also been used for sponge taxonomy (Bergquist
et al. 1986; Blumenberg 2003; Thiel et al. 2002). Such
characters can be particularly useful for sponges that do not
possess characters essential for sponge systematics, the socalled ‘‘sponges without skeleton’’ (Boury-Esnault et al.
1995; Bultel-Ponce et al. 1999). Moreover, sponges harbour an important diversity of cell types and associated
microorganisms (Taylor et al. 2007), which serve also as
taxonomical characters (Muricy and Dı́az 2002; Vishnyakov and Ereskovsky 2009).
Homoscleromorpha, the focus of this study, is a small
sponge clade composed of a single family, Plakinidae
(Solé-Cava et al. 1992), represented by seven genera
(Muricy and Dı́az 2002) and 22 valid species in the
Mediterranean (Ereskovsky et al. 2009b). Taxonomy of
Homoscleromorpha is highly challenging and their phylogeny remains debated, because many species are devoid
of skeleton (Borchiellini et al. 2004; Ereskovsky 2009a;
Nichols 2005; Philippe et al. 2009). This lack of convenient
morphological character makes Oscarella lobularis a typical example of a sponge species that has been considered
for a long time as ‘‘cosmopolitan’’ and finally turned out to
be a highly diversified complex of different species (Boury-Esnault et al. 1992; Muricy et al. 1996), with potential
new species. For such a problematic sponge group, it has
been necessary to describe in details the cytology and
associated bacteria for each species identification (Muricy
1999; Muricy et al. 1999).
Homoscleromorpha chemical diversity has seldom been
studied, with the exception of tropical representatives of
the Plakortis and Corticium genera. Lacking a good
knowledge of their metabolome, the aim of our study was
to propose more holistic approach to Homoscleromorpha
chemotaxonomy using metabolic fingerprinting rather than
attempting to identify their entire chemical diversity. Our
first objective was thus to develop a rapid and reproducible
Metabolic fingerprinting as an indicator of biodiversity
method to assess metabolic diversity within Homoscleromorpha and to easily discriminate species. The next step
was to relate the observed metabolic diversity with crude
extract bioactivity, and with the diversity of cell types and
symbiotic bacteria known for each species. Finally we
evaluated the relevance of such a rapid assessment method
to build a metabolic fingerprint classification of all studied
Homoscleromorpha, compared with a classification
obtained using a DNA marker, the mitochondrial gene
coding for Cytochrome Oxydase I (COI).
2 Materials and methods
2.1 Sponge material
We studied 10 Homoscleromorpha species (Table 1;
Fig. 1). These sponges are generally thin and encrusting.
They are all characterized by a reduced skeleton and some
of them, notably Oscarella species and Pseudocorticium
jarrei, lack spicules entirely. When present, the skeleton is
usually formed by a combination of small siliceous tetractine spicules of a particular type, called calthrops (genus
Corticium, Plakina) and/or its derivatives called diods and
triods (genera Plakina, Plakortis). The determination of
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Oscarella species is highly difficult and requires a thorough
study of anatomical and cytological characters (Muricy
et al. 1996), especially mesohyl cells and endobiotic bacterial morphotypes. A new species, Oscarella sp. nov. is
currently under description (our team, work in progress).
In the Mediterranean, Homoscleromorpha sponges
dwell in sciaphilic hard-substrate communities, such as the
coralligenous banks or semi-dark and dark submarine
caves (Ereskovsky et al. 2009b). They can be found
from the Gibraltar Strait to the Levantine Basin of the
Mediterranean.
For this study, each species was represented by two or
three different specimens except for two rare species,
Oscarella microlobata and Oscarella viridis, restricted to
dark submarine cave, for which it was not possible to
collect more than one specimen. In order to better assess
the intra-specific variability, Oscarella lobularis and
Oscarella tuberculata were each represented by 12 different individuals.
2.2 Sampling and taxonomy
Sponges were collected by SCUBA diving during field
trips of the ECIMAR programme (www.ecimar.org) in
the Mediterranean (2007–2009): Gibraltar Strait (Ceuta,
Table 1 Main morphological and cytological characters used to differentiate Homoscleromorpha species
Species
Skeleton
Bacteria
Mesohyl Cell types
Oscarella
tuberculata
Absent
2 morphotypes, rare
1 vacuolar type
References
Boury-Esnault et al. (1992)
Vishnyakov and Ereskovsky
(2009)
Oscarella lobularis Absent
3 morphotypes, medium
density
2 vacuolar types
Oscarella viridis
3 morphotypes, medium
density
1 crescent type, 1 vacuolar type
4 morphotypes, abundant
2 granular types, 1 type with
inclusions
Absent
Boury-Esnault et al. (1992)
Vishnyakov and Ereskovsky
(2009)
Muricy et al. (1996)
Vishnyakov and Ereskovsky
(2009)
Oscarella
microlobata
Absent
Muricy et al. (1996)
Oscarella sp. nov.
Absent
2 morpotypes, medium
density
3 types with inclusions
Our team, work in progress
Pseudocorticium
jarrei
Absent
5 morphotypes, abundant
4 types with inclusions
Boury-Esnault et al. (1995)
Plakina jani
Siliceous diods, triods,
calthrops
8 morphotypes, abundant
1 vacuolar type, 1 type with
inclusions
Muricy et al. (1999)
Plakina trilopha
Siliceous diods, triods,
calthrops
8 morphotypes, abundant
1 vacuolar type, 1 type with
inclusions
Muricy et al. (1998)
Muricy et al. (1999)
Corticium
candelabrum
Siliceous calthrops
6 morphotypes, abundant
1 vacuolar type, 1 type with
inclusions
Muricy and Dı́az (2002)
Plakortis simplex
Siliceous diods, triods
Abundant but no
description
No description
Muricy and Dı́az (2002)
Vishnyakov and Ereskovsky
(2009)
Muricy et al. (1998)
De Caralt (2007)
Laroche et al. (2007)
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J. Ivanišević et al.
