toxics

Article
The Effects of Single and Combined Stressors on
Daphnids—Enzyme Markers of Physiology and Metabolomics
Validate the Impact of Pollution
Anna Michalaki 1 , Allan Robert McGivern 1 , Gernot Poschet 2 , Michael Büttner 2 , Rolf Altenburger 3
and Konstantinos Grintzalis 1, *

1 School of Biotechnology, Dublin City University, D09 Y5NO Dublin, Ireland

2 Centre for Organismal Studies (COS), Heidelberg University, 69120 Heidelberg, Germany
3 Department of Bioanalytical Ecotoxicology, Helmholtz-Centre for Environmental Research—UFZ,
04318 Leipzig, Germany
* Correspondence: konstantinos.gkrintzalis@dcu.ie

Abstract: The continuous global increase in population and consumption of resources due to human
activities has had a significant impact on the environment. Therefore, assessment of environmental
exposure to toxic chemicals as well as their impact on biological systems is of significant importance.
Freshwater systems are currently under threat and monitored; however, current methods for pollution
assessment can neither provide mechanistic insight nor predict adverse effects from complex pollution.
Using daphnids as a bioindicator, we assessed the impact in acute exposures of eight individual
chemicals and specifically two metals, four pharmaceuticals, a pesticide and a stimulant, and their
composite mixture combining phenotypic, biochemical and metabolic markers of physiology. Toxicity
levels were in the same order of magnitude and significantly enhanced in the composite mixture.
Citation: Michalaki, A.; McGivern,
Results from individual chemicals showed distinct biochemical responses for key enzyme activities
A.R.; Poschet, G.; Büttner, M.;
Altenburger, R.; Grintzalis, K. The
such as phosphatases, lipase, peptidase, β-galactosidase and glutathione-S-transferase. Following
Effects of Single and Combined this, a more realistic mixture scenario was assessed with the aforementioned enzyme markers and
Stressors on Daphnids—Enzyme a metabolomic approach. A clear dose-dependent effect for the composite mixture was validated
Markers of Physiology and with enzyme markers of physiology, and the metabolomic analysis verified the effects observed, thus
Metabolomics Validate the Impact of providing a sensitive metrics in metabolite perturbations. Our study highlights that sensitive enzyme
Pollution. Toxics 2022, 10, 604. markers can be used in advance on the design of metabolic and holistic assays to guide the selection
https://doi.org/10.3390/ of chemicals and the trajectory of the study, while providing mechanistic insight. In the future this
toxics10100604 could prove to become a useful tool for understanding and predicting freshwater pollution.
Academic Editor: Xavier Cousin
Keywords: Daphnia magna; mixture toxicology; combined stressors; mortality; biochemical
Received: 19 July 2022
markers; metabolomics
Accepted: 10 October 2022
Published: 12 October 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in 1. Introduction
published maps and institutional affil- Humans have a significant impact on the physical environment in many ways such
iations.
as overpopulation, pollution, fossil fuels and deforestation, which are responsible for the
observed climate change, increased pollution, and decrease in air, water and soil quality.
Therefore, assessment of environmental exposure to toxic chemicals as well as their impact
on biological systems is of significant importance. Until recently, most approaches in water
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
monitoring were based on the detection of individual chemicals, the physicochemical
This article is an open access article
and microbiological parameters in a typical water analysis [1] and the determination of
distributed under the terms and abundance and diversity of fauna and flora, which were subsequently compared against
conditions of the Creative Commons water quality standards [2]. However, these measurements have several weaknesses such
Attribution (CC BY) license (https:// as their cost for analytical quantification, the limited detection of a small number of possible
creativecommons.org/licenses/by/ contaminants in the environment and their detection limits, which cannot always cover the
4.0/). presence of low concentrations of pollutants. In addition, such approaches fail to produce a

Toxics 2022, 10, 604. https://doi.org/10.3390/toxics10100604 https://www.mdpi.com/journal/toxics

Toxics 2022, 10, 604 2 of 14

diagnostic insight concerning the type of stressor and at the same time they are unable to
predict future impact early enough to avoid ecological damage.
More advanced approaches propose the use of models and well-characterized multi-
response systems to assess the responses of pollutants based on an understanding of
the underlying mechanisms moving towards effect-based methods [3], taking into ac-
count complex mixture interactions [4] and explaining toxicity effects via adverse outcome
pathways [5]. As a general approach, model species are exposed to single chemicals in
laboratory studies to assess their individual mechanisms. However, since in the environ-
ment organisms are not just confronted with single pollutants but rather combinations of
different stress factors in complex mixtures, their effects have been within the scope of
current research, thereby identifying markers for pollution [6].
Focusing on the freshwater ecosystem, in this study, the impact of eight individual
pollutants from diverse categories of commonly encountered pollutants were initially
assessed individually on Daphnia magna. These chemicals represent four pharmaceuticals,
two metals, one pesticide and a stimulant, which are commonly encountered threats for
freshwater and marine species in the environment. Furthermore, for a realistic scenario,
their composite mixture was assessed in non-lethal concentrations. Using lethality as a
surrogate measure of toxicity and biochemical markers of physiology, individual toxicity
and mixture effects for eight pollutants were assessed. To compare and validate how
biochemical markers could provide meaningful information for environmental complex
contamination, an in-depth assessment of the impact of the composite mixture at three
stress intensities was subsequently performed on the metabolic level.

