EFFECTS OF PHARMACEUTICALS ON AQUATIC MICROORGANISMS
tion in the spectra. Three replicates of each of the samples were randomlyapplied to the ZnSe plates, and triplicate spectra were obtained using differentpositions in each well; a total of nine spectra (called technical replicates) persample were collected. Each plate was loaded onto an HTS-XT motorizedmicroplate module under computer control by the OPUS software (version 4)(53). Spectra were collected with an Equinox 55 FT-IR spectrometer (BrukerOptics Ltd.) in transmission mode using a deuterated triglycine sulfate detectorover a wavelength range from 4,000 to 600 cm⫺1 and with a resolution of 4 cm⫺1.
Sixty-four spectra were co-added to improve the signal-to-noise ratio. The spec-tra were displayed in terms of absorbance (Fig. 1 shows typical example spectra).
Analysis of FT-IR spectroscopy data. (i) Spectral preprocessing. The ASCII
data were imported into Matlab version 7.1 (The MathWorks, Inc., Natick, MA),and in the initial step spectral regions which were dominated by CO vibrations
arising from the atmosphere (2,403 to 2,272 cm⫺1 and 683 to 656 cm⫺1) wereremoved and filled with a linear trend. The spectra were corrected using ex-tended multiplicative scatter correction (EMSC), which normalizes andsmoothes spectra by application of a polynomial smoothing function (28). Theresulting preprocessed spectra were used for subsequent multivariate analyses.
(ii) Multivariate analysis. The protocol used for multivariate analysis was a
protocol developed previously (1, 15). Principal component analysis (PCA) is anunsupervised method for reducing the dimensionality of multivariate data whilepreserving the variance. This transformation was performed prior to canonicalvariate analysis (CVA). CVA is a supervised learning method that seeks tominimize within-group variance while it maximizes between-group variance, andit can be used in conjunction with PCA to discriminate between groups on the
FIG. 1. Typical processed FT-IR spectra for P. aeruginosa PA14
basis of retained principal components (PCs), given a priori knowledge of group
exposed to 80 g ml⫺1 (R)-propranolol, (S)-propranolol, and (⫾)-
membership of spectral replicates (26, 52). In this study, PC-CVA models were
propranolol. Control samples (Con) which were not exposed to pro-
constructed with a priori knowledge of the biological replicates. In order to make
pranolol were included. The spectra are offset for clarity.
sure that these models were not over- or undertrained, validation was performedusing the full cross-validation method, where two of the biological replicateswere used for model training and the third replicate was projected into the model
decrease in the turbidity of the culture [39]) for the exponential phases using the
for cluster validation (20). Finally, CVA also allowed statistical significance to be
following equation: ⫽ 2.303(log OD2 ⫺ log OD1)/t ⫺ t ), where is the
displayed on the score plots, and circles were used to indicate the 95% 2
specific growth rate or death rate, log OD1 is the log
optical density at time
confidence region constructed around each group mean based on the 2 distri-
point 1, log OD2 is the log
optical density at time point 2, t is time point 1,
bution with 2 degrees of freedom (24).
and t is time point 2. The growth rate data (see below) were used to select a
Partial least-squares (PLS) (27) regression is a multivariate linear regression
reduced range of concentrations for further investigation; the concentrations
method which allows the quantitative relationship between different variables
selected were based on identifiable differences in the growth rate which did not
(e.g., API concentration and FT-IR spectra) to be modeled, and it can deal
result in cell death.
efficiently with data sets that are highly correlated. In this study, PLS regression
Batch growth. Bacterial cultures were exposed in triplicate to a range of
was employed to predict the API concentration values from the FT-IR spectros-
concentrations of the chosen API; both the pure enantiomers and a range of
copy data. Like the PC-CVA, the regression models were calibrated with two of
enantiomeric mixtures were used (Table 1). An aliquot (1 ml) of sterile water was
the three biological replicates, and the third replicate was used as an independent
added to an additional set of bacterial samples as a control. Samples were
test set to validate the model and establish whether the models could generalize.
maintained at 15°C and 200 rpm in a Multitron orbital shaker for 24 h.
Aliquots (2 ml) were taken in triplicate from each flask and centrifuged (5 min, 0°C,
RESULTS AND DISCUSSION
16,089 ⫻ g) to harvest the biomass. The supernatant and pelleted biomass werestored at ⫺80°C before further analysis.
