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dissertation entitled

kIKETICS CHARACTERIZATION OF CONETABOLI 21M;
COMMUNITIES AA/D ADAPT/Snot] T 0 MoflépowTH SUBSTRATE

presented by

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MSU IeAn AfflrmetIve Action/Equal Opportunlty Inetttwon
Wm:

KINETICS CHARACTERIZATION OF COMETABOLIZING COMMUNITIES AND
ADAPTATION TO N ONGROWTH SUBSTRATE

By

Wang-kuan Chang

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Civil and Environmental Engineering

1996

ABSTRACT

KINETICS CHARACTERIZATION OF COMETABOLIZING COMMUNITIES AND
ADAPTATION TO NONGROWTH SUBSTRATE

By

Wang-kuan Chang

Many compounds of environmental and toxicological significance are transformed by
cometabolism. For routine engineering application of cometabolism, several issues must be
addressed. First, a generally accepted kinetic model is needed to describe transformation of
growth and nongrowth substrates. Second, methods are needed to evaluate model
parameters. Finally, changes in the cometabolic activity of a community due to long term,

repeated exposure to nongrowth substrates need to be investigated.

An unstructured model for cometabolism is presented and verified experimentally in this
research. The model includes the effects of cell growth, endogenous cell decay, product
toxicity, and competitive inhibition with the assumption that cometabolic transformation
rates are enhanced by reducing power obtained from oxidation of growth substrates. A
theoretical transformation yield is used to quantify the enhancement resulting from
oxidation. A systematic method for evaluating model parameters is described. The
applicability of the model is evaluated by comparing experimental data for methanotrophic
cometabolism of TCE with model predictions from independently measured model
parameters. Propagation of errors is used to quantify errors in parameter estimates and in
the final prediction. The model predicts TCE and methane transformation successfully for

a wide range of concentrations of TCE (0.5 - 9 mg/L) and methane (0.05 - 6 mg/L). The

model was also sucessfully applied for the simplified case of nongrowth substrate

transformation of HCFCs and HFCs by resting cells.

To describe adaptive changes within cometabolizing communities, the verified
cometabolism model was analyzed in terms of the "fitness" concept. Fitness was quantified
in term of measurable kinetic parameters. The selection gradient for each parameter was
defined as the partial derivative of fitness with respect to that parameter. The gradient
indicates the selection acting on each fitness component, with other components held
constant. It appears possible to use selection gradients as criteria for the stability of a

community under a given perturbation.

Finally, a methanotrophic mixed culture and a phenol-degrading culture were repeatedly
exposed to different levels of TCE. Changes of community structure were monitored and
the effects of TCE exposure were evaluated. Various phenotypic parameters were measured
to monitor change within each community. Molecular methods of community analysis
(ARDRA and DGGE) were used to monitor shifts in microbial community structure. The
results indicate that the phenol-degrading community is very stable despite repeated
exposure to TCE. Greater instability in a methanotrophic mixed culture is predicted and
observed. A stable community is able to maintain its structure and performance under
nongrowth substrate exposure. Based on the results of this study, a diverse community

seems to have higher stability.

To the Memory of My Father.
Efiik

iv

ACKNOWLEDGMENTS

I want to offer very special thanks to my advisor, Dr. Craig Criddle, without whom this
work would never have happened. He is the one who finally convinced me to tackle it, and

he did everything possible to help me to accomplish this research.

Of the many people who helped during the research and writing of this work. I would
especially like to thank Dr. Larry Fomey, Dr. Robert Hickey, and Dr. Thomas Voice, for
their infinite patience and invaluable advice; I am grateful as well to Dr. Denise Searles,
Hector Ayala, and Deborah Hogan, for their help in culture isolation and community
analysis; and Dr. Chien-chun Shih and Dr. Xianda Zhao and Yanlyang Pan, for their

friendship and for the right stuff at the right time.

For sheer inspiration, energy, joy, and faith in the future, I am grateful to my wife, Shu-

chu, who have added a whole lot of color to my life.

TABLE OF CONTENTS

LIST OF TABLE ................................................................................... ix
LIST OF FIGURES ............................................................................... xi
NOMENCLATURE ............................................................................... xiv
CHAPTER 1
INTRODUCTION ................................................................................ 1
COMETABOLIC TRANSFORMATION ............................................. 1
ADAPTATION OF COMETABOLIZIN G COMMUNITIES ...................... 2
RESEARCH OBJECTIVES ............................................................ 5
OUTLINE OF THE THESIS ........................................................... 6
REFERENCES ........................................................................... 7
CHAPTER 2
BACKGROUND .................................................................................. 8
COMETABOLISM PRINCIPLES ..................................................... 8
General concept .................................................................. 8
Energy requirement ............................................................. 10
Substrate interaction ............................................................. 11
Product toxicity .................................................................. 12
Cometabolic community ........................................................ 12
QUANTIFICATION OF COMETABOLISM ........................................ 13
Cometabolism by resting cells ................................................. 13
Cometabolism by growing cells ............................................... 15
MODEL MICROBIAL SYSTEMS FOR COMETABOLISM ...................... 2O
Methanotrophic communities .................................................. 21
Phenol-oxidizing bacteria ....................................................... 25
MODEL NONGROWTH SUBSTRATES ............................................ 29
HCFC/CFCs ..................................................................... 30
Trichloroethylene ................................................................ 3 l
Methanotrophic transformation of trichloroethylene ........................ 32
MODEL REACTOR SYSTEMS FOR COMETABOLISM ......................... 35
REFERENCES ........................................................................... 38
CHAPTER 3
KINETICS OF COMETABOLISM BY RESTING CELLS ................................. 45
ABSTRACT .............................................................................. 45
INTRODUCTION ....................................................................... 46
Rationale for experimental system ............................................. 47

vi

MATERIALS AND METHODS ....................................................... 48

Chemicals ........................................................................ 48
Analytical techniques ............................................................ 48
Measurement of Henry's constants ........................................... 48
Culture conditions ............................................................... 49
Batch biotransformation experiments ......................................... 50
Modeling transformation of HCFCs/HFCs .................................. 50
RESULTS ................................................................................. 52
Henry's Law constants ......................................................... 52
HCFCs/HFC transformation rates ............................................ 53
DISCUSSION ............................................................................ 61
REFERENCES ........................................................................... 64
CHAPTER 4
KINETICS OF COMETABOLISM BY GROWING CELLS ............................... 66
ABSTRACT .............................................................................. 66
INTRODUCTION ....................................................................... 66
Rationale for experimental system ............................................. 68
MATERIALS AND METHODS ....................................................... 69
Culture and culture conditions ................................................. 69
Analytical methods .............................................................. 70
Batch biotransformation experiments ......................................... 71
Model development ............................................................. 72
Model verification ............................................................... 74
RESULTS AND DISCUSSION ....................................................... 77
REFERENCES ........................................................................... 93
CHAPTER 5
THEORY OF ADAPTATION OF COMETABOLIZIN G COMMUNITIES .............. 96
INTRODUCTION ....................................................................... 96
MODEL DEVELOPMENT .............................................................. 97
Adaptive change of microbial communities .................................. 97
Fitness of cometabolizing cultures ............................................ 100
Selection gradients .............................................................. 102
Implications of theory for different model systems ......................... 104
MODEL ANALYSIS .................................................................... 106
RESULTS AND DISCUSSION ....................................................... 106
REFERENCES ........................................................................... 118
CHAPTER 6
CHANGE IN COMMUNITY STRUCTURE IN RESPONSE TO LONG TERM
TCE EXPOSURE: METHANOTROPHIC MIXED CULTURE IN CI-IEMOSTAT ..... 119
INTRODUCTION ....................................................................... 119
MATERIALS AND METHODS ....................................................... 123
Culture conditions ............................................................... 123
Perturbation of TCE ............................................................. 124
Monitoring of consortia ......................................................... 128
Adaptation of cometabolizing community in chemostat ..................... 129
General procedures for biotransformation measurement ................... 131
Community Analysis ............................................................ 132
RESULTS ................................................................................. 133

vii

DISCUSSION ............................................................................ 138
REFERENCES ........................................................................... 140

CHAPTER 7
CHANGE IN COMMUNITY STRUCTURE IN RESPONSE TO LONG TERM
TCE EXPOSURE: PHENOL-DEGRADING COMMUNITY IN SEQUENCING

BATCH REACTOR .............................................................................. 143
INTRODUCTION ....................................................................... 143
MATERIALS AND METHODS ....................................................... 146

Culture conditions ............................................................... 146
Perturbation of TCE ............................................................. 147
Monitoring of consortia ......................................................... 147
Adaptation of cometabolizing community in SBR ........................... 148
General procedures for biotransformation measurement ................... 151
Community Analysis ............................................................ 153
RESULTS ................................................................................. 154
DISCUSSION ............................................................................ 164
REFERENCES ........................................................................... 167

CHAPTER 8

ENGINEERING SIGNIFICANCE ............................................................ 169

CHAPTER 9

SUMMARY AND CONCLUSIONS ........................................................... 171
CONCLUSIONS ......................................................................... 171
FUTURE RESEARCH .................................................................. 173

viii

LIST OF TABLES

Table 2.1 Organic compounds subject to cometabolism and accumulated products ..... 9

Table 2.2 Microorganisms exhibiting the phenomenon of cometabolism ................. 10
Table 2.3 Summary of cometabolic transformation model by resting cells ............... 16
Table 2.4 Summary of cometabolic transformation model by growing cells ............. 19
Table 2.5 Examples of cometabolic transformation of trichloroethylene .................. 21
Table 2.6 Tentative classification Scheme for Methanotrophic Bacteria ................... 23
Table 2.7 Kinetic coefficients of methane utilization ........................................ 24
Table 2.8 Kinetic coefficients of TCE cometabolic transformation by

methanotrophic communities ..................................................................... 25
Table 2.9 Kinetic coefficients of phenol utilization .......................................... 28
Table 2.10 Kinetic coefficients of TCE cometabolic transformation by phenol-

oxidizing bacteria .................................................................................. 29
Table 2.11 Properties and purity of HCFCs and HFC evaluated in this work ............. 31
Table 2.12 General information and properties of trichloroethylene ........................ 32
Table 2.13 TCE transformation and product formation by methanotrophs ................. 34
Table 3.1 Measured values of Henry's constant vs. temperature .......................... 54
Table 3.2 Temperature regression for Henry's constant .................................... 55
Table 3.3 Salting-out coefficients .............................................................. 55

