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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

scummins@usc.edu.au

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.

References

Adamson, K.J., Wang, T., Zhao, M., Bell, F., Kuballa, A.V., Storey, K.B., Cummins, S.F.,

2015. Molecular insights into land snail neuropeptides through transcriptome and

comparative gene analysis. BMC Genomics. 16, 308.

Alon, G., Shore, L.S., Steinberger, Y., 2007. Correlation between levels of sex hormones

(progesterone, testosterone, and estrogen) and ecophysiological-behavior stages in two

species of desert snails (Sphincterochila zonata and Sphincterochila prophetarum) in the

Northern Negev Desert. Gen. Comp. Endocrinol. 151, 122-127.

Badimon, L., Vilahur, G. and Padro, T., 2016. Systems biology approaches to understand the

effects of nutrition and promote health. Br. J. Clin. Pharmacol. (Epub ahead of print).

Barker, G.M., 2001. The biology of terrestrial molluscs. CABI Publishing, Wallingford

Oxon.

Becker, W., 1980. Metabolic interrelationship of parasitic trematodes and molluscs,

especially Schistosoma mansoni in Biomphalaria glabrata. Z Parasitenkd. 63, 101-111.

Becker, W., 1983. Purine metabolism in Biomphalaria glabrata under starvation and

infection with Schistosoma mansoni. Comp. Biochem. Physiol. B. 76, 215-219.

Beitz, E., 2000. TEXshade: shading and labeling of multiple sequence alignments using

LATEX2 epsilon. Bioinformatics 16, 135-139.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

Bezerra, J.C., Kemper, A., Becker, W., 1999. Profile of organic acid concentrations in the

digestive gland and hemolymph of Biomphalaria glabrata under estivation. Mem. Inst.

Oswaldo. Cruz. 94, 779-784.

Bishop, T., Ocloo, A., Brand, M.D., 2002. Structure and function of mitochondria in

hepatopancreas cells from metabolically depressed snails. Physiol. Biochem. Zool. 75, 134-

144.

Bose, U., Hewavitharana, A.K., Vidgen, M.E., Ng, Y.K., Shaw, P.N., Fuerst, J.A., Hodson,

M.P., 2014. Discovering the recondite secondary metabolome spectrum of Salinispora

species: a study of inter-species diversity. PLoS One 9, e91488.

Brooks, S.P.J., Storey, K.B., 1995. Evidence for aestivation specific proteins in Otala lactea.

Mol. Cell. Biochem. 143, 15-20.

Bruce, S.J., Jonsson, P., Antti, H., Cloarec, O., Trygg, J., Marklund, S.L., Moritz, T., 2008.

Evaluation of a protocol for metabolic profiling studies on human blood plasma by combined

ultra-performance liquid chromatography/mass spectrometry: From extraction to data

analysis. Anal. Biochem. 372, 237-249.

Challet, E., le Maho, Y., Robin, J.P., Malan, A., Cherel, Y., 1995. Involvement of

corticosterone in the fasting-induced rise in protein utilization and locomotor activity.

Pharmacol. Biochem. Behav. 50, 405-412.

Chng, Y.R., Ong, J.L., Ching, B., Chen, X.L., Wong, W.P., Chew, S.F. and Ip, Y.K., 2014.

Molecular characterization of argininosuccinate synthase and argininosuccinate lyase from

the liver of the African lungfish Protopterus annectens, and their mRNA expression levels in

the liver, kidney, brain and skeletal muscle during aestivation. J. Comp. Physiol. B. 184, 835-

853.

Crespi, E.J., Denver, R.J., 2005. Roles of stress hormones in food intake regulation in anuran

amphibians throughout the life cycle. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 141,

381-390.

de Souza, S.C., Carvalho, J.E., Abe, A.S., Bicudo, J.E.P.W., Bianconcini, M.S.C., 2004.

Seasonal metabolic depression, substrate utilisation and changes in scaling patterns during the

first year cycle of tegu lizards (Tupinambis merianae). J. Exp. Biol. 207, 307-318.

Dunn, W.B., Broadhurst, D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N.,

Brown, M., Knowles, J.D., Halsall, A., Haselden, J.N., Nicholls, A.W., Wilson, I.D., Kell,

D.B., Goodacre, R., 2011. Procedures for large-scale metabolic profiling of serum and

plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.

