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Evaluation of ATR-FTIR for analysis of bacterial cellulose impurities
Journal of Microbiological Methods 144 (2018) 145–151
Contents lists available at ScienceDirect
Journal of Microbiological Methods journal homepage: www.elsevier.com/locate/jmicmeth
Evaluation of ATR-FTIR for analysis of bacterial cellulose impurities ⁎
T
Mark E. Fuller , Christina Andaya, Kevin McClay Aptim Federal Services, 17 Princess Road, Lawrenceville, NJ 08648, United States
A R T I C L E I N F O
A B S T R A C T
Keywords: Bacterial cellulose Biocellulose Gluconacetobacter ATR-FTIR Impurity
This research evaluated the utility of using the large amount of spectral data obtained during attenuated total reflection Fourier-transform infrared spectrophotometry (ATR-FTIR) analysis of dried biocellulose (BC) to estimate the type and concentration of potential bacterial cells impurities present in the BC. Pre-cleaned BC was contaminated with known concentrations of representative nucleic acid, lipid, and protein impurities, as well as whole bacterial cells. These impurity standards were then subjected to ATR-FTIR analysis, and the resulting spectral data were used to develop models to estimate the concentrations of impurities in differentially processed BC. Results indicated that ATR-FTIR is a useful tool for estimating impurities in BC, and may also be applicable for measurement of levels of non-cellulose biomolecules added to BC for various purposes.
1. Introduction
2. Materials and methods
There is an increasing interest in the use of bacterial biopolymers in the energy, electronics, food, and medical industries (Ullah et al., 2016; Esa et al., 2014; Rajwade et al., 2015). Of particular interest is bacterial cellulose, or biocellulose (BC). BC is produced by a wide range of bacterial species (Ross et al., 1991), although most efforts have focused on the Acetobacteraceae group (Florea et al., 2016), and specifically Gluconacetobacter spp. Limited industrial production of BC to smallscale operations for specialized uses, such as preparation of the traditional food nata de coco (Halib et al., 2012), and as temporary wound dressings (Fontana et al., 1990). Most efforts to date to increase BC production have focused on adjustments of culture conditions, including evaluation of different feedstocks (Jung et al., 2010; Keshk, 2014a), medium additives (Keshk & Sameshima, 2006; Keshk, 2014b), and culturing method(Kouda et al., 1997; Okiyama et al., 1992). As BC production and processing parameters are adjusted, there is a need to monitor BC purity. This has routinely been achieved using both X-ray diffractometry (XRD) and attenuated total reflection Fouriertransform infrared spectrophotometry (ATR-FTIR) (Czaja & Romanovicz, 2004; Zeng et al., 2011), with the resulting data being using to calculate crystallinity indexes. However, the exact nature of any impurities that might exist in the BC has usually not been reported. The current study was undertaken to determine if the data obtained using ATR-FTIR analysis could be used to both identify and quantitate likely impurities (e.g., protein, lipids, DNA, other polymers) in BC. The effectiveness of three levels of post-processing for removing impurities was also examined.
2.1. Chemicals
⁎
The sources of the potential BC impurities were as follows: protein, bovine serum albumin (Sigma-Aldrich, A0281); lipid, 1,2-dipalmitoylsn-glycero-3-phosphocholine (Sigma-Aldrich, P4329); nucleic acid, salmon testes DNA (Sigma-Aldrich, D7656). The purity of all other chemicals was reagent grade or higher. 2.2. Bacterial strains Gluconacetobacter hansenii ATCC 53582 was purchased from the American Type Culture Collection (ATCC). Strain 399 was isolated from a kombucha “mushroom” starter culture, and was identified by genomic analysis as a Gluconacetobacter sp. closely related to Gluconacetobacter hansenii ATCC 23769 (unpublished data). A BC negative mutant of 399 designated as 399A (Fuller, 2017), was also used. Strains were routinely grown in either ATCC Medium 1765 or a basal salts medium (BSM; pH 7 S·U) (Hareland et al., 1975) amended with glucose (5 g/L). 2.3. BC production and processing BC was produced by strains ATCC 53582 and Gx 399 for this testing during growth in BSM with glucose (5 g/L) in 2 L Ziploc bags. Cells were aseptically harvested from multiple ATCC Medium 1765 agar plates and suspended in 40 mL of BSM. The cell suspension was passed
Corresponding author at: Aptim Federal Services, LLC, 17 Princess Road, Lawrenceville, NJ 08648, United States. E-mail address: [email protected] (M.E. Fuller).
https://doi.org/10.1016/j.mimet.2017.10.017 Received 26 September 2017; Received in revised form 30 October 2017; Accepted 31 October 2017 0167-7012/ © 2017 Elsevier B.V. All rights reserved.
Journal of Microbiological Methods 144 (2018) 145–151
M.E. Fuller et al.
then diluted to an optical density at 600 nm of 5. Assuming that a bacterial cell weighs 1 × 10− 9 mg (dry weight), then the OD 5 cell solution contained 2.5 mg/mL of bacterial dry mass. The cell solution was applied to the coupon similarly to the single impurities, then coupons were dried and stored in a desiccator until FTIR analysis as with the other impurities.