Fig. 1 In situ pictures of the Mediterranean Homoscleromorpha
species from this study. a Oscarella tuberculata; b Oscarella
lobularis; c Oscarella viridis; d Oscarella microlobata; e Oscarella
sp. nov.; f Pseudocorticium jarrei; g Corticium candelabrum;
h Plakina trilopha; i Plakina jani (Plakjan) and Plakortis simplex
(Plaksim)
Spain), Costa Blanca (Mazzaròn, Spain), Riou Archipelago
(Maire, Jarre and Riou Islands, Marseille, France), 3PP
Cave (La Ciotat, France) and Gulf of Porto (North West of
Corsica, France). The sampling was carried out every year
in the summer period, from mid-June to mid-July. For
chemical analyses, samples were frozen immediately after
collection and kept at -20°C until freeze drying. Small
pieces of samples were stored separately in 95% ethanol,
for morphological and genetic analyses. For the latter
analyses, ethanol was replaced several times to avoid
dilution with intercellular seawater. Another small piece of
each was fixed for cytological analysis (see below).
Some species were identified using external morphological characters (Corticium candelabrum, Pseudocorticium jarrei) and spicule preparations (Plakina spp. and
Plakortis simplex). For all Oscarella species fine cytological analyses were necessary. For these studies, Oscarella
specimens were fixed in a mixture of 25% glutaraldehyde,
0.4 M cacodylate buffer and filtered (0.2 lm) sea water
(1 vol.:4 vol.:5 vol.) and postfixed in a 2% solution of
osmium tetroxide in seawater. Specimens were then
embedded in Araldite resin for semi-thin and ultra-thin
sections. Semi-thin sections were stained with toluidin blue
and observed in light microscopy (Light microscope WILD
M20). Ultra-thin sections were stained with uranyl acetate,
contrasted with lead citrate and finally observed under
transmission electron microscope JEOL JEM-2000FX.
Information regarding bacteria and cell type diversity was
gathered from bibliography (Boury-Esnault et al. 1995;
Boury-Esnault et al. 1992; Muricy 1999; Muricy et al.
1999; Muricy et al. 1996; Muricy and Dı́az 2002).
Plakortis simplex was an exception as this is the only case
where no cytology study has been reported.
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2.3 Secondary metabolites extraction
Frozen samples of different Homoscleromorpha species
were freeze-dried and ground to obtain a homogenous
powder. Approximately 1 g of the powder was extracted
three times in a row during 10 min with 10 ml of MeOH/
CH2Cl2 1:1 in an ultrasonic bath. The filtrates of each
extraction were combined, mixed with 0.5 g of C18 powder
and evaporated to dryness. The C18 supported crude
extracts were then loaded onto C18 Solid Phase Extraction
Metabolic fingerprinting as an indicator of biodiversity
(SPE) cartridges (2 g, Phenomenex Strata). The conditioned columns were first washed with 10 ml of H2O for
desalting and then eluted with 10 ml of MeOH/CH2Cl2 1:1
in a 10 ml volumetric flask. The resulting organic phase
was used for HPLC–DAD–ELSD–MS analysis.
2.4 HPLC–DAD–ELSD–MS analysis
On-line HPLC–MS analysis was performed using VWRHitachi HPLC system and a Bruker Esquire 3000 Plus mass
spectrometer equipped with electrospray ionisation (Katajamaa et al. 2006). Mass spectra were recorded in the
positive and negative modes alternatively. The HPLC
system was equipped with an autosampler (VWR-Hitachi
L-2220), a photoDiode Array Detector (DAD, VWRHitachi L-2455), and an Evaporative Light-Scattering
Detector (ELSD, Chromachem, Eurosep, France). HPLC
separation was achieved on a Phenomenex Gemini
C6-phenyl column (250 mm 9 3 mm, 5 lm) using a linear
gradient elution of H2O/ACN/formic acid from 90:10:0.1
(isocratic from 0 to 5 min), to 0:100:0.1 (isocratic from 35
to 45 min, flow 0.5 ml/min). The column temperature was
maintained at 30°C. The injection volume was set to 20 ll.
For UV fingerprints, the DAD detector was set to scan from
200 to 800 nm, and chromatograms were extracted at 210,
254 and 280 nm. The ELSD fingerprints were obtained
after mobile phase nebulization at 38°C following the
evaporation at 50°C. The mass spectrometry detector
(MSD) parameters were set as follows: nebulizer sheath
gas, N2 (50 psi); dry gas, N2 (12 l/min); capillary temperature, 350°C; capillary voltage, 4000 V in negative ion ESI
mode, 4000 V in positive ion ESI mode; lens 1 voltage,
5 V in (-) ESI, -5 V in (?) ESI; lens 2 voltage, 60 V in
(-) ESI, -60 V in (?) ESI; isolation width for the MS
experiments, m/z 1.0; collision gas, He; and collision
energy, 35%. Data were collected between m/z 50 and
1200. A sample of purified ecdysteroids was used as an
external standard to test the retention time repeatability of
the online analysis.
