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Applied and Environmental Microbiology, August 2000, p. 3415-3420, Vol. 66, No. 8
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Quantitative Determination of the Biodegradable
Polymer Poly(
-hydroxybutyrate) in a Recombinant Escherichia
coli Strain by Use of Mid-Infrared Spectroscopy and
Multivariative Statistics
Mustafa
Kansiz,1
Helen
Billman-Jacobe,2 and
Don
McNaughton1,*
Department of
Chemistry1 and Department of
Microbiology,2 Monash University, Melbourne,
Victoria 3168, Australia
Received 8 February 2000/Accepted 9 May 2000
 |
ABSTRACT |
Fourier transform infrared (FTIR) spectroscopy in combination with
the partial least squares (PLS) multivariative statistical technique
was used for quantitative analysis of the poly(
-hydroxybutyrate) (PHB) contents of bacterial cells. A total of 237 replicate spectra from 34 samples were obtained together with gas
chromatography-determined reference PHB contents. Using the PLS
regression, we were able to relate the infrared spectra to the
reference PHB contents, and the correlation coefficient between the
measured and predicted values for the optimal model with a standard
error of prediction of 1.49% PHB was 0.988. With this technique, there
are no solvent requirements, sample preparation is minimal and simple,
and analysis time is greatly reduced; our results demonstrate the
potential of FTIR spectroscopy as an alternative to the conventional
methods used for analysis of PHB in bacterial cells.
| |
INTRODUCTION |
Many prokaryotic microorganisms
synthesize polyhydroxyalkanoates (PHAs) as carbon and energy
reserves (2, 5, 8, 25, 26), and poly(-hydroxybutyrate)
(PHB) is the most common PHA. The general chemical structure of these
compounds is shown in Fig. 1.
The composition of the alkyl side chain (the R group) and the number of
methylene units together determine the identity of the monomer.
For PHB, the number of methylene units is 1 and the R group is
CH3. A number of organisms have been developed in an effort
to optimize the yield of PHB, and recently accumulations of PHB
at levels up to 80% of dry cell weight have been obtained under
optimum growth conditions (25). PHB is an attractive
alternative to the environmentally unfriendly petrochemically derived
plastics because its copolymer with polyhydroxyvalerate is a
biodegradable molecule with properties similar to those of polyolefins,
such as polypropylene and polyethylene. For this reason industrial fermentation of PHB has been the focus of a number of studies (1,
38, 39), while other studies have been directed towards the use
of PHB-producing organisms in the breakdown of wastewater (32,
42).
The most common method available at present for analysis of PHAs in
bacterial cells is gas chromatography (GC) (6, 22, 31). The
GC method involves hydrolysis and subsequent methanolysis or
propanolysis of the PHAs in whole cells in the presence of sulfuric
acid and chloroform. This method is time-consuming and laborious and involves extensive use of solvents. Other methods of PHA
analysis include gravimetry, infrared (IR) spectroscopy of
chemically extracted PHB (23), fluorimetry (10),
and cell carbon analysis (37). To aid in the development of
more efficient fermentation processes and to monitor production, rapid
feedback on the state of fermentation in terms of the PHA content of
cells is required.
Fourier transform infrared (FTIR) spectroscopy is a routine chemical
technique used to study molecular structure, but when it is applied to
a large collection of intact microbial cells, the resulting spectra
reflect the total biochemical composition of the cells
(28). IR spectroscopy can thus provide a total, simultaneous chemical analysis. The observed bands in spectra of
PHA-producing bacteria are due to the major cellular
constituents, such as proteins, lipids, polysaccharides, nucleic
acids, and PHAs (9, 15, 19, 20, 28). Because FTIR
spectra can be considered chemical profiles of samples, the spectra can
be used to predict the concentration of an analyte (e.g., the
concentration of PHB). FTIR spectroscopy has been used previously to
monitor water-soluble extracellular analytes in fermentation systems
for ethanol (13, 30), lactic acid (12, 30),
and glucose and acetic acid (11) and, very recently, for
quantification of a recombinant protein (27). In a recent
study Schuster et al. (35) used FTIR spectroscopy to monitor
the physiological state of bacterial cells as an indirect method to
determine the stage of fermentation. Quantification of PHB and major
cell components by using dispersive IR spectroscopy and evaluation of
data by using an absorption coefficient at selected wave number values have been developed by Zagreba et al. (43) and have been
used for PHB analysis by Savenkova et al. (33). Grube et al.
