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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2022, Vol. 16 Issue (5) : 62    https://doi.org/10.1007/s11783-022-1567-y
RESEARCH ARTICLE
Determination of growth kinetics of microorganisms linked with 1,4-dioxane degradation in a consortium based on two improved methods
Yi Xiong1, Boya Wang1, Chao Zhou2, Huan Chen3, Gang Chen1, Youneng Tang1()
1. Department of Civil and Environmental Engineering, FAMU-FSU College of Engineering, Florida State University, Tallahassee, FL 32310, USA
2. Geosyntec Consultants Inc., Huntington Beach, CA 92648, USA
3. National High Magnetic Field Laboratory, Tallahassee, FL 32310, USA
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Abstract

● Evaluated three methods for determining the consortia’s growth kinetics.

● Conventional method is flawed since it relies on the total biomass concentration.

● Considering only selected bacterial taxa improved the accuracy.

● Considering oligotrophs and copiotrophs further improved the accuracy.

The conventional method for determining growth kinetics of microbial consortia relies on the total biomass concentration. This may be inaccurate for substrates that are uncommon in nature and can only be degraded by a small portion of the microbial community. 1,4-dioxane, an emerging contaminant, is an example of such substrates. In this work, we evaluated an improved method for determining the growth kinetics of a 1,4-dioxane-degrading microbial consortium. In the improved method, we considered only bacterial taxa whose concentration increase correlated to 1,4-dioxane concentration decrease in duplicate microcosm tests. Using PEST (Parameter Estimation), a model-independent parameter estimator, the kinetic constants were estimated by fitting the Monod kinetics-based simulation results to the experimental data that consisted of the concentrations of 1,4-dioxane and the considered bacterial taxa. The estimated kinetic constants were evaluated by comparing the simulation results with experimental results from another set of microcosm tests. The evaluation was quantified by the sum of squared relative residual, which was four orders of magnitude lower for the improved method than the conventional method. By further dividing the considered bacterial taxa into oligotrophs and copiotrophs, the sum of squared relative residual further decreased.
Keywords Biodegradation      1,4-Dioxane      Kinetics      Microbial consortium      16S rRNA     
Corresponding Author(s): Youneng Tang   
Issue Date: 29 April 2022
 Cite this article:   
Yi Xiong,Boya Wang,Chao Zhou, et al. Determination of growth kinetics of microorganisms linked with 1,4-dioxane degradation in a consortium based on two improved methods[J]. Front. Environ. Sci. Eng., 2022, 16(5): 62.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-022-1567-y
https://academic.hep.com.cn/fese/EN/Y2022/V16/I5/62
Reported culture* μmax (d−1) Y (mg protein/mg dioxane) Ks (mg/L) b (d−1) Reference
Pseudonocardia dioxanivorans CB1190 2.40 0.02−0.09 160 ± 44 − Mahendra & Alvarez-Cohen, 2006
Pseudonocardia dioxanivorans CB1190 0.74 ±0.06 0.45 ± 0.09 6.3 ± 0.2 0.05 ±0.01 Barajas-Rodriguez & Freedman, 2018
Mycobacterium dioxanotrophicus PH-06 − 0.16 78 ± 10 − He et al., 2017
Pseudonocardia benzenivorans B5 0.07 0.03 330 ± 82 − Kämpfer & Kroppenstedt, 2004; Mahendra & Alvarez-Cohen, 2006
Afipia sp. D1 1.20 0.19 25.8 − Sei et al., 2013
Mycobacterium sp. D6 0.62 0.19 20.6 − Sei et al., 2013
Mycobacterium sp. D11 0.22 0.18 69.8 − Sei et al., 2013
Pseudonocardia sp. D17 0.48 0.22 59.7 − Sei et al., 2013
Acinetobacter baumannii DD1 − 0.41 − − Zhou et al., 2016
Rhodanobacter sp. ASY5 7.68 − − − Pugazhendi et al., 2015
Xanthobacter flavus DT8 3.60 0.35 17.5 − Chen et al., 2016
Xanthobacter sp. YN2 0.6 0.27 410.91 − Ma et al., 2021
Rhodococcus aetherivorans JCM 14343 0.18 0.03 59.2 − Inoue et al., 2016, 2018
Pseudonocardia sp. N23 5.52 0.32 79.9 − Yamamoto et al., 2018
Enriched culture FS 0.01 0.58 93.9 – Nam et al., 2016
Enriched culture AS 0.19 0.34 181.3 Nam et al., 2016
Industrial activated sludge 0.24−1.03 0.18−0.50 9.9 − Grady et al., 1997
Industrial activated sludge 0.12 − 181.9 − Zenker et al., 2002
