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Global metabolite analysis of the land snail Theba pisana hemolymph duringactive and aestivated states
U. Bose, E. Centurion, M.P. Hodson, P.N. Shaw, K.B. Storey, S.F. Cummins
PII: S1744-117X(16)30037-5DOI: doi: 10.1016/j.cbd.2016.05.004Reference: CBD 401
To appear in: Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics
Received date: 10 November 2015Revised date: 18 May 2016Accepted date: 25 May 2016
Please cite this article as: Bose, U., Centurion, E., Hodson, M.P., Shaw, P.N.,Storey, K.B., Cummins, S.F., Global metabolite analysis of the land snail Theba pisanahemolymph during active and aestivated states, Comparative Biochemistry and Physiology- Part D: Genomics and Proteomics (2016), doi: 10.1016/j.cbd.2016.05.004
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Global metabolite analysis of the land snail Theba pisana hemolymph during active and aestivated states
Bose, Ua,c., Centurion, Ea., Hodson, M.Pb,c., Shaw, P. Nc., Storey, K. Bd., S. F. Cumminsa,* aGenecology Research Centre, Faculty of Science, Health, Education and Engineering,
University of the Sunshine Coast, Maroochydore DC, Queensland, Australia, 4558 bMetabolomics Australia, Australian Institute for Bioengineering and Nanotechnology, The
University of Queensland, St Lucia, 4072 cSchool of Pharmacy, The University of Queensland, St Lucia, 4072 dInstitute of Biochemistry & Department of Biology, Carleton University, 1125 Colonel By
Drive, Ottawa, ON, Canada K1S 5B6
*Corresponding Author Scott Cummins,
Genecology Research Centre, Faculty of Science, Health, Education and Engineering,
University of the Sunshine Coast,
Maroochydore DC, Queensland, Australia, 4558
Tel: +61 7 5456 5501
Abstract The state of metabolic dormancy has fascinated people for hundreds of years, leading to
research exploring the identity of natural molecular components that may induce and
maintain this state. Many animals lower their metabolism in response to high temperatures
and/or arid conditions, a phenomenon called aestivation. The biological significance for this
is clear; by strongly suppressing metabolic rate to low levels, animals minimise their
exposure to stressful conditions. Understanding blood or hemolymph metabolite changes that
occur between active and aestivated animals can provide valuable insights relating to those
molecular components that regulate hypometabolism in animals, and how they afford
adaptation to their different environmental conditions. In this study, we have investigated the
hemolymph metabolite composition from the land snail Theba pisana, a remarkably resilient
mollusc that displays an annual aestivation period. Using LC-MS-based metabolomics
analysis, we have identified those hemolymph metabolites that show significant changes in
relative abundance between active and aestivated states. We show that certain metabolites,
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including some phospholipids [e.g. LysoPC(14:0)], and amino acids such as L-arginine and
L-tyrosine, are present at high levels within aestivated snails. Further investigation of our T.
pisana RNA-sequencing data elucidated the entire repertoire of phospholipid-synthesis genes
in the snail digestive gland, as a precursor towards future comparative investigation between
the genetic components of aestivating and non-aestivating species. In summary, we have
identified a large number of metabolites that are elevated in the hemolymph of aestivating
snails, supporting their role in protecting against heat or desiccation.
Key words: T. pisana, aestivation, non-aestivated, metabolites, metabolomics, phospholipid,
LC-MS
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1. Introduction
Aestivation describes a mechanism whereby an animal enters a state of dormancy or
inactivity in response to extreme heat or arid conditions (Storey and Storey, 1990). Similar to
hibernation, the metabolic rate of these animals lowers so that survival time can be
maximized. Both vertebrates and invertebrates have the ability to aestivate, with many studies
focused in particular on anurans [e.g. frogs (Kayes et al., 2009)], lungfish (Hiong et al.,
2013), and terrestrial gastropods [e.g. snails (Hermes-Lima et al., 1998)]. The biochemical
and molecular aspects that enable their metabolic depression have been widely studied and
reviewed recently (Storey and Storey, 2012).
Measuring and analysing the animal’s metabolome can provide important insights into
the molecular dynamics important to hypometabolism, including uric acid cycle, succinate
pathway in hypoxia, and protection of macromolecules by sugars and polyols and fluidity of
membranes. For instance, aestivated animals are known to store fuel (i.e. amino acids,
carbohydrates, lipids or ketone bodies) for energy production (Frick et al., 2008a; Frick et al.,
2008b). Also, hibernating mammals store large amounts of lipid, which are primarily used
during hibernation exclusively for energy production (Storey 1990). Non-mammalian species
have also been shown to rely on stored metabolites (i.e. lipids during dormancy), for example
salamanders (Pusey, 1990), tegu lizards (se Souza et al., 2004) and molluscs (Barker, 2001).
During adverse conditions of heat/aridity, land snails tend to climb tall plants or find shade to
begin the aestivation process, where their standard metabolic rate decreases to ~16% of
normal (Hand and Hardewig, 1996). They subsequently secrete a special mucus, which
creates a thin film covering the opening of the shell (epiphragm), sealing it closed yet still
allowing transfer of gases (Li and Graham, 2007). This prevents significant water loss, which
is imperative for survival. In gastropods, some metabolite components have been revealed
(Nicolai et al., 2011), yet further investigation is necessary to delineate the role and
modulation of these during aestivation.