2.5 Chemometric data analysis
The ±ESI–MS raw data were processed by Bruker Daltonics Data Analysis version 3.4. The metabolite diversity
was assessed by an estimation of the number of compounds
based on ELSD chromatograms. Filtering and baseline
correction for ELSD chromatograms were defined by the
following parameters: RT (Retention Time) range
0–50 min, Signal/Noise threshold C5, Minimum Relative
Area C1% and Minimum Relative Intensity C1%. Semiquantifications were performed assessing the proportion of
the major compounds with the ELSD chromatograms. This
relative composition of the extracts was expressed as a
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Relative Fraction Area (RFA in %). We chose an artificial
limit of H2O/ACN 50:50 as the border between what were
called ‘‘polar’’ (eluted during H2O/ACN gradient from
90:10 to 50:50) and ‘‘apolar’’ compounds (eluted during
H2O/ACN gradient from 50:50 to 0:100).
ESI–MS chromatogram (Base Peak Chromatogram––
BPC) analyses were exported as line spectra and converted
to netCDF file format to process unit mass resolution data
in centroid mode including fragmentation (MSn) scans,
with MZmine toolbox (Katajamaa et al. 2006). MZmine
peak detection was achieved by centroid mass detection
using a parameter of ‘‘noise level’’ set to identify the representative data point of each peak in spectrum domain.
This value sets the minimum intensity for a centroid data
point to be considered as part of a peak (noise level = 104).
The highest intensity chromatogram builder was used to
eliminate the noise and define each peak by ‘‘min time
span’’ (15 s.), ‘‘min height’’ (104) and ‘‘m/z tolerance’’
(0.1). The peak deconvolution was performed using local
minimum search respecting the following parameters:
chromatographic threshold 85%, RT range of 15 s., minimum relative height 5% and minimum absolute height of
104. Alignment was performed using absolute RT tolerance
of 30 s. and m/z tolerance of 0.1. The data matrix,
including the retention index coupled with molecular mass
of each chemical constituent as a variable, and their presence or absence (0 or 1) as values, was gap-filled using the
‘‘same RT and m/z range filler’’ option. Hierarchical
Cluster Analyses (HCA) were performed with Statistica
software (StatSoft Inc., U.S.A.) to classify samples on the
basis of fingerprint similarities, and thus to determine
relationships between different species and genera. The
first step was the measure of similarity between samples
using Euclidean distance, after which the agglomerative
clustering was applied using the complete linkage algorithm (Varmuza and Filzmoser 2009).
2.6 Bioactivity assay
The standardised MicrotoxÒ bioassay (Microbics, Carlsbad, CA, USA) (Marti et al. 2003) was used to assess the
bioactivity of crude organic phase (obtained after SPE) of
all extracted Homoscleromorpha. Crude organic extracts
were dissolved in artificial seawater and up to 1% acetone
was added to improve dissolution of the extracts. Solutions
were tested in four diluted concentrations. According to the
species, the initial concentration was set at 500 lg/ml or
1000 lg/ml and a dilution factor of 2 was applied between
each following tested concentration. The bioactivity was
quantified by measuring the direct effect on the metabolism
of the bioluminescent bacterium Vibrio fischeri indicated
by a decrease in light emitted and expressed as an EC50
value.
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2.7 DNA extraction and sequencing
DNA of Oscarella species and Plakina trilopha were
extracted using the QIAamp DNA Mini Kit (QIAGEN).
The primers C1-J2165 and C1-Npor2760, were used to
amplify the partition I3-M11 portion of the COI mitochondrial gene (Erpenbeck et al. 2002). Amplification was
performed in a 20 ll total-reaction volume with: 2 ll of
each primer (10 lM), 3.2 ll dNTPs (10 mM), 4 ll polymerase buffer 59, 2.5 ll MgCl2 (25 mM), 0.1 ll Taq
polymerase (5 Ull-1), and 1.2 ll of extracted DNA.
Polymerase Chain Reaction (PCR) was performed on a
thermocycler Mastercycler gradient PCR-S (Eppendorf)
thermocycler with an initial step of 5 min at 94°C followed
by 40 amplification cycles (denaturation at 94°C for
1 min.; annealing at 42°C for 1 min; and extension at 72°C
for 1 min), and a final extension step at 72°C for 5 min.
PCR products were directly sequenced in each primer
direction by the sequencing platform ‘‘BioGenOuest’’
(Roscoff, France). The best match from BLAST searches
of GenBank was with a sequence from Oscarella carmela
(Wang and Lavrov 2007). The COI sequences of Plakina
jani, Corticium candelabrum, Pseudocorticium jarrei and
Plakortis simplex were kindly provided by Dennis V.
Lavrov.
2.8 Molecular classification
Sequences were aligned using BioEdit version 7.0.5.2 (Hall
1999). The Akaike Information Criterion was used in
jMODELTEST (Posada 2008) to determine the best-fit
model of nucleotide substitution, which was the ‘‘transitional model’’ (Posada 2003) with a proportion of invariable sites and gamma correction (TIM2 ? I ? G).
Supports for Neighbour Joining (NJ) and Maximum Likelihood (ML) reconstructions were evaluated by 10000
bootstrap replicates, respectively in PAUP* 4B10 (Swofford 2002.) and PhyML 3.0 programmes (Guindon and
Gascuel 2003).