Effects of the chiral APIs on the bacterial growth rates. A
Quantitative analysis of API concentration by HPLC. Concentrations of
number of aquatic microorganisms were exposed to the chiral
atenolol and propranolol were determined by HPLC (Agilent 1100 series). Thesupernatant samples were allowed to thaw at room temperature and were filtered
APIs atenolol and propranolol, and growth rates, death rates,
(0.22 m; Millipore) in order to remove any microbial cells remaining in the
and maximum optical densities were determined to monitor
medium. Aliquots (25 l) were injected onto the HPLC column in a random
the effects of the APIs on culture progress; Fig. 2 shows data
order. Each sample was injected three times during the analysis, resulting in
for 0, 10, 50, 90, and 130 g ml⫺1 of the (R) and (S) enantio-
three analytical replicates for each biological sample. The HPLC system was
mers and the racemic mixtures. Slight variations in the specific
equipped with a Chirobiotic V2 column (250 mm by 4.6 mm [inside diameter];particle size, 5 m; ASTEC, Whippany, NY) and a UV detector operating at a
growth rates were observed for the Pseudomonas species ex-
wavelength of 230 nm. The column was eluted with an isocratic mixture of
posed to different concentrations (10 to 130 g ml⫺1) of (R)-,
methanol and water (90:10, vol/vol) and 1.0% triethylamine acetate (TEAA)
(S)-, and (⫾)-propranolol. There was a considerable difference
buffer (pH 5.0). The pH of the buffer was adjusted with acetic acid prior to the
in the growth rates of species, and a marked effect was ob-
addition of methanol. The measurements were obtained at 25 ⫾ 1°C at a flowrate of 1 ml min⫺1 (4).
served for P. aeruginosa PA14 exposed to propranolol. In con-
Analysis of microbial cells by FT-IR spectroscopy. Ninety-six-well zinc sele-
trast, minimal changes were detected in the growth rates, death
nide plates were cleaned by rinsing them with 2-propanol and deionized water
rates, and maximum amounts of biomass of both Pseudomonas
(three times) and were allowed to dry at room temperature (18, 53). The cell
species exposed to 10 to 130 g ml⫺1 of (R)-, (S)- and (⫾)-
pellets stored at ⫺80°C were allowed to thaw at room temperature and washed
in order to remove any traces of residual API. Ice-cold sterile water (2 ml) wasadded to each sample and gently vortexed. The samples were centrifuged for 10
An interesting effect was observed for P. aeruginosa PA14
min (0°C, 16,089 ⫻ g), and the supernatants were discarded; this cycle was
exposed to both of the propranolol enantiomers and the race-
repeated three times. A final 100-l aliquot of sterile water was added to each
mate. At concentrations of 50 to 70 g ml⫺1 there appeared to
sample, and the solution was vortexed. Aliquots (20 l) of each resuspended
be no death of the microbial cells. In contrast, for cells exposed
sample were applied to ZnSe plates and oven dried at 50°C for 10 min. Dryingwas used to minimize any signal arising from the absorption of water in the
to 10 to 40 g ml⫺1 and to 80 to 130 g ml⫺1 the death rate
mid-IR region, which would mask the biologically important chemical informa-
was equivalent to that of the control cells. This was probably
WHARFE ET AL.
APPL. ENVIRON. MICROBIOL.
FIG. 2. Specific growth rate data for P. putida KT2440 and P. aeruginosa PA14 exposed to 0 to 130 g ml⫺1 of propranolol or atenolol. The
maximum optical densities (OD) at 600 nm and specific death rates are also shown. The data are averages from five biological replicates, and theerror bars indicate standard deviations.