2. Materials and Methods

2.1. Culturing of Daphnids and Toxicity Exposures
Daphnids were maintained in glass beakers in OECD media (final concentrations
0.29 g CaCl2 .2H2 O/L, 0.123 g MgSO4 .7H2 O/L, 0.065 g NaHCO3 /L, 0.0058 g KCl/L,
2 µg Na2 SeO3 /L, pH 7.7) at a density of 80 adults per 4 L of media and under a 16h:8h
of light:dark photoperiod at 20 ◦ C [7]. For experiments, neonates (<24 h) were collected
from the third brood of their mothers and cultured until four days old, and then used for
experiments. Typically, in experiments with daphnids, acute toxicity is performed with
neonates (<24 h); however, in several cases this has proven to be not reproducible, mainly
because of the time window of neonate selection which extends to up to 24 h, thus resulting
in a less homogenous population for experiments affecting toxicity [8]. Furthermore, as
acute exposures are performed in the absence of food, the animals experience an additional
stress of starvation which we avoided by allowing them to grow over a period of four days
prior to exposure to the chemicals. Based on the selection of 24 h exposure periods, the
chemicals were only added once following the general outlined procedure of the OECD
guidelines [9]. The chemicals used in this study were aluminium (CAS 16828-11-8), lithium
(CAS 7447-41-8), acetylsalicylic acid (CAS 50-78-2), diltiazem (CAS 33286-22-5), metformin
(CAS 115-70-4), propranolol (CAS 318-98-9), glyphosate (CAS 1071-83-6) and nicotine (CAS
54-11-5). All chemicals were of highest purity >99.9%.
For exposures, 15 four-day-old animals were exposed to each chemical separately
in a final volume of 100 mL OECD media with four replicates per concentration tested.
Toxicity curves were plotted for 24 h exposures and EC values were calculated. A mixture
was constructed for all chemicals and further assessed for its toxicity (Figure 1). All plots
were calculated using the Four parameter logistic (4PL) model, following the equation
Span = Top − Bottom and Y = Bottom + (Top-Bottom)/(1 + 10ˆ((LogIC50-X)*HillSlope)),
using the GraphPad software. The parameters top and bottom were commonly fixed to 100
and 0, accordingly.
Toxics 2022, 10, x FOR PEER REVIEW 3 of 15

Toxics 2022, 10, 604 3 of 14

using the GraphPad software. The parameters top and bottom were commonly fixed to
100 and 0, accordingly.