The hemolymph metabolic profile of the land snail Helix pomatia was investigated by
comparing two different populations (mountain and valley) in both active and hibernated
states, revealing significant biochemical differences (Nicolai et al., 2012). Active snail
populations were characterized by relatively lower amino acids and higher carbohydrate
concentrations (Nicolai et al., 2012). A study on the freshwater snail Pomacea canaliculata
has shown the upregulation of purine synthesis proteins (bifunctional purine biosynthesis
protein and bifunctional purine biosynthesis protein) during aestivation (Sun et al., 2013). In
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the purine biosynthesis pathway, uric acid is the final product, thus, the upregulation of these
two proteins relates directly to the accumulation of uric acid, which is also observed in land
snails. It has also been reported that protein catabolism is upregulated that produces uric acid,
over a period of 45 days aestivation in the American apple snail (Giraud-Billoud et al., 2011).
Advances in genomics have meant that investigation of enzymatic pathways
responsible for metabolite biosynthesis is relatively easy to achieve. Therefore, following
metabolomics investigation to elucidate those relevant metabolite(s) (from a mixture),
integrated metabolomics and genomic workflows enable for metabolomics profiles to be
correlated with gene expression changes between tissues or varying metabolic activities
(Badimon et al., 2016; Maansson et al., 2016). This is emerging as a powerful tool to
understand metabolic processes.
In this study, we aimed to identify and compare the metabolomic hemolymph profiles
of the snail Theba pisana during various states of activity and aestivation. T. pisana is native
to the Mediterranean region, yet has become an invasive species in other parts of the world,
often resulting in agricultural damage (Odendaal et al., 2008) and is thus of great agricultural
and economic importance. We hypothesize that there are clear aestivation-associated
differences in the hemolymph metabolome compared with active states, including an
abundance of certain amino acids and phospholipid classes found predominantly during
aestivation. We integrate metabolomics and genomic workflows to identify enzymes
reportedly critical for biosynthesis of abundant lipids and amino acid identified in this study.
2. Materials and methods
2.1 Ethics Statement All use of animals for this research was carried out in accordance with the recommendations
set by the Animal Ethics Committee at the University of the Sunshine Coast. Approval for the
importation of T. pisana from South Yorke Peninsula, South Australia was granted from the
Department of Agriculture, Forests and Fisheries (DAFF) QLD.
2.2 Snails T. pisana were collected from Warooka (34°59′24″S, 137°23′56″E) pasturelands in
the grain belt of South Yorke Peninsula, South Australia. Animals were transported to a lab-
based snail facility at the University of the Sunshine Coast during the months of April and
May 2013. Animals used in this study did not appear to harbour any parasites, based on
external visual inspection. Adult snails were separated from juveniles based upon the
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diameter of the peristome (T. pisana 10-12.5 ± 0.3 mm), and housed in moist nylon mesh-
covered pens on a 12 h light/dark photoperiod. All snails were maintained at 22°C for 1 week
and provided with water and food (cucumber and carrot) ad libitum. Snails were induced into
aestivation by placing into glass jars without food or water and kept in an incubator on a
cycle of 12 h at 30°C with light, 12 h at 20°C dark, to emulate South Australian summer
conditions. The positions of the snails were marked on the jars after 14 days. After this
acclimation period, snails that had not moved following a further 21 days were deemed to be
in aestivation, from which the “aestivation period” started (Figure 1). Snails could be
recovered from aestivation through brief hydration (spraying with water). Snails were
separated into 5 groups for hemolymph collection and analysis: (i) snails active for 1 week
after 4 weeks of aestivation; (ii) snails active for 2 weeks after 4 weeks of aestivation; (iii)
snails aestivating for 2 weeks; (iv) snails aestivating for 4 weeks and; (v) snails induced to
arouse following 4 weeks of aestivation and sampled at 1 h post-arousal (Figure 1).
2.3 Global metabolite profiling In metabolomic analysis, nuclear magnetic resonance (NMR) and mass spectrometry (MS)
are the most commonly employed techniques for untargeted measurement of the metabolite
complement of a biofluid, extract or tissue (Bruce et al., 2008). MS-based techniques, usually
coupled with HPLC or UPLC (but also CE and GC), provide better resolution/sensitivity,
allowing for detailed metabolic phenotypes to be obtained (Dunn et al., 2011). Therefore, to
maximise the opportunity of capturing as much of the snail hemolymph metabolome as
possible with limited sample volume, our analysis was performed using MS in multimode
ionisation at both positive and negative polarities, allowing differential ionisation of multiple
metabolite classes.
Difficulties associated with sample preparation can arise primarily due to the challenge of
maintaining metabolic concentration levels equal to that before extraction (Nicolai et al.,
2005). For this reason, we performed snail hemolymph extraction followed by immediate
preparation at low temperature (Movie S1). Once samples were obtained, comparative
metabolome analysis was performed, including determination of semi-quantitative changes in
metabolites. The semi-quantitative data was derived from the ion current integral of each
extracted peak, a peak being denoted by its mass (m/z) and retention time. Peaks are
combined as compounds based upon proximal retention time and associations with the
molecular ion (isotopic, fragment and adduct peaks). This compound data was then used to
compare across the sample set to determine if there are group-dependent differences.