Table 1 The staged BC processing protocol used during this research. Processing level Minimal
Partial
Full
Step
Procedure
X
X
X
1
X
X X
X X
2 3
X
X
4
X X
5 6
BC pellicle was removed from the incubation vessel (flask, Ziploc bag, etc.) and squeezed by hand to wring out excess medium. The pellicle was rinsed with distilled water The pellicle was placed in 1 M NaOH overnight at room temperature The pellicle was rinsed with distilled water until the pH of the rinsate was approximately 8. The pellicle was placed in 96% ethanol for 2 h The pellicle was rinsed thoroughly with distilled water
2.5. Analytical Coupons of dry BC (2.5 cm × 3.75 cm) were analyzed using an Agilent Cary 660 Fourier-Transform infrared (FTIR) spectrophotometer equipped with a Pike MIRacle attenuated total reflection (ATR) assembly with a ZnSe crystal and a liquid nitrogen cooled linearized mercury cadmium telluride (MCT) detector (Agilent Technologies, Wilmington, DE, USA). Data was collected using Agilent Resolutions software (v. 5.2 and 5.3). Spectra were obtained over a wavenumbers from 4000 to 650 cm− 1. Parameters of the standard analysis method are shown in Appendix A. Data was collected from 10 to 20 distinct locations across the surface of the BC coupon. The ATR clamp and platform were cleaned with a cotton swab dampened with isopropyl alcohol and allowed to dry between analysis of each coupon.
repeatedly through a 20 gauge syringe needle using a 60 mL disposable syringe to break up clumps prior to inoculating the Ziploc bags. Bags were incubated at room temperature without agitation by placing them on baking sheets. BC was harvested once a firm pellicle had formed, which usually three to four weeks. Based on the existing literature, a staged BC processing protocol was developed as shown in Table 1. Different processing levels were used for some experiments, designated as “minimal” (steps 1 and 2), “partial” (steps 1 to 4), and “full” (steps 1 to 6). Pellicle were dried after processing (room temp or 35 °C), and stored in sealed Ziploc bags until analysis or further use. Triplicate pellicles were produced in 2 L Ziploc bags using strains ATCC 53582 and Gx 399. Each pellicle was then split into three roughly equal portions, and subjected to the three levels of processing described in Table 1. BC coupons were subjected to ATR-FTIR as described in Section 2.5, and collected FTIR spectra were analyzed as described in Section 2.6 to measure the levels of impurities.
2.6. Data analysis and impurity modeling Panorama Pro (v3.2.15.0) (LabCognition Analytical Software GmbH & Co., Cologne, Germany) was used to extract impurity-related information from a subset of the collected FTIR spectra over a subset of the collected wavenumbers from 3052 to 800 cm− 1. The Partial Least Squares (PLS) calibration models in the Chemometrics add-on module were used to extract impurity-specific signals in the spectra, and to generate calibrations which could then be used to estimate the impurity levels in samples of BC. 3. Results and discussion 3.1. Spectra from different impurities/concentrations
2.4. Preparation of BC spiked with impurities ATR-FTIR analysis clearly demonstrated expected classes of BC impurities could be detected in a concentration-depended manner. Fig. 1 presents FTIR spectra of BC contaminated with varying levels of protein, lipid, and nucleic acid. Different regions in the FTIR spectra corresponded to functional groups of the different types of impurities (e.g., unsaturated alkyl chain in lipids, amine groups in proteins, etc.; see Table B.1 for wavenumber - functional group correspondence information). The coupons contaminated with whole cells showed a response in regions of the FTIR spectra that are indicative of functional groups associated with protein, lipid, and nucleic acid (e.g., peaks in the wavenumber range of 1750–1400) (Fig. 1D). The use of ATR during the FTIR analysis measured the impurity signal in the top few microns of the BC coupon. However, since it was assumed the impurities were distributed homogeneously throughout the BC matrix, the ATR measurement was deemed to be representative and reproducible. Further work beyond the scope of this research would be needed to validate this assumption.