Table 3.4 Kinetic coefficients for HCFC/HFC transformation by methanotrophic
mixed culture MM] ............................................................................... 60

Table 3.5 Kinetic coefficients for HCFC/HFC transformation by methanotrophic
mixed culture MM] ............................................................................... 60

Table 4.1 Kinetic and stoichiometric parameters for methane utilization, growth,
and TCE degradation for methanotrophic mixed culture MMl .............................. 83

ix

Table 4.2 Stoichiometry of TCE cometabolism by methanotrophic mixed culture

MMl for different initial TCE concentrations .................................................. 83
Table 4.3 Electron flow in methanotrophic mixed culture MMl for different initial

TCE concentrations ............................................................................... 84
Table 4.4 The effect of methane concentration on observed transformation yield ....... 84
Table 5.1. The effects of fitness components on fitness ..................................... 104
Table 6.1 Operation conditions for chemostat ................................................ 126
Table 6.2 Endogeneous decay constant for methanotrophic mixed cultures during
long-term TCE exposure ......................................................................... 135
Table 7.1 TCE feed concentration for long-term TCE ....................................... 150
Table 7.2 Summary of bench-scale SBR operating condition .............................. 151

Table 7.3 Theoretical transformation capacity for phenol—degrading reactor
communities ........................................................................................ 158

Table 7.4 Endogeneous decay constant for phenol-degrading communities during
long-term TCE exposure in SBRs ............................................................... 159

LIST OF FIGURES

Figure 1.] Flow of reducing power in cometabolizing system ............................. 3
Figure 1.2 General scheme of cometabolic transformation ................................. 4

Figure 3.1 Biotransformation of HCFC-22 by methanotrophic mixed culture
MM] ................................................................................................ 56

Figure 3.2 Biotransformation of HCFC-142b by methanotrophic mixed culture
MM] ................................................................................................ 57

Figure 3.3 Biotransformation of HFC-134a by methanotrophic mixed culture
MM] ................................................................................................ 5 8

Figure 3.4 Biotransformation of HCFC-123 by methanotrophic mixed culture
MM] ................................................................................................ 59

Figure 4.] Approach for prediction of degradation of growth and nongrowth
substrates by the proposed model ............................................................... 79

Figure 4.2 The sensitivity equations for parameters estimated nonlinearly from
model ............................................................................................... 81

Figure 4.3 Biotransformation of TCE and methane by methanotrophic mixed
culture ................................................................................................ 85

Figure 4.4 Biotransformation of TCE and methane by methanotrophic mixed
culture ............................................................................................... 86

Figure 4.5 The effect of methane concentration on TCE and methane degradation

rate .................................................................................................. 87
Figure 4.6 The observed transformation capacity as a function of the

concentrations of growth and nongrowth substrates .......................................... 91
Figure 4.7 The observed transformation yield as a function of the concentration

of growth and nongrowth substrates ............................................................ 92
Figure 5.1 Fitness space of cometabolizing community .................................... 102

Figure 5.2 Dimensionless selection gradient, (P/W)(8W/8P) as a function of
methane and TCE concentrations for methanotrophic mixed culture when both
substrates are present separately ................................................................. 110

xi

Figure 5.3 Dimensionless selection gradient, (P/W)(8W/3P) as a function of
phenol and TCE concentrations for phenol degrading culture when both substrates
are present separately ............................................................................. 1 11

Figure 5.4 Dimensionless selection gradient, (P/W)(6W/8P) as a function of TCE
concentration for methanotrophic mixed culture at a specified methane
concentration ....................................................................................... 1 12

Figure 5.5 Dimensionless selection gradient, (P/W)(BW/8P) as a function of
methane concentration for methanotrophic mixed culture at a specified TCE
concentration ....................................................................................... 1 13

Figure 5.6 Sensitivity equation for qS as a function of TCE concentration for
methanotrophic mixed culture at a specified methane concentration ......................... 114

Figure.5.7 Sensitivity equation for qs as a function of methane concentration for
methanotrophic mixed culture at a specified TCE concentration ............................. 1 15

Figure 5.8 Sensitivity equation for qc as a function of TCE concentration for
methanotrophic mixed culture at a specified methane concentration ......................... 116

Figure 5.9 Sensitivity equation for qc as a function'of methane concentration for
methanotrophic mixed culture at a specified TCE concentration ............................. l 17

Figure 6.2 TCE feed concentration for long-term TCE exposure experiment in
chemostate .......................................................................................... 126

Figure 6.3 Changes of Cell density of methanotrophic mixed cultures in respond
to long-term TCE exposure ...................................................................... 134

Figure 6.4 Observed growth yield for methanotrophic mixed cultures during long-
term TCE exposure ................................................................................ 135

Figure 6.5 Transformation capacity for methanotrophic mixed cultures during
long-term TCE exposure ......................................................................... 135

Figure 6.6 Fitness of methanotrophic mixed cultures in respond to long-term TCE
exposure in chemostats ........................................................................... 136

Figure 6.7 ARDRA fingerprints of control and TCE-exposed methanotrophic

communities ........................................................................................ 137
Figure 7.1 Experimental setup for bench-scale sequencing batch reactors ................ 149
Figure 7.2 The operating mode of SBR in a cycle ........................................... 150
Figure 7.3 Changes in cell density of the phenol-degrading reactor communities. ...... 156

Figure 7.4 Second order rate coefficients of TCE transformation by phenol-
degrading reactor communities. ................................................................. 157

Figure 7.5 Maximum specific rate of phenol utilization by phenol-degradin g
reactor communities ............................................................................... 158

xii

Figure 7.6 Long-term changes in observed yield for the phenol-degrading reactor
communities ........................................................................................ 159

Figure 7.7 Fitness of phenol-degrading communities during period when the
exposed reactor received an influent concentration 25 mg/L TCE ........................... 160

Figure 7.8 Change in the second order rate coefficient for TCE transformation in a
typical SBR operating cycle ...................................................................... 161

Figure 7.9 TCE concentration in gas (Cg) and liquid (Cw) phase during the fill
and react periods for a feed solution of 25 mg/L TCE ........................................ 162

Figure 7.10 DGGE fingerprints from control and TCE-exposed communities in
sequencing batch reactors ........................................................................ 163

xiii

NOMENCLATURE

English symbols

996”

a: 1:0

E

a

a '3»: fix.

a.
G

is:

05

953333

“(139

(7; )obs

(TQM

first-order endogenous decay constant (d" )

concentration of nongrowth substrate (mg nongrowth substrate/L)

initial concentration of nongrowth substrate (mg nongrowth substrate/L)

final concentration of nongrowth substrate at t = 00 (mg nongrowth

substrate/L)

Selection gradient for fitness component, P

Henry's constant of nongrowth substrate (-)

Henry's constant of growth substrate (-)

maximum specific rate of utilization of the nongrowth substrate (mg nongrowth
substrate/mg cells-d)

maximum specific rate of utilization of growth substrate (mg growth substrate/mg
cells-d)

= kc / Kc , second order rate coefficient of utilization of the nongrowth substrate
(Umg cells-d)

inhibition coefficient indicating the effect of nongrowth substrate concentration
on growth substrate utilization rate (mg nongrowth substrate/L)

inhibition coefficient indicating the effect of growth substrate concentration on
nongrowth substrate utilization rate (mg growth substrate/L)

half-saturation coefficient of nongrowth substrate (mg nongrowth substrate/L)
half-saturation coefficient of growth substrate (mg growth substrate/L)

mass of nongrowth substrate (mg)

mass of growth substrate (mg)

Malthusian parameter

specific rate of utilization of nongrowth substrate (mg nongrowth substrate/mg
cells-d)

specific rate of utilization of growth substrate (mg growth substrate/mg cells-d)

concentration of growth substrate (mg growth substrate/L)

time (d)

=- qclp , observed transformation capacity (mg nongrowth substrate/mg cells),
theoretical transformation capacity in the absence of endogenous decay (mg
nongrowth substrate/mg cells)

= qc/q, , observed transformation yield (mg nongrowth substrate/mg growth
substrate)

xiv

7; theoretical transformation yield (mg nongrowth substrate/mg growth substrate)
VG volume of gas phase (L)

VL volume of liquid phase (L)

W fitness

X active organism concentration (mg cells/L)

X 0 initial concentration of active organism (mg cells/L)

Ym maximum yield or true growth yield (mg cells/mg growth substrate)

Y observed yield (mg cells/mg growth substrate)

Greek symbols

[1 specific growth rate of organism (day'l)

um maximum specific growth rate of organism (day'l)
no specific growth rate of organism of the ancestor community (day-1)

XV

CHAPTER 1
INTRODUCTION

The phenomenon of cometabolism was first reported by Leadbetter and Foster (1959).
Since then, many microbial cometabolizing populations have been identified. Many
compounds transformed by cometabolism are toxic and, therefore, of environmental
concern because of deliberate or inadvertent release into waters and soils. Cometabolism
may be a useful tool for removal of such contaminants, particularly those not readily
catabolized, from natural environments and engineered systems. Quantitative understanding

of cometabolic transformations will enable rational engineering design.