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A methanol:water-based extraction method was used to capture both polar and non-
polar metabolites from the snail hemolymph (Want et al., 2013). Methanol, an organic
solvent, has the additional benefit of denaturing and thus causing precipitation of
macromolecules such as proteins. Although untargeted, the choice of solvent will dictate that
the resulting extracts contained much of the snail hemolymph. Following the identification of
these metabolites, from active and aestivated individuals, multiple parallel data analysis lines
of investigation were performed: (i) generation of PCA models to identify outliers and to
identify the reason for “outlying” samples, such as instrumental drift, artefacts and biological
variability, (ii) determination of patterns; to identify any clusters, groupings or trends in the
data set between or among experimental groups (iii) use of any structure in the data to
determine further analysis steps and to identify clear relationships in the data relating to the
biological question(s) of interest, (iv) generation of supervised models to delineate
differences not easily resolved by PCA, (v) break down of analyses into rational subsets to
determine specific comparisons of interest and finally, (vi) selection of features (variables)
that are responsible for the differences between groups of interest and a subsequent database
search for identity (e.g. HMDB and METLIN).
2.4 Collection of hemolymph and isolation of metabolites The location of the heart was clearly visible upon illumination of the whole snail. The area of
the shell protecting the heart was removed by scalpel blade and fine tweezers were used to
carefully remove the outer membrane. Hemolymph was collected via heart puncture using a
needle with syringe (18 gauge x 2.5 inch). After collection of ~300 µL hemolymph sample
from the snails (n=3), 1.5 mL of pre-chilled methanol:water (1:1) was added and extraction of
small molecules was performed as previously described (Bose et al., 2014). Blank and pooled
quality control (QC) samples were also prepared by following the same sample extraction
protocol. To prepare QC samples, 50 µL of hemolymph samples were taken from individual
groups, pooled them together and extracted the metabolites for LC-MS analysis. Briefly,
samples were filtered through a 0.45 µm PVDF membrane (Waters) and the filtrate was then
centrifuged (16,000 � g for 10 min at 4°C). The supernatant was collected (cells removed)
and subjected to freeze-drying, and the lyophilised samples stored at -80°C until subsequent
analysis. Three biological replicates from 5 different sample groups were used for LC-MS
metabolomics analysis (Figure 1).
2.5 Liquid chromatography-mass spectrometry-based metabolomic (LC-MS) analysis
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Freeze-dried samples were resuspended to 15% of the original volume by adding 30 µL
methanol and then 120 µL of MilliQ (Millipore) water to produce a 20:80 methanol:water
solution. The extract solution was stored at -80°C until use for chemical analysis. Before LC-
MS analysis, samples were thawed and kept at ~4°C prior to injection.
The chromatographic separation of compounds and extracts was performed using Ultra High
Performance Liquid Chromatography (UHPLC) on an Agilent 1290 series system (Agilent
Technologies, USA). The UHPLC was coupled to an Agilent 6520 high-resolution accurate
mass (HRAM) QToF mass spectrometer equipped with a multimode source (Agilent) and
controlled using MassHunter acquisition software, (B. 02.01 SP3; Agilent). Separation was
achieved using a 4.6�150 mm, 2.7 µm Poroshell 120 EC-C18 column (Agilent). The
chromatographic analysis was performed using 0.1% (v/v) aqueous formic acid (mobile phase
A) and acetonitrile + 0.1 % (v/v) formic acid (mobile phase B) at a flow rate of 0.20 mL/min.
The column was pre-equilibrated for 15 min with 80% A and 20% B. After injection, the
composition of mobile phase was changed from 20% B to 100% B over a period of 50 min,
composition held at 100% B for 5 min, and then returned to the starting composition of 20%
B over the next 5 min. The column was re-equilibrated using 20% B for 15 min prior to the
next injection. The total chromatographic run time was 75 min. All chromatographic runs
contained initial blank runs and pooled QC samples (Sangster et al., 2006) intercalated
throughout the High Performance Liquid Chromatography (HPLC) run to control for any
acquisition-dependent variation. In total, 30 samples (observations), 15 samples (5 groups x 3
biological replicates) in addition to 12 QC’s and 3 blanks were analysed by LC-MS to control
for any acquisition-dependent variation. The injection volume was 20 µL.
Mass spectrometry data were acquired in positive and negative ionization mode. A
dual nebulizer electrospray source was used for continuous introduction of reference ions. In
MS mode the instrument scanned from m/z 100 to 1700 for all samples at a scan rate of 3
cycles/second. This mass range enabled the inclusion of two reference compounds, a lock
mass solution including purine (C5H4N4 at m/z 121.050873, 10 µmol L-1) and hexakis (1H,
1H, 3H-tetrafluropentoxy)-phosphazene (C18H18O6N3P3F24 at m/z 922.009798, 2 µmol L-1).
Multimode (i.e. simultaneous Electrospray Ionisation [ESI] and Atmospheric Pressure
Chemical Ionization [APCI]) was employed to optimally ionize compounds after
chromatographic separation.
2.6 Chemometric analyses and compound identification
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Data processing was performed using Agilent MassHunter Qualitative software (Version
B.05.00). The Molecular Feature Extractor (MFE) algorithm within MassHunter Qualitative
analysis software was used to extract chemically qualified molecular features from the LC-
QToF-MS data files. For empirical formula generation, the Molecular Formula Generator
(MFG) algorithm was used. This algorithm uses a wide range of MS information, for instance
accurate mass measurements, adduct formation, multimer formation and isotope patterns to
generate a list of candidate compounds. The maximum elemental composition
C60H120O30N30P5S5C13Br3 was used to generate formulae. MFG can automatically eliminate
unlikely candidate compounds and rank the putative molecular formulae according to their
mass deviation, isotopic pattern accuracy and elemental composition.