(16) have recently used this method for quantitative
analysis of major cell components. In this study we monitored an
intracellular analyte, PHB. For quantification, the spectra had to be
calibrated with known reference values for the analyte, as determined
by an established method, such as GC. This allowed the development of
mathematical models in which the spectra were related to the analyte
concentrations. The most common multivariative methods used for this
purpose are the principal-component regression (PCR) and partial
least-squares (PLS) methods.
In a recent study Hong et al. investigated identification of different
PHAs in whole bacterial cells by using FTIR spectroscopy (21), but to our knowledge no work has been carried out
previously on quantification of PHAs in intact cells. FTIR spectroscopy
also has the following advantages: very small samples are required (~0.4 mg of biomass); speed (analysis time, ~30 min); no solvent is
required; and minimal sample manipulation is required.
We describe here a study of intracellular PHA contents in which we used
FTIR spectroscopy and multivariative statistics, and we show that this
combination method is a promising alternative to the conventional
methods used for PHB analysis.
| |
MATERIALS AND METHODS |
Bacterial strains and plasmid DNA.
PHB accumulation was
studied in a recombinant Escherichia coli DH5
clone
containing the Acinetobacter sp. pha locus on
plasmid pJKD1425. E. coli(pJKD1425) carried all of the genes
required for PHB biosynthesis under the control of the
Acinetobacter promoters (34).
Culture methods.
E. coli was maintained for the long
term on a 33% glycerol solution at 
70°C and was subcultured weekly
on Luria-Bertani (LB) agar plates. For maintenance of the plasmid, 100 µg of ampicilin per cm3 was added as required.
Seeder cultures were prepared in 10-cm
3 portions of LB
medium (0.5 g of yeast extract per liter, 1 g of tryptone per
liter,
1 g of NaCl per liter) supplemented with 0.5% glucose and
100
µg of ampicilin per cm
3 by inoculating the medium
with single colonies from agar plates.
The cultures were then grown
aerobically at 37°C for 24
h.
E. coli shake flasks (120 rpm) containing 100 cm
3 of LB medium supplemented with 0.5% glucose and 100 µg of ampicilin per cm
3 were inoculated with
2-cm
3 portions of a 24-h-old seeder culture, and the
preparations were
incubated aerobically at 37°C for 24 to 48
h.
Sample preparation.
Over the course of the culture period,
34 samples with a range of PHB contents were collected aseptically and
analyzed by IR spectroscopy and GC. For the IR spectroscopy analysis, 1 cm3 of a culture was collected and centrifuged at
13,000 × g for 5 min. The growth medium was removed by
washing, resuspension in isotonic saline, and further centrifugation.
The final pellet was resuspended in 80 µl of isotonic saline, and 20 µl of this solution was deposited onto a type KRS-5 (thallium
bromide-iodide crystal) IR-transparent substrate. Additional serial
dilutions of the remaining solution were prepared, and 20-µl portions
were deposited. The deposits were dried in a vacuum desiccator for 10 to 15 min before spectra were acquired immediately.
For the GC analysis, 10-20-cm
3 portions of cultures were
collected at the same time that samples were collected for the IR
spectroscopy analysis. These samples were centrifuged at
13,000
×
g for 5 min, the supernatants were removed,
and the remaining
pellets were freeze-dried and stored at
20°C
until
analysis.
Spectral acquisition.
Spectra were recorded with a Bruker
model IFS-55 FTIR spectrometer coupled to a Bruker IR microscope fitted
with a liquid N2-cooled mercury-cadmium-tellurium detector.
The Bruker system was controlled with an IBM-compatible PC running
OPUS, version 2.2, software. Absorbance spectra were collected at
wavenumber values between 3,650 and 700 cm
1 with spectral
resolution of 8 cm1, and 10 scans were coadded and
averaged. A Blackman-Harris four-term apodization function was used
along with a zero-filling factor of 2.
To minimize differences between spectra due to baseline shifts, the
spectra were baseline corrected by using the Rubber Band
algorithm of
the OPUS, version 2.2, software and 200 baseline
points and excluding
the CO
2 bands. Spectra were normalized to
the amide I band
at 1,654 cm
1 to account for any differences in deposit
thickness. Six to 12
spectra were recorded for each sample deposit to
assess precision
and to ensure that representative spectra of each
sample deposit
were
collected.