Figure1.1.Experimental
Figure Experimentaldesign.
design.Four-day-old
Four-day-olddaphnids
daphnids were
wereexposed
exposedto to
eight chemicals
eight chemicalsindividually
individu-
for
ally24for
h. 24
Description of theofcombined
h. Description mixture
the combined effecteffect
mixture at EC5atwas
EC5used
was to assess
used its toxicity
to assess at 24 h.
its toxicity at 24
h.
2.2. Sample Homogenization and Biochemical Assays
2.2. Sample
FifteenHomogenization and Biochemical
animals per biological Assays
replicate were pooled together and homogenized in
0.4 mL Fifteen
bufferanimals
using aper biological
pestle replicate were
homogenizer. pooled together
The homogenate wasand homogenized
cleared in 0.4
by centrifuga-
mL buffer
tion (9000×using
g for a5 pestle 5 ◦ C), and theThe
min athomogenizer. homogenate
clear supernatant waswascleared by centrifugation
collected and assessed
(9000× g for 5for
immediately min at 5 °C),
enzyme and thePhosphatases
activity. clear supernatantwerewas collected
assayed and
in 100 mM assessed immedi-
acetic acid pH
ately
4.5 (forfor enzyme
acid) or 100activity.
mM boric Phosphatases
acid pH 9.8were assayed in
(for alkaline) 100 the
using mMsubstrate
acetic acid pH 4.5 (for
p-nitrophenyl
acid) or 100
phosphate andmM boric acid
monitoring pH
the 9.8 (for alkaline)
production using theatsubstrate
of p-nitrophenol p-nitrophenyl
405 nm after phos-
its alkalinization.
Similarly,
phate andthe activities the
monitoring of galactosidase
production ofand lipase wereatquantified
p-nitrophenol 405 nm afterby the generation of
its alkalinization.
nitrophenol
Similarly, the from the catalysis
activities of o-nitrophenyl-β-galactoside
of galactosidase or p-nitrophenyl
and lipase were quantified butyrate,
by the generation of
respectively,
nitrophenol from in phosphate buffer
the catalysis ofpH 7.2. Lactate dehydrogenase
o-nitrophenyl-β-galactoside (LDH) activitybutyrate,
or p-nitrophenyl was as-
sessed from the
respectively, in consumption
phosphate buffer of NADH
pH 7.2.inLactate
a reaction with substrate
dehydrogenase of pyruvate
(LDH) activity (5 mM)
was as-
at 340 nm [10]. Glutathione-S-transferase (GST) activity was measured
sessed from the consumption of NADH in a reaction with substrate of pyruvate (5 mM) by the formation
of
at a340
complex
nm [10].between reduced glutathione (GST)
Glutathione-S-transferase with 1-chloro-2,4-dinitrobenzene
activity was measured by the in phosphate
formation
buffer
of a complex between reduced glutathione with 1-chloro-2,4-dinitrobenzene inin
pH 7.2 at 340 nm [11,12]. For reduced thiols, samples were homogenized 100 mM
phosphate
acetic
bufferacid pHat4.5
pH 7.2 and
340 nmquantified
[11,12]. Forfollowing the protocol
reduced thiols, samplesof were
Grintzalis et. al. [13].
homogenized Protein
in 100 mM
was quantified
acetic acid pH by 4.5aand
sensitive Bradford
quantified protocol
following the[14].
protocol of Grintzalis et. al. [13]. Protein
was quantified by a sensitive Bradford protocol [14].
2.3. Metabolomic Analysis
Fifteen animals
2.3. Metabolomic were snap frozen in liquid nitrogen and analysed in the Metabolomics
Analysis
Core Technology Platform at the University of Heidelberg. For metabolite extraction,
Fifteen animals were snap frozen in liquid nitrogen and analysed in the Metabolom-
the frozen sample material was ground with a micropestle in 190 µL 100% methanol
ics Core Technology Platform at◦ the University of Heidelberg. For metabolite extraction,
and incubated for 15 min at 70 C with vigorous shaking. After the addition of 100 µL
the frozen sample material was ground with a micropestle in 190 µ L 100% methanol and
100% chloroform, samples were shaken for 5 min at 37 ◦ C. To separate polar and organic
incubated for 15 min at 70 °C with vigorous shaking. After the addition of 100 µ L 100%
phases, 200 µL HPLC-grade water was added and samples were centrifuged for 10 min at
chloroform, samples were shaken for 5 min at 37 °C. To separate polar and organic phases,
11,000× g. While avoiding the interphase containing cellular debris, 300 µL of the polar
200 µ L HPLC-grade water was added and samples were centrifuged for 10 min at 11,000×
(upper) phase were transferred to a glass vial and dried using a vacuum concentrator
g. While avoiding the interphase containing cellular debris, 300 µ L of the polar (upper)
(Eppendorf Concentrator Plus) without heating. Sequential on-line methoximation and
phase were transferred to a glass vial and dried using a vacuum concentrator (Eppendorf
silylation reactions were performed using an MPS autosampler (Gerstel, Mülheim Ruhr,
Concentrator Plus) without heating. Sequential on-line methoximation and silylation re-
Germany). Methoximation was performed by adding 20 µL 20 mg/mL methoxyamine
actions were performed
hydrochloride usingSt.anLouis,
(Sigma 226904, MPS MO,
autosampler (Gerstel, (Sigma
USA) to pyridine Mülheim Ruhr,and
270970) Germany).
incuba-
Methoximation
◦ was performed by adding 20 µ L 20 mg/mL methoxyamine
tion at 37 C for 90 min in an MPS Agitator Unit (250 rpm). For silylation reactions, 45 µL hydrochloride
(Sigma
of 226904, St. Louis, MO, USA) to pyridine (Sigma
N-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA; 270970)
Sigmaand incubation
69479) was added at 37and
°C
for 90 min in an MPS Agitator ◦ Unit (250 rpm). For silylation reactions,
samples were incubated at 37 C for 30 min with gentle shaking. Before injection, samples 45 µ L of N-Methyl-
N-(trimethylsilyl)trifluoroacetamide
were incubated for 45 min at RT. For GC/MS (MSTFA; Sigmaa69479)
analysis, GC-ToF was added
system and
was samples
used were
consisting
incubated
of an Agilent at 37 °C Gas
7890 for 30 min with gentle
Chromatograph shaking.
(Agilent, Before
Santa injection,
Clara) fitted samples were incu-
with a Rxi-5Sil MS
bated for 45 min at RT. For GC/MS analysis, a GC-ToF system was
column (30 m × 0.25 mm × 0.25 µm; Restek) coupled to a Pegasus BT Mass Spectrometer used consisting of an
Agilent 7890 Gas Chromatograph (Agilent, Santa Clara) fitted with
(LECO). The GC was operated with an injection temperature of 250 C and a 1 µL sample ◦a Rxi-5Sil MS column
(30 m
was × 0.25 with
injected mm a× split
0.25 µ m; Restek)
ratio of 10. Thecoupled to a Pegasus
GC temperature BT Mass
program Spectrometer
started with a 1 min(LECO).
hold
The GC was operated with an injection temperature of 250 °C
at 40 C followed by a 6 C/min ramp up to 210 C, a 20 C/min ramp up to 330 ◦ C and
◦ ◦ ◦ ◦ and a 1 µ L sample was
ainjected
bake-out with
at 330 ◦
a split
C ratio
for 5 of
min10.using
The GC temperature
Helium program
as a carrier started
gas with with alinear
constant 1 minvelocity.
hold at
40 °C
The ToFfollowed by a 6 °C/min
mass spectrometer was ramp up towith
operated 210 °C,
ion asource
20 °C/min ramp uptemperatures
and interface to 330 °C andofa
250 ◦ C, a solvent cut time of 9 min and a scan range (m/z) of 50–600 with an acquisition
rate of 17 spectra/second. The ChromaTof v5.50 software (LECO Corporation, Saint Joseph,
MI, USA) was used for data processing.
Toxics 2022, 10, 604 4 of 14

2.4. Statistical Analysis

The biochemical data were presented as mean ± standard deviation (SD) and were
analysed and plotted with the GraphPad Prism software. For biochemical analysis, statis-
tically significant differences were compared by Student’s t-test over unexposed control
with a p-value of 0.05 for single chemical exposures with a null hypothesis that differ-
ences would be observed due to chance. For the different concentrations of the mix-
ture, one-way ANOVA followed by comparisons with the control was performed and
a test of a linear trend was validated. For metabolomic data (provided in Supplemen-
tary Materials), the values of peak area intensities were standardised by z scoring and
then processed for multivariate statistical analysis with the freeware software Multi Ex-
periment Viewer [15] to perform principal component analysis (PCA) and hierarchical
clustering with Pearson distance metrics. A significant analysis of microarrays (SAM) be-
tween each exposed group and control was performed to identify significant fold changes
in metabolites.