The LC-MS molecular feature-extracted files (.cef) were further processed using
Agilent Mass Profiler Professional (MPP) software (Agilent) to extract and align
peaks/features from the chromatograms of all extracts of the sample groups (observations),
resulting in a total of 600 and 645 putative metabolite features (variables) in positive and
negative mode, respectively. An unpaired t-test was performed to aid in feature selection.
Entities were filtered based on their p-values calculated from statistical analysis (t-test
unpaired and Benjamini-Hochberg multiple testing correction). To generate volcano plots a
p-value cut-off (p<0.0001) was applied. To aid the data-mining process, the LC-QToF-MS
data file of the blank sample was also analysed to extract features and to use as a background
reference.
The resulting data matrix (30 observations; 600 variables (positive mode) and 30
observations and 645 variables (negative mode) was then exported as .csv and imported to
SIMCA-P (version 13.0.3.0; MKS Umetrics AB, Umea, Sweden). All data were log
transformed, mean centred and scaled to unit variance prior to multivariate analysis.
Unsupervised analysis using Principal Component Analysis (PCA) was initially performed to
reveal any outliers resulting from technical/instrumental/processing procedures and to assess
any groupings or trends in the dataset. Principal Component Analysis (PCA) combines the
several linear dimensions to produce a small number of dimensions. Each linear combination
is in fact an Eigen Vector of the similarity matrix associated with the dataset. These linear
combinations (called Principal Axes) are ordered in decreasing order of associated Eigen
Value. Typically, two or three of the top few linear combinations in this ordering serve as
very good set of dimensions to project and view the data in. These dimensions capture most
of the information in the data. To obtain more clear information about groups, clustering
analysis (CA) was used. CA is also and efficient way to organize metabolites and conditions
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in the dataset into clusters. In this current study, Hierarchical clustering was used to show
relationships among the metabolites and experimental sample groups. Thereafter, OPLS-DA
was used to maximize the covariance between the measured data (peak intensities in MS
spectra) and the response variables (predictive classifications). The quality of the fit of the
model can be explained by R2 and Q2 values. To identify metabolites specific to aestivating
and active snail hemolymph, we break down total datasets into rational subsets to determine
specific comparisons of interest, for example active vs. aestivated snail. The scores and
loadings plots from these analyses, which describe the multivariate relationships of the
observations and variables respectively, along with other metrics such as S-plots and VIP
lists, were then used to determine the features (metabolites) that contribute to the differences
between the experimental groups. Finally, univariate statistical analysis was performed by
using One-way ANOVA for metabolites of interest. Metabolite distributions were displayed
by box-whisker plots, giving the arithmetic mean value for each metabolite and the standard
error as box and whiskers for 1.96 times the category standard deviation to indicate 95%
confidence intervals, assuming normal distributions. To identify metabolites present in
different quantities fold change analysis was used between multiple groups that are
significant to the given cut-off or threshold values. Fold change was calculated between a
condition ‘aestivated 2 weeks’ and one or more other conditions ‘aestivated 4 weeks’ and
‘active’. The ratio between Condition 2 and Condition 1 is calculated (Fold change =
Condition 2/Condition 1). Fold change gives the absolute ratio of normalized intensities
between the average intensities of the samples grouped. The entities satisfying the
significance analysis (p <0.0001) are passed on for the fold change analysis >16 fold for this
experiment.
In this study compound identification was performed by interrogating the METLIN and
HMDB databases using the m/z values of the mined compounds from metabolic profiles
through MFE and MFG. The search parameters implemented were as follows: mass tolerance
(accurate mass) ≤ 5 ppm, maximum number of peaks to search when peaks are not specified
graphically = 5, charge carriers (positive ions) = H+, K+, Na+, negative ions = H loss and
HCOO− and neutral loss = −H2O. The scoring algorithm for database searches uses not only
accurate mass, but also isotope abundance and spacing. The mass position of the M+1 and
M+2 isotopes are calculated based on the number and types of elements contributing to them,
and the mass spacing from the M to the M+1 and M+2 isotopes can be measured with low- to
sub-ppm accuracy and provide confidence for compound identification.
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2.7 Identification of phospholipid biosynthesis enzyme genes in T. pisana digestive gland Sequences of gastropod phospholipid pathway enzyme genes were obtained from the
National Center for Biotechnology Information (NCBI; www.ncbi.nlm.nih.gov), and then
used as queries for tBLASTn searches of a T. pisana assembled digestive gland transcriptome
(SRAfile: SRP056280). BLAST searches were performed using the CLC Main Workbench
Version 6.0 with an e-value cut-off 10-3. Multiple sequence alignments were created with the
Molecular Evolutionary Genetics Analysis (MEGA) software version 5.1 (Kumar et al.,
2012). Sequence presentation and shading of multiple sequence alignments was performed
using the LaTEX TEXshade package (Beitz, 2000).