The base BC to which defined concentrations of the impurities was added was produced by strain ATCC 53582 under static incubation in 2 L Ziploc bags. The complete BC washing procedure described in Section 2.3 above was used, followed by several rinses in chloroform to assure maximum purity. The sheets of base BC were cut into coupons (approximately 3 cm × 3 cm). The impurities were prepared as aqueous stock solutions, except for lipid, which was prepared in chloroform. Impurities were applied to the BC coupons in stages, adding the impurity solution to the surface in 10 to 20 μL aliquots and spreading it gently around until the central 90% of the coupon surface was wetted. The coupon was then allowed to dry at room temperature. The process of applying and drying was repeated until the correct mass of the impurity had been delivered to the coupon. Final impurity concentrations for protein, lipid, DNA, and polymer were (0.1% to 10%, w:w, dry wt basis). All coupons thoroughly dried at room temperature and stored in a desiccator until FTIR analysis. This method of applying the various impurities to the BC coupons was assumed to have resulted in a reasonable distribution of the compounds throughout the BC matrix as the carrier evaporated. However, additional analyses would have been required to confirm this assumption, and were beyond the scope of this research. Coupons were also contaminated with whole bacterial cells as the complex impurity most likely to be expected in the BC. Strain 399A cells were grown on glucose, washed twice in sterile purified water, and
3.2. Impurity modeling The Partial Least Squares 2 calibration model in Panorama Pro was used as it allowed multiple regression of several of the impurities at once. The PLS2 model appeared to yield good results for nucleic acid and lipid (Fig. 2), with regression coefficients (r2) for actual vs. predicted concentration of > 0.8 using five factors extracted from the
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coefficient of 0.87 for actual vs. predicted concentrations. The whole cell impurity was also modeled successfully using the PLS1 model (r2 = 0.86) (Fig. 3B). 3.3. Impurities vs. processing levels ATR-FTIR spectra from differentially processed BC and BC with known impurities is shown in Fig. 4. A qualitative inspection of the spectra indicated that if bacterial impurities were present, most were at very low levels, and few differences could be observed between minimally processed BC and fully processed BC. Application of the impurity models to the raw FTIR data yielded quantitative estimates of the levels of the different impurities, as presented in Table 2. Estimates of the defined impurities (e.g., nucleic acid, lipid, protein) were all < 1%. In general, estimates of nucleic acid and lipid were below the lowest impurity standard of 0.1%, even for the minimally processed BC. Estimates of protein were more scattered, and, surprisingly, a higher percentage of protein impurity was detected for fully processed compared to minimal processed BC produced by ATCC 53582. The reason for this apparent inversion of expected increased purity with increased processing is not known. A simple transposition of the minimally processed and fully processed samples is not likely, given that the estimates of DNA and lipid did not follow the same pattern as the estimates of protein. Also, the BC produced by strain Gx 399 yielded the expected results (e.g., decreased protein with increased processing). Estimates of whole cell impurity, which rely on a complex mix of FTIR signals from all the cellular components (e.g., a mix of different proteins, lipids, nucleic acids, other biopolymers) indicated that increased processing was needed to remove cells entrained in the BC matrix. Additionally, while lower initial estimates of cells were observed for BC from ATCC 53582 compared to Gx 399, the processing steps appeared to remove more of the Gx 399 cells than the ATCC 53582 cells from their respective BC matrices. This may reflect that ATCC 53582 produces a BC pellicle that is more dense than Gx 399, thereby increasing the difficulty in removing embedded cells. Regardless, the results clearly indicate that more than simple rinsing with distilled water is needed to remove cellular debris from BC material. 4. Conclusions This research provided proof-of-concept results that ATR-FTIR analysis of dried BC can be used to estimate the type and concentration of potential bacterial cells impurities present in the BC material. Application of the analytical method for differentially processed BC allowed determination of the effects of processing on residual cellular impurities. This method used readily available sources of defined biomolecules as impurities, some of which are not bacterial. Bovine serum albumin was used as the protein impurity, and salmon teste DNA was used as the nucleic acid impurity. For more refined estimates, bacterial protein, nucleic acid, and lipid could be used to prepare the standards. In this research, the whole cell impurity standards likely provided the most representative estimate of overall impurity level in the BC materials. While this research used air dried BC, the basic methodology would be applicable to freeze dried (or otherwise processed) BC, as long as the same BC is used for both the “base BC” controls and the impurity standards. While the goal of this research was to evaluate the use of ATR-FTIR for estimating impurity type and concentration in BC, it also indicates that the method could be tailored to monitor the levels of other biomolecules that may be added to BC for various purposes. For instance, this method could be useful for measuring the levels of compounds being added to BC for specific applications, such as a carrier for antibiotics (Nguyen et al., 2008) or growth factors (Shi et al., 2012), and when using BC as an enzyme immobilization scaffold (Wu & Lia, 2008).
Fig. 1. FTIR spectra of BC contaminated with varying levels of A) protein, B) lipid, C) nucleic acid, and D) whole cells. Lines represent average of 20 scans. Shaded regions indicate area of the spectra indicative of impurity associated functional groups.
spectra dataset. However, the PLS2 calibration model did not work well for the full protein dataset (regression coefficient of < 0.6; data not shown). Use of only the protein dataset with another model, PLS1, yielded much better results (Fig. 3A), resulting in a regression
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Fig. 2. PLS2 model of predicted vs. actual impurity concentrations for A) nucleic acid and B) lipid. Equations represent best fit linear regressions. Shaded regions indicate 95% confidence interval around the best fit line.
Fig. 3. PLS1 model of predicted vs. actual impurity concentrations for A) protein and B) whole cells. Equations represent best fit linear regressions. Shaded regions indicate 95% confidence interval around the best fit line.
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Fig. 4. FTIR spectra of BC produced by two different bacterial strains and subjected to various levels of processing. Spectra for BC contaminated with 10% whole cells, lipid, nucleic acid, and protein are shown for reference.