3. Results
3.1. Toxicity of Individual Chemicals and Their Mixture
Acute exposure of daphnids to eight individual chemicals—Al, Li, acetylsalicylic acid,
diltiazem, metformin, propranolol, glyphosate and nicotine—was assessed via toxicity
curves (Figure 2) and the calculation of the effective concentration (EC) values (Table 1). As
described, in this study we used older (four-day-old) daphnids and not the most sensitive
neonates. The selection of this stage was made mainly because of the amount of tissue
required but most importantly to avoid differences observed in some chemicals and their
action because of the time window of the 0–24 h for collection of neonates. Specifically, it is
well known that neonates have differences in toxicity responses as their collection could
be from 0 to 24 h prior to the exposure, as shown for example for metals [8]. With this
in mind and based on our previous experience, choosing a four-day stage as a starting
point achieves better homogeneity allowing all individuals to grow to a level at which
they will have a more unified response. Finally, in a freshwater population, all ages of
daphnids are present, and therefore the neonate may serve as a more sensitive stage, but is
not restrictive to the selection for this organism. As expected, the EC values recorded were
in a similar order of magnitude but higher than reported EC50 values in the literature for
neonates as neonates are more sensitive. In addition, a composite mixture in the ratio of
the components’ EC5 (Table 1) was further explored for its toxicity in a range of dilutions
to construct a full toxicity curve (Figure 3). For this toxicity curve the dose–response
relationship was plotted as log10 EC5 , and low (10% EC5 ), medium (20% EC5 ), and high
(30% EC5 ) concentrations were selected for the mixture exposures of daphnids for 24 h as
non-lethal concentrations.

Table 1. EC values (in mg/L) from toxicity curves and mixture ratio at EC5 *.

Chemical EC50 Hill Slope EC5 % in Mixture

Aluminium sulfate hexadecahydrate 59.4 5.282 34 4.53
Lithium chloride 93.7 9.354 68.4 9.11
Acetylsalicylic acid 75.8 13.03 60.5 8.06
Diltiazem hydrochloride 80.8 16.37 67.5 8.99
Metformin 145 9.534 106.5 14.19
Propranolol 83.6 3.864 39 5.19
Glyphosate 61.3 56.17 1.69 0.225
Nicotine 455 14.76 373 49.69
* presented precision does not signal significance but serves the purpose for reusability.
 Toxics 2022, 10, 604 5 of 14
Toxics 2022, 10, x FOR PEER REVIEW 5 of 15

Figure 2. Acute toxicity curves for individual chemicals in this study. Data represent average ±
Figure 2. Acute toxicity curves for individual chemicals in this study. Data represent average ±
standard deviation (N = 4 replicates).
standard deviation (N = 4 replicates).
 Acetylsalicylic acid 75.8 13.03 60.5 8.06
Diltiazem hydrochloride 80.8 16.37 67.5 8.99
Metformin 145 9.534 106.5 14.19
Propranolol 83.6 3.864 39 5.19
Toxics 2022, 10, 604 Glyphosate 61.3 56.17 1.69 6 of 0.225
14
Nicotine 455 14.76 373 49.69
* presented precision does not signal significance but serves the purpose for reusability.

Figure
Figure 3. 3. Acute
Acute toxicity
toxicity curve
curve of composite
of composite mixture
mixture of chemicals.
of chemicals. Data Data represent
represent average
average ± standard
± standard
deviation
deviation (N(N
= 6=replicates).
6 replicates).

3.2.Enzyme
3.2. Enzyme Responses
Responses to Single
to Single Chemicals
Chemicals and and
TheirTheir Mixture
Mixture
Exposure
Exposure to to individual
individual stressors
stressors at5EC
at EC 5 revealed
revealed distinct
distinct responses
responses in theinactivity
the activity
of of
enzymes
enzymes among
among thethe different
different pollutants
pollutants (Table(Table 2). Acetylsalicylic
2). Acetylsalicylic acid, followed
acid, followed by nico-
by nicotine,
tine, induced
induced fewer changes
fewer changes in enzymeinactivities,
enzyme while
activities,
on thewhile
otheron themetals
hand, other were
hand,themetals
most were
impactful
the moststress by decreasing
impactful stress byalldecreasing
enzyme activities with the
all enzyme exception
activities withofthe
GSTexception
which was of GST
increased by aluminium and not lithium. Interestingly, there is not a specific
which was increased by aluminium and not lithium. Interestingly, there is not a specific pattern on
the responses
pattern triggered
on the as for
responses example,as
triggered propranolol
for example, only resulted in increases
propranolol in activities
only resulted in increases
ofinboth phosphatases
activities of both and peptidase. and
phosphatases In relation to reduced
peptidase. thiols,
In relation tofour stressors
reduced (lithium,
thiols, four stress-
aluminium, nicotine and metformin) decreased their levels, while on
ors (lithium, aluminium, nicotine and metformin) decreased their levels, while on the contrary, four
the con-
chemicals (acetylsalicylic acid, propranolol, diltiazem and glyphosate) increased the levels
trary, four chemicals (acetylsalicylic acid, propranolol, diltiazem and glyphosate) in-
of reduced thiols.
creased the levels of reduced thiols.
Exposure to the eight chemical mixtures resulted in dose-dependent changes for
all enzyme biomarkers assessed (Table 3). Both acid (ACP) and alkaline (ALP) phos-
phatase activity was increased in the ranges of 18–37% and 36–41%, respectively, in a
concentration-dependent manner relative to the intensity of the mixture. Furthermore,
GST activity showed a trend to increase between 29% and 52%. On the other hand, β-
galactosidase and lipase decreased between 12% and 42% dose-dependently with the stress
intensity. Peptidase and lactate dehydrogenase, and reduced thiols, were also decreased
in response to the concentration of the mixture, by 21% to 56%, respectively. The latter
observed decrease in thiols could also be correlated with the increase in the activity of
GST, which uses glutathione as a substrate to detoxify xenobiotics, and potentially other
thiol-consuming enzymes.
Toxics 2022, 10, 604 7 of 14

Table 2. Biochemical markers of daphnid physiology upon exposure to a mixture of eight chemicals.
Data represent mean ± standard deviation (N = 4) of enzyme activity. Enzyme activity was expressed
as units/mg protein for lactate dehydrogenase (LDH), lipase, β-galactosidase and phosphatases,
as munits/mg protein for GST, and for reduced thiols in nmoles/mg protein. Bold font indicates
statistically significant difference by Student’s t-test compared with the unexposed control.