because the lower concentrations (⬍40 g ml⫺1) of propran-
the higher concentrations of propranolol a slight increase in
olol had very little effect on metabolism so cells quickly
the amount of biomass was immediately followed by a notice-
reached the stationary and death phases and because the
able decrease in the optical density of the culture (death
higher concentrations (⬎80 g ml⫺1) had a negative impact on
phase), the maximum amount of biomass was severely inhib-
metabolism and killing cells (as also indicated by the fact that
ited by the presence of the API. Our observations suggest that
the final turbidity measurements were significantly lower than
the API has different effects depending on the concentration
the turbidity measurements for the control cells), while the
applied. At lower concentrations the growth is not affected by
intermediate concentrations (50 to 70 g ml⫺1) slowed growth
the API, and at high concentrations death occurs during the
but the cells did not enter the death phase. Inspection of the
growth period. However, at intermediate concentrations pro-
growth curves indicated that there was a second phase of
duction of biomass occurs throughout the growth period (onset
growth several hours into the stationary phase. While a second
of the death phase may have been observed if the growth had
phase may indicate that there is utilization of a secondary
been monitored for extended periods). The APIs were not
carbon source, this was not observed in the control cultures
metabolized during growth (Table 2), and we hypothesize that
and thus is not the likely explanation for this observation. The
the intermediate concentrations of propranolol affected either
biomass of the culture decreased as the concentration of the
the transport of nutrients into the cell or the rate of metabo-
API increased, and thus the original carbon source was poten-
tially not depleted at the onset of stationary phase. While at
The growth rate data for the cells exposed to propranolol
EFFECTS OF PHARMACEUTICALS ON AQUATIC MICROORGANISMS
TABLE 2. Quantification of propranolol from HPLC data for bacterial cells exposed to different ratios of (R)-propranolol
to (S)-propranolol at a concentration of 50 g ml⫺1
Amt (g ml⫺1) with the following ratio of (R)-propranolol to (S)-propranololb:
P. putida KT2440
P. aeruginosa PA14
a Control experiments (with labeled medium) were performed to determine the experimental effect on the drug concentration (i.e., loss of API during incubation
in growth medium).
b The values are averages for five measurements. The values in parentheses are standard deviations.
clearly showed that the specific growth rate decreases as the
quantitative drug effect was explored further using FT-IR spec-
concentration of the API increases. This trend was also ob-
troscopy and P. putida KT2440 exposed to (⫾)-propranolol.
served in the maximum optical density data and the death rate
Quantitative effects of APIs on bacteria measured using
data for both pseudomonads. Our findings indicate that pro-
FT-IR spectroscopy. In order to assess possible quantitative
pranolol has considerably different effects on the two Pseudo-
effects of propranolol on the phenotype of P. putida KT2440,
monas species. These findings are rather surprising as these
we employed partial least-squares (PLS) regression analysis to
species are genetically closely related. Estimates have shown
investigate whether the effect on the phenotype as measured
that there is greater similarity (60% of the predicted coding
using FT-IR spectroscopy was directly proportional to the con-
sequences) between these two pseudomonads than between
centration of API applied (Fig. 3). A clear linear relationship
any other complete microbial genomes obtained to date (32).
was observed between the concentration of (⫾)-propranolol to
In addition, comparative genome analysis has shown that 85%
which the P. putida KT2440 cells were exposed and the meta-
of the genes in the P. putida KT2440 genome have homologues
bolic fingerprint. In addition, we were able to predict the con-
in the P. aeruginosa PAO1 genome (46).
centration of propranolol to which the bacterial cells were
The toxic effects of the APIs observed here for aquatic
exposed with an accuracy of 95.45%. This is perhaps not sur-
organisms have been previously reported. Toxicity studies car-
prising as the inhibitory effect of the propranolol on the cells
ried out by Choi and coworkers with the crustacean Thamno-
was proportional to the concentration of API. This was a clear
cephalus platyurus and a fish species (Oryzias latipes) showed
phenotypic effect as we were unable to collect a propranolol
that propranolol caused acute toxicity in T. platyurus at a con-
spectrum at these concentrations when using FT-IR spectros-
centration of 10.61 g ml⫺1 and in O. latipes at a concentration
copy. We also performed PLS regression analysis with the
of 11.40 g ml⫺1. In contrast to the results presented here,
profiles of P. aeruginosa PA14 exposed to the intermediate
these workers found that atenolol did not have toxic effects in
concentrations of propranolol to determine if the secondary
the aquatic organisms at the concentrations that they used(⬍100 g ml⫺1) (9). In addition, toxicity studies have beencarried out with a range of APIs (including propranolol) usingthe Japanese medaka fish (O. latipes), an amphipod (Hyalellaazteca), and two crustaceans (Ceriodaphnia dubia and Daphniamagna). It was found that propranolol had the greatest effecton the organisms studied. The crustacean C. dubia displayedresponses to toxicity at a concentration of 0.25 g ml⫺1. Pro-pranolol was the only API investigated which was found tohave acute toxic effects in the Japanese medaka fish. Theseeffects were observed at a concentration of 0.5 g ml⫺1 (19).