Table 2 Impurity estimates in differentially processed BC. Strain
ATCC 53582
Gx 399
a b c d
Processingb
Minimal Partial Full Minimal Partial Full
% (w:w)ab Nucleic acid
Lipid
Protein
Cells
< 0.1c < 0.1 0.03 < 0.1 < 0.1 < 0.1
< 0.1 < 0.1 0.01 < 0.1 < 0.1 < 0.1
0.31 < 0.1 0.72 ± 0.25 0.66 < 0.1 0.09
2.41 ± 0.24d 0.10 0.44 ± 0.04 4.61 ± 0.30 < 0.1 < 0.1
Estimates based on FTIR data collected from 10 discrete locations on each replicate coupon. Average of three replicate coupons taken from BC subjected to different levels of processing. Negative impurity estimates were interpreted as less than the lowest impurity standard, < 0.1%. Average and standard deviation shown if non-negative impurity estimate values were obtained from more than one replicate. Otherwise, the single non-negative value is shown.
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Acknowledgments
to thank Mr. Frank Weston, Agilent Technologies, for his excellent technical assistance during the ATR-FTIR analysis setup. Views, opinions, and/or findings contained herein are those of the authors and should not be construed as an official Department of Defense position or decision unless so designated by other official documentation.
The investigators acknowledge and thank the Strategic Environmental Research and Development Program (SERDP, Project WP-2333, Contract W912HQ-13-C-0065). The authors would also like Appendix A. ATR-FTIR instrument parameters Table A.1
Method parameters for ATR-FTIR analysis of BC samples. Method summary Number of scans Sample Background Resolution (cm− 1) Scan range (cm− 1) Total Usable Advanced settings Collect Speed Electronic lowpass filter (kHz) Interfergram sampling interval Sensitivity Interferogram symmetry Spectrometer configuration Beam splitter Source aperture Beam attenuator throughput Spectral processing 1) Collection 2) Compute 3) Ratio 4) Truncate 5) ATR correct
32 32 4 4000–650 6000–400
25 kHz 17.4 1 1 Symmetrical double-sided KBr 0.5 cm− 1 at 4000 cm− 1 100%
Appendix B. Functional group wavenumber correspondence Table B.1 Regions of FTIR spectra associated with impurity functional groups. Functional groupsa
Wave number Range (cm− 1)
Associated impurity
CH, CH2, CH3 NH2, CeN, C]O CeN PeOC
3000–2800 1800–1500 1300–1150 1100–1050
Lipid Lipid, protein, nucleic acid Nucleic acid Lipid, nucleic acid
a
Functional group vs. wavenumber associations adapted from https://www2.chemistry.msu.edu/faculty/reusch/virttxtjml/spectrpy/ infrared/infrared.htm and http://www.wag.caltech.edu/home/jang/genchem/infrared.htm.
(pending)). Halib, N., Amin, M.C.I.M., Ahmad, I., 2012. Physicochemical properties and characterization of nata de coco from local food industries as a source of cellulose. Sains Malaysiana 41, 205–211. Hareland, W.A., Crawford, R.L., Chapman, P.J., Dagley, S., 1975. Metabolic function and properties of 4-hydroxyphenylacetic acid 1-hydroxylase from Pseudomonas acidovorans. J. Bacteriol. 121, 272–285. Jung, H.-I., Lee, O.M., Jeong, J.-H., Jeon, Y.-D., Park, K.-H., Kim, H.-S., An, W.-G., Son, H.-J., 2010. Production and characterization of cellulose by Acetobacter sp. V6 using a cost-effective molasses-corn steep liquor medium. Appl. Biochem. Biotechnol. 162, 486–497. Keshk, S.M.A.S., 2014a. Bacterial cellulose production and its industrial applications. Journal of Bioprocessing & Biotechniques 4. Keshk, S.M.A.S., 2014b. Vitamin C enhances bacterial cellulose production in Gluconacetobacter xylinus. Carbohydr. Polym. 99, 98–100.