Li Acetyl Nicotine
Control Al Salicylic Acid Propranolol Diltiazem Glyphosate Metformin

ALP 7.53 ± 0.88 2.2 ± 0.21 8.8 ± 0.31 8.13 ± 0.68 13.98 ± 1.19 10.6 ± 0.34 5.42 ± 0.53 7.77 ± 0.79 4.7 ± 1.18
(−71%) (+84%) (+41%) (−28%) (−38%)
1.8 ± 0.07 3.4 ± 0.13 7.44 ± 0.46 3.17 ± 0.71
ACP 5 ± 0.69 (−64%) (−32%) 5.73 ± 0.09 (+49%) 5.7 ± 0.24 5 ± 0.21 4.38 ± 0.3 (−37%)
1.85 ± 0.1 3.13 ± 0.05 4.72 ± 0.71
βGAL 11.63 ± 0.2 (−84%) (−73%) 12.36 ± 0.87 9.96 ± 1.14 11.7 ± 1.02 11.7 ± 0.42 11.1 ± 1.25 (−59%)
50.2 ± 2.3 104.6 ± 13.5 190 ± 12.5 187.8 ± 9.6 84.2 ± 16.7
Lipase 165 ± 10.1 (−70%) (−37%) 181.64 ± 3.7 153 ± 16.4 (+15%) (+14%) 169.4 ± 11.8 (−49%)
53 ± 10.7 158 ± 11.8 240 ± 17.6 387 ± 41.5 340 ± 8.9 217 ± 27.5 138 ± 27.8
Peptidase 286 ± 21.8 291 ± 41.8
(−82%) (−45%) (−16%) (+35%) (+19%) (−24%) (−52%)
LDH 80.32 ± 6.52 63.8 ± 5.33 83 ± 5.22 84.6 ± 4.33 77.7 ± 2.51 86.42 ± 7.88 72.59 ± 6.85 65.5 ± 3.15 67.4 ± 4.91
(−21%) (−18%) (−16%)
GST 212 ± 21.7 34.3 ± 19.7 274 ± 7.6 198.6 ± 6.6 215.6 ± 24.5 151.4 ± 6 134.7 ± 7.2 155.6 ± 4.8 254.8 ± 9.2
(−84%) (+29%) (−29%) (−37%) (−27%)
Reduced 36.7 ± 1.2 51.9 ± 1.7 79.9 ± 2 74.1 ± 2.5 70.5 ± 2.2 73.6 ± 3.9 50.7 ± 1.3 57.7 ± 3.2
64.9 ± 3.5 (−43%) (−20%) (+23%) (+14%) (+8.6%) (+13.5%) (−22%) (−11%)
thiols

Table 3. Biochemical markers of daphnid physiology upon exposure to a mixture of eight chemi-
cals. Data represent mean ± standard deviation (N = 4) of enzyme activity. Enzyme activity was
expressed as units/mg protein for lactate dehydrogenase (LDH), lipase, β-galactosidase and phos-
phatases, as munits/mg protein for GST, and for reduced thiols in nmoles/mg protein. Bold font
indicates statistically significant difference by one-way ANOVA followed by comparison with the
unexposed control.

Control 10% 20% 30%

11.67 ± 1.01 11.64 ± 0.32 11.22 ± 1.19
ALP 8.28 ± 0.19
(+41%) (+41%) (+36%)
3.62 ± 0.44 4.05 ± 0.27 4.29 ± 0.45
ACP 3.08 ± 0.14
(+18%) (+31%) (+37%)
3.19 ± 0.09 2.6 ± 0.13 2.11 ± 0.09
βGAL 3.62 ± 0.06
(−12%) (−28%) (−42%)
15.34 ± 1.31 10.43 ± 1.18 10.85 ± 1.24
Lipase 17.73 ± 0.59
(−13%) (−41%) (−39%)
73.8 ± 6.81 77.18 ± 6.58
Peptidase 95.65 ± 4.44 93.09 ± 12.29
(−23%) (−20%)
32.9 ± 10.11 18.41 ± 4.3
LDH 54.79 ± 2.32 50.44 ± 7.07
(−40%) (−67%)
193.41 ± 7.56 227.61 ± 23.68
GST 149.52 ± 3.36 169.14 ± 13.02
(+29%) (+52%)
158.82 ± 12.12 115.69 ± 17.18 88.69 ± 35.97
Reduced thiols 201.44 ± 31.76
(−21%) (−43%) (−56%)