3. Results and discussion 3.1 Hemolymph metabolite identification in positive mode ionisation The hemolymph metabolite profiles of T. pisana during aestivation and active periods were
acquired using LC-MS. The total dataset (all extracted compounds detected within the m/z
range of 100–1700; 600 detected features) was first evaluated using PCA. The PCA scores
plot shows the separation of metabolite samples based on the five metabolic time points; the
scores plot for the first two components of the model are shown in Figure 2A. A five
component PCA model explains 66.5% of the variance in the dataset. Snail hemolymph
samples collected at 2 weeks and 4 weeks aestivation showed similarities in their chemical
profiles, whereas hemolymph samples collected during active periods showed distinctly
different patterns of metabolites. To further delineate the similarities and differences between
groups, a Hierarchical cluster analysis (HCA) was performed (Figure S1A); results show
similar pattern of sample groupings previously found in PCA. The variables responsible for
any groupings in the data can be determined from the loadings plot in Figure 2B; as an
example the presence of a compound with 174.1131 m/z with a retention time (RT) of 6.40
min (Figure 2C) distinguishes the samples in the lower half of Figure 2B from other
samples. This finding is confirmed by extracted ion chromatograms (EICs) created for m/z
174.1131 (Figure 2C) and the box-and-whisker plot of the data for that variable (Figure 2D).
Through database searching, this compound was putatively identified as corticosterone
(11β,21-dihydroprogesterone). In a previous study using the rat model, increased
corticosterone was found to play a critical role in protein catabolism and locomotor activity
during prolonged fasting (Challet et al., 1995). Another study on the spadefoot toad showed
that corticosterone participated in regulating energy balance and food intake across three
developmental life stages (Crespi and Denver, 2005). More specifically, increased levels of
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corticosterone in the adults plays and important role in the regulation of stress-induced
changes in feeding behaviour and regulation of daily food intake (Crespi and Denver, 2005).
We found another compound in the snail hemolymph at m/z 315.2035, which was
tentatively identified as the steroid androstanediol. Previous investigations into desert frogs
and desert snails have highlighted changes in steroid hormones during aestivation and activity
(Alon et al., 2007).
It is important to note that the multivariate analysis was used to identify
important/discriminatory compounds/features within the dataset and that the confirmation of
their importance should always be achieved by extracting the representative data to ascertain
the behaviour of these compounds across the sample set (Bose et al., 2015). A representation
of identified compounds using the positive mode MS analysis is listed in Table 1, while a
more comprehensive list can be found in Table S1. Numerous hemolymph metabolites from
aestivating and active snails remain unknown as they do not match to anything in the
databases used for this study. Their elucidation would involve purification followed by mass
spectrometry and NMR analysis, which is outside the scope of the current study.
To further assess the dataset and to identify specific metabolite changes during
aestivation and active states, we broke down the data analysis into smaller subsets, that is, 2
weeks active compared with aestivated 2 and 4 weeks. Separation of the groups of samples
according to their aestivating time points can be observed as scores for the first two PCA
model components, as shown in Figure 2E. This two component PCA model explains 66%
of the variance in the dataset. Samples from snails aestivated for 2 and 4 weeks showed
similarities within their chemical profiles, whereas samples from snails active for 2 weeks
show a distinctly different pattern of metabolites. The loadings plot visualizes the correlation
and influence of the metabolites (variables). As shown in Figure 2F, the variables to the
extreme left and right are influential, dependent upon activity. In a comparison of aestivation
and active states, the variables (metabolites) to the left of the loadings plot were more
abundant during the active stage and conversely, the variables to the right of the plot were
more abundant in hemolymph during aestivation. Figures 2G-H show example EICs and
box-and-whisker plots used in molecular identification, respectively.
To identify metabolite changes during the different metabolic stages, unpaired t-tests
were used to compare metabolite changes between hemolymph samples at 2 and 4 weeks
aestivation as well as between one hour and two weeks active. From the positive mode
ionisation data, six of metabolites were identified that were elevated by >16-fold after 4
weeks aestivation as compared with two weeks active snail (log fold change (FC)> 16, p
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<0.0001) (Table S2). A comparison of the 2 weeks active snails against 2 weeks aestivated
animals, showed that 23 metabolites (log FC> 16, p <0.0001) were found to be elevated in
the 2 weeks active hemolymph samples, while 1 metabolite was decreased, as compared with
the 2 week aestivated samples (Table S3). Likewise, a comparison between 2 weeks active
snails and 4 weeks aestivated snail hemolymph showed that 33 metabolites (log FC> 16,
p<0.0001) were relatively higher in abundance at 4 weeks aestivation and 13 metabolites
were present at relatively lower levels during aestivation (Table S4).
The amino acid linking required for protein synthesis is known to play a major role in
cell energy expenditure, accounting for approximately 18-26% of total cellular ATP turnover
(Hawkins, 1991). During periods of high foraging activity in snails, amino acid
concentrations increase, while a decrease in total amino acids during aestivation or
hibernation might indicate a depression of protein synthesis (Brooks and Storey, 1995). We
found reduced levels of amino acids, such as L-arginine, L-tyrosine and gamma-
glutamyltyrosine during 2 or 4 weeks of aestivation as compared with the active state
including. At 2 weeks active, snail hemolymph L-tyrosine concentration was reduced by
17.78 fold, when compared to four weeks aestivation.
A few studies in snails have shown down-regulation of protein synthesis in tissues
during aestivation, including the land snail (Helix aspersa) (Pakay et al., 2002). However,
analysis of amino acid levels in the digestive-gland gonad (DGG) complex of the freshwater
snail Biomphalaria glabrata during 7 days aestivation, showed that alanine levels increased
during this period (Vasta et al., 2010), while increases in lactate, succinate, malate, and
acetate have also been observed in the DGG (Bezerra et al., 1999).