Previous studies have suggested that -blockers do not affect
microbes due to the absence of the API receptors in theseorganisms (10, 23). However, in another study conducted byour group we reported that (⫾)-propranolol significantly re-duced the amount of lipid storage components of the algaMicrasterias hardyi 649/15 and caused a marked reduction inthe cellular protein content (34). In addition, the findings ob-tained by metabolic fingerprinting suggested that the pheno-type was altered during exposure to this API (34). To our
FIG. 3. Partial least-squares regression model for P. putida KT2440
knowledge, no further studies on the metabolic effects of pro-
exposed to various concentrations (0 to 100 g ml⫺1 in steps of 10 gml⫺1) of (⫾)-propranolol. The model was trained with FT-IR spec-
pranolol in aquatic microorganisms have been carried out.
troscopy data using two of the biological replicates and was validated
The effects on the growth dynamics of the bacteria are likely to
using the third biological replicate. The PLS regression model was
reflect changes in the metabolic potential of the cells, and this
built using 10 factors.
WHARFE ET AL.
APPL. ENVIRON. MICROBIOL.
TABLE 3. Quantification of atenolol from HPLC data for bacterial cells exposed to different ratios of (R)-atenolol
to (S)-atenolol at various concentrations
Amt (g ml⫺1)b
Concn of atenolol
(R) enantiomer
(S) enantiomer
(R) enantiomer
(S) enantiomer
P. putida KT2440
P. aeruginosa PA14
a Control experiments (with labeled medium) were performed to determine the experimental effect on the drug concentration (i.e., loss of API during incubation
in growth medium).
b The values are averages for five measurements. The values in parentheses are standard deviations.
growth effect was proportional to the concentration of API
a PC-CVA score plot represents the degree of similarity or
applied (data not shown). Under these conditions it was not
dissimilarity between the samples. A smaller distance indicates
possible to obtain a correlation between the drug concentra-
greater similarity, and a larger distance indicates that there are
tion and the FT-IR spectroscopy data. Therefore, the presence
greater differences between samples. Loading plots provide an
of the drug may have led to more complex biochemical per-
indication of which regions of the spectrum are used to define
turbations in the organisms that we were unable to model using
the patterns of separation, which allows meaningful biochem-
PLS regression.
ical interpretation of the results. FT-IR spectroscopy analysis
Quantitative analysis of API concentration with HPLC.
demonstrated that each of the propranolol enantiomers had a
Chiral HPLC analysis was performed to quantify the amounts
metabolic effect on the cells of P. aeruginosa PA14 at a con-
of the enantiomers remaining at the end of the growth period.
centration of 80 g ml⫺1 (Fig. 4a) compared to the control.
This analysis was used to explore the effects shown by the
The PC-CVA score plot clearly shows that the control samples
growth rate data. In addition, control experiments were per-
separate across PC-canonical variate 1 (CV1), which accounts
formed to determine the effect of the experiment on the drug
for the greatest variance in the data according to the putative
concentration (i.e., the loss of API during incubation in growth
class assignment; as discussed above, this finding was perhaps
medium). To determine the effects of the enantiomers on the
not surprising given the effect of the APIs on the bacterial
growth of the pseudomonads, a range of ratios of propranolol
growth dynamics (Fig. 2). In addition, the samples exposed to
enantiomers were employed using a concentration of 50 g
(⫾)-propranolol are clearly separate from the samples exposed
ml⫺1. The findings of the HPLC analysis (Table 2) demon-
to each of the enantiomers [(R) and (S)], which in this analysis
strated that neither of the enantiomers was degraded during
showed no separation across the first two PC-CV scores. As
batch growth. In addition, the pseudomonads were exposed to
described above, two of the three biological replicates were
a range of concentrations of the API atenolol (Table 3); the
used for calibration (indicated by black type in Fig. 4), and the
concentrations selected were chosen based on the growth ratedata. There was no notable indication of API degradationduring growth of the bacteria.
TABLE 4. Concentrations of propranolol and atenolol at which a
Effects of chiral APIs on FT-IR spectroscopy metabolic fin-
significant effect on the bacterial phenotype was observed
gerprints. In order to investigate whether there were any
using FT-IR spectroscopy
chirality-specific phenotypic changes in the various aquatic
Concn at which effect was observed
bacteria, a single drug concentration with which there was no
(g ml⫺1)a
observable difference in the growth rate between the enantio-
mers was chosen for investigation.