3.3. The Metabolic Responses to Mixture Exposure

An untargeted metabolomic analysis revealed a significant number of changes in the
metabolism of daphnids upon exposure to the different intensities of the combined stress,
thus supporting the observations in enzyme activities. Principal component analysis (PCA)
and hierarchical clustering (HCL) show a clear grouping and clustering, respectively, of
the metabolic profiles based on the intensity of the combined mixture stress (Figure 4).
There is a clear trend (Figure 4 green arrow) of increase in intensity towards PC1. This
is also supported by the significance analysis of microarrays analysis (SAM) (Figure 5),
which allows the identification of significant changes based on the differential expression
between sets of samples. Although this analysis is used in microarrays, it is also applicable
to metabolomic data [16]. Statistically, this analysis provided a number of significantly up-
or down-regulated metabolites with increasing stress intensities. For example, even from
 thus supporting the observations in enzyme activities. Principal component analysis
(PCA) and hierarchical clustering (HCL) show a clear grouping and clustering, respec-
tively, of the metabolic profiles based on the intensity of the combined mixture stress (Fig-
ure 4). There is a clear trend (Figure 4 green arrow) of increase in intensity towards PC1.
This is also supported by the significance analysis of microarrays analysis (SAM) (Figure
Toxics 2022, 10, 604 5), which allows the identification of significant changes based on the differential expres- 8 of 14
sion between sets of samples. Although this analysis is used in microarrays, it is also ap-
plicable to metabolomic data [16]. Statistically, this analysis provided a number of signif-
icantly up- or down-regulated metabolites with increasing stress intensities. For example,
the low
evenstress, undecanol
from the and
low stress, β-alanine
undecanol were
and down-regulated,
β-alanine and with and
were down-regulated, the increase
with the in the
intensity of the
increase mixture
in the stress
intensity of thetomixture
20%, citronellol and
stress to 20%, ethanoloamine
citronellol were also were
and ethanoloamine decreased,
and also
at 30%, methionine,
decreased, phenylalanine
and at 30%, methionine, and glycine were
phenylalanine addedwere
and glycine to the list of
added to significantly
the list
decreased metabolites.
of significantly On the
decreased other hand,
metabolites. On all
thestress
other intensities up-regulated
hand, all stress threonine and
intensities up-regu-
lated threonine
putrescine, and putrescine,
while other metaboliteswhile
ofother metabolites
the TCA of the TCA
cycle (citric acid,cycle (citricacid),
fumaric acid, fu-
the urea
cyclemaric acid), the
(ornithine) urea
and cycleacids
amino (ornithine) andvaline)
(proline, amino acids
appear(proline, valine)
increased appear
only at theincreased
middle (20%)
only at
and high the middle
(30%) (20%)
intensity, andindicating
thus high (30%)differences
intensity, thus
withindicating differences
the escalation of thewith the effect.
stress
escalation of the stress effect.

022, 10, x FOR PEER REVIEW 9 of 15

Figure 4. Multivariate statistical analysis of metabolomic data. PCA analysis and HCL shows the
Figure 4. Multivariate statistical analysis of metabolomic data. PCA analysis and HCL shows the
grouping and clustering of samples. The green arrow in PCA shows the gradual change in the
grouping and clustering of samples. The green arrow in PCA shows the gradual change in the met-
metabolicthe
abolic profiles following profiles following
intensity the intensity
of the mixture stress.of the mixture stress.

Figure 5. Significance Analysis of Microarrays (SAM) for each exposure compared with the unex-
Figure 5. Significance Analysis of Microarrays (SAM) for each exposure compared with the unexposed
posed control reveals the gradual change in the most significant metabolites.
control reveals the gradual change in the most significant metabolites.

4. Discussion
4.1. The Effects of Individual Stressors
There is a great number of chemicals simultaneously present in the environment, and
a study of their effects separately is impossible in the actual environment. In most labora-
Toxics 2022, 10, 604 9 of 14

4. Discussion
4.1. The Effects of Individual Stressors
There is a great number of chemicals simultaneously present in the environment, and a
study of their effects separately is impossible in the actual environment. In most laboratory
studies, the individual effects of single stressors are assessed in controlled experiments to
understand their underlying mechanisms of toxicity.
Metal contamination is a major concern in aquatic ecosystems and, therefore, it is
important to find reliable indicators of metal stress on aquatic organisms. In this study,
aluminium and lithium were selected as two metal stressors commonly present in freshwa-
ter ecosystems. The adverse effects of both these metals have been studied in a variety of
aquatic organisms such as sea urchins, fishes and snails [17], and in daphnids the reported
EC50 for neonates is in a similar range to the EC50 reported in our study. In one study,
aluminium exposure resulted in the differential expression of 155 genes [18] and its toxicity
is believed to be mediated due to its capacity to strongly bind to phosphorus, thus reducing
its availability. Ionic aluminium is able to inhibit extracellular phosphatases, and in our
study this was the case for a decrease in the activity of acid phosphatase but not for alkaline
phosphatase. Lithium has also been shown to exert toxic effects on aquatic organisms
such as the fathead minnow and Ceriodaphnia dubia, implicating the role of other elements
such as sodium decreasing lithium toxicity [17]. Furthermore, in relation to daphnids,
lithium exposure resulted in 143 genes being differentially expressed, some even by over
three-fold [19]. In some studies, lithium led to significant metabolite variations, specifically
in amino acids as well as uracil and the osmolyte glycerophosphocholine, thus revealing
toxicity-mediated effects by impairing energy production and ionoregulation [20]. In the
present study, both metals had a significant impact through decreasing most biochemical
markers when applied independently. Furthermore, the observed increase in GST activity
upon aluminium exposure has also been reported by others as a response to heavy metals
in plants [21] and to toxins in daphnids [22].
With the demographic trend of an ageing of populations and the high accessibility
of medication and drugs, pharmaceuticals have been highlighted as a class of emerging
pollutants. This in turn is also amplified by their improper disposal and limited removal
from waste water treatment plants. Non-steroidal anti-inflammatory drugs (NSAIDs) are
probably the most widely used medication worldwide. In our study, acetylsalicylic acid,
best known as aspirin, was studied for its impact on daphnids as a representative NSAID.
Acetylsalicylic acid has been reported to decrease survival rates, fecundity and growth
in daphnids [23], and the reported EC50 values are similar to the ones observed in this
study. In daphnids, it has been shown that acetylsalicylic acid mediates its toxicity via
an induction of oxidative stress with an increase in lipid and protein oxidation which
is accompanied by DNA damage. In response to the aforementioned effects on oxida-
tive stress, changes in antioxidant enzyme activities such as superoxide dismutase and
catalase have been recorded [24]. A more holistic approach revealed that three genes are
significantly up-regulated and four genes are significantly downregulated in response to
acetylsalicylic acid [25]. However, in our study, acetylsalicylic acid had a less significant
impact, decreasing only the activity of peptidase.
Diltiazem is a non-dihydropyridine calcium channel blocker prescribed to treat high
blood pressure and to control angina [26]. Diltiazem inhibits calcium influx into both
cardiac and smooth muscles during depolarisation, and as such, it is most probable that
toxic effects would occur through the disruption of regulation of cellular calcium levels.
Maintenance of appropriate calcium levels is important for many physiological processes in
all organisms, including daphnids. At low concentrations of 500 ng/L, diltiazem has been
shown to increase the heart rate of Daphnia magna, along with oxygen consumption, thus
resulting in energy imbalance and a higher demand for energy [27]. Although in our study
we did not assess phenotypic endpoints, diltiazem decreased GST activity and increased
alkaline phosphatase and lipase. The latter observed increase in lipase activity could be
seen as consistent with the decrease in lipids reported in other studies [27].
Toxics 2022, 10, 604 10 of 14