References Czaja, W., Romanovicz, D., Brown Jr., R.M., 2004. Structural investigations of microbial cellulose produced in stationary and agitated culture. Cellulose 11, 403–411. Esa, F., Tasirin, S.M., Rahman, N.A., 2014. Overview of bacterial cellulose production and application. Agriculture and Agricultural Science Procedia 2, 113–119. Florea, M., Reeve, B., Abbott, J., Freemont, P.S., Ellis, T., 2016. Genome sequence and plasmid transformation of the model high-yield bacterial cellulose producer Gluconacetobacter hansenii ATCC 53582. Sci. Rep. 6, 23635. Fontana, J.D., Souza, A.M., Fontana, C.K., Torriani, I.L., Moreschi, J.C., Gallotti, B.J., Souza, S.J., Narcisco, G.P., Bichara, J.A., Farah, L.F.X., 1990. Acetobacter cellulose pellicle as a temporary skin substitute. Appl. Biochem. Biotechnol. 24, 253–264. Fuller, M.E., 2017. Applying synthetic biology principles to increase biocellulose (BC) production. Strategic Environmental Research and Development Program (WP-2333
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and its composites in biomedicine. Appl. Microbiol. Biotechnol. 99, 2491–2511. Ross, P., Mayer, R., Benziman, M., 1991. Cellulose biosynthesis and function in bacteria. Microbiol. Mol. Biol. Rev. 55, 35–58. Shi, Q., Li, Y.g., Sun, J., Zhang, H.R., Chen, L., Chen, B., Yang, H., Wang, Z., 2012. The osteogenesis of bacterial cellulose scaffold loaded with bone morphogenetic protein2. Biomaterials 33, 6644–6649. Ullah, H., Santos, H.A., Khan, T., 2016. Applications of bacterial cellulose in food, cosmetics and drug delivery. Cellulose 23, 2291–2314. Wu, S.-C., Lia, Y.-K., 2008. Application of bacterial cellulose pellets in enzyme immobilization. J. Mol. Catal. B Enzym. 54, 103–108. Zeng, X., Liu, J., Chen, J., Wang, Q., Li, Z., Wang, H., 2011. Screening of the common culture conditions affecting crystallinity of bacterial cellulose. J. Ind. Microbiol. Biotechnol. 38, 1993–1999.
Keshk, S., Sameshima, K., 2006. Influence of lignosulfonate on crystal structure and productivity of bacterial cellulose in a static culture. Enzym. Microb. Technol. 40, 4–8. Kouda, T., Yano, H., Yoshinaga, F., 1997. Effect of agitator configuration on bacterial cellulose productivity in aerated and agitated culture. J. Ferment. Bioeng. 83, 371–376. Nguyen, V.T., Gidley, M.J., Dykes, G.A., 2008. Potential of a nisin-containing bacterial cellulose film to inhibit Listeria monocytogenes on processed meats. Food Microbiol. 25, 471–478. Okiyama, A., Shirae, H., Kano, H., Yamanaka, S., 1992. Bacterial cellulose I. Two-stage fermentation process for cellulose production by Acetobacter aceti. Food Hydrocoll. 6, 471–477. Rajwade, J.M., Paknikar, K.M., Kumbhar, J.V., 2015. Applications of bacterial cellulose
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Contents lists available at ScienceDirect
Journal of Microbiological Methods journal homepage: www.elsevier.com/locate/jmicmeth
Evaluation of ATR-FTIR for analysis of bacterial cellulose impurities ⁎
T
Mark E. Fuller , Christina Andaya, Kevin McClay Aptim Federal Services, 17 Princess Road, Lawrenceville, NJ 08648, United States
A R T I C L E I N F O
A B S T R A C T
Keywords: Bacterial cellulose Biocellulose Gluconacetobacter ATR-FTIR Impurity
This research evaluated the utility of using the large amount of spectral data obtained during attenuated total reflection Fourier-transform infrared spectrophotometry (ATR-FTIR) analysis of dried biocellulose (BC) to estimate the type and concentration of potential bacterial cells impurities present in the BC. Pre-cleaned BC was contaminated with known concentrations of representative nucleic acid, lipid, and protein impurities, as well as whole bacterial cells. These impurity standards were then subjected to ATR-FTIR analysis, and the resulting spectral data were used to develop models to estimate the concentrations of impurities in differentially processed BC. Results indicated that ATR-FTIR is a useful tool for estimating impurities in BC, and may also be applicable for measurement of levels of non-cellulose biomolecules added to BC for various purposes.
1. Introduction
2. Materials and methods
There is an increasing interest in the use of bacterial biopolymers in the energy, electronics, food, and medical industries (Ullah et al., 2016; Esa et al., 2014; Rajwade et al., 2015). Of particular interest is bacterial cellulose, or biocellulose (BC). BC is produced by a wide range of bacterial species (Ross et al., 1991), although most efforts have focused on the Acetobacteraceae group (Florea et al., 2016), and specifically Gluconacetobacter spp. Limited industrial production of BC to smallscale operations for specialized uses, such as preparation of the traditional food nata de coco (Halib et al., 2012), and as temporary wound dressings (Fontana et al., 1990). Most efforts to date to increase BC production have focused on adjustments of culture conditions, including evaluation of different feedstocks (Jung et al., 2010; Keshk, 2014a), medium additives (Keshk & Sameshima, 2006; Keshk, 2014b), and culturing method(Kouda et al., 1997; Okiyama et al., 1992). As BC production and processing parameters are adjusted, there is a need to monitor BC purity. This has routinely been achieved using both X-ray diffractometry (XRD) and attenuated total reflection Fouriertransform infrared spectrophotometry (ATR-FTIR) (Czaja & Romanovicz, 2004; Zeng et al., 2011), with the resulting data being using to calculate crystallinity indexes. However, the exact nature of any impurities that might exist in the BC has usually not been reported. The current study was undertaken to determine if the data obtained using ATR-FTIR analysis could be used to both identify and quantitate likely impurities (e.g., protein, lipids, DNA, other polymers) in BC. The effectiveness of three levels of post-processing for removing impurities was also examined.