3 Results
3.1 Standardised metabolic fingerprinting
The standardised sample preparation protocol followed by
the optimised online HPLC–UV–ELSD–MS analysis has
been demonstrated to be a rapid and efficient method to
obtain a reproducible chemical fingerprint of each sample
(Figs. 3, 4, 6). A large panel of polar and apolar compounds was detected and separated on C6-phenyl column
applying the specified elution gradient. Chemical fingerprints were obtained using three distinct detectors: ELSD,
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UV (at 210, 254 and 280 nm) and MS±. ELSD data provided an estimation of the extracts composition and the
expression level (RFA) of the most concentrated metabolites (Fig. 3b). UV profiles gave additional information on
the presence of chromophores in the observed compounds.
ESIMS data offered structural information on each detected compound, defined by their retention time (RT min)
and molecular mass (m/z). To maximise the metabolome
characterization, mass responses were recorded in both
positive and negative ion mode. In general, more information was obtained with the ESI? mode than with ESImode. This is probably due to the composition of the
mobile phase which contains formic acid and facilitates the
compounds ionisation. The RT variability tested using the
external standard did not exceed 0.034 s (standard deviation B0.11%) in the optimized HPLC–MS conditions,
which warranted high repeatability.
3.2 Metabolite diversity and bioactivity of crude
extracts
According to ELSD fingerprints, the highest metabolic
diversity is recorded in Oscarella spp. species (with the
exception of Oscarella microlobata) and Plakortis simplex,
with 22–28 annotated metabolites (Fig. 2a). Pseudocorticium jarrei and Plakina trilopha follow with a mean
diversity of 20 and 18 metabolites respectively. The lowest
metabolite diversity is recorded in O. microlobata (16),
Plakina jani (15) and Corticium candelabrum with only 12
detected compounds in our conditions (Fig. 2a). Despite
the lower metabolite diversity the latter three species as
well as P. jarrei display high expression level, with at least
one or few major compounds exceeding RFA of 30% and
sometimes attaining even 60%. Corticium candelabrum
and P. jani exhibit the highest metabolite expression level,
with at least three detected compounds with a RFA higher
than 15%. In C. candelabrum, the major compound (RFA
32.7%) is characterized by RT = 18.8 min and m/z 470,
two other being highly apolar and not detectable by MS±
at RT = 42.1 min (RFA 31%) and 43 min (RFA 23%)
respectively. In P. jani, the three major compounds are
highly apolar and not detected by MS± at RT = 41.4 min
(RFA 39.3%), 42 min (RFA 19.8%) and 43 min (RFA
15.5%). In P. trilopha, the three major compounds are also
highly apolar with RT at 39.8 min and m/z 391 (12.2%)
and two others not detectable by MS± at RT = 42 min
(8.8%) and 43 min (39.1%). Two latter highly apolar
metabolites with RT 42 and 43 min seem to be common to
these three species. Oscarella microlobata and Oscarella
sp. display also a high metabolite expression level, corresponding to rather polar metabolites, with at least three
detected compounds having a RFA higher than 10%. In
Oscarella sp., two highly expressed families of compounds
Metabolic fingerprinting as an indicator of biodiversity
295
Fig. 2 Secondary metabolite diversity in relation to bioactivity and
bacteria and mesohyl cell diversity. a Secondary metabolite diversity
within Homoscleromorpha species evaluated on the basis of HPLC–
ELSD analysis. b Bioactivity assessment of species organic extracts.
Measurements with the standardized Microtox Assay. High EC50
values indicate low extract activity. In a and b bars indicate standard
errors. c Correlation between the bacteria and cell diversity (present
in the sponges’ mesohyl) and the estimated compound diversity.
d Correlation between the bacteria and cell diversity (present in the
sponges’ mesohyl) and the organic extract bioactivity. For c and d,
curved dotted lines represent 95% confidence bounds of the
correlation values
were observed, the first with compounds at RT = 26.9 min
(m/z 452, RFA 9.3%), 27.7 min (m/z 466, RFA 33%) and
29.2 min (m/z 482, RFA 18.7%) and the second including
major compounds at RT = 38.6 min (m/z 365, RFA 8.6%),
and 42 min (not detectable by MS±, RFA 11.1%). The
major compounds of O. microlobata have a RT at 3.8 min
(m/z 194, RFA 16.4%), 35.9 min (m/z 399, RFA 5.3%) and
two highly apolar compounds, not detectable by MS±, at
RT 41.2 min (RFA 11%) and 42 min (RFA 48.7%).
Pseudocorticium jarrei displays the highest ‘‘apolar’’
metabolite diversity and expression level. Five highly
apolar major metabolites were noticed at RT 39.5 min
(RFA 6.6%), 40.5 min (m/z 395, RFA 16.8%), 41.2 min
(m/z 488, RFA 7.5%), 41.8 min (m/z 502, RFA 60%) and
44.1 min (RFA 6.2%). For the other species, O. tuberculata, O. lobularis (see Sect. 3.3), O. viridis and P. simplex,
few major metabolites were detected, with a high diversity
of minor and rather apolar compounds. Most of our studied
species display a high diversity of apolar compounds
illustrated by a high ‘‘apolar’’/‘‘polar’’ compounds ratio
varying from 7.5 ± 5 (Oscarella sp. nov., P. trilopha,
O. tuberculata, O. viridis) to 20 ± 6 (P. jarrei, P. simplex
and O. lobularis). In contrast, C. candelabrum, O. microlobata and P. jani have the lowest apolar compounds
diversity, ‘‘apolar’’/‘‘polar’’ ratios being respectively 1, 2.2
and 2.75. Therefore, beside the presence of highly expressed
apolar compounds the latter species and Oscarella sp. are
also characterized by the presence of rather polar metabolites, most of them having RFA higher than 5%.