Metformin is a medication used under many brand names and is the most common
drug prescribed to treat type 2 diabetes and polycystic ovary syndrome. Because of its
wide audience of patients, it is very much consumed and, thus, commonly present in the
aquatic ecosystem [28]. Metformin may interact with a variety of molecular targets across
species [29]. In relation to its actions on aquatic species, there is literature on its impact
on fecundity and behaviour in fish [30,31] and therefore, metformin has been attributed
as an endocrine disruptor [32]. There are few biochemical data available for daphnids;
however, the mechanism of action of metformin has been linked to the induction of the
hypoxia-inducible factor (HIF) α and β genes [33]. In our study, metformin proved to
be a strong stressor and decreased all enzyme activities simultaneously, thus showing a
significant impact on daphnids.
Propranolol is a drug that belongs to the category of β-blockers, which are prescribed
for the treatment of stable coronary ischaemic disease [34]. Propranolol has been detected
in the freshwater environment in significant high levels, and although it is designed for
human therapeutic usage, it exerts its effects on non-target organisms. As a drug with
a significant bioaccumulation effect, propranolol at a very low concentration has been
shown to bioaccumulate in daphnids at up to 1.6× its original concentration over 10 gener-
ations [35]. Propranolol is toxic to neonates with an ambient EC50 of 7.5 mg/l [36], which
is significantly lower than the one determined in our study, which could be attributed to
the higher sensitivity of neonates when compared with the four-day-old daphnids used
here. Propranolol may exert its toxic effects in daphnids in an organ-specific manner, such
as reducing the heart rate [37], in addition to decreasing fecundity (at 0.22 to 0.44 mg/L)
and completely inhibiting it (at 0.88 mg/L). It is worth noting that in transgenerational
exposures, the second generation of daphnids was less sensitive to propranolol [38] al-
though in other studies even subtle environmentally relevant concentrations may induce
physiological changes [39]. Propranolol has been reported to increase GST activity and
inhibit glutathione peroxidase (GPx), an enzyme that removes peroxyl radicals and hy-
droperoxides [40], thus affecting the antioxidant defence system of daphnids. Interestingly,
in our study, propranolol increased the activity of both phosphatases and peptidase.
N-phosphonomethyl glycine is a well-known herbicide called glyphosate, which has
been employed extensively in agriculture, and thus can be found in the environment as a
consequence of agricultural or urban run-off and leaching into local surface waters [41].
There have long been concerns over its implications in the environment and for public
health. Glyphosate has been shown to cause morphological alterations in zooplanctonic
organisms and crustaceans [42], and to impact carbon and fat metabolism and the micro-
biome [43] as well as the heart rate of daphnids [44]. Our results support these findings,
as an increase in lipase and peptidase was observed along with a decrease in alkaline
phosphatase and GST. The latter is in agreement with similar decreases in fish and could
be explained as failure of detoxification processes and development of oxidative stress [45].
In addition, recent studies on daphnids showed that glyphosate exposure can modify the
mRNA transcription and enzymatic activity of GST and lipid peroxidation [46] and even
exacerbate its toxicity in the presence of microplastics [47].
The last chemical studied was a stimulant, nicotine, which is widely consumed within
a number of products in daily life and is a key ingredient of tobacco. There is a general
lack of data on the impact of nicotine on freshwater organisms, and many existing studies
focus solely on mortality or heart-rate as physiology endpoints. Nicotine has been found to
reduce fecundity in daphnids by decreasing the number of neonates released per individual.
It also triggers the production of male offspring [48] and induces antenna, carapace and
spine malformations [49] in daphnids. In our study, nicotine decreased the activities
of peptidase, LDH and GST, and the latter could indicate a possible induction of stress
in daphnids.
Toxics 2022, 10, 604 11 of 14