P. putida KT2440
During the investigation of the chiral API-specific effects on
P. aeruginosa PA14
microorganisms, the four bacteria were exposed to a number of
M. luteus 2.13
drug concentrations (Table 1). A summary of the statistically
B. natatoria 2.1
significant differences in the data for the drug enantiomers,
a Effects were considered significant in PC-CVA plots if confidence regions
enantiomer ratios, and racemate is shown in Table 4.
were statistically different at the 95% 2 limit; that is, below the concentration
PC-CVA was carried out in order to investigate any chiral-
indicated all bacteria had equivalent phenotypes (overlapping clusters) andabove the concentration indicated there was clear differentiation.
ity-specific effects on the microorganisms as determined by
b ND, not determined as propranolol appeared to produce the most notable
FT-IR spectroscopy. The distance between samples plotted on
effects in earlier experiments.
EFFECTS OF PHARMACEUTICALS ON AQUATIC MICROORGANISMS
FIG. 4. PC-CVA score (left side) and loading (right side) plots for FT-IR spectroscopy data for P. aeruginosa PA14 exposed to (R)-propranolol,
(S)-propranolol, and (⫾)-propranolol at a concentration of 80 g ml⫺1 (a and b), for P. putida KT2440 exposed to different ratios of (R)-propranolol to(S)-propranolol at a concentration of 50 g ml⫺1 (c and d), and for P. aeruginosa PA14 exposed to (R)-atenolol, (S)-atenolol, and (⫾)-atenolol at aconcentration of 80 g ml⫺1 (e and f). In the score plots black type indicates the two biological replicates used to train the PC-CVA models. Gray typeindicates the third biological replicate, which was used to validate the PC-CVA model. Black circles indicate the 95% confidence interval around thegroup centroid, and gray circles indicate the 95% confidence region around the group sample population. In the loading plots the loading for PC-CV1is indicated by black lines and the loading for PC-CV2 is indicated by gray lines. C, control; M, racemic mixture; R, (R) enantiomer; S, (S) enantiomer;R:s, ratio of (R) enantiomer to (S) enantiomer of 75:25; r:S, ratio of (R) enantiomer to (S) enantiomer of 25:75.
third biological replicate was projected into the model (indi-
the 95% confidence intervals for the groups are also indicated
cated by gray type). The majority of the projected data are
in Fig. 4 and show that for three of the groups there is a distinct
grouped with the appropriate calibration samples, indicating
separation in CVA score space. This analysis demonstrated
that the separation shown in the model was valid. Moreover,
that the microbial cells exposed to (R)- and the microbial cells
WHARFE ET AL.
APPL. ENVIRON. MICROBIOL.
exposed to (S)-propranolol clustered together, indicating that
left side and the results for the 25:75 mixture of (R)-propran-
there were no metabolic differences in the microbial cells ex-
olol and (S)-propranolol (indicated by r:S) and for the pure
posed to the two pure enantiomers. It was very surprising that
enantiomers on the right side. The 75:25 mixture of (R)-pro-
the cultures exposed to the racemate formed a distinct cluster
pranolol and (S)-propranolol (indicated by R:s) falls between
separate from the clusters containing cultures exposed to the
these two groups. The clear separation of P. putida KT2440
pure enantiomers or control samples in the CVA space. The
exposed to the racemate supports the observations on the
loading plot (Fig. 4b) indicates that very specific changes in
effect of propranolol on the metabolic fingerprints of P. aerugi-
the metabolic fingerprints of the microbial cells account for the
nosa PA14 described above.
patterns of separation observed for the control and drug-ex-
In contrast, no phenotypic variation was observed in the
posed samples in the scores plot. The major chemical changes
metabolic fingerprints of the samples exposed to (R)-, (S)-, and
occur in the protein (1,681 to 1,629 cm⫺1) and carbohydrate
(⫾)-atenolol (Fig. 4e and f) as the 95% 2 confidence regions
(1,155 to 999 cm⫺1) regions of the FT-IR spectrum; there was
overlap. This analysis clearly showed that there was no differ-
a less pronounced contribution from lipid species (2,951 to
ence between the three treatments. This result differs from the
2,845 cm⫺1), which changed in the same direction as the car-
results for P. aeruginosa PA14 exposed to propranolol (Fig. 4a)
bohydrates. To investigate the effects of the racemate and
and indicates that atenolol does not have a chirality-specific
enantiomers on the biochemical components of the cells fur-
metabolic effect on P. aeruginosa PA14.