PHB reference analysis.
The PHB reference measurements were
obtained by using a modified acidic methanolysis method of Braunegg
et al. (6). The modification was found to be necessary
because individual weighing of approximately 3 to 4 mg of biomass
resulted in relatively high errors even when an analytical balance was
used. To reduce the errors associated with weighing of the samples, the
following sample transfer method was used. An accurately weighed 20- to 30-mg portion of freeze-dried cell mass was placed in a
polytetrafluoroethylene-lined screw-cap test tube and crushed to a fine
powder. To this was added a volume of chloroform which resulted in a
freeze-dried cell mass concentration of 3 to 4 mg/cm3 of
chloroform, and the resulting solution was sonicated for ca. 30 min or
until the solution was completely homogenized. One cubic centimeter of
this solution was then placed in another polytetrafluroethylene-lined screw-cap test tube, to which were added 0.85 cm3 of
methanol containing ca. 0.5 mg of benzoic acid per cm3
as an internal standard and 0.15 ml of concentrated sulfuric acid.
The reaction mixture was heated at 100°C for 2 h and cooled rapidly; then 1 cm3 of distilled water was added, and the
mixture was vortexed for 1 min. The organic and aqueous layers were
allowed to separate, and 0.5 µl of the lower organic layer was
injected into the GC. The GC used was a Varian model 3700 GC equipped
with a DB-Wax capillary column (15 m by 0.53 mm; internal film
thickness, 1.0 µm) coupled to a Hewlett-Packard model 3396A
integrator. The injector and flame ionization detector temperatures
were set at 200 and 250°C, respectively. The temperature was set at
80°C and then increased at a rate of 10°C/min until the maximum
temperature, 200°C, was reached. Under these conditions, the
retention times for the PHB derivative (methyl 3-hydroxybutyric
acid) and the benzoic acid internal standard were 4.4 and 5.8 min, respectively.
Data analysis.
Two multivariative statistical methods, the
PCR and PLS methods, were employed to create calibration models to
relate the spectra obtained to the reference measurements obtained. The
regression coefficients and the standard errors of prediction (SEP) for
the PLS regression models were used as measurements of the "goodness of fit." The SEP is the standard deviation of the spread of the errors (the difference between the measured and predicted values). The
SEP is calculated from a leave-out-one cross-validation (7, 14,
17, 24). These statistical analyses were performed by using the
Unscrambler 6.11 Camo ASA computer software package.
Derivative spectra were also used in the preprocessing of data prior to
PLS regression. Spectral derivatives can remove remaining
spectral
contributions from a sample deposit that are not corrected
for by the
baseline correction and normalization functions. The
spectral
derivative technique can also be considered a pseudoresolution
enhancement technique, because the derivatives are able to highlight
slight variations in the slopes and contours of bands and hence
increase the accessible spectral information. These two advantages
of
spectral derivatives, therefore, can combine to reduce the
number of
factors required to obtain a minimum SEP by removing
the need for extra
factors to model spectral variations that are
not related to the
analyte of
interest.
| |
RESULTS AND DISCUSSION |
The IR spectrum of a sample represents its total chemical
composition, because every chemical compound in the sample makes its
own distinct contribution to the absorbance spectrum. The distinctness
of an individual spectrum, which is determined by the chemical
structure of each component and the degree to which each component
contributes to the spectrum, is directly related to the concentrations
of the components of the sample. It is on this basis that
quantification of analytes is performed.
The representative spectra in Fig. 2 are
results of the cumulative absorbances for all of the chemical species
present, which resulted in the relatively broad spectral features that
are typical of biological samples. These spectra are dominated by the
absorbances for the major cellular constituents, namely, PHB (in the
case of PHB-producing microorganisms) and cellular proteins. The major PHB bands are the intense ester carbonyl stretch at 1738 to 1728 cm1 and a number of strong bands at wavenumber values
between 1,450 and 1,000 cm1 due to methyl
(CH3) and methylene (CH2) deformations and C-O stretches. The major spectral features due to the organism itself are
the protein absorbances apparent as strong features in the lower trace
of Fig. 2. These are the amide I band at 1,654 cm1, which
is due primarily to the amide carbonyl stretching vibration, and the
amide II band at 1,540 cm1, which is due mostly to N-H
bending vibrations. A more detailed description of band assignments is
presented in Table 1.