We did identify another metabolite, uric acid, to be highly abundant in aestivated T.
pisana hemolymph, as well as at 1 h post-arousal (Table S1). Pulmonate snails are known to
increase their levels of endogenous antioxidants during aestivation to minimize oxidative
stress injury upon arousal (Ferreira et al., 2003; Hermes-Lima et al., 1998). For example, in
the apple snail P. canaliculata, uric acid accumulation likely functions as a non-enzymatic
antioxidant in the arousing snail (Giraud-Billoud, 2011). Uric acid concentrations also
increase in starved and parasite-infected B. glabrata (Becker, 1980; Becker, 1983). In T.
pisana, we find that enzymes associated with uric acid biosynthesis are present (Table S8
and Figure S2).
3.2 Metabolite identification in negative mode ionisation
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To increase capture of the hemolymph metabolome of the snails, LC-MS data were also
acquired in negative ionisation mode. From the multivariate analysis of negative mode LC-
MS data, the PCA scores plot shows separation based on the aestivation time periods and
activity (Figure 3A). A five component PCA model explains 59% of the variance in the
dataset. Clustering analysis also shows that hemolymph samples from snails that were
aestivated for 2 or 4 weeks showed similarities in their chemical profiles, whereas samples
collected during the active periods showed a distinctly different pattern of metabolites
(Figure S1B). A loadings plot is shown in Figure 3B, while Figures 3C and D show EIC
and box-and-whisker plot for m/z 134.0219, respectively.
To obtain more information from the dataset and identify specific metabolite changes during
aestivation and active states, we divided the data analysis into smaller subsets, 2 weeks active
and compared with aestivated 2 and 4 weeks. PCA generated a two-component model that
explained 55% of the variance in the dataset. The first two component scores of the model are
shown in Figure 3E. The loadings plot shows the correlation and model influence of the
metabolites (variables). As shown in Figure 3F, the variables to the extreme left are unique
to hemolymph from aestivation periods whereas the active-associated metabolites are
comparatively more abundant in the far right of the loadings plot. Figures 3G and 3H show
the EICs and box-and-whisker plots used in the tentative identification of m/z 230.0902 and
m/z 134.0219, respectively.
Unpaired t-tests were used to assess the differences in metabolite profiles between
different sample groups (i.e. active snails after 2 weeks, 2 weeks aestivation and 4 weeks
aestivation) for data collected under negative ionisation mode and to select variables based
upon arbitrary but stringent cut-offs i.e. p<0.0001 and FC>16-fold. In a comparison of 2
weeks aestivation with 4 weeks aestivation samples, 9 compounds were lower and 10
compounds were elevated after 4 weeks (Table S5). Comparison of active snails and 2 weeks
aestivated snails showed that a total of 17 compounds were significantly altered during the
aestivation period; amongst them 6 compounds were elevated in two weeks active period and
11 compounds were reduced in two weeks aestivation as compared to the active state (Table
S6). Likewise, comparison between hemolymph from 2 week active snails and 4 weeks
aestivated snails showed that a total of 29 compounds were modulated differently after 4
weeks aestivation, with the levels of 9 compounds increased and levels of 20 compounds
decreased in 4 weeks aestivation as compared with 2 weeks active (Table S7). It is notable to
mention that using less stringent cut-off values to compare different test groups always
produces true positives, which are always worthy of further investigation.
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Argininosuccuinate synthase (Ass) and arginosuccinate lyase (Asl) are involved in
arginine synthesis during aestivation for different physiological purposes. Studies on African
lungfish have shown that mRNA expression of ass and asl expressions were increased
significantly during onset of aestivation (Chng et al., 2014), which helps to increase arginine
production to support increased urea synthesis. However, in long-term lungfish aestivation (6
months), studies show a relative decrease in the amount of arginine synthesis in
tissues/organs. In this present study, we have found that arginine concentration was increased
at two weeks aestivation, while at four weeks aestivation the relative amount had decreased.
Figure S3A shows the pathway for biosynthesis of L-arginine and associated major enzymes
that were identified within the T. pisana transcriptome database (Table S8). Additionally, we
have found that the amino acid glutamate was significantly increased during two and four
weeks of aestivation. In African lungfish liver, a study has shown an increase in glutamate
dehydrogenase (GDH) activity at aestivation, a key enzyme for final step in glutamate
biosynthesis. This suggested that a small amount of energy can be gained through increased
amino acid catabolism, directed towards the generation of tricarboxylic acid cycle
intermediates, which can ultimately use amino acids as fuel stores during aestivation (Frick et
al., 2008a). Our analysis of T. pisana transcriptome data (Zhao et al., 2016) has shown that
there is GDH present during aestivation (Figure S3B).
Neurotransmitters such as dopamine, serotonin and norepinephrine have previously
been identified and quantitated in the aestivated nervous system and hearts of land snails,
where they have positive ionotropic and chronotropic effects (Rofalikou et al., 1999). This
finding is likely associated with the regulation of heart rate (Walker, 1986). In accordance
with this, our findings show that hemolymph dopamine is abundant at four weeks aestivation
(Table S5). In the H. pomatia, the inactivation to reactivation process is correlated with
decreases in the neurotransmitter metabolites dopamine and serotonin in the CNS, yet there is
an increase in the peripheral organs (Hernádi et al., 2008), possibly through hemolymph
transport.