The bioactivity of crude organic extracts, expressed in
EC50 values, ranges from 36 lg/ml for O. tuberculata (the
most bioactive species), to 268 lg/ml for P. jarrei
(Fig. 2b). Oscarella species and P. simplex are the most
bioactive species, with EC50 ranging from 36 to 111 lg/ml,
always higher than the bioactivity of Plakina, Corticium
and Pseudocorticium genera (EC50 values from 167 to
268 lg/ml). In overall, species with the highest secondary
metabolites diversity, especially a high number of minor
and rather apolar compounds, also display the highest
bioactivity. However, two exceptions explain that no statistical correlation was found between metabolite diversity
and species bioactivity. Oscarella microlobata appears as
123
296
an exception having a low ‘‘apolar’’/‘‘polar’’ ratio and
exhibiting a high bioactivity (EC50 59 lg/ml), whereas
P. jarrei has the lowest bioactivity (268 lg/ml) despite a
high metabolites diversity, mainly composed of apolar
compounds.
We found, however, a correlation between the metabolite diversity and the number of mesohyl cells with inclusions and symbiotic bacteria found in the sponge mesohyl.
This correlation is negative (Fig. 2c, R = -0.756,
P = 0.01). Species with the lowest number of bacteria and
cell morphotypes in their mesohyl display the highest
metabolite diversity and bioactivity (Fig. 2c, d). With the
exception of O. microlobata which harbours the highest
diversity of main bacterial morphotypes (4) in the genus,
other Oscarella species display only a few cell types with
inclusions (1–3) and rare bacterial morphotypes (1–2) in
their mesohyl. On the contrary, Plakina species, C. candelabrum and P. jarrei present a high diversity of main
bacterial morphotypes (5–8) and high numbers of bacterial
cells in their mesohyl. The latter are the species with the
lowest, albeit well expressed, metabolite diversity.
Plakortis simplex was not included in this correlation
analysis due to the lack of precise cytological data.
3.3 Intraspecific versus interspecific variability
Intraspecific and interspecific variability of metabolic fingerprints were compared on two sister species O. tuberculata and O. lobularis sampled in different regions of the
Western Mediterranean (Ceuta, Marseille and Corsica).
The inter-individual/intraspecific variability of HPLC–
ELSD and HPLC–MS metabolic fingerprints is both
quantitative, marked by the expression level of some
J. Ivanišević et al.
compounds, and qualitative, related to the occurrence of
some minor compounds (Fig. 3). Superimposed ESI(?)MS
(Fig. 4a) and ELSD (Fig. 4b) metabolic fingerprints of
both species provide evidence of a subtle but clear chemical divergence between the two sister species. A large part
of their metabolic profiles, from RT 28 to 40 min, is
qualitatively similar, characterized by several common
metabolites, including the major one (according to the
ELSD profiles: RFA 30.5% for O. tuberculata and 66.4%
for O. lobularis) which is eluted at RT 30.6 min characterised by m/z 506. A few distinct compounds are exclusive
of each species (Fig. 4). Two prominent peaks at RT
42.8 min, m/z 415 (25.8%) and 44.0 min, m/z 417 (RFA
28.3%) are only found in O. tuberculata extracts. The
O. lobularis profiles present one distinct, specific compound eluted at RT 24.4 min (RFA 5%, Fig. 4b) and
absorbing at all three UV wavelengths. Unfortunately, this
compound was not detected by MS? and - in m/z range
from 50 to 1200.
All HPLC–MS fingerprints of both species were defined
by up to 30 peaks or variables added as vectors in a data
matrix to perform a Hierarchical Cluster Analysis (HCA,
Figs. 4, 5). To build the classification of samples, the peak
at RT 30.6 min and m/z 506 shared by both species was
assigned as a reference peak (Fig. 4.). The alignment of all
HPLC–MS fingerprints provided a classification with two
distinct groups, the chemotypes of the same species being
grouped together. The intraspecific variability is evaluated
at 40% of divergence (linkage distance) within O. tuberculata samples and 54% for O. lobularis samples. Both
remain significantly lower than the interspecific variability,
with 70% of divergence (Fig. 5). Within each species,
samples originating from the same geographic region are
grouped together.
Fig. 3 Intraspecific variability illustrated by HPLC–ESI(?)MS (BPC) chromatograms of two Oscarella tuberculata specimens sampled in two
distant Mediterranean sites: Marseille, France (top graph) and Ceuta, Gibraltar Straits (lower graph). Major ions m/z are indicated above peaks
123
Metabolic fingerprinting as an indicator of biodiversity
Fig. 4 Interspecific variability illustrated by HPLC–ESI(?)MS
(BPC) (top) and HPLC–ELSD (below) chromatograms of two sister
species Oscarella tuberculata (grey lower line) and Oscarella
297
lobularis (black top line). Major m/z (on BPC) and retention times
in minutes (on HPLC–ELSD) are indicated above peaks
Fig. 5 Hierarchical cluster
analysis of specimens belonging
to the Oscarella sister-species,
O. tuberculata and O. lobularis,
based on HPLC–ESI(?)MS
metabolic fingerprint alignment.
Oscatub: O. tuberculata,
Oscalob: O. lobularis. Ma1–
Ma7 indicate different sampling
sites in Marseille, France, Co1–
Co3 in Corsica, France, Ce1–
Ce5 in Ceuta, Gibraltar Straits,
Spain and Cb1–Cb3 in Costa
Blanca, Spain
3.4 Interspecific relationships
within Homoscleromorpha sponges
using metabolic fingerprints
When the fingerprints of all species are aligned (Fig. 6a, b),
HCA classification always groups together specimens
belonging to the same species.