Industrial activated sludge 1.49 0.54 1.65 0.312 Zenker et al., 2002
Enriched culture 1.99 0.3 12.6 ±7.6 − Zenker et al., 2002
* All values were reported in the temperature range of 25–30 °C, except for Zenker et al. (2002), which were reported at 35 °C.
Tab.1  Summary of reported dioxane-degrading microorganisms and their kinetics
Fig.1  Experimental results from the microcosm experiments for constants determination: (a) Concentrations of dioxane; (b) Concentrations of proteins (error bars represent the standard deviations based on quadruplicate sampling and analysis).
Fig.2  NMDS analysis: an overview of the change of microbial community in the microcosm experiments for constants determination. (Note: Different colors represent different dates of sampling. For each date, one set of samples from Bottle A and duplicate sets of samples from Bottle B were analyzed. Typically, the analysis is believed to be reliable if the stress level is smaller than 0.1 (Kruskal, 1964)).
Name Category Correlation coefficient p-value Reported as dioxane-degrading bacteria in pure culture studies Reported as potential dioxane-degrading bacteria in mixed culture studies Reference
Pseudonocardia dioxanivorans CB1190 Copiotrophs* 9.8 × 10−2 2.7 × 10−2 Yes Yes Aoyagi et al., 2018; Mahendra & Alvarez-Cohen, 2006
Afipia Oligotrophs† 2.5 × 10−2 4.2 × 10−7 Yes Yes Nam et al., 2016; Sei et al., 2013
Xanthobacteraceae 1.2 × 10−2 3.5 × 10−7 Yes Yes Chen et al., 2016, 2021; Nam et al., 2016
Burkholderiaceae 2.9 × 10−3 6.1 × 10−3 Yes Yes Mahendra & Alvarez-Cohen, 2006; Nam et al., 2016
Saccharimonadales 9.6 × 10−3 8.9 × 10−7 No Yes Chung et al., 2019
Rhodopseudomonas 3.5 × 10−3 8.2 × 10−7 No Yes Aoyagi et al., 2018
Dokdonella 4.6 × 10−3 3.9 × 10−2 No Yes Nam et al., 2016
Pedomicrobium 2.9 × 10−3 1.1 × 10−2
Mesorhizobium 3.2 × 10−3 4.4 × 10−4
Blastocatella 2.8 × 10−3 2.4 × 10−2
Chlorobi bacterium OLB5 1.9 × 10−3 3.0 × 10−3 No No −
JGI 0001001-H03 2.9 × 10−3 1.6 × 10−2
Bauldia 1.3 × 10−3 8.3 × 10−3
Ellin6067 1.6 × 10−3 6.4 × 10−4
Mine drainage metagenome 3.5 × 10−3 3.5 × 10−12
Tab.2  Microbial groups likely responsible for dioxane degradation in this study
Fig.3  Linear regression analysis for the concentration of degraded dioxane and the concentration of microbial groups from Bottles A and B in the microcosm experiments for constants determination: (a) An example oligotrophs: Afipia (p-value = 4.22 × 10–7, correlation coefficient = 0.025); (b) The only copiotrophs: Pseudonocardia dioxanivorans CB1190 (p-value = 0.027, correlation coefficient = 0.098). (Note: in Fig. (b), the diamonds and squares represent the samples taken before and after the dioxane was degraded from 50 to 30 mg/L, respectively. Only the data corresponding to the diamonds (higher dioxane concentrations or less dioxane degradation) were used for regression analysis.)
Methods μmax (d−1) Y (mg protein/mg dioxane) Ks (mg dioxane/L) b (d−1) Total SSRR
Conventional method 0.07 0.08 69.71 0.01 61,900
Improved method based on one set of kinetics 0.33 0.45 0.56 0.22 5.6
Improved method based on two sets of kinetics Copiotrophs 2.50 0.44 160.00 0.28 4.0
Oligotrophs 0.26 0.40 0.44 0.16
Tab.3  Summary of the constants determined by the three methods
Fig.4  Comparison of dioxane concentrations from the microcosm experiments for constants determination and the three methods for determining kinetics: a) the conventional method, b) the improved method with one set of kinetics, and c) the improved method with two sets of kinetics.
Fig.5  Comparison of biomass concentrations from the microcosm experiments for constants determination and the three methods for determining kinetics: (a) the conventional method, (b) the improved method with one set of kinetics, and (c) the improved method with two sets of kinetics. (Note: The symbols for experiments represent the average. For standard deviation of the quadruplicate measurements of proteins and variation among the triplicate measurements of taxa relative abundance, refer to Fig. 1(b) and S1, respectively).
Fig.6  Comparison of results from the microcosm experiments for evaluation by the three modeling methods.
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