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FIG. 2.
Representative spectra of samples with different PHB
contents and a sample containing no PHB. a.u., arbitrary units.
|
|
Spectral reproducibility.
Six to 12 replicate spectra obtained
from different areas within each sample deposit were recorded to ensure
that representative spectra were collected from each deposit. These
spectra were then baseline corrected and normalized to the amide I
band. The replicate spectra exhibited little variation, as shown in
Fig. 3, in which six spectra are overlaid
together.
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FIG. 3.
Six replicate spectra from the same deposit of a sample
containing 27.3% PHB. All spectra have been baseline corrected and
normalized to the amide I band. a.u., arbitrary units.
|
|
The baseline correction function attempts to correct for the spectral
artifacts caused by variations in the scattering, diffraction,
and
refraction properties of the samples as the IR beam passes
through a
sample. The normalization function attempts to correct
for differences
in spectral absorbances due to different sample
thicknesses within the
same deposit. Normalization is performed
to the amide I band because
there is no spectral contribution
from PHB in this spectral region. It
has been shown that the protein
content per cell does not vary
substantially for a wide range
of PHB contents, and so the protein
content of the cells provides
an ideal internal standard for
normalization (
26).
The manual sample preparation procedure results in unavoidable
irregularities in deposition from sample to sample and even
within a
sample. The differences add to the total error of the
model by
contributing a certain amount of imprecision. The precision
of values
predicted from an IR multivariative model is calculated
by using
replicate spectral measurements. A PLS regression was
performed with
all 237 replicate spectra from 34 samples, which
allowed us to
calculate the average predicted value for each sample,
the standard
deviation, and the relative standard deviation (RSD)
for the predicted
values for the replicates of each sample. The
resulting data could then
be used to assess the reproducibility
of the IR spectra. An analysis of
the spread of the predictions
for replicate spectra for each sample
showed that only one replicate
spectrum out of the entire data set was
an outlier; i.e., the
predicted value was more than 2 standard
deviations from the mean.
This was found to be the case for the
regressions performed with
the spectra themselves, as well as the
regressions performed with
first and second derivatives of the
spectra.
The replicate spectra in Fig.
3 can thus be considered highly
reproducible and show that baseline correction and normalization
almost
completely accounted for and corrected for sample deposit
heterogeneity.
To detect outliers that were due to errors in the reference
measurements, as opposed to errors in IR measurements, the replicate
spectra for each sample were averaged, and a PLS regression (performed
with spectra and first and second derivative spectra) was performed
by
using the averages instead of the replicate spectra. The results
showed
that there was one consistent outlier (more than 2 SEP
from the
reference value), which was removed from the data set;
this left 33 samples together with their replicates. An examination
of the raw
spectra for the outlier sample revealed that they all
had unusually
high absorbance values. The mercury-cadmium-tellurium
detector used in
this study is nonlinear at high absorbances,
and so only spectra with
maximum absorbances of <0.7 should have
been used in the
analysis.
PLS regression results.
Due to the long computational times
required, whole-spectrum PLS regressions were not performed for the
replicate spectrum data sets. However, a PLS regression performed with
the whole spectra of averaged replicates (33 spectra, one from each
sample) resulted in data that were virtually identical to data from PLS regression performed with the 1,800- to 900-cm1 spectral
region. This was due to the fact that virtually all of the PHB spectral
information is in the 1,800- to 900-cm1 region. The
results of this PLS regression are shown in Table 2. A regression coefficient plot, which
shows the variables which correlated most closely with the PHB content,
and a solution cast spectrum of pure PHB are shown in Fig.
4. The regression coefficient plot, which
is essentially a mathematical extraction of the PHB spectrum from the
spectra of the whole organism, shows that the spectral range from 1,800 to 900 cm1 contains most of the chemical information
related to PHB.
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FIG. 4.
Spectrum of a solution-cast film of pure PHB (line A)
and regression coefficient plot from a PLS regression performed with
entire underivatized spectra (line B). The major spectral differences
are indicated.
|
|
Figure
4 also shows the differences between the solution-cast spectrum
of PHB and the plot of regression coefficients. The
major difference is
the shift of the position of the ester carbonyl
stretch in the solution
cast spectrum of PHB from 1,728 to 1,738
cm
1 in the
regression coefficient plot. There are also a number of
other
differences in the intensities of bands, particularly those
at 1,280 and 1,188 cm
1, as indicated in Fig.