COMETABOLIC TRANSFORMATION

Under aerobic conditions, a number of organic compounds are transformed by
cometabolism. These transformation are typically mediated by aerobic organisms
possessing nonspecific oxygenase activity capable of oxidizing hydrocarbons as growth
substrates and other compounds as nongrowth substrates. Transformation of growth
substrate yields carbon or energy for the organisms; transformation of nongrowth substrate
wastes the energy and reducing reserves of the cell. In the latter case, the transformation is
termed cometabolic. This work focuses on monooxygenase activity. Monooxygenases
(MO) cometabolically attack a broad range of compounds, including halogenated aliphatic
compounds (RX):

MO
RX+02+2e-+2H+ —> RXO+H20

2
In a true sense, cometabolism is not metabolism (energy yielding) but fortuitous

transformation of a compound by pathways that do not yield energy to the organisms.
Enhancing these fortuitous side-reactions is a goal of engineered transformation using
microorganisms. However, this goal may conflict with the primary objective of the
microbe: the use of electrons for growth and respiration. The "dilemma" that microbes face
in cometabolism is shown in Figure 1.1. In normal metabolism, the fraction of electrons
used for energy generation (fe) plus the fraction of electrons used for synthesis (f8) will
equal one. In cometabolism, a fraction of the electrons removed from the electron donor
may also be used in cometabolic reactions (fco). Since the energy and the products of the
transformation are unavailable for microbial use, fs-I-fe will decrease in the presence of a
compound that is cometabolized. Thus, the successful removal of contaminants by
cometabolism depends, at least in part, upon the electron donor requirements and the

efficiency with which electrons can be directed to cometabolic transformation.

ADAPTATION OF COMETABOLIZING COMMUNITIES

Cometabolic transformation is a complex phenomenon, especially when both growth and
nongrowth substrates are simultaneously present. Significant declines in methane
conversion rates by methanotrophs following exposure to TCE are observed for both
resting and formate-fed cells, suggesting toxic effects caused by TCE or its transformation
products (Alvarez-Cohen and McCarty 1991a,b). The presence of toxic transformation
products can be expected to have some impact on the development of microbial
communities during long-term exposure to nongrowth substrate (Figure 1.2). Changes in
populations are likely related to the level of exposure to nongrowth substrate, turnover of

transformation products and utilization of growth substrate.

 

| Growth substrate 1

 

e

l

 

 

 

f ,
Nongrowth substrate a CO S >| Cells

 

 

 

 

 

 

1

Electron acceptor

 

 

 

 

Figure 1.1 Flow of reducing power in cometabolizing system

The accumulation of stable nongrowth substrate breakdown products in pure cultures
indicates that pure cultures are not able to mineralize nongrowth substrates. However,
research shows that many mixed cultures and communities can achieve mineralization.
Since pure cultures with oxygenases typically suffer from product toxicity, other
populations capable of utilizing these toxic products may play an important role in
detoxification. Under conditions of prolonged or repeated exposure to nongrowth
substrate, selection pressures may favor shifts in the microbial community structure so as

to enhance detoxification.

 

Nongrowth

Growth
substrate \ substrate

\

Enhanced \ \
transformation

 

 

 

 

 
  
 
       
   
     

 

 

 

. . Cells . .
Competltrve . Competitlve
Inhibition NonSPeCIfic Inhlbltlon
enzyme

Finite
transformation \\
capacity
Useful products Useless
and and/or
biomass toxic products

 

 

 

 

 

 

Figure 1.2 General scheme of cometabolic transformation. Dashed lines indicate the effects
of substrates or products on transformation.

5
Different reactor environments may also have different effects on the adaptation of

cometabolizing microbial communities. Communities in batch and plug flow reactors are
exposed to high concentration of halogenated compound whereas continuous well-mixed

systems are exposed to low concentrations continuously. Consequently, selective pressures
in batch or plug flow reactors might be expected to favor cometabolizing communities with

higher rate of transformation of halogenated compounds.

RESEARCH OBJECTIVES

The objectives of this research are to evaluate and verify a model for cometabolic
transformation of nongrowth substrate and transformation of growth substrates and to use
this model to characterize adaptive changes during long-term exposure to nongrowth
substrate. The hypothesis are:

l. Cometabolism can be quantified using a model based on saturation kinetics and
incorporating terms for the loss of microbial biomass caused by endogenous decay,
depletion of cofactors and product toxicity.

2. Under conditions of long term periodic exposure to nongrowth substrate, total biomass
will be negatively affected by cometabolism, but can recover by means of changes in
community structure. These changes can be characterized by phenotypic, morphological
and genotypic parameters.

3. Exposure to nongrowth substrate forces selective changes in the community and
enhanced nongrowth substrate degradation as secondary populations capable of detoxifying
reaction products become more prevalent.

4. Simple cometabolizing communities derived from growth upon a single rate-limiting
substrate can be treated as a single population in terms of fitness or growth. Both genotypic
and phenotypic adaptation within populations will contribute to phenotypic changes of the
whole community. For the characterization of the community, the phenotypic properties

eValuated will be apparent values for the whole community.

6
5. Adaptive changes of community structure can be described in term of a fitness parameter

defined as the ratio of specific growth rate during nongrowth substrate exposure to specific
growth rate before exposure to nongrowth substrate. The value of fitness at any instant will
be the total result of phenotypic adaptation of the community. The sensitivity of change in
each phenotypic property to fitness can also be evaluated as a measure of community

stability.

OUTLINE OF THE THESIS

This work was conducted in three phases. The first phase, in chapters 3 and 4, entails
presentation and verification of a model for cometabolism kinetics. In chapter 3, the model
is verified for resting cells using transformation of HCFC/HFCs by a methanotrophic
mixed culture as a model system. In chapter 4, the model is extended to growing cells and
verified experimentally for the methanotrophic mixed culture with trichloroethylene as the
nongrowth substrate. In second phase of this work (chapter 5), the model verified in
chapters 3 and 4 is analyzed in terms of " fitness " concept to describe adaptive changes
within cometabolizing communities, Fitness is quantified in term of measurable kinetic
parameters. The final phase in chapters 6 and 7 focuses on long-term adaptations of
cometabolic model systems in response to long-term repeated exposure to nongrowth
substrate. A methanotrophic mixed culture in a chemostat and a phenol-degrading
community in a sequencing batch reactor were chosen as model systems representing
extreme cases for adaptation. The engineering significance of this work is described in
chapter 8. Finally, the dissertation concludes with a summary of the most important results

and possible future studies.

REFERENCES

1.Alvarez-Cohen, L., and P. L. McCarty. 19913. A cometabolic biotransformation model

for halogenated aliphatic compounds exhibiting product toxicity. Environ. Sci. Technol.
25:1381-1387.

2.Alvarez-Cohen, L., and P. L. McCarty. 1991b. Effects of toxicity, aeration, and
reductant supply on trichloroethylene transformation by a mixed methanotrophic culture.
Appl. Environ. Microbiol. 57:228 - 235.

3.Leadbetter, E. R., and J. W. Foster. 1959. Oxidation products formed from gaseous
alkanes by the bacterium Pseudomonas methanica. Arch. Biochem. Biophys. 82:491-492.

CHAPTER 2
BACKGROUND

COMETABOLISM PRINCIPLES

General concept

Many compounds of environmental and toxicological significance are transformed by
cometabolism. In this study, cometabolism is defined as transformation of a nongrowth
substrate that depends upon the previous or concurrent utilization of a growth or
nongrowth substrate (Criddle 1993; Horvath 1972). A growth substrate is defined as an
electron donor that supports growth. An energy substrate is defined here as an electron
donor that provides reducing power and energy for the transforming population, but does

not by itself support growth.

The term "co-oxidation" was first used to described cometabolism because the original
observations all involved oxidations. Subsequently, reductive transformations were
discovered that did not facilitate growth of the transforming organisms but still depended
upon the concurrent or previous utilization of a growth or energy substrate. These "co-
reduction" reactions led to the use of the broader term cometabolism. It now appears that
certain cometabolic transformations are also hydrolytic. Thus, in addition to the well
known examples of co-oxidation, we now recognize the potential for "co-reductions" and
"co-hydrolyses". All of the known co-oxidations occur only under obligate aerobic

conditions, while most co-reductions occur under anaerobic conditions

9
Cometabolism is important for many transformations, including some polynuclear aromatic

hydrocarbons, halogenated aliphatic and aromatic hydrocarbons, and pesticides. Many of
these compounds express toxic properties and are environmental concern. This
phenomenon has been observed so frequently that it appears to represent a very important
type of microbial metabolism (Table 2.1). Many microbial species exhibit the phenomenon
of cometabolism. Table 2.2 lists those microorganisms which have been clearly shown to

possess transformation by cometabolism.

Table 2.] Organic compounds subject to cometabolism and accumulated products

 

 

-Compound Product

Ethane Acetic acid

Propane Propionic acid, acetone

Butane Butanoic acid, methyl ethyl ketone

m-Chlorobenzoate 4-Chlorocatechol, 3-chlorocatechol

o-Fluorobenzoate 3-F1uorocatechol, fluoroacetate

2-Fluoro-4-nitrobenzoate 2-Fluoroprotocatechuic acid

4—Chlorocatechol 2-Hydroxy-4-chloro-muconic semialdehyde

3, 5-Dichlorocatechol 2-Hydroxy-3,5-dichloro—muconic
semialdehyde

3—Methylcatechol 2-Hydroxy-3~methyl-muconic
semialdehyde

o—Xylene o-Toluic acid

p-Xylene p-Toluic acid, 2, 3-dihydroxytoluic acid

Pyrrolidone Glutamic acid

Cicerone Cinerolone

naphthalene salicyclic acid

n-Butylbenzene Phenylacetic acid

Ethylbenzene Phenylacetic acid

n-Propylbenzene Cynnamic acid

p-Isopropyltoluene p-Isopropylbenzoate

n-Butyl-cyclohexane Cyclohexaneacetic acid

2, 3, 6-Trichlorobenzoate 3, 5-Dichlorocatechol

2, 4, 5-Trichlorophenoxy acetic acid 3, 5-Dichlorocatechol

p-p'-Dichlorodiphenyl methane p-Chlorophenylacetate

l, 1-Diphenyl—2, 2, 2-trichloroethane 2-Phenyl-3, 3, 3-trichloropropionic acid

1, 1, l-Trichloroethene l, 2—dichloroethene

Trichloroethylene Trichloroacetate, 2,2,2-trichloro-ethanol,
dichloroacetate

 

Source: (1) Horvath 1972; (2) Dalton and Stirling 1982

10

Table 2.2 Microorganisms exhibiting the
phenomenon of cometabolism

 

ficroorganism
Acetobacterium woodii
Achromobacter sp.
Acinetobacter sp.
Arthrobacter sp.
Aspergillus niger
Azotobacter chroococcum
Azotobacter vinelandii
Bacillus megaterium
Bacillus sp.
Brevibacterium sp.
Clostridium sp.