2.1. Chemicals
⁎
The sources of the potential BC impurities were as follows: protein, bovine serum albumin (Sigma-Aldrich, A0281); lipid, 1,2-dipalmitoylsn-glycero-3-phosphocholine (Sigma-Aldrich, P4329); nucleic acid, salmon testes DNA (Sigma-Aldrich, D7656). The purity of all other chemicals was reagent grade or higher. 2.2. Bacterial strains Gluconacetobacter hansenii ATCC 53582 was purchased from the American Type Culture Collection (ATCC). Strain 399 was isolated from a kombucha “mushroom” starter culture, and was identified by genomic analysis as a Gluconacetobacter sp. closely related to Gluconacetobacter hansenii ATCC 23769 (unpublished data). A BC negative mutant of 399 designated as 399A (Fuller, 2017), was also used. Strains were routinely grown in either ATCC Medium 1765 or a basal salts medium (BSM; pH 7 S·U) (Hareland et al., 1975) amended with glucose (5 g/L). 2.3. BC production and processing BC was produced by strains ATCC 53582 and Gx 399 for this testing during growth in BSM with glucose (5 g/L) in 2 L Ziploc bags. Cells were aseptically harvested from multiple ATCC Medium 1765 agar plates and suspended in 40 mL of BSM. The cell suspension was passed
Corresponding author at: Aptim Federal Services, LLC, 17 Princess Road, Lawrenceville, NJ 08648, United States. E-mail address: [email protected] (M.E. Fuller).
https://doi.org/10.1016/j.mimet.2017.10.017 Received 26 September 2017; Received in revised form 30 October 2017; Accepted 31 October 2017 0167-7012/ © 2017 Elsevier B.V. All rights reserved.
Journal of Microbiological Methods 144 (2018) 145–151
M.E. Fuller et al.
then diluted to an optical density at 600 nm of 5. Assuming that a bacterial cell weighs 1 × 10− 9 mg (dry weight), then the OD 5 cell solution contained 2.5 mg/mL of bacterial dry mass. The cell solution was applied to the coupon similarly to the single impurities, then coupons were dried and stored in a desiccator until FTIR analysis as with the other impurities.
Table 1 The staged BC processing protocol used during this research. Processing level Minimal
Partial
Full
Step
Procedure
X
X
X
1
X
X X
X X
2 3
X
X
4
X X
5 6
BC pellicle was removed from the incubation vessel (flask, Ziploc bag, etc.) and squeezed by hand to wring out excess medium. The pellicle was rinsed with distilled water The pellicle was placed in 1 M NaOH overnight at room temperature The pellicle was rinsed with distilled water until the pH of the rinsate was approximately 8. The pellicle was placed in 96% ethanol for 2 h The pellicle was rinsed thoroughly with distilled water
2.5. Analytical Coupons of dry BC (2.5 cm × 3.75 cm) were analyzed using an Agilent Cary 660 Fourier-Transform infrared (FTIR) spectrophotometer equipped with a Pike MIRacle attenuated total reflection (ATR) assembly with a ZnSe crystal and a liquid nitrogen cooled linearized mercury cadmium telluride (MCT) detector (Agilent Technologies, Wilmington, DE, USA). Data was collected using Agilent Resolutions software (v. 5.2 and 5.3). Spectra were obtained over a wavenumbers from 4000 to 650 cm− 1. Parameters of the standard analysis method are shown in Appendix A. Data was collected from 10 to 20 distinct locations across the surface of the BC coupon. The ATR clamp and platform were cleaned with a cotton swab dampened with isopropyl alcohol and allowed to dry between analysis of each coupon.
repeatedly through a 20 gauge syringe needle using a 60 mL disposable syringe to break up clumps prior to inoculating the Ziploc bags. Bags were incubated at room temperature without agitation by placing them on baking sheets. BC was harvested once a firm pellicle had formed, which usually three to four weeks. Based on the existing literature, a staged BC processing protocol was developed as shown in Table 1. Different processing levels were used for some experiments, designated as “minimal” (steps 1 and 2), “partial” (steps 1 to 4), and “full” (steps 1 to 6). Pellicle were dried after processing (room temp or 35 °C), and stored in sealed Ziploc bags until analysis or further use. Triplicate pellicles were produced in 2 L Ziploc bags using strains ATCC 53582 and Gx 399. Each pellicle was then split into three roughly equal portions, and subjected to the three levels of processing described in Table 1. BC coupons were subjected to ATR-FTIR as described in Section 2.5, and collected FTIR spectra were analyzed as described in Section 2.6 to measure the levels of impurities.