Nat. Protoc. 6, 1060-1083.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

Ferreira, M.V.R., Alencastro, A.C.R., Hermes-Lima, M., 2003. Role of antioxidant defenses

during estivation and anoxia exposure in the freshwater snail Biomphalaria tenagophila

(Orbigny, 1835). Can. J. . Zool. 81, 1239-1248.

Frick, N.T., Bystriansky, J.S., Ip, Y.K., Chew, S.F., Ballantyne, J.S. 2008a. Carbohydrate and

amino acid metabolism in fasting and aestivating African Lungfish (Protopterus dolloi).

Comp. Biochem. Physiol. A Mol. Integr. Physiol. 151, 85-92.

Frick, N.T., Bystriansky, J.S., Ip, Y.K., Chew, S.F., Ballantyne, J.S. 2008b. Lipid, ketone

body and oxidative metabolism in the African lungfish, Protopterus dolloi following 60 days

of fasting and aestivation. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 151, 93-101.

Giraud-Billoud, M., Vega, I.A., Tosi, M.E.R., Abud, M.A., Calderon M.L., Castro-vazquez,

A 2011. Antioxidant and molecular chaperone defences during estivation and arousal in the

South American apple snail Pomacea canaliculata. J. Exp. Biol. 216, 614-622.

Giraud-Billoud, M., 2011. Uric acid deposits and estivation in the invasive apple-snail,

Pomacea canaliculata. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 158, 506-512.

Guppy, M., Reeves, D.C., Bishop, T., Withers, P., Buckingham, J.A., Brand, M.D., 2000.

Intrinsic metabolic depression in cells isolated from the hepatopancreas of estivating snails.

FASEB J. 14, 999-1004.

Hand, S.C., Hardewig, I., 1996. Downregulation of cellular metabolism during environmental

stress: mechanisms and implications. Annu. Rev. Physiol. 58, 539-563.

Hawkins, A.J.S., 1991. Protein turnover: a functional appraisal. Funct. Ecol. 5, 222-233.

Hermes-Lima, M., Storey, J.M., Storey, K.B., 1998. Antioxidant defenses and metabolic

depression. The hypothesis of preparation for oxidative stress in land snails. Comp. Biochem.

Physiol. B Biochem. Mol. Biol. 120, 437-448.

Hernádi, L., Hiripi, L., Gyıri, J., Szabó, H., Vehovszky, Á., 2008. The terrestrial snail, Helix

pomatia, adapts to environmental conditions by the modulation of central arousal. Acta. Biol.

Hung. 59, 47-53.

Hiong, K.C., Loong, A.M., Chew, S.F., IP, Y.K., 2005. Increases in urea synthesis and the

ornithine-urea cycle capacity in the giant African snail, Achatina fulica, during fasting or

aestivation, or after the injection with ammonium chloride. J. Exp. Zool. A. Comp. Exp. Biol.

303, 1040-1053.

Hiong, K.C., Ip, Y.K., Wong, W.P., Chew, S.F., 2013. Differential gene expression in the

brain of the African lungfish, Protopterus annectens, after six days or six months of

aestivation in air. PLoS One 8, e71205.

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

Kayes, S.M., Cramp, R.L., Franklin, C.E., 2009. Metabolic depression during aestivation in

Cyclorana alboguttata. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 154, 557-563.

Kumar, S., Stecher, G., Peterson, D., Tamura, K., 2012. MEGA-CC: computing core of

molecular evolutionary genetics analysis program for automated and iterative data analysis.

Bioinformatics 28, 2685-2686.

Li, D., Graham, L.D., 2007. Epiphragmin, the major protein of epiphragm mucus from the

vineyard snail, Cernuella virgata. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 148, 192-

200.

Maansson, M., Vynne, N.G., Klitgaard, A., Nybo, J.L., Melchiorsen, J., Nguyen, D.D.,

Sanchez, L.M., Ziemert, N., Dorrestein, P.C., Andersen, M.R. and Gram, L., 2016. An

integrated metabolomic and genomic mining workflow to uncover the biosynthetic potential

of bacteria. mSystems, 1, e00028-15.