F lavobacterium sp.
Hydrogenomonas sp.
Methylomonas sp.
Microbacterium sp.

M icrococcus certficans
Micrococcus sp.
Nitrosomonas europaea
Nocardia erythropolis
Nocardia sp.
Pseudomonas sp.

P. fluorescens

P. methanica

P. putida

Rhodococcus sp.
Streptomyces aureofaciens
Trichodemra viride
Vibrio sp.
_Xgnth0monas sp. _
Source: (1) Horvath 1972;
(2) Dalton and Stirling 1982 ; (3) Criddle 1993

 

Energy requirement

Many aerobic cometabolic reactions are catalyzed by non-specific oxygenase enzymes that
use 02 as the electron acceptor and NADH as the reducing energy source to oxidize both
growth and nongrowth substrates (Colby et al. 1977; Fox et al. 1990; Nelson et al. 1987;
Wackett et al. 1989). These enzymes are the methane monooxygenases (MMO) of
methanotrophs (Fox et al. 1990; Oldenhuis et al. 1989; Tsien et a1. 1989), ammonia

monooxygenases of nitrifiers (Arciero et al. 1989; Hyman et a1. 1988; Rasche et a1. 1990;

11
Vannelli et al. 1990), propane monooxygenases (Arciero et al. 1989; Hyman et al. 1988;

Vannelli et a1. 1990; Wackett et a]. 1989), certain toluene mono- and dioxygenases, and
certain phenol monooxygenases (Winter et al. 1989; Zylstra et al. 1989). After the initial
oxidation step, growth substrates are further degraded to regenerate reducing energy
(NADH), which promotes more substrate oxidation. However, the oxidation of nongrowth
substrate in the absence of growth substrate can cause the depletion of NADH in cells since
NADH is not regenerated. Thus, energy or reducing power must be present to transform
the nongrowth substrate. Transformation can not be sustained if growth substrate is not

supplied continuously or intermittently.

Substrate interaction

In the metabolism of multiple substrates, competitive inhibition is frequently reported. A
number of substrate interactions have been observed during hydrocarbon degradation by
cometabolism involving monooxygenases and dioxygenases. Saéz and Rittmann (1991,
1993) reported that batch experiments on the simultaneous utilization of phenol and 4-
chlorophenol by Pseudomonas putida PpG4 demonstrated 4-chlorophenol inhibited the
oxidation of growth substrate and the cometabolic degradation of 4—chlorophenol was
proportional to the rate of phenol oxidation. Competitive inhibition between phenol and
trichloroethylene (TCE) was also observed for the degradation by Pseudomonas cepacia
G4(Folsom et al. 1990). Studies on TCE degradation by methanotrophs (Alvarez-Cohen
and McCarty 1991a; Anderson and McCarty 1994; Broholm et al. 1992; Chang and
Alvarez-Cohen 1995a; Saéz and Rittmann 1993) indicated that competitive inhibition was
generally present between methane (growth substrate) and TCE (nongrowth substrate).
Chang et a]. (1993) also revealed competitive inhibition and cometabolic patterns using
paired substrates (benzene, toluene, and p-xylene). Some research also concluded that the

degradation of nongrowth substrates is enhanced in the presence of growth or energy

12
substrate (Chang and Alvarez-Cohen 1995a; Chang et al. 1993; Criddle 1993; Saéz and

Rittmann 1993).

Product toxicity

Several examples indicate that cometabolism by pure cultures does not typically result in the
mineralization of nongrowth substrates (Table 2.1). Horvath (1971) reported that
cometabolism of 2, 3, 6-trichlorobenzoate resulted in accumulation of 3, 5-dichlorocatechol
and development of a toxic environment for the cells. Several researchers have shown that
stable and toxic TCE breakdown products accumulate in pure cultures of methanotrophs.
This suggests that methanotrophic bacteria in isolation are not be able to effectively
mineralize TCE (Henry and Grbié -Galié 1990; Little et al. 1988; Oldenhuis et al. 1989).
Significant declines in methane conversion rates following exposure to TCE were observed
for both resting and formate-fed cells, suggesting toxic effects caused by TCE or its
transformation products (Alvarez-Cohen and McCarty 1991a; Alvarez—Cohen and McCarty
1991b).

Cometabolic community

The products of cometabolic transformation accumulate in pure cultures, but, in a mixed
culture, they are typically used by other microorganisms. As a result, cometabolic
transformations are key initiatory reactions in pathways that ultimately result in the
complete degradation of many environmental pollutants. Some research shows that
methanotrophic mixed cultures have advantages for complete degradation of TCE. Since
methanotrophs suffer from product toxicity in TCE transformation, heterotrophs in mixed
culture may play an important role in detoxification. Heterotrophic bacteria in the
methanotrophic mixed cultures apparently can degrade most of the water-soluble

breakdown products from l4C-TCE, decreasing levels of water-soluble radiolabel and

l3

increasing production of 14C02 (Little et al. 1988). Futherrnore, Uchiyama and co-
workers reported that a heterotrophic bacterium in a methanotrophic mixed culture,
Xanthobacter autotrophicus, can can oxidize dichloroacetic and glyoxylic acid completely
and can reduce trichloroacetic acid levels (Uchiyama et al. 1992). These results indicate that
heterotrophic bacteria in microbial communities play an important role in complete
degradation of nongrowth substrates. Another good example is the initial cometabolic
transformation of PNAs, which is typically followed by a series of degradation steps
leading to C02. The initial step is not mediated by the same organisms as the mineralization

steps.

QUANTIFICATION OF COMETABOLISM

Cometabolism by resting cells

Nongrowth substrates can be transformed by resting cells in the absence of growth
substrates. Under these conditions, cells utilize nongrowth substrate in the absence of

growth substrates. Transformations of growth substrate by cells can generally be described

using a saturation kinetic expression:

S
K+S

S

 

CI. =k,( ) (1)

Transformations of nongrowth substrate by resting cells can also be described using

saturation kinetics:

 

=k
4‘ ‘(K +C

C

) (2)

If the substrate concentration is low (S << K,, C << K, ), the specific transformation rate is

directly proportional to the substrate concentration:

14

%=ks (»
q, = k' C (4)

At high substrate concentration (S >> Ks, C>> K), the specific rate of substrate

transformation is independent of substrate concentration( qs = ks , qc = kc).

In some cases, a growth substrate inhibits its own transformation at high concentration. To
describe this situation, Haldane kinetics is often used:
k S
4. = 4'37 (5)
Ks + S + —-
K

As discussed previously, loss of cometabolic transformation activity can occur as a result
of endogenous decay and product toxicity. To evaluate the loss of cell activity during the

transformation of nongrowth substrate, first order decay of biomass is usually used:

— = -bX (6)

To account for the loss of transformation activity in resting cells caused by a depletion of
reducing power (in the absence of growth or energy substrate) and by product toxicity, the
concept of "transformation capacity" is applied. Transformation capacity was first defined
as (Alvarez-Cohen and McCarty 1991a):

dC

(72),,” = Ti} (7)

15
Alvarez-Cohen and McCarty assumed that biomass transformation capacity was equal to

the mass of nongrowth substrate ultimately degraded divided by the initial biomass used.
Criddle (1993) defined a “theoretical” biomass transformation capacity by correcting for
losses caused by endogenous decay. The later definition represents a theoretical maximum

value in the absence of external reducing power.

Most models proposed to describe transformations of nongrowth substrate under resting
cell condition are a combination of Eq. (2), (6), and (7). Criddle (1993) proposed a model
that unified the earlier models. This model assumed that nongrowth substrate is degraded
with saturation kinetics and endogenous decay and that loss of cometabolic activity can be
attributed to product toxicity are incorporated into cell decay term. A surrunary of these

models is shown in Table 2.3.

Cometabolism by growing cells

Earlier cometabolic models presented in the literature focus on resting cells, yet frequently,
both the rates and extent of cometabolism are enhanced in the presence growth substrate
and energy substrates. Competitive inhibition between growth and nongrowth substrates
(Folsom et al. 1990; Strand et al. 1990) or between multiple nongrowth substrates

(Alvarez-Cohen and McCarty 1991c) is also observed. When there is competitive inhibition

between the growth substrate and the nongrowth substrate, (12),,” and (KC )0,” replace K,

and KC respectively in Eq. (1) and Eq. (2), where:

C

(Ks )obs _ Ks(1+ E) (8)
S

(Kclob, — Kc(1+ F) (9)

13

Transformations of growth and nongrowth substrate by cells under the conditions of

competitive inhibition can generally be described as follow:

16
Table 2.3 Summary of cometabolic transformation model by resting cells

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Differential equations for substrate
Model utilization rate Integrated form Ref.
dC k or dX _ c _ —k X0 _b,
1a -'Z=K:+C and E—-bx Kcln(E;)+C—C0——;—(1-e ) 1’2
_£_ chXO e—bt
dt K + C
dC c__kx0e —br
“3 dt —kc CX andd dt bX C- Coe b 23
c<< K, 80 ”g = chXoe'b'
—i—C;kx and—--bX Sarneasmodel3where (T) -k—‘
1° dr dt _ C 0’” _ b 4
dC -
C>> Kc SO -E = kCCXOe b'
_d_C__ chX
and— = (T )0,” ,= _L Kc ln CXo
2a d—t— —KC + C dxl kc (( C0 “Ebb: — 0) FCO 5
kCC(X -——(C —C) X
dc 0 (a)... 0 ) +<T. >...n n{—"}>
so —— = — F
dt Kc + C 1
where F: X0 -——)—(C0 — C)
c obs
dC ' —k' F':
-_ _ F ‘
2b dt = k CX and: = (T )obs C = C0 20 k. F} 4
dC X0 - ————e_ (
C<< Kc SO -7 = ch(X0 _ 1; (C0 _ C» (Tc)obs
where F' = X0 — C0
(7;)obs
dC d S kc
2c mat—=1: X an —=(T)0,,, ameasmodel 3where (T )obs—Z- 4
dC I
so -— = k x —— C -
C>> Kc d: c( 0 cm )obs ( 0 C3)
dX
3 E-‘bX and 'd_C=(T)obS C=C0_(T~C)0bsxo(1_e-bl) 6
so —i(-:-- —bT Xoe ""
d_t
_d_C_ k CX and
4 d, = K +C No integrated form 4
(_1x_ = —bX-1—(——k cx )
d: T K + C

 

 

 

 

Reference: (1) Galli and McCarty 1989; (2) Schmidt et al. 1985; (3) Criddle et al. 1990; (4) Criddle 1993;
(5) Alvarez-Cohen and McCarty I991a; (6) Saéz and Rittmann 199].