Intraspecific divergence always remains lower than the
interspecific variability. Two separate and well supported
groups are discriminated by HCA within Homoscleromorpha. Group A comprises all the Oscarella species and
P. jarrei and a group B gathers Plakina species, C. candelabrum and P. simplex (Fig. 7). Within the group A,
there are two distinct clusters: A1 with O. tuberculata,
O. lobularis and O. viridis, and A2 with Oscarella sp. nov.,
P. jarrei and O. microlobata. However, the divergence
between these two clusters is not well supported (only 4%).
Within cluster B, the presence of three separate groups
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298
J. Ivanišević et al.
Fig. 6 Homoscleromorpha
sponge interspecific variability
in metabolic fingerprints,
illustrated by HPLC–ESI(?)MS
(BPC) of a species forming
subgroup A2: Oscarella
microlobata, Oscarella sp. nov.
and Pseudocorticium jarrei,
b species forming group B:
Corticium candelabrum,
Plakina jani and Plakina
trilopha. Major m/z are
indicated above peaks
corresponding to the Plakina, Corticium and Plakortis
genera is well supported by up to 34% of divergence.
According to the observed linkage distances, P. jani and
P. trilopha, O. tuberculata and O. lobularis, and then
Oscarella sp. nov. and P. jarrei appear as closely related
species pairs.
3.5 Interspecific relationships
within Homoscleromorpha sponges using COI
A fragment of 409 bp on the I3-M11 portion of the mitochondrial COI gene was aligned for all analysed species. No
insertion/deletion was detected. A strong congruence can be
123
found between the mitochondrial DNA reconstruction and
the classification based on metabolic fingerprints (Fig. 8).
Groups A and B are clearly separated in two clades whatever the phylogenetic reconstruction method used and are
supported by maximal bootstrap values. Within the clade
A, the NJ reconstruction strongly supported the two subclades within Oscarella: the subclade A1 with O. tuberculata, O. lobularis and O. viridis and the subclade A2 with
Oscarella sp. nov., P. jarrei and O. microlobata. NJ tree is
also representative of the topology found by ML reconstruction which produced a slightly different result with
O. microlobata being placed at the base of both subclades,
although well within clade A. Relationships within group B
lack bootstrapping support whatever the method.
Metabolic fingerprinting as an indicator of biodiversity
299
Fig. 7 Hierarchical cluster
analysis of Homoscleromorpha
sponge species based on the
alignment of their HPLC–
ESI(?)MS metabolic
fingerprints. Numbers from 1 to
3 refer to different specimens of
one species
Fig. 8 Classification of Homoscleromorpha sponge species based on
the I3-M11 portion of the COI mitochondrial gene (left tree)
compared to the classification based on metabolic fingerprints (right
tree). COI molecular phylogenetic reconstruction using Neighbour
Joining method is compared to the metabolic fingerprint classification
established on Euclidean linkage distance. Values on nodes indicate
the percentage of bootstrap replicates (over 10,000 replicates)
4 Discussion
markers for Corticium and Plakina (Van Soest and
Braekman 1999). Plakinamine A and B, first reported from
the genus Plakina (Rosser and Faulkner 1984) were
afterwards identified in different specimens of the Corticium genus. Due to the high bioactivity displayed by
specimens of the Corticium genus, an exhaustive chemical
study was undertaken, which led to the isolation and
characterization of numerous steroidal alkaloids called plakinamines (A–K), lokysterolamines and cortistatins (A–L)
(Aoki et al. 2006; Aoki et al. 2007; Borbone et al. 2002; De
Marino et al. 1999; Jurek et al. 1994; Lee et al. 2001;
McCarthy et al. 1992; Ridley and Faulkner 2003; Watanabe et al. 2007). They display significant antimicrobial,
4.1 Metabolic fingerprinting and secondary metabolite
diversity
Secondary metabolites of Mediterranean Homoscleromorpha species have been poorly studied. Previous studies
mainly referred to tropical and often unidentified species of
the Plakortis, Corticium and Plakina genera that displayed
bioactivity and thus were believed to have a potential
biomedical value. These studies also provided several
putative chemotaxonomic markers. Steroidal alkaloids
called plakinamines are considered as synapomorphic
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300
cytotoxic and antiproliferative activities. In our study, the
HPLC–MS fingerprints of Plakina species and C. candelabrum displayed molecular ions at m/z of 427, 429, 469
and 470 (RT = 18–20 min) suggesting the presence of
steroidal alkaloids whose correspondence to previously
described ones remains to be ascertained.
The existence of chemical markers at species level has
been suggested after the isolation and structural identification of the major compounds from O. tuberculata and
O. lobularis, the only studied Mediterranean species studied so far. Rare alkylpyrrole-aldehydes and sterol endoperoxides were found in O. tuberculata (Aiello et al. 1991)
whereas only the methyl ester of 6–11-eicosadienoic acid
was identified from O. lobularis (Loukaci et al. 2004). Our
LC–ELSD–MS metabolic fingerprints underlined more
subtle chemical differences between the two sister species
than previously reported. Although few specific compounds can be detected, a large part of the metabolic fingerprints is almost identical, including the major compound
recorded in both species. This major metabolite, with an
RT = 30.6 min and m/z of 506 is a lysophospholipide
currently under description (our team, work in progress).