3.3 Analysis of phospholipid synthesis pathway genes in T. pisana digestive gland
As shown in Table 1, we found that some phospholipids were relatively abundant within the
hemolymph of either active or aestivated snails. These phospholipids, including LysoPE
(lysophosphatidylethanolamine) and LysoPS (lysophosphatidylserine), are synthesised via a
well-recognised enzymatic pathways (Fig. 4) (Pavlovic and Bakovic, 2013). The digestive
gland of molluscs is a key organ for metabolism, and is involved in the production of
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digestive enzymes, the absorption of digested food, and the centre for lipid metabolism and
for storage of lipids for energy expenditure during reproduction and starvation (Barker,
2001). Several investigations have analysed changes in the land snail digestive gland during
metabolic depression (Bishop et al., 2002; Brooks and Storey, 1995; Guppy et al., 2000;
Stuart et al., 1998) including the analysis of phospholipid and protein composition of
mitochondrial membranes. The general consensus is that aestivating snails remodel their
mitochondrial membranes to reduce the rates of proton pumping and leaking (Stuart et al.,
1998).
To date, the molecular components of phospholipid synthesis have not been described in
land snails, but the availability of a digestive gland transcriptome derived from T. pisana
(Adamson et al., 2015), enabled their identification in this study. Using BLASTp analysis
within our T. pisana gene database (ThebaDB; http://thebadb.bioinfo-minzhao.org/), we
identified snail homologue enzyme genes, including those within the CDP-ethanolamine and
CDP-choline pathway (Figure 4A and File S1). All T. pisana proteins encoded shared the
characteristic domains of the individual enzymes (Figure 4B) and significant amino acid
identity with the corresponding enzymes from multiple other species, including mammals,
fish and insects (File S2). Their identification provides an important step towards defining the
molecular genetic markers that may contribute to an animal’s ability to aestivate. This may
also help to elucidate those aestivation-specific proteins of around 50 and 90 kDa that have
been noted as up-regulated within the digestive gland of another aestivating land snail, Otala
lactea (Brooks and Storey, 1995).
4. Conclusions In this study we describe the global metabolite profiling of the hemolymph of a land snail
during aestivating and active states. The initiation and maintenance of quiescence states in T.
pisana were accompanied by physiological adjustments of metabolites such as amino acids,
uric acid and phospholipids. Phospholipids are associated with fat deposits in many
aestivated invertebrates, and relative increases in the concentration of certain hemolymph
phospholipids within aestivated snails indicate that they probably produce different classes of
phospholipids for energy production. The production of large quantities of amino acids (i.e.
glutamic acid, tyrosine and arginine) during aestivation is in agreement with the hypothesis
that anaerobic pathways are an important part of overall metabolism. Utilised this non-
targeted approach enables us to gain an unbiased view of those metabolites that should be
further investigated, via targeted approaches. Future studies will be carried out by using more
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targeted approaches to identify ‘unknown’ metabolites that appear to be significantly
different between active and aestivating snail. Noteworthy, combined metabolomics and
transcriptomics analyses have given meaningful insights into the metabolites and their
enzymatic biosynthesis during different conditions, and this approach could be used further to
elucidate more biochemical pathways associated with hypometabolism. The abundance of
metabolites observed in this study may assist in elucidating the molecular mechanisms
regulating the phenomenon of metabolic rate depression, for which there is currently no
unequivocal model.
Acknowledgements
This work was supported by grants from the Australian Research Council (KBS, SFC) and
the Grains Research Development Corporation (SFC). We wish to thank Kevin J. Adamson
for sample collection.
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Legends
Figure 1. Schematic showing the timeline for collection of hemolymph for metabolite
analysis.
Figure 2. Chemoinformatic analyses of metabolite variations in hemolymph at different
stages of aestivation and activity in Theba pisana (positive mode ionization). (A)
Principal component analysis (PCA) scores plot, principal component 1 (PC1) (t[1]) versus
principal component 2 (PC2) (t[2]) showing the variation in the chemical profiles of
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hemolymph samples from snails that were estivated two weeks (blue), estivated four weeks
(red), one hour active after aestivation (purple), active after one week (green) and active after
two weeks (black). Each symbol represents one snail sample described by all detected
features (metabolites). (B) Inspection of the 2-D loadings plot for PC1 vs. PC2 reveals the
variables responsible for the spatial arrangement of samples. (C) Extracted ion chromatogram
(EIC) of m/z 174.1131 from three sample groups, showing clear species differences in the
abundance of this metabolite after 4 weeks aestivation (red), 2 weeks aestivation (blue) or 2
weeks active (black). (D) Box-and-whisker plot of the abundance of the 174 ion in the three
sample groups (p <.001). (E) PCA scores plot, PC1 (t[1]) versus PC2 (t[2]) showing the
variation in the chemical profiles of samples from snails estivated two weeks (blue), estivated
four weeks (red) and active after two weeks (black). (F) Loadings plot showing the variables
responsible for the spatial arrangement of samples. (G) Extracted ion chromatogram (EIC) of
m/z 203.1151 from three sample groups, showing clear species differences in the abundance
of this metabolite after 4 weeks aestivation (red), 2 weeks aestivation (blue) or 2 weeks active
(black). (H) Box-and-whisker plot of the abundance of the 203 ion in the three sample groups
(p < .001).