17
S

K,(1+—C—)+s
K.

lC

 

6]. =k.( ) (10)

c
s

KC(I+——)+C
K.

(S

 

q. =k..( ) (11)

To describe the observed enhancement in the rate of cometabolism in the presence of
growth substrate, modification of Eq (10) or Eq (11) were proposed. Chang and Alvarez-
Cohen (1995a) proposed a general model that includes reducing energy explicitly as a
limiting reactant during cometabolism. They applied the model to describe degradation of
TCE by methanotrophs. The effects of reducing power were separated from toxicity effects
and quantified by supplying the cells with formate. For methanotrophs, forrnate provides
energy as NADH, but does not support growth. Thus, the experimental approach of Chang
and Alvarez-Cohen is somewhat specific to organisms for which energy substrates can be

identified that do not support growth.

By contrast, Criddle (1993) introduced a term, the growth substrate transformation
capacity, or theoretical transformation yield, to quantify the enhancement of cometabolism

resulting from oxidation of the growth substrate:

4. = (T,.q. + k.)( CS ) (12)

K I+— +C
.( K”)

(S

 

Use of Eq. (12) does not require explicit quantification of reducing power and may be
applied to growth and energy substrates. Eq. (12) is related to the Luedeking-Piret (LP)

model that is commonly used in fitting product formation data from many different

fermentation (Bailey and Ollis 1986). In the LP model, qp = all + B, where qp = specific

18

rate of product formation, a = the growth-associated product yield, and ,8 = nongrowth-

associated product formation rate. The LP model can be modified to apply to

cometabolism. Consequently, qc =(ap+fi)/ Yp”, where Yp” =the mass of product

formed by cometabolism of a unit mass of nongrowth substrate. The same result is derived

with Eq. (12) when C >> Kc. Thus, the LP model is obtained as a limiting case of Eq.

(12).

The most widely used model of cell growth and decay is the Monod expression as modified

by Herbert et a] (1956):
Y kS
=Y —-b=—”"‘——b 13
# qu K +S ( )

S

For cells that are growing, decaying, and simultaneously carrying out cometabolic
transformation, Eq. (13) is an inadequate description of growth. A modification proposed
by Criddle (1993) and used by Anderson and McCarty (1994) and by Chang and Alvarez-
Cohen (1995a) is:

‘1.

=Y — ——‘ 14
u ’7qu T ( )

C

Eq(l4) indicates that an increase in the rate of cometabolism causes a decrease in the
specific growth rate of a cometabolizing population. While this appears to be true for pure
cultures, it may not be true for mixed cultures where organisms capable of using the
products of cometabolism for growth are frequently present. Eq(10), (12), and (14)
provide the framework for a complete kinetic description of cometabolism by growing or

resting cells and were used throughout this research. A summary of these models is shown

in Table 2.4.

19
Table 2.4. Summary of cometabolic transformation model by growing cells

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

'l
|
Model Differential equations for substrate utilization rates and growth rate Ref. l
; -__1_£_ k,s (g) I
1 ‘15 th K,+s ___l_ig ‘K. 1 I
X ‘1‘ (1+—S—+—C—) l
K: KC 1
q __i£_ k,s q __i£€_ ch
2 X ‘1‘ K,(1+—)+s X ‘1‘ K,(1+-§—)+C 2
1 (IX
=——=r —b
u Lit q.
as 1 dC
____._. +
3 (1+5: qc X d1 or], lab 3
q _ 1 dS_ K,
3__——" n2 2
X dt K,(1+Z—2-)+S+§—
K2 Kh
1 (1X
=——=Y -b
u x it 4.
q =_l£S__ kss q —.__1__‘.1£- ch
4 X ‘1’ K,(1+£)+s c X ‘1‘ Kc(1+i)+c 4
KC [(5
1
=X - (C -C)
0 (Tc)obs 0
5 q 1dS_ k,s q_ ldC_ ch
s‘—_—- c-—_—"'
X ‘1’ K,(1+—IS—)+S X ‘1' Kc(l+i)+C 5
c s |
1 (IX q l
=——=Y — —- c
” x d: 4’ (a)... l
1 d5 R KS 1 dC R k C '
qs=———=( )( 5 ) qC=———=( )( C ) |
6 th KR-i-R K,(1+—C—)+s th KR+R Kc(1+-k§-)+C 6 ;
1 dX' q l
=__=y _b.._£
‘1 X dt 4 Tc ;
1 (15 as 1 dC ch
7 qs=———= qc=-——=(7}q.+kc)( )
th KAN-[EMS X dt Kc(1+ )+C 7
fr is l
-i__ _ _4_c I

 

Renferece: (1 trandt a] 1990; (2) Broholm et a1 1992; (3) Saéz and Rittmann 1993; (4) Alvme_hen
and McCarty 1991a; (5)Anderson and McCarty 1994; (6) Chang and Alvarez-Cohen 1995a; (7) Criddle
1993.

20
MODEL MICROBIAL SYSTEMS FOR COMETABOLISM

Several aerobic bacteria with non-specific oxygenase activity are capable of oxidizing
halogenated hydrocarbons. Microorganisms possessing this ability include toluene-
oxidizing bacteria (Nelson et al. 1987; Wackett and Gibson 1988), methane-oxidizing
bacteria (Little et al. 1988; Oldenhuis et al. 1989; Tsien et al. 1989), ammonia-oxidizing
bacteria (Arciero et a]. 1989; Hyman et al. 1988; Rasche et al. 1990; Vannelli et al. 1990),
and propane-oxidizing bacteria (Wackett et al. 1989). The enzymes which have been
implicated in catalyzing halocarbon oxidations are toluene mono- and dioxygenase (Winter
et al. 1989; Zylstra et al. 1989), methane monooxygenase (Fox et al. 1990; Oldenhuis et al.
1989; Tsien et a]. 1989), ammonia monooxygenase (Arciero et a]. 1989; Hyman et al.
1988; Rasche et al. 1990; Vannelli et al. 1990), and propane monooxygenase (Wackett et
al. 1989), respectively. A variety of non-specific oxygenases that attack TCE in aerobic

environments are listed in Table 2.5.

As discussed previously, a diverse range of cometabolizing activities are present in the
environment. Non-specific oxygenase activities within communities are the focus of this
work. Such activity can be found in many hydrocarbon-degrading communities, the most
well studied of which is the methanotrophs. Accordingly, a simple well-defined
methanotrophic community was selected as a model cometabolizing community for this
work. The second community selected was a phenol-degrading enrichment. This
community was chosen because of its high growth rates, its high transformation capacity
for TCE, and ease of handling of phenol in laboratory studies. Additional discussion of
both methanotrophic and phenol-degrading communities are provided in the following

sections.

21
Table 2.5 Examples of cometabolic transformation of trichloroethylene

 
  
     
     
   
   
    
  
    

       

 

 

 

 

 

 

 

 

      
 

 

i Microorganisms Growth substrate References
l Pseudomonas cepacia strain phenol, toluene, o-cresol 1, 2
. G4 phenol 3, 4
Pseudomonas putida Fl toluene 5, 6
Strain 46-1 toluene 7, 8
Methylosinus trichosparium methane 9, 10
OB3b methanol 11
forrnate 12
IIMethylocystis sp. strain M methane 13
14, 15
Mycabacterium vaccae propane 16
J 0B5
Nitrosomonas europaea ammonia 17, 18, 19
Xanthobacter strain Py 2 propylene 20, 21
Genetically engineered toluene 21
Escherichia coli

 

 

 

 

 

References: (1) Ne son et a1. 1986; (2) Nelson et a . 1 87; (3) F0 som et a . 1 9 ; ( )
Folsom &Chapman 1991; (5) Nelson et a1. 1988; (6) Wackett & Gibson 1988; (7) Fox et
al. 1990; (8) Little et al. 1988; (9) Oldenhuis et al. 1989; (10) Oldenhuis et al. 1991; (11)
Tsien et al. 1989; (12) Newman & Wackett 1991; (13) Nakajima et al. 1992; (14)
Uchiyama et al. 1989; (15) Uchiyama et al. 1992; (16) Wackett et al. 1989; (17) Arciero et

al. 1989; (18) Hyman et al. 1988; (19) Rasche et al. 1991; (20) Ensign et al. 1992; (21)
Zylstra et al. 1989.

Methanotrophic communities

Methanotrophs are classified into two major groups depending on their internal cell
structure and carbon assimilation pathway. Type I organisms assimilate one-carbon
compounds via a unique pathway, the ribulose monophosphate cycle, whereas Type II
organisms assimilate C-l intermediates via the serine pathway. The requirement for 02 as a
reactant in the initial oxidation of methane explains why all methanotrophs are obligate
aerobes, whereas some organisms using methanol as electron donor can grow
anaerobically (with nitrate or sulfate as electron acceptor). Both groups of methanotrophs

contain extensive internal membrane systems, which appear to be related their methane-

22
oxidizing ability. Type I methanotrophs are characterized by internal membranes arranged

as bundles of disk-shaped vesicles distributed throughout the organism whereas Type II
methanotrophs possess paired membranes running along the periphery of the cell. Type I
methanotrophs are also characterized by a lack a complete tricarboxylic acid cycle (the
enzyme a-ketoglutarate dehydrogenase is absent), whereas Type II methanotrophs possess
a complete cycle. In addition, most Type II methanotrophs can fix molecular nitrogen
whereas Type I organisms do not. The classification and characteristics of methanotrophic

bacteria are listed in Table 2.6.