Among specific compounds, the apolar and highly
UV-absorbing compounds at m/z 415, 403, 417 (Cimino
et al. 1975) seem to be exclusive to O. tuberculata species
and were identified as the previously-described 5-alkylpyrrole-2-carboxaldehydes (Cimino et al. 1975). Loukaci
et al. (2004) proposed chemical markers for these two sister
species whereas they were unable to perform an exhaustive
chemical analysis. In general, that kind of approach is
limited to a structural elucidation of only a few major
compounds, usually the easiest to purify. Although non
exhaustive, such studies require a lot of time and biological
material (usually several hundred grams of dry weight) to
achieve the characterisation of only very small, target part
of the secondary metabolome.
Our metabolic fingerprint approach is rapid and reproducible, and only 0.1–1 g of dry material is necessary to
obtain valuable information, which was a real advantage
for the study of a comprehensive set of small and fragile
specimens to assess natural variability at different levels.
This approach can provide a more comprehensive view on
the overall secondary metabolite production and thus on a
wide spectrum of potential chemotaxonomic markers.
Nevertheless, we must emphasize that some highly polar
compounds may not be soluble in MeOH/CH2Cl2 (1:1)
solvent mixture. As a consequence, our HPLC metabolic
windows do not include the highly polar metabolites. The
complementary information lost in the highly polar fraction
of the metabolome mainly concerns compounds derived
from the primary metabolism (Regalado et al. 2010) and
some polar peptides mainly produced by microorganisms
through the Non-Ribosomal Peptide Synthetase pathway
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J. Ivanišević et al.
(Sieber and Marahiel 2005). As the degree of specificity
of associated microorganisms is poorly known for the
Homoscleromorpha sponges, we assume that this part of
the metabolome would not represent significant additional
clues to our chemo-taxonomical study.
Our metabolic fingerprint approach has highlighted a
higher diversity and expression level of apolar metabolites
than previously suspected in Homoscleromorpha sponges.
These compounds may correspond to diverse and elaborated membrane constituents, such as unconventional esters
and fatty acids, phospholipids and sterols, which are often
characteristic of marine sponges (Ayanoglu et al. 1988;
Bergquist et al. 1986; Djerassi and Silva 1991; Kornprobst
2005). Some of them may also have a functional role and
be responsible of a part of the sponge bioactivity (Lee et al.
2007; Plouguerne et al. 2010).
4.2 Secondary metabolite diversity in relation
to bioactivity, mesohyl cell type and bacterial
diversity
Our results indicate that bioactivity of crude sponge
extracts does not depend entirely on the diversity and
expression level of secondary metabolites. The origin of
the bioactivity is a very complex issue, difficult to study
because of possible synergetic effects between metabolites.
Crude extracts can display a high bioactivity with the
purified compounds being inactive, whereas purified compounds can be highly active even when they represent a
minority of the crude extract. However, the examples of
species that share one or few major compounds might be
useful to draw some inferences on the putative contribution
of these metabolites to the whole species bioactivity. In the
case of the highly bioactive O. tuberculata and O. lobularis, their bioactivity might be due to their common major
compound at RT 30.6 and m/z 506, rather than to the
alkylpyrrole carboxaldehydes which is only present in
O. tuberculata. Even if Oscarella sp., O. microlobata
and P. jarrei share the presence of several compounds,
Oscarella species are up to five times more bioactive than
P. jarrei. This discrepancy might be explained by the high
expression level of polar bioactive compounds in Oscarella
which are absent in P. jarrei. Finally, C. candelabrum and
Plakina species seem to share the presence and high
expression level of two highly apolar metabolites (RT 42
and 43 min). But the highest bioactivity displayed by
C. candelabrum might be related to the high expression
level of its major polar compound at RT 18.8 min and m/z
470, which is absent in Plakina species.
Among the secondary metabolome both highly polar
and highly apolar groups of compounds are less explored
because they require specific purification processes. Our
methodology using a phenylhexyl rather than a C18 HPLC
Metabolic fingerprinting as an indicator of biodiversity
column allowed elution of a large quantity of highly apolar
compounds (more apolar than cholesterol as revealed by
TLC). Lipidic compounds usually represent a high proportion of marine sponge extracts. This part of metabolome
might include original molecules with potential bioactivities whose ecological role for the producing organism
remains to be elucidated. Our study points out a number of
highly bioactive Homoscleromorpha species producing a
large diversity of apolar compounds, and highlights thus
the need to explore this part of their metabolome.
To date, it is generally well accepted that microorganisms associated to sponges are involved in the production
of secondary metabolites. However, only few studies
actually provide evidence that symbiotic bacteria do contribute to this metabolism (Bewley et al. 1996; BultelPonce et al. 1999; Bultel-Ponce et al. 1998; Molinski
1993). Sponge mesohyl cells also are known to produce or
store secondary metabolites (Flowers et al. 1998; Uriz et al.
1996). This makes generalisations on the origin and
localisation of secondary metabolite production risky.
From our results, Homoscleromorpha species harbouring
only few and rare bacteria display the highest metabolite
diversity and bioactivity, thus supporting the hypothesis
that a significant part of the metabolome might have its
origin in sponge cells, rather than bacterial symbionts.
However, investigating more thoroughly the putative role
of microsymbionts in the whole sponge metabolome would
require another complete study, additional sampling (of
several tiny species, sometimes rare or limited to dark
submarine caves) but also the development of new analytical protocols.