2.6. Data analysis and impurity modeling Panorama Pro (v3.2.15.0) (LabCognition Analytical Software GmbH & Co., Cologne, Germany) was used to extract impurity-related information from a subset of the collected FTIR spectra over a subset of the collected wavenumbers from 3052 to 800 cm− 1. The Partial Least Squares (PLS) calibration models in the Chemometrics add-on module were used to extract impurity-specific signals in the spectra, and to generate calibrations which could then be used to estimate the impurity levels in samples of BC. 3. Results and discussion 3.1. Spectra from different impurities/concentrations
2.4. Preparation of BC spiked with impurities ATR-FTIR analysis clearly demonstrated expected classes of BC impurities could be detected in a concentration-depended manner. Fig. 1 presents FTIR spectra of BC contaminated with varying levels of protein, lipid, and nucleic acid. Different regions in the FTIR spectra corresponded to functional groups of the different types of impurities (e.g., unsaturated alkyl chain in lipids, amine groups in proteins, etc.; see Table B.1 for wavenumber - functional group correspondence information). The coupons contaminated with whole cells showed a response in regions of the FTIR spectra that are indicative of functional groups associated with protein, lipid, and nucleic acid (e.g., peaks in the wavenumber range of 1750–1400) (Fig. 1D). The use of ATR during the FTIR analysis measured the impurity signal in the top few microns of the BC coupon. However, since it was assumed the impurities were distributed homogeneously throughout the BC matrix, the ATR measurement was deemed to be representative and reproducible. Further work beyond the scope of this research would be needed to validate this assumption.
The base BC to which defined concentrations of the impurities was added was produced by strain ATCC 53582 under static incubation in 2 L Ziploc bags. The complete BC washing procedure described in Section 2.3 above was used, followed by several rinses in chloroform to assure maximum purity. The sheets of base BC were cut into coupons (approximately 3 cm × 3 cm). The impurities were prepared as aqueous stock solutions, except for lipid, which was prepared in chloroform. Impurities were applied to the BC coupons in stages, adding the impurity solution to the surface in 10 to 20 μL aliquots and spreading it gently around until the central 90% of the coupon surface was wetted. The coupon was then allowed to dry at room temperature. The process of applying and drying was repeated until the correct mass of the impurity had been delivered to the coupon. Final impurity concentrations for protein, lipid, DNA, and polymer were (0.1% to 10%, w:w, dry wt basis). All coupons thoroughly dried at room temperature and stored in a desiccator until FTIR analysis. This method of applying the various impurities to the BC coupons was assumed to have resulted in a reasonable distribution of the compounds throughout the BC matrix as the carrier evaporated. However, additional analyses would have been required to confirm this assumption, and were beyond the scope of this research. Coupons were also contaminated with whole bacterial cells as the complex impurity most likely to be expected in the BC. Strain 399A cells were grown on glucose, washed twice in sterile purified water, and
3.2. Impurity modeling The Partial Least Squares 2 calibration model in Panorama Pro was used as it allowed multiple regression of several of the impurities at once. The PLS2 model appeared to yield good results for nucleic acid and lipid (Fig. 2), with regression coefficients (r2) for actual vs. predicted concentration of > 0.8 using five factors extracted from the
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coefficient of 0.87 for actual vs. predicted concentrations. The whole cell impurity was also modeled successfully using the PLS1 model (r2 = 0.86) (Fig. 3B). 3.3. Impurities vs. processing levels ATR-FTIR spectra from differentially processed BC and BC with known impurities is shown in Fig. 4. A qualitative inspection of the spectra indicated that if bacterial impurities were present, most were at very low levels, and few differences could be observed between minimally processed BC and fully processed BC. Application of the impurity models to the raw FTIR data yielded quantitative estimates of the levels of the different impurities, as presented in Table 2. Estimates of the defined impurities (e.g., nucleic acid, lipid, protein) were all < 1%. In general, estimates of nucleic acid and lipid were below the lowest impurity standard of 0.1%, even for the minimally processed BC. Estimates of protein were more scattered, and, surprisingly, a higher percentage of protein impurity was detected for fully processed compared to minimal processed BC produced by ATCC 53582. The reason for this apparent inversion of expected increased purity with increased processing is not known. A simple transposition of the minimally processed and fully processed samples is not likely, given that the estimates of DNA and lipid did not follow the same pattern as the estimates of protein. Also, the BC produced by strain Gx 399 yielded the expected results (e.g., decreased protein with increased processing). Estimates of whole cell impurity, which rely on a complex mix of FTIR signals from all the cellular components (e.g., a mix of different proteins, lipids, nucleic acids, other biopolymers) indicated that increased processing was needed to remove cells entrained in the BC matrix. Additionally, while lower initial estimates of cells were observed for BC from ATCC 53582 compared to Gx 399, the processing steps appeared to remove more of the Gx 399 cells than the ATCC 53582 cells from their respective BC matrices. This may reflect that ATCC 53582 produces a BC pellicle that is more dense than Gx 399, thereby increasing the difficulty in removing embedded cells. Regardless, the results clearly indicate that more than simple rinsing with distilled water is needed to remove cellular debris from BC material. 4. Conclusions This research provided proof-of-concept results that ATR-FTIR analysis of dried BC can be used to estimate the type and concentration of potential bacterial cells impurities present in the BC material. Application of the analytical method for differentially processed BC allowed determination of the effects of processing on residual cellular impurities. This method used readily available sources of defined biomolecules as impurities, some of which are not bacterial. Bovine serum albumin was used as the protein impurity, and salmon teste DNA was used as the nucleic acid impurity. For more refined estimates, bacterial protein, nucleic acid, and lipid could be used to prepare the standards. In this research, the whole cell impurity standards likely provided the most representative estimate of overall impurity level in the BC materials. While this research used air dried BC, the basic methodology would be applicable to freeze dried (or otherwise processed) BC, as long as the same BC is used for both the “base BC” controls and the impurity standards. While the goal of this research was to evaluate the use of ATR-FTIR for estimating impurity type and concentration in BC, it also indicates that the method could be tailored to monitor the levels of other biomolecules that may be added to BC for various purposes. For instance, this method could be useful for measuring the levels of compounds being added to BC for specific applications, such as a carrier for antibiotics (Nguyen et al., 2008) or growth factors (Shi et al., 2012), and when using BC as an enzyme immobilization scaffold (Wu & Lia, 2008).