ther, we calculated difference spectra [for example, the meta-
Due to the chirality-specific effects on the pseudomonads
bolic fingerprint of the racemate was subtracted from that of
observed when they were exposed to propranolol, two addi-
the (R) enantiomer] for each combination of interest. The
tional bacteria were used to investigate the effects of propran-
resulting data were then used to determine the relative
olol. Propranolol had a very noticeable metabolic effect on B.
changes in the lipid and amide components of the cells when
natatoria 2.1 at concentrations of 40 and 50 g ml⫺1 (Fig. 5a
they were exposed to drugs. Inspection of the difference spec-
and b) and on M. luteus 2.13 at a concentration of 50 g ml⫺1
tra revealed that the bacterial cells exposed to the racemate
(Fig. 5c and d). The greatest difference observed in these
contained lower levels of amides and higher levels of lipids
analyses was the difference between the control and API-ex-
than the cells exposed to either of the enantiomers. This sug-
posed samples. To investigate the more subtle differences be-
gests that the racemate has less metabolic effect on the bacte-
tween the cultures exposed to the different enantiomers and
rial cells, and this suggestion is supported by the PC-CVA
the racemate, the control samples were removed from the
scores plot, in which the cells exposed to racemate are between
analysis. The B. natatoria 2.1 samples in the PC-CVA score
the control cells and the enantiomer-exposed cells across PC-
plot are separated across the first CV with respect to the
CV1. As discussed above, HPLC analysis suggested that deg-
enantiomers. The (S) and (R) enantiomers are clearly sepa-
radation or significant uptake of the APIs did not occur in the
rated in the CVA space, and the racemate is located between
microbial cells. Therefore, it is unlikely that the increase in the
them. A concentration effect was also observed in the meta-
levels of proteins shown by the FT-IR spectra of propranolol-
bolic fingerprints across CV2. This is in contrast to the chiral-
exposed cells was due to expression of enzymes in order to
ity-specific effects of this API on the two pseudomonads, in
metabolize this API. It is more probable that this effect was
which the greatest variation was found between the cells ex-
due to expression of an efflux system to remove the API from
posed to the racemate and the cells exposed to the enantio-
the bacterial cells. In addition, propranolol is a lipophilic API
mers. The loading data for B. natatoria 2.1 indicate that the
which is known to interact with cell membranes of mammalian
major chemical changes occur in the lipid (2,936 to 2,851
cells, and the observed reduction in the level of lipids in ex-
cm⫺1) region of the FT-IR spectrum and at 1,748 to 1,654
posed cells was likely due to interactions of the API with the
cm⫺1. Vibrations in this region may be attributed to the CAO
bacterial cell wall. Propranolol is routinely administered to
stretching of esters and carboxylic acids; however, this region is
humans as the racemate. The (S) enantiomer accounts for the
dominated by amide I. The FT-IR spectra demonstrate that
majority of the -blocking effect, while the (R) enantiomer has
the cells exposed to (S)-propranolol contained lower levels of
a predominantly membrane-stabilizing effect (3, 17, 36, 51).
lipids but higher levels of amide and carbohydrate than the
We hypothesized that the results for the racemate [(⫾)-pro-
cells exposed to the (R) enantiomer. This suggests that (S)-
pranolol] were different from the results for either of the en-
propranolol has a greater biological effect on the bacterial
antiomers because of the difference in the physical properties
cells. The effect of this API on M. luteus 2.13 and B. natatoria
between the racemate and the enantiomers (8, 42).
2.1 is perhaps more predictable as the different metabolic ef-
To explore the chirality-specific effect observed in the exper-
fects of the enantiomers are linearly additive. The difference
iments described above further, the pseudomonads were ex-
between the phenotypic effects on these organisms following
posed to various ratios of (R)-propranolol to (S)-propranolol
exposure to propranolol and the phenotypic effects on pseudo-
at a concentration of 50 g ml⫺1. The results of the chemo-
monads is probably a consequence of the metabolic differences
metric analysis of the FT-IR spectra also showed that there was
between the bacteria.
a metabolic difference between the microbial cells exposed to
To our knowledge, the -blockers atenolol and propranolol
different ratios of (R)-propranolol to (S)-propranolol and the
have not previously been studied to examine chirality-specific
control cells (data not shown), and the data for the controls
effects in microbial systems. Nevertheless, the effects of APIs in
were removed prior to PC-CVA so that only chirality-specific
these systems are highly relevant, as microorganisms populate
changes were observed. The results of this PC-CVA are shown
the lower trophic levels in food webs. Therefore, differences in
in Fig. 4c, which shows the results for P. putida KT2440 ex-
the population dynamics could represent significant effects
posed to 50 g ml⫺1 (⫾)-propranolol (indicated by R:S) on the
on the whole freshwater community (23).