The methylotrophs are capable of growth on a variety of organic compounds; however,
they cannot use methane as carbon and energy sources. All methanotrophs can grow on
methane, many are also able to utilize methanol and formaldehyde, and a few can use a
wider range of organic compounds. Most methanotrophs are obligate methylophiles, which
means that they are incapable of growth on compounds that contain carbon-carbon bonds.
The responsible enzyme of methanotrophic bacteria, monooxygenase, catalyzes the
incorporation of one oxygen atom from molecular oxygen into methane to produce
methanol. The lack of substrate specificity of the monooxygenase enzyme enables it ability
to oxidize a broad range of compounds, including halogenated aliphatic compounds.
Monooxygenases can hydroxylate many alkanes and aromatic compounds and form
epoxides from alkenes (Semprini et al. 1992). The epoxides are unstable and hydrolyze to
acids. Since some products of these reactions are not further metabolized by

methanotrophs, a community of microorganisms is necessary for mineralization.

23
Table 2.6 Tentative classification Scheme for Methanotrophic Bacteria*

 

 

Characteristic Groupl GroupX Group H

Morphology Straight rod Coccus Straight, curved
or pear-shaped
rod

Membrane arrangement
Bundles of vesicular

disks + + -
Paired peripheral
membranes - - +
Motility i - i:
Resting stage Azotobacter-type Azotobacter-type Lipid cyst or terminal
cyst cyst exospore
Rosette - - + (most strains)
Major carbon Rump Rump Serine
assimilation pathway
Autotrophic C02 - + -
fixation
Complete TCA cycle - - +
Nitrogenase - + +
Isocitrate dehydrogenase
NAD’ and NAD(P)+
specific + ' -
NAD l specific
NAD(P)‘ specific - + -
- - +
Glucose-6—dehydrogenase +( NADP + — specific) +(NADP l - specific) "a”
gigggefigwwc +(NADP ’ - specific) +(NADP; - specific) '
Predominant fatty acid 16** 16 18
carbon-chain length
Growth at 45°C Variable + _
Mol% G+C of DNA 50-54 62.5 61.7-63.1

 

* Green 1992.

Kinetic coefficients for methane utilization and TCE transformation reported in literature are
summarized in Table 2.7 - 2.8. Although units have been standardized for purposes of
comparison, differences in experimental protocol and methods of reporting sometimes
made an accurate and complete comparison impossible. Clearly, broadly accepted
unstructured models of cometabolism and standardized experimental protocols are needed
to enable fair comparison of organisms from different sources and assist in the design and

operation of engineering systems.

24

Table 2.7 Kinetic coefficients of methane utilization

 

 

 

 

 

 

 

 

 
  
 

 

  
 

 

   

ks Ks k. 3 Re 3. .
mg cells/ mg CH4] mg CH4/L [lmg cell- day-1
mg CH4 mg cell- day
, day
" Methanotrophic 0.34 (obs.) 1.13 0.67 Strand et
7 mixed culture 0.5] (true) J al. 1990
f Methanotrophic 0,221 1,728: 0.2 0.12 Broholm et 7
1, mixed culture (Ki=12 a1. 1992
. mg/L for
1 TCE) _
T Methanotrophic 0.33 0.94 1.07 0.18 Chang .
j mixed culture &Alvarez-
1 Cohen '
; 1995a
. Methanotrophic 0.35 Alvarez-
, mixed culture Cohen
I &McCarty
, 1991a ;
Mefiylosinus 8.37 1.47 7.49 Oldenhuis '
trichosporium et al. 1991 .
OB3b :
Methanotrophic 0.14 Henry &
mixed culture Grbié
-Galié ;
1991 l
Methylomonas 0.26-0.73 0.16-1.63 Henry & ’
Grbic’:
~Galic’: I
1990 l

  

 

 

on protein content.

 

 

 

 

   

25

Table 2.8 Kinetic coefficients of TCE cometabolic transformation by methanotrophic
communities

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Culture! growth substrate kc 6 (TV )0,”
mg TCE/ mg TCE/ L leg mg TCE/ mg mg TCE/
mg cell—day cell-day growth mg cell
substrate
Methanotrophic mixed 0.009
culture/ methane
Methanotrophic mixed (1011a
culture/ methane
Methanotrophic mixed 1.03 3.84 0.05
culture/ methane (- formate) (- formate) (- formate)
4.17 6.95 0.1
(+fonnate) (+fonnate) (+fonnate)
l' Methanotrophic mixed 0.017 0.05
culture/ methane (- formate) (- formate)
0.034 0.1
(+fonnate) (+fonnate)
Methanotrophic mixed 0.84 0.69 0.043
culture/ methane (- formate) (- formate) (-formate)
4.8 7.9 0.061
(+fonnate) (+fonnate) (+formate)
Methanotrophic mixed 5.] 7.3 0.013 0.036
culture/ methane (+fonnate) (+fonnate)
Methanotrophic mixed 0.84 1.5 0.042
culture/ methane
Methylosinus trichosporium 41.6 18.1
OB3b/ methane (+fonnateL (+fonnate)
Methylosinus trichosporium 54.9 19.] 2.88
OBBbl methane
Methylomonas sp. MM2/ 0.046-0.29 0.51-1.35 0.003-
methane 0.86
Methanotrophic mixed 0.61
culture/ methane
sMMO from Methylosinus 64 4.83
m'chosporium
OB3b/methane

 

 

 

 

 

a. biomass based on protein content.

b. References: (1) Strand et al. 1990; (2) Broholm et al. 1992; (3) Chang and Alvarez-Cohen 1995a; (4) Chang and
Alvarez-Cohen 1995b; (5) Alvarez-Cohen and McCarty I991a; (6) Alvarez-Cohen and McCarty 1991b; (7)
Alvarez-Cohen and McCarty 1991c; (8) Brusseau et al. 1990; (9) Oldenhuis et a1. 1991; (10) Henry and Grbié
-Galié 1990; (11) Fox et a1. 1990.

Phenol-oxidizing bacteria

Another group of microorganism that exhibit cometabolic oxygenase activity are bacteria
that degrade aromatic compounds. aromatic-oxidizing microorganisms usually grow faster

than methanotrophs by one or two orders of magnitude. The faster growth kinetics and

26
relative ease of addition of aromatic compounds give aromatic-oxidizing microorganisms

certain pratical advantages in reactor systems. Some Pseudomonas species produce
aromatic oxygenases that can degrade halogenated alkenes, including TCE (Nelson et al.
1987; Wackett and Gibson 1988). Other research established that Pseudomonas putida
PpG4 would utilize phenol as growth substrate and cometabolize 4—chlorophenol (Saéz and
Rittmann 1991; Saéz and Rittmann 1993). Previous results (Gottschalk 1986) supported
that the initial step of 4-chlorophenol degradation, a monooxygenase-mediated attack on 4-
chlorophenol, requiring Oz and NADPH as cosubstrates. Phenol oxidation supplies the
electrons needed to regenerate the NAPDH cosubstrate. Phenol and toluene are typical
inducing agents for this activity. In aromatic oxygenase systems, TCE is degraded to
formate, carbon monoxide, and glyoxylic acid in pure culture(Wackett and Householder
1989; Winter et al. 1989). P. cepacia G4, degraded TCE to C02, Cl' and unidentified,

nonvolatile products (Nelson et al. 1987; Nelson et al. 1986).

Microorganisms with phenol- or toluene-degrading ability include bacteria, such as
Pseudomonas (Beltrame et al. 1980; Yang and Humphrey 1975), Nocardia (Rizzuti and
Augueliaro 1982), and Bacillus (Buswell 1975); yeast, such as trichosporon (Gaal and
Neujahr 1979); and multicellular fungi, such as Fusarium (Anselmo et a1. 1985). Although
many of these cultures (Pseudomonas strain G4, Pseudomonas putida F1, Pseudomonas
putida BS, Pseudomonas putida PpFl) can transform TCE (Nelson et al. 1987; Nelson et
al. 1986; Nelson et al. 1988; Wackett and Gibson 1988), the ability to degrade aromatic
compounds does not always correlate with the ability to degrade TCE. The MMO system of
the methanotrophs appears to be somewhat more consistent in its ability to degrade TCE

although the rates of oxidation vary substantially.

Phenol-oxidizing microorganisms have demonstrated effective transformation of cis- and

trans- dichloroethylene and trichloroethylene in laboratory and in-situ field studies

27
(Hopkins et al. 1993a). The phenol-oxidizing microorganisms appear to have a much

higher capacity to degrade trichloroethylene than the methanotrophs. Trichloroethylene
degradation of 90 percent has been achieved with 99.8 percent removal of injected phenol.
Separate laboratory studies suggest that the addition of noncompetitive external reducing
power may significantly increase the transfomation potential. Trichloroethylene
transformation capacities were enhanced by the addition of aliphatic compounds, the
greatest enhancement being with formate or lactate (Hopkins et al. 1993b). Kinetic
coefficients for phenol utilization and TCE transformation reported in the literature are

summarized in Table 2.9 - 2.10.