Nicolai, A., Filser, J., Lenz, R., Bertrand, C., Charrier, M., 2012. Quantitative assessment of

haemolymph metabolites in respect to the physiological state in two populations of the land

snail Helix pomatia. Physiol. Biochem. Zool. 85, 274-284.

Nicolai, A., Filser, J., Lenz, R., Bertrand, C., Charrier, M., 2011. Adjustment of metabolite

composition in the haemolymph to seasonal variations in the land snail Helix pomatia. J.

Comp. Physiol. B 181, 457-466.

Nicolai, A., Vernon, P., Lee, M., Ansart, A., Charrier, M., 2005. Supercooling ability in two

populations of the land snail Helix pomatia (Gastropoda: Helicidae) and ice-nucleating

activity of gut bacteria. Cryobiology. 50, 48-57.

Odendaal, L.J., Haupt, T.M., Griffiths, C.L., 2008. The alien invasive land snail Theba pisana

in the West Coast National Park: Is there cause for concern? Koedoe 50, 93-98.

Pakay, J.L., Withers, P.C., Hobbs, A.A., Guppy, M., 2002. In vivo downregulation of protein

synthesis in the snail Helix apersa during estivation. Am. J. Physiol. Regul. Integr. Comp.

Physiol. 283, R197-204.

Pavlovic, Z., Bakovic, M., 2013. Regulation of phosphatidylethanolamine homeostasis - The

critical role of CTP:Phosphoethanolamine Cytidylyltransferase (Pcyt2). Int. J. Mol. Sci. 14,

2529-2550.

Pusey, B.J., 1990. Seasonality, aestivation and the life history of the salamanderfish

Lepidogalaxias salamandroides (Pisces: Lepidogalaxiidae). Environ. Biol. Fish. 29, 15-26.

Rofalikou, E., Pappa-Louisi, A., Michaelidis, B., 1999. Effects of estivation on the levels of

biogenic amines and related compounds in the ganglia, the auricle, and the ventricle of the

heart of the pulmonate land snail Helix lucorum (L): Evidence for physiological roles of

ACC

EPTE

D M

ANU

SCR

IPT

ACCEPTED MANUSCRIPT

serotonin and dopamine in the control of heart function during estivation. J. Exp. Zool. 283,

137-146.

Storey, K.B., Storey, J.M., 1990. Metabolic rate depression and biochemical adaptation in

anaerobiosis, hibernation and estivation. Q Rev. Biol. 65, 145-174.

Storey, K.B., Storey, J.M., 2012. Aestivation: signaling and hypometabolism. J. Exp. Biol.

215, 1425-1433.

Stuart, J.A., Gillis, T.E., Ballantyne, J.S., 1998. Compositional correlates of metabolic

depression in the mitochondrial membranes of estivating snails. Am. J. Physiol. 275, R1977-

1982.

Sun, J., Mu, H., Zhang, H., Chandramouli, K.H., Qian, P., Wong, C.K.C, Qiu, J., 2013.

Understanding the regulation of estivation in a freshwater snail through iTRAQ-based

comparative proteomics. J. Proteome Res. 12, 5271-5280.

Vasta, J.D., Fried, B., Sherma, J., 2010. Determination of estivation-induced changes in the

amino acid content of Biomphalaria glabrata snails by high performance thin-layer

chromatography-densitometry. J. Liq. Chromatogr. Relat. Technol. 33, 1028-1037.

Walker, R.J., 1986. Transmitters and modulators - neurobiology and behaviour, part 2, in:

A.O.D. Willows (Ed.), The mollusca. Academic Press, New York, pp. 279–485.

Want, E.J., Masson, P., Michopoulos, F., Wilson, I.D., Theodoridis, G., Plumb, R.S.,

Shockcor, J., Loftus, N., Holmes, E. and Nicholson, J.K., 2013. Global metabolic profiling of

animal and human tissues via UPLC-MS. Nat. Protoc. 8, 17-32.

Zhao, M., Wang, T., Adamson, K.J., Storey, K.B., Cummins, S.F., 2016. Multi-tissue

transcriptomics for construction of a comprehensive gene resourse for the terrestrial snail

Theba pisana. Sci. Rep. 8, 20685.

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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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.