4.3 Homoscleromorpha metabolic fingerprints
and implications for systematics
Intraspecific metabolic variability was proved to be rather
high, especially between samples originating from distant
geographic areas. The origin of this variability is difficult
to ascertain as it can be influenced by a number of environmental factors, and also by the genetic diversity (Becerro et al. 1997; Bundy et al. 2009; Lopez-Legentil et al.
2006; Lopez-Legentil and Turon 2005; Paul et al. 2007).
However, intraspecific variability was always lower than
interspecific variability which seems to be strongly determined by the genetic heritage characterizing each species.
Homoscleromorpha are presently accepted as a monophyletic group containing a single family, Plakinidae (BouryEsnault et al. 1992; Muricy 1999; Muricy and Dı́az 2002;
Solé-Cava et al. 1992). However they have been once been
divided in two families on the basis of traditional morphological characters: the Plakinidae Schulze, 1880 with
five genera containing spicules (Corticium, Plakina, Plakinastrella, Plakortis and Placinolopha) and Oscarellidae
301
Lendenfeld, 1887 with the genus Oscarella Vosmaer,
1884, without skeleton. The description of the new genus
and species Pseudocorticium jarrei (Boury-Esnault et al.
1995) motivated grouping of all Homoscleromorpha in a
single family. Indeed, this sponge lacks spicules as Oscarella, but is more similar to Corticium in some histological
and biochemical traits. However, our results clearly show
that Homoscleromorpha should be divided in two taxonomic groups, on the basis of both, their metabolic fingerprints and our preliminary phylogenetic analysis. This
hypothesis is strongly supported by the recent findings
suggesting the rehabilitation of the family Oscarellidae, to
which the genera Oscarella and Pseudocorticium belong so
far (Gazave 2010, Gazave et al. submitted). Sponges of the
genera Corticium, Plakina and Plakortis form a ‘‘spiculate’’ Homoscleromorpha group, whereas Oscarella species and P. jarrei constitute an ‘‘aspiculate’’ group. All
Oscarella species are thin and lobate sponges of various
size and colour. They have a reduced mesohyl, a very thin
ectosome and a sylleibid organisation of their aquiferous
system (Ereskovsky 2009a). Metabolic and molecular
divergences observed among Oscarella species are supported by several morphological and cytological characters. The two sister species O. tuberculata and O. lobularis
are difficult to distinguish in the field. They have the same
shape and size and they are both variable in colour.
However their consistencies are different, rather soft for
O. lobularis and cartilaginous for O. tuberculata. They can
be also distinguished at the cytological level, O. tuberculata harbouring a specific and highly abundant vacuolar
cells (Boury-Esnault et al. 1992; Muricy et al. 1996) where
secondary metabolites might be produced or stored.
Oscarella viridis can easily be distinguished from all other
Oscarella species by its light green colour, very soft and
fragile consistency and specific cells with granular inclusions (Muricy et al. 1996). Pseudocorticium jarrei and
some Mediterranean Oscarella species (O. microlobata
and Oscarella sp. nov.) share the presence of particular
cells with paracrystalline inclusions (Boury-Esnault et al.
1995; Muricy et al. 1996). Sponges of the ‘‘spiculate’’
group (except Plakina genus) as well as P. jarrei have a
thicker growth-form, with a well developed mesohyl, true
cortex and leuconoid organization of their aquiferous system (Muricy and Dı́az 2002). Although P. jarrei shares
morphological characters with both Homoscleromorpha
groups, our results indicate its closer relationship to the
Oscarella species. Inter-generic relationships among the
‘‘spiculate’’ group seem to be well resolved with metabolic
fingerprints, but they are not as clear with mitochondrial
DNA phylogeny. Further molecular studies will probably
help elucidating the phylogenetic relationships within this
sponge clade, by adding more species and more molecular
markers into the analysis (Gazave 2010, Gazave et al.
123
302
submitted). In the same way, metabolic fingerprinting
should be complemented by metabolite identification and
by the definition of potential synapomorphic characters.
5 Conclusions
Metabolic fingerprinting was applied for the first time to
the chemosystematics of marine organisms. Our results on
Homoscleromorpha sponges demonstrate that the metabolic fingerprint approach can be a valuable and rapid
assessment method: (1) to differentiate chemotypes within
a given species and study intraspecific variability, and (2)
to discriminate between species and classify samples
within the same genus and even different genera or families. Therefore, metabolic fingerprints could be applied as a
complementary tool to morphology and molecular genetics
in sponge systematics. Our work highlights the presence of
two distinct taxonomical groups among the Homoscleromorpha sponge clade and demonstrates the need for a
revision of its classification using the best diagnostic
characters, from the molecular and biochemical to the
cytological level. In marine natural product research, the
structural identification of metabolites with original properties remains challenging. In this field, metabolic fingerprints could be useful as prospecting tools, offering a
snapshot of the metabolite pool or chemical heritage of an
organism.
Acknowledgments We sincerely thank to N. Penez and G. Culioli
(Université du Sud Toulon-Var, France) for their assistance in HPLC–
MS analyses and Daria Tokina (Zoological Institute of RAS, St.
Petersburg, Russia) for technical assistance. We gratefully acknowledge the scientific help of J. Vacelet, N. Boury-Esnault, C. Borchiellini, A. Ereskovsky, (Centre d’Océanologie de Marseille, France),
B. Banaigs (Université de Perpignan Via Domitia, France), M. Mehiri
and D. Cabrol-Bass (Université de Nice Sophia Antipolis, France).
We are also grateful to R. Graille and B. DeLigondes for diving and
sampling assistance. This work was funded by the ECIMAR program
(ANR-06-BDIV-001) of the French National Agency for Research.
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