4. These differences
are due to differences
in the physical state of the PHB. The solution
cast PHB crystallizes
almost instantly after solvent evaporation, while
the PHB in the
cells remains in an amorphous state even after complete
drying.
This observation confirms the results of recent studies on the
physical state of PHB in vivo, which showed that PHB is in an
amorphous
state (
3,
36). The PHB in cells, however, does
slowly begin
to crystallize 10 to 15 min after drying. This causes
spectral changes
with time, which means that spectra of samples
must be obtained at the
same time after complete drying. FTIR
spectroscopy has also been used
to investigate and even predict
the degree of crystallinity of PHB
samples (
4).
An initial PLS regression performed with the entire replicate spectrum
data set before outlier removal (237 spectra from 34
samples) resulted
in a relatively poor correlation coefficient
(between predicted and
measured values) of 0.983 and an SEP of
1.77% PHB (nine factors).
These results were improved by outlier
removal, as described above. The
analyses in which we used the
raw and spectral first derivative
provided the best results; the
correlation coefficient and SEP were
0.988 and 1.49% PHB (10 factors),
respectively, for the raw spectrum
PLS regression. The SEP in
this optimized model was only 17% greater
than the standard error
of calibration, which can be considered the
theoretical "best"
fit. The results obtained by using first and
second derivatives
showed no further improvement, although slightly
fewer factors
were required. This indicates that spectral normalization
and
baseline removal remove most of the spectral variation not related
to the analytes of interest. This shows that the validation samples
from the leave-out-one cross-validation analysis were predicted
almost
as well as the calibration samples were, which indicated
that the model
was valid and robust. These results and the results
of other PLS
regressions are summarized in Table
2. The optimal
number of factors in
a particular model was chosen by examining
a plot of SEP versus number
of
factors.
A plot of predicted PHB content versus measured PHB content is shown in
Fig.
5, which graphically shows the
excellent fit.
PCR was also employed as an additional multivariative
technique
to relate the spectral information to the analyte
concentrations.
The results proved to be practically identical to the
PLS results.
It is not possible to measure the absolute accuracy of the IR method
compared to the accuracy of the reference method without
resorting to
more fundamental reference analysis. Such fundamental
analysis would be
almost impossible as it would involve creating
known standard samples
of PHB in bacteria. It is, however, possible
to estimate the role which
imprecision in the IR and reference
methods plays in determining the
overall accuracy of the method.
A comparison of the RSD of the
replicate spectra per sample to
the RSD of the triplicate reference
measurements showed that in
general the IR method was more precise,
indicating that most of
the error with the model is due to errors in
reference measurements.
Grube et al. (
16) obtained similar
results for quantification
of major cell
components.
In this study we demonstrated the ability of FTIR spectroscopy in
combination with multivariative statistics to quantitatively
determine the PHB contents of bacterial cells, particularly recombinant
E. coli cells. To further improve the
attractiveness of the technique,
investigations into the
possibility of using one data set for
spectra for analysis of PHB
from different organisms (i.e., samples
with different biological
backgrounds) are being conducted. This
could mean that one model
derived from spectra of different organisms
could be used to predict
the PHB contents of different bacterial
species. This technique has
several advantages, including no solvent
requirement, minimal, simple
sample preparation, and greatly reduced
analysis time (ca. 30 min from
sampling to quantification), and
shows that FTIR spectroscopy can be
used as an alternative to
the conventional methods used to analyze PHB
in bacterial cells.
The technique, which is also useful as a rapid
screening tool
for new organisms, is not limited to analysis of PHB
fermentations
and could also be useful in analyses of other
fermentation
products.
| |
ACKNOWLEDGMENTS |
We thank Victor Guzman for assistance with the GC analysis and
J. K. Davies, Department of Microbiology, Monash University, for
supplying the recombinant E. coli.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Department
of Chemistry, Monash University, Wellington Rd., Clayton
3168, Victoria, Australia. Phone: 61 3 9905 4525. Fax: 61 3 9905 4597. E-mail: d.mcnaughton{at}sci.monash.edu.au.
| |
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Applied and Environmental Microbiology, August 2000, p. 3415-3420, Vol. 66, No. 8
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