28

Table 2.9 Kinetic coefficients of phenol utilization

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 ture Y Us] a. k. K. K.- Re 8-
l mg ce 1/hr m henol/ m henol/ m henol/ l
"18 Pbenol 11.35.11-111 L g p L g p l
l Mixed culture NA 0. 13]- 5-266 142-] 199 1 l
; 0.363 l
LMixed culture 0.7-0.9 0.66 16.5 634.4 2 l
P putida Sp 0.55 0.1 19 5.27 377 3 g

P. putida 0.53-1.84 0.5-1.23 8-20 4 i

Mixed culture 0.326 19.2 229 5 1

Mixed culture 0.545 0.260 24.5 173 6

(non filament)

Mixed culture 0.616 0.223 5.8 934 6

(filaments)

Mixed culture 0.2] 630.41 2

Mixed culture 0.45 0.1 17 245 7

Mixed culture 0.07 2

Mixed culture 0.0] 1- 8

0.030

P. fluorescens 0.08 9

NocarJia 0.37 9

P. cepacia G4 2.6 0.8 43 10

Mixed culture 0.55 1 1

Mixed culture 0.051- 12

0.135

 

 

 

 

 

 

 

=——————_—_——
Refences: (1) D'Adamo et al. 1984; (2) Auteinrieth et al. 199]; (3) Kotturi et a1. 1991; (4)
SoKol 1988; (5) Szetela and Winnicki 198]; (6) Pawlowsky and Howell 1973; (7)

Beltrame et al. 1980; (8) Tischler and Eckenfelder 1969; (9) Rizzuti and Augueliaro 1982;
(10) Folsom et al. 1990; (1]) Chang and Alvarez-Cohn 1995b; (12) Shih et al. 1996.

 

29

Table 2.10. Kinetic coefficients of TCE cometabolic transformation by phenol-oxidizing
bacteria

 

 

 

 

 

 

 

‘ Culegrtu/owth subsate —
L/mg
mg cell-day 031le growth mg cell
. substrate
! Mixed culture! phenol 0.1 1 0.24 1
. (-phenol) (-phenol)
0.01 0.03
, (+phenol) (+phenol)
' Mixed culture! phenol 0.017 0.031 2
(-phenol) (-phenol)
0.019 0.034
(+phenol) (+phenol)
Mixed culture/ phenol 0.12-0.20 3
Mixed culture/ phenol 0.10 0.35 0.0026- 4
0.11
Pseudomonas cepacia G4/ 0.74 0.40 5
phenol
Pseudomonas putida Fll 0.0162 6
toluene

 

 

 

 

 

 

 

E
a. biomass based on protein content.
b. References: (1) Hopkins et al. 199321; (2) Chang and Alvarez-Cohen 1995b; (3) Coyle et al. 1993; (4) Shih et
a1. 1996; (5) Folsom et al. 1990; (6) Wackett and Gibson 1988.

MODEL NONGROWTH SUBSTRATES

Halogenated hydrocarbons containing one or two carbon atoms constitute a significant
fraction of the hazardous substances from industrial , domestic, and agricultural sources.
These compounds tend to be mobile and persistent in soil and groundwater. Some have the
potential for ozone depletion. For this work, the model compounds studied as nongrowth
substrates were selected hydrochlorofluorocarbons (HCFCs), a
hydrofluorocarbons(HFC), and trichloroethylene(TCE). The ban on CFCs has promoted
the widespread use of HCFCs and HFCs. The presence of hydrogen makes HCFCs and
HFCs more susceptible to tropospheric oxidation than the CFCs, and thus less likely to
migrate into the stratosphere. To date, there is relatively little information on the fate of

HCFCs or HFCs in aquatic environments. Trichloroethylene is a commonly detected

30
groundwater contaminant and is classified as a priority pollutant by the U. S.

Environmental Protection Agency. A detailed discussion of these model compounds and

their known properties for biodegradation is provided in the following sections.

HCFC/CFCS

Chlorofluorocarbons (CFCs) are widely used refrigerants and aerosols in industry and
domestic life. Over the past decade, they have been implicated as agents of depletion of
stratospheric ozone and as contributors to global warming (Molina and Rowland
1974)(Molina & Roland 1974; Rowland & Molina 1975). As a result, worldwide
production of CFCs will be banned under the terms of Montreal Protocol. Nevertheless,
CFCs will continue to be released into the environment due to past production and
continued use. In aerobic aquatic environments, CFCs are recalcitrant, but they are
transformed anaerobically (Denovan and Strand 1992; Lesage et a1. 1992; Lovely and

Woodward 1992; Semprini et al. 1992).

The ban on CFCs has inspired a major research effort to assess two classes of CFC
substitutes - the hydrochlorofluorocarbons (HCFCs) and the hydrofluorocarbons (HFCs).
HCFCs and HFCs are one- and two-carbon aliphatics, similar in structure and physical
properties to the CFCs, but containing one or more hydrogen atoms. The presence of
hydrogen makes HCFCs and HFCs more susceptible to tropospheric oxidation than the
CFCs, and thus less likely to migrate into the stratosphere. To date, there is relatively little
information on the fate of HCFCs or HFCs in aquatic environments. Lessage et al. (1992)
reported transformation of HCFC-123a to HCFC-133 and HCFC-l33b under
methanogenic conditions. DeFlaun et al. (DeFlaun et al. 1992) reported aerobic
transformation of three HCFCs and one HFC by Methylosinus trichosporium OB3b. The
properties and purity of chemicals used as model nongrowth substrates in this work are

summarized in Table 2.1 1.

31
Table 2.1] Properties and purity of HCFCs and HFC evaluated in this work.

 

 

Solubility in Boiling DEnsity,
water,wt%, point,°C, g/cm3, Purity,
Compound Chemical name @25°C @760mm Hg @25°C %
HCFC-22 chlorodifluoro- 0.30 -40.8 1.194 99.9788
methane
HCFC-142b l-chloro-1,1— 0.14 -9.2 1.108 99.9609
difiuoroethane
HCFC-123 1,1-dichloro- 0.21* 27.9 1.48* **
2,2,2-
trifluoroethane
HFC-134a 1,2,2,2- ** -26.2 1.206 99.8483
tetrafluoro-
ethane

 

*@ 21.13C ; **Data unavailable from manufacturers.

Trichloroethylene

Trichloroethylene (T CE) has been widely used in industry for many years as a popular dry
cleaning solvent, an excellent degreasing agent, an extraction agent in decaffeinating coffee,
and in several other ways. Because of improper handling, inadequate disposal techniques,
or accidental spillage, it is commonly found in soil and groundwater near industrial sites
(Barbash and Roberts 1986; Verschueren 1983). The presence of TCE and other low-
molecular-weight chlorinated aliphatic hydrocarbons in groundwater threatens drinking
water supplies (Roberts et al. 1982) and endangers human health because of the toxicity
and suspected or demonstrated carcinogenicity of these chemicals (Miller and Guengerich
1983). In 1976, TCE was included on the EPA list of hazardous substances. It has become
the subject of extensive governmental regulation. Moreover, TCE is partially degraded
anaerobically to vinyl chloride, which is more toxic than TCE and is a known carcinogen
(Parsons et al. 1984; Vogel and McCarty 1985). Therefore, TCE is the most frequently
reported contaminant at hazardous waste sites on the National Priority List of the US.

Environmental Protection Agency .

32
Trichloroethylene (C12C=CHC1) is a synthetic, chlorinated organic chemical that fulfills all

requirements for the degreasing solvent. It has high solvency for oils, greases, waxes, tars,
resins, lubricants, and coolants generally found in the metal-processing industry. TCE is
only slightly soluble in water (about 1100 ppm at 77°F) and forms an azeotrope with water,
resulting in a mixture with a lower boiling point and vapor density. It is considered to be a
highly volatile compound and favors environmental partitioning to the air rather than water.

TCE is destroyed by photooxidation in the atmosphere, with a half-life of about one day.

Table 2.12 General information and properties of trichloroethylene1

 

 

:roperty Value
ChemicaT Abstracts Service (CAS) number 79-01-6
Chemical formula (:2H(313
Molecular weight 131.40
Physical state Colorless liquid
Boiling point 86.7°C
Melting point -73°C
Density 1.4 g/mL at 25°C
Vapor pressure 77 mm Hg at 25°C
Water solubility 1 g/L at 20°C
Henry's constant (dimensionless)2 0.392 at 25°C
Log octanol/ water partition coefficient 2.29
Odor threshold 0.5 mg/L in water;

2.5-900 mg/m3 in air

Air concentration conversion factor 5.46 mg / m3=1 ppm

References: (1)1Ware 1988; (2) Gossett 1987.

 

Methanotrophic transformation of trichloroethylene

In 1985, Wilson and co-workers reported on the possibility of aerobic oxidation of TCE by
soil microorganisms that were provided natural gas as a primary source of energy (Wilson
and Wilson 1985). Since them, the ability of methane-utilizing bacteria to cometabolize
TCE has been reported and confirmed by several researchers (Fliermans et al. 1988; Fogel
et a]. 1986; Little et a1. 1988). It is generally believed that the enzyme methane

monooxygenase (MMO) oxidizes TCE to epoxides, which spontaneously hydrolyzes to

33
glyoxylate and dichloroacetate under acidic conditions, and carbon monoxide and formate

under basic conditions (Henry and Grbié -Galié 1986; Henschler et al. 1979; Little et a1.
1988; Miller and Guengerich 1982). Glyoxylate and dichloroacetate are oxidized to carbon
dioxide by heterotrophic bacteria. Formate and carbon monoxide are oxidized to carbon

dioxide by methanotrophs.

Later, researchers reported that besides products resulting from epoxide hydrolysis,
intramolecular halide or hydride migration can occur yielding 2,2,2-trichloroacetaldehyde
(chloral hydrate) (Fox et al. 1990). Chloral hydrate can be reduced further to
trichloroethanol or oxidized to trichloroacetic acid (Newman and Wackett 1991). Chloral is
toxic and may be responsible for the product toxity observed during TCE transformation by
methanotrophs. Trichloroacetate degraded slowly in one methanotrophic mixed culture
(Uchiyama et al. 1989). Formation of significant levels of Trichloroacetate and epoxide
degradation products by Methylocystis sp. strain M indicates that chlorine migration and
epoxide formation proceed in parallel (Nakajima et a1. 1992). Other research also shows
that these pathways proceed in parallel in Methylocystis trichosporium OB3B (Fox et al.
1990; Newman and Wackett 1991; Oldenhuis et al. 1989). Taken together, these reports
suggest that both pathways occur simultaneously in type II methanotrophs. On the other
hand, reports published to date suggest that type I methanotrophs transform exclusively by
the epoxide pathway (Henry and Grbic’: -Galié 1986; Little et al. 1988). Methanotrophs and

the products produced via transformation pathways are summarized in Table 2.13.