4.2. Mixture Effects and Omics in Toxicology

Organisms in their natural environments are exposed to composite mixtures of several
individual chemicals at low concentrations. This is an issue, as chemical interactions within
these mixtures can result in unintuitive results. The individual components were mixed
at an EC5 ratio; however, the mixture, as expected, was significantly more toxic than its
constituents alone. This is known as synergy, meaning that the components act together,
and this poses a major problem in the context of environmental risk assessments. These
results are not easily predicted, so new methods are needed to understand the mechanisms
and interactions within these mixtures. Effect-based methods gain more attention in the
literature as complementary strategies to the chemical analytical characterisation of complex
pollution patterns and they can provide new metrics for pollution assessment [50].
For mixture prediction, there exist two concepts—Concentration Addition (CA) and
Independent Action (IA)—that allow the calculation of expectable combined effects based
on individual components’ bioactivities and mixture exposure knowledge. Both concepts
are based on knowledge of the single-compound toxicities and the assumption of no in-
teraction. CA assumes that the individual components behave as simple dilutions of one
another, which is commonly interpreted as being the case for compounds of a mixture
sharing a strictly similar mechanism of action. However, IA supports the completely inde-
pendent action of chemicals, which is commonly interpreted as the compounds of a mixture
having dissimilar mechanisms of action. In a recent study on daphnids where mortality
was used as the only endpoint, four contaminants (sodium fluoride, boric acid, ammonium
hydroxide and acetaminophen) were assessed in mixtures. Regardless of the assumption
of dose- or response-additivity, independent action slightly outperformed concentration
addition in most of the combinations of these multiple-class compounds [51]. In this study,
we considered that individual responses were low but we did not perform any analysis
of non-interactive mixture modelling, but rather we chose to elaborate for observation of
biochemical markers of enzyme activities, in order to understand the biological responses
of daphnids when exposed to low (10% EC5), medium (20% EC5) and high (30% EC5)
concentrations of the composite chemical mixture. As it was hypothesised, a clear dose
response in connection to the stress intensity was observed, which could be attributed to a
synergy effect in the above meaning. However, a more in-depth level of knowledge would
require more sophisticated measurements. Holistic or omics methodologies are widely
used in toxicological research and have a pivotal role in the understanding of mechanisms
of toxicity [52]. These analytical approaches extend from the epigenome to the transcrip-
tome, proteome and metabolome level. Metabolomics is the study of low-molecular-weight
metabolites and metabolic pathways within living organisms, and is a fast-evolving re-
search field with pioneering investigations in relation to the biochemical responses to
toxicants. Metabolism is a decisive parameter for physiology and is of central importance
for adaptation of all life forms. Therefore, the unique strength of metabolomics is that it
measures the functional status of an organism as the alterations in metabolic levels are the
primary adaptation mechanism in organisms [53]. This is because by nature metabolism
responds fast to environmental stimuli which allows the analysis and interpretation of the
organism’s response at a molecular pathway level. In our experiments, alterations in the
TCA cycle, the urea cycle and metabolism of amino acids were highlighted as perturbed
with intensity of stress. Although our analysis was a preliminary discovery analysis and
not targeted to any pathway, targeted methods have identified perturbations in specific
pathways [54] or mapped these changes on specific tissues [55] in these crustaceans. Fur-
thermore, this study focused on a composite mixture as a stress pool and not on a specific
chemical; however, lithium, for example, has been recently assessed in its nanoparticle
form to affect amino acid, starch and glucose metabolism, which could also be reflected in
our study from changes in the relevant catabolic enzymes [56].
Metabolomic analysis in daphnids has been highlighted as a key mechanistic tool
for environmental monitoring [57] and can provide valuable fitness information at the
molecular level [58]. Our study verified that simple biochemical markers of enzyme
Toxics 2022, 10, 604 12 of 14

activities show similar patterns in changes with sensitive holistic metabolomic analysis. In
this context, simple enzyme activity endpoints can be used as a first step to evaluate and
design metabolomic detailed studies safely. To our knowledge, this is the first study where
a direct link was observed in daphnids between the activities of key enzymes relevant to
the physiology of daphnids and metabolomic analysis, thus verifying the employment of
the first as potent markers in toxicology assessment.

Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/toxics10100604/s1, Table S1: Metabolomic data.
Author Contributions: Conceptualization, K.G. and R.A.; methodology, K.G., G.P. and M.B.; in-
vestigation, A.M. and A.R.M.; resources, K.G. and R.A.; data curation, M.B.; writing—original
draft preparation, K.G.; writing—review and editing, K.G., R.A., G.P. and M.B.; supervision, K.G.;
funding acquisition, K.G. and R.A. All authors have read and agreed to the published version of
the manuscript.
Funding: This research was funded by SCIENCE FOUNDATION IRELAND under grant number
[18/SIRG/5563 Metabolomic approaches in mechanistic toxicology] and the ALEXANDER VON
HUMBOLDT FOUNDATION to support Grintzalis with a research visit fellowship to the UFZ. The
APC was free of charge as this was an invited article with KG being the guest editor of the special
issue “Toxicity of Contaminants on Aquatic Organisms”.
Institutional Review Board Statement: Ethical review and approval were waived for this study,
due to the fact that daphnids are regarded as “animals” in terms of being members of the kingdom
Animalia, however, they are not “animals” as defined in regulation SI543 of 2012 on the protection
of animals used for scientific purposes. Therefore, the study does not require authorization from
the Health Products Regulatory Authority (HPRA), while is also in line with the aim of working
under the 3Rs (reduce, refine, replacement) strategy, since daphnids are commonly used in ecology
and ecotoxicology as replacements of more evolutionary advanced species (i.e., fishes), posing no
ethical implications.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments: The authors acknowledge the administrative support from Science Foundation
Ireland, the UFZ and the Alexander von Humboldt Foundation to enable travelling and connection
between Dublin City University and the UFZ. Furthermore, the state-of-the-art metabolomic analysis
was performed at the Metabolomics Core Technology Platform of the University of Heidelberg.
Conflicts of Interest: The authors declare no conflict of interest.

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