EFFECTS OF PHARMACEUTICALS ON AQUATIC MICROORGANISMS
FIG. 5. PC-CVA score (left side) and loading (right side) plots for FT-IR spectroscopy data for B. natatoria 2.1 exposed to (R)-propranolol,
(S)-propranolol, and (⫾)-propranolol at concentrations of 40 and 50 g ml⫺1 (a and b) and for M. luteus 2.13 exposed to (R)-propranolol,(S)-propranolol, and (⫾)-propranolol at a concentration of 50 g ml⫺1 (c and d). In the score plots black type indicates the two biological replicatesused to train the PC-CVA models. Gray type indicates the third biological replicate, which was used to validate the PC-CVA model. Black circlesindicate the 95% confidence interval around the group centroid, and gray circles indicate the 95% confidence region around the group samplepopulation. In the loading plots the loading for PC-CV1 is indicated by gray lines and the loading for PC-CV2 is indicated by black lines. M, racemicmixture; R, (R) enantiomer; S, (S) enantiomer.
Conclusion. The growth data clearly showed that propran-
propranolol at concentrations at which no difference in the
olol had a biological effect on all of the microorganisms
growth rates was observed. The FT-IR spectroscopy analysis
studied. At the higher concentrations tested growth was
revealed that propranolol affected both the lipid and protein
retarded, and in most cases the death rate increased; asso-
contents of the bacterial cells. We hypothesize that this was
ciated changes were observed in the metabolic fingerprints.
likely due to the interaction of the APIs with the microbial cell
The loading plots from the PC-CVA of API-exposed and
walls. A more predictable effect on the metabolic fingerprints
unexposed P. aeruginosa PA14 cells (Fig. 4b) indicate that
was noted during the analysis of exposure of B. natatoria 2.1
propranolol has a widespread effect on bacterial cells, and
and M. luteus 2.13 to propranolol, in which the racemate fell
this effect was also observed in the other bacteria studied.
between the (R) and (S) enantiomers in the PC-CVA. Rather
The results of the HPLC analysis showed that this API was
surprisingly, the most significant effect on the two pseudo-
not degraded during the growth period, and this suggests
monads was the effect of the racemate, while the enantiomers
that the observed changes in the multivariate analysis of the
had identical effects on the phenotypes of the cells. It is pos-
metabolic fingerprints were not due to degradation of the
sible that the physical properties of the racemate were signif-
API but more likely were a secondary effect of the drug.
icantly different from those of the (R) and (S) enantiomers and
Despite the genetic similarity of the two pseudomonads
that this was reflected in how the cells responded to exposure
studied, our findings show that propranolol had different
to this API. This possibility will be investigated in the future.
effects in the two species. In contrast, the growth data show
In conclusion, we have shown that chirality-specific effects
that no effects were observed in the atenolol-exposed cul-
do occur in bacteria, which may have implications for environ-
tures, and this finding was reflected in the results of the
mental ecosystems as APIs are regularly found in the aquatic
multivariate analyses of the bacterial fingerprints (Fig. 4e).
environment. We believe that FT-IR spectroscopy with appro-
All four aquatic bacteria were exposed to the enantiomers of
priate chemometrics is a very powerful method for investigat-
WHARFE ET AL.
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J. Med. Toxicol. (2011) 7:205–212DOI 10.1007/s13181-011-0162-6 2,4-Dinitrophenol (DNP): A Weight Loss Agentwith Significant Acute Toxicity and Risk of Death Johann Grundlingh & Paul I. Dargan &Marwa El-Zanfaly & David M. Wood Published online: 8 July 2011 # American College of Medical Toxicology 2011 Abstract 2,4-Dinitrophenol (DNP) is reported to cause Keywords Dinitrophenol . Weight loss . Toxicity. Fatality
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