Figure 3. Chemoinformatic analyses of metabolite variations in hemolymph of Theba
pisana in aestivating and active states (negative mode ionization). (A) Principal
component analysis (PCA) scores plot, PC1 (t[1]) versus PC2 (t[2]) showing the variation in
the chemical profiles in hemolymph samples from snails estivated two weeks (blue),
estivated 4 weeks (red), active for 1 hour (purple), active for 1 week (green) and active for 2
weeks (black). Each symbol represents one snail sample described by all detected features
(metabolites). (B) Inspection of the 2-D loadings plot for PC1 vs. PC2 reveals the variables
responsible for the spatial arrangement of samples. (C) Extracted ion chromatogram (EIC) of
m/z 134.0219 from three sample groups, showing clear differences in the abundance of this
metabolite after 4 weeks aestivation (red), 2 weeks aestivation (blue) and active after 2 weeks
(black). (D) Box-and-whisker plot of the abundance of the 134 ion in the three sample groups
(p <.001). (E) PCA scores plot, PC1 (t[1]) versus PC2 (t[2]) showing the variation in the
chemical profiles for estivated two weeks (blue), estivated 4 weeks (red) and active after 2
weeks (black). (F) Loadings plot shows the variables responsible for the spatial arrangement
of samples. (G) Extracted ion chromatogram (EIC) of m/z 230.0902 from three sample
groups, showing clear differences in the abundance of this metabolite after 4 weeks
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aestivation (red), 2 weeks aestivation (blue) and active after 2 weeks (black). (H) Box-and-
whisker plot of the abundance of the 230 ion in the three sample groups (p <.001).
Figure 4. Characterisation of phospholipid biosynthesis enzymes present within T.
pisana hepatopancreas. (A) Pathway showing major enzymes required for the synthesis of
phosphatidylethanolamine (PE) and phosphatidylserine (PS). (B) Schematic representation of
the major T. pisana enzymes required for PE and PS synthesis.
Figure 1
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Figure 2
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Figure 3
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Figure 4
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Table 1. List of a select group of metabolites shown to be different within the hemolymph of
active versus aestivated Theba pisana. √ indicates presence at relatively high level of
abundance (i.e. > 150000 counts) snails active for 1 week; (ii) snails active for 2 weeks; (iii)
snails aestivated for 2 weeks; (iv) snails aestivated for 4 weeks and; (v) snails induced to
arouse and sampled at 1 h post-arousal.
Compound ID Active 1
hour Active 1
week Active 2 weeks
Aestivated 2 weeks
Aestivated 4 weeks
PG(14:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z)) � − � � �
PG(14:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z)) � � � � �
PI(20:0/16:0) � � � � �
PIP3(18:1(9Z)/18:1(9Z)) � � � � �
18-Hydroxycortisol � � � � �
Cholesterol sulfate � � � � �
PGP(18:2(9Z,12Z)/20:3(5Z,8Z,11Z)) � � � � �
LysoPC(20:3(5Z,8Z,11Z)) � � � � �
LysoPC(20:3(5Z,8Z,11Z)) � � � � �
LysoPC(10:0) � − � � �
N-Formyl-L-glutamic acid � � � � �
PG(14:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z)) � − � � �
PGP(18:2(9Z,12Z)/20:3(5Z,8Z,11Z)) � − � � �
PG(13:0/18:4(6Z,9Z,12Z,15Z)) � − � � �
3-Hydroxyadipic acid 3,6-lactone � − � � �
Glutamate � − � � �
L-Acetylcarnitine � − � � �
Guanosine 2',3'-cyclic phosphate � − � � �
LysoPC(14:0) � − � � �
LysoPC(14:0) � − � � �
PIP[3'](16:0/18:1(9Z)) � − � � �
Gamma-Glutamyltyrosine � − � � �
Phosphoserine � − � � �
LysoPE(0:0/20:1(11Z)) � − � � �
PG(16:1(9Z)/18:3(9Z,12Z,15Z)) � − � � �
PG(13:0/18:4(6Z,9Z,12Z,15Z)) � − � � �
CE(16:0) � − � � �
PG(14:1(9Z)/20:5(5Z,8Z,11Z,14Z,17Z)) � − � � �
S-(Hydroxymethyl)glutathione � − � � �
Dihydroferulic acid 4-O-glucuronide � − � � �
3-Hexenedioic acid � − � � �
N-Methyl-a-aminoisobutyric acid � − � � �
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ACCEPTED MANUSCRIPT
N-Acetylleucine � − � � �
4'-O-Methyldelphinidin 3-O-beta-D-glucoside
� − � �
�
L-Glutamic acid � − � � �
L-Tyrosine � − � � �
L-arginine � − � � �
2-Hydroxyglutarate � − � � �
N-Acryloylglycine � − � � �
LysoPE(0:0/20:4(5Z,8Z,11Z,14Z)) � − � � �
LysoPC(10:0) � − � � �
Dopamine − − − � −
Uric acid − − − � �
Palmitic amide � − � � �
PIP3(18:1(9Z)/18:1(9Z)) � − � � �
PG - Phosphotidylglycerol; PIP - Phosphotidylinositol phosphate; PC- Phosphotidylcholine;
PIP3- Phosphotidylinositol 3-phosphate; PI- Phosphotidylinositol; PGP-
Phosphoglycerolphosphate; LysoPC- Lysophosphotidylcholine; LysoPE-
Lysophosphotidylethanolamine; CE- Cholesterol-fatty acid ester.