Fig. 1. FTIR spectra of BC contaminated with varying levels of A) protein, B) lipid, C) nucleic acid, and D) whole cells. Lines represent average of 20 scans. Shaded regions indicate area of the spectra indicative of impurity associated functional groups.
spectra dataset. However, the PLS2 calibration model did not work well for the full protein dataset (regression coefficient of < 0.6; data not shown). Use of only the protein dataset with another model, PLS1, yielded much better results (Fig. 3A), resulting in a regression
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Fig. 2. PLS2 model of predicted vs. actual impurity concentrations for A) nucleic acid and B) lipid. Equations represent best fit linear regressions. Shaded regions indicate 95% confidence interval around the best fit line.
Fig. 3. PLS1 model of predicted vs. actual impurity concentrations for A) protein and B) whole cells. Equations represent best fit linear regressions. Shaded regions indicate 95% confidence interval around the best fit line.
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Fig. 4. FTIR spectra of BC produced by two different bacterial strains and subjected to various levels of processing. Spectra for BC contaminated with 10% whole cells, lipid, nucleic acid, and protein are shown for reference.
Table 2 Impurity estimates in differentially processed BC. Strain
ATCC 53582
Gx 399
a b c d
Processingb
Minimal Partial Full Minimal Partial Full
% (w:w)ab Nucleic acid
Lipid
Protein
Cells
< 0.1c < 0.1 0.03 < 0.1 < 0.1 < 0.1
< 0.1 < 0.1 0.01 < 0.1 < 0.1 < 0.1
0.31 < 0.1 0.72 ± 0.25 0.66 < 0.1 0.09
2.41 ± 0.24d 0.10 0.44 ± 0.04 4.61 ± 0.30 < 0.1 < 0.1
Estimates based on FTIR data collected from 10 discrete locations on each replicate coupon. Average of three replicate coupons taken from BC subjected to different levels of processing. Negative impurity estimates were interpreted as less than the lowest impurity standard, < 0.1%. Average and standard deviation shown if non-negative impurity estimate values were obtained from more than one replicate. Otherwise, the single non-negative value is shown.
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Acknowledgments
to thank Mr. Frank Weston, Agilent Technologies, for his excellent technical assistance during the ATR-FTIR analysis setup. Views, opinions, and/or findings contained herein are those of the authors and should not be construed as an official Department of Defense position or decision unless so designated by other official documentation.
The investigators acknowledge and thank the Strategic Environmental Research and Development Program (SERDP, Project WP-2333, Contract W912HQ-13-C-0065). The authors would also like Appendix A. ATR-FTIR instrument parameters Table A.1
Method parameters for ATR-FTIR analysis of BC samples. Method summary Number of scans Sample Background Resolution (cm− 1) Scan range (cm− 1) Total Usable Advanced settings Collect Speed Electronic lowpass filter (kHz) Interfergram sampling interval Sensitivity Interferogram symmetry Spectrometer configuration Beam splitter Source aperture Beam attenuator throughput Spectral processing 1) Collection 2) Compute 3) Ratio 4) Truncate 5) ATR correct
32 32 4 4000–650 6000–400
25 kHz 17.4 1 1 Symmetrical double-sided KBr 0.5 cm− 1 at 4000 cm− 1 100%
Appendix B. Functional group wavenumber correspondence Table B.1 Regions of FTIR spectra associated with impurity functional groups. Functional groupsa
Wave number Range (cm− 1)
Associated impurity
CH, CH2, CH3 NH2, CeN, C]O CeN PeOC
3000–2800 1800–1500 1300–1150 1100–1050
Lipid Lipid, protein, nucleic acid Nucleic acid Lipid, nucleic acid
a
Functional group vs. wavenumber associations adapted from https://www2.chemistry.msu.edu/faculty/reusch/virttxtjml/spectrpy/ infrared/infrared.htm and http://www.wag.caltech.edu/home/jang/genchem/infrared.htm.
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