34

Table 2.13 TCE transformation and product formation by methanotrophs

 

 

 

 

 

 

 

 

ehaotrop V _ I Tranmsforatron pth Product reported Referne l
1 Type 1. Methylomonas _ FA“, GA‘, DCAA‘ Henry & Grbié -Galié l
. sp. strain MM2 1986 l
} Type 1, strain 46-] 1# GA, DCAA Little et al. 1988 l
l Type II, Methylosinus l&2# Chloral, TCetOH* Oldenhuis et al. 1989 l
‘ trichosporium OB3b l
. |
Soluble MMO from 1&2 FA, GA, CO, DCAA, Fox et al. 1990 l
i Methylosinus Chloral l
; trichosporium OB3b l
l Methylosinus 1&2 FA, GA, CO, DCAA, Newman and Wackett l
; trichosporium OB3b Chloral, TCAA“, TCetOH 1991 l
l
; -Methylococcus _ Chloral Newman and Wackett ;
1 capsulatus, 1991 i
l -Methylosinus, sporium,
l -Methylosporovibrio I
. methanica 812 I
. l
I Type II, Methylocystis 1&2 FA, GA. DCAA, Chloral, Nakajima er al. 1992 l
2 sp. strain M 1CAA,'ICetOH 1
i l
i Type II, Methylocystis 1&2 'ICAA Uchiyama et al. 1992
1 sp. strain M +
l Xanthobacter
l autotrophicus DA4

 

 

 

l
l
l
1

*FA: formic acid
GA: glyoxylic acid
DCAA: dichloroacetic acid
TCetOH: 2,2,2-trichloroethanol
TCAA: trichloroacetic acid
#Path 1: epoxidation
Path 2: chloride migration

Recent studies indicate that TCE transformation capacity is not only a function of the
availability of reducing power, but also of the specific cometabolized compound and the
toxicity of its transformation products (Alvarez-Cohen and McCarty 1991a; Henry and
Grbic’: -Galié 1991a; Wackett and Householder 1989). Formate addition resulted in
increased initial specific TCE transformation rates and elevated transformation capacity.
Significant declines in methane conversion rates following exposure to TCE were observed
for both resting and formate-fed cells, suggesting toxic effects caused by TCE or its

transfomation products (Alvarez-Cohen and McCarty 1991a; Alvarez-Cohen and McCarty

35
1991b). Not many researchers have reported on the toxicity of the specific products.

Oldenhuis and co-workers suggested that TCE epoxide can be expected to bind covalently
to proteins and nucleic acids. Other possible reactive metabolites that might bind
irreversibly are chloral, dichloroacetyl chloride, and formyl chloride (Oldenhuis et al.
1991). Organisms or communities capable of degrading a large amounts of TCE should

possess detoxification systems or populations that degrade these compounds.

The presence of toxic transformation products can be expected to have some impacts on the
development of microbial communities during long-term TCE exposure. Changes in the
populations are likely related to the level of TCE exposure, turnover of transformation
products and utilization of growth substrate. Lackey et al. (1994) used total-recycle
expanded-bed bioreactors to evaluate the degradation potential of TCE by a microbial
consortium. Ester-linked phospholipid fatty acid profiles (PLFAME) were used to monitor
the change of TCE-affected community during short-term perturbation. The results showed
that a propane-utilizing bacterial biomaker increased as TCE was degraded and propane
consumed. However, the relationship between community structure and extent of TCE

exposure was not clear for these short-term exposures.

MODEL REACTOR SYSTEMS FOR COMETABOLISM

In order to study the development of microbial communities, a culture or community of
microorganisms must be grown under defined conditions. Reactor configurations that have
been evaluated include: completely-stirred tank reactor (Coyle et al. 1993; Landa et al.
1994), fed-batch reactor (Strand et al. 1990), fixed-bed reactors (Strand et a1. 1991;
Strandberg et al. 1989), expanded-bed reactor(Lackey et al. 1994; Phelps et al. 1990) and
multi-stage systems(Alvarez—Cohen and McCarty 199ld; Folsom and Chapman 1991).

This work focuses on dispersed growth systems.

36
Three basic modes of dispersed growth culture are widely used. Simple batch systems are

used to study substrate-sufficient growth with maximum specific growth rate. Continuous
culture in chemostats permits full control over specific growth rate with a given
environment or conversely, the environment may be varied with the specific growth rate
held constant. A unique feature of chemostats is that a time-independent steady state can be
attained which enables one to determine the relationship between microbial behavior
(genetic and phenotypic expression) and the environmental conditions. The last basic mode
is fed-batch systems. In such systems, the culture is provided with a substrate feed which

permits substrate-limited growth with a decreasing specific growth rate.

The three basic reactor systems described in last section affect the process parameters
differently. Characteristically, batch systems show the four phases of growth (lag,
logarithmic, stationary, and decline). In a chemostat culture at steady state, all the
environmental factors are constant. In a variable volume fed-batch system in the "quasi
steady state" all the environmental factors are virtually constant except the growth-limiting
substrate; in constant volume fed-batch system with substrate-limiting growth, all nutrient

concentrations vary throughout the cycle.

Several model systems have been considered for the evaluation of cometabolism of TCE.
The cases here are much different from the traditional growth study involving only growth
substrates. In most cases, both growth and nongrowth substrates were injected into model
systems. Thus, the interactions between both substrates are important. The questions
discussed in these research can be divided into three categories: verification of kinetic
models, evaluation of reactor operations and monitoring of development of microbial

communitines.

37
The presence of toxic transformation products can be expected to have some impacts on the

microbial community structure during long-term TCE exposure. Changes in the
populations are likely related to the level of TCE exposure, turnover of transformation
product and utilization of growth substrate. These factors will have different effects in
simple mixed cultures compared to complex communities and in batch reactors compared to
continuous reactors. In batch reactors, for example, the microbial community is exposed to
a range of growth and nongrowth substrate concentrations. This may select for a more
diverse community with "specialist" organisms that occupy a variety of niches created by
substrate concentration gradients. In contrast, a chemostat favors selection of specific
populations at a fixed specific growth rate. This can be expected to result in a less diverse
culture. Since nongrowth substrates are not mineralized by cometabolizing species and
since heterotrophs are known to play important roles in detoxification, more diverse

cultures should have advantages for cometabolism.

As discussed previously, batch system and chemostat represent two extremes for
cometabolizing nongrowth substrate. One purpose of this work is to assess the effect of
TCE exposure on microbial communities. Therefore, both systems were chosen as model
systems for this study. Compared to conventional batch reactors, sequencing batch reactors
(SBRs) have several advantages for cometabolic transformations. An SBR can alternate
between periods of growth on growth substrates and periods of cometabolism of
nongrowth substrate, eliminating the possibility of competitive inhibition for enzymes
between growth and nongrowth substrates. SBRs also have a great ability to periodically
change environmental condition, selecting or enriching specific microbial populations.
Thus, SBRs offer a better model environment to study the dynamic changes of microbial

communities than conventional batch system.

38
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44

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CHAPTER 3

KINETICS OF COMETABOLISM BY RESTING CELLS"

ABSTRACT

This research investigated the potential for methanotrophic biotransformation of three
HCFCs -- chlorodifluoromethane (HCFC-22); 1-chloro-1,1-difiuoroethane (HCFC-142b);
and 1,1-dichloro-2,2,2-trif]uoroethane (HCFC-123); and one HFC -- 1,2,2,2-
tetrafluoroethane (HFC-134a). All of these compounds were biotransformed to differing
degrees by methanotrophic mixed culture MM 1. Rates of transformation were obtained by
monitoring disappearance of the target compounds from the headspace in batch
experiments. Henry's constants were determined over a range of conditions to enable
estimation of the intrinsic rates of transformation. Intrinsic rates of transformation were
obtained by combining a second order rate expression with an expression describing loss
of transformation activity due to either endogenous decay or product toxicity. For HCFC-
123 and HFC-134a, the independently measured endogenous decay rate for mixed culture
MM] (0.594/day) was sufficient to account for the observed loss of transformation activity
with time. However, the endogenous decay rate did not account for the loss of
transformation activity for HCFC-22 and HCFC-l42b. A model based on product toxicity
provided a reasonable representation of the loss of transformation activity for these
compounds. The order of reactivity was HCFC-22 > HCFC-l42b > HFC-134a > HCFC-
123, with second order rate coefficients of 0.014, 0.0096, 0.00091, and 0.00054 L/mg-
day, respectively. Transformation capacities for HCFC-22 and HCFC-142b were 2.47

 

* This study was published in W, 6: 1-9, 1995.

45

46
and 1.1] ug substrate/mg biomass, respectively.

INTRODUCTION

Cometabolic transfomation of nongrowth substrates under resting cell conditions is a
specific case for cometabolism. Under this condition, reducing power or energy reserve is
consumed for nongrowth substrate transformation with little or no energy return to
microorganisms in the community. Several researchers have chosen resting cell conditions
to study cometabolic transformation because interaction between growth and nongrowth
substrates is not present. Kinetic models for cometabolic transformation by resting cells
have been developed by several researchers (Alvarez-Cohen and McCarty 1991a; Criddle et
a]. 1990; Galli and McCarty 1989; Saéz and Rittmann 1991; Schmidt et al. 1985). Criddle
(1993) proposed a mode] that unified the earlier models. This model assumed that
nongrowth substrate is degraded with saturation kinetics and endogenous decay and that

loss of cometabolic activity due to product toxicity can be incorporated into the cell decay

term.

This chapter focuses on the methanotrophic biotransformation of three HCFCs (HCFC-22:
chlorodifiuoromethane, HCFC-l42b: 1-chloro-1,l-difiuoroethane, and HCFC-123: 1,1-
dichloro-2,2,2-trifluoroethane) and one HFC (HFC-134a: 1,2,2,2-tetrafluoroethane) in a
defined methanotrophic mixed culture. The unified model was used to predict the
cometabolic transformation of HCFCs/HFC. The headspace method was used to monitor
disappearance of target compounds. To obtain intrinsic kinetic data using this method,
Henry's constants are needed. Because these constants were not available for the
compounds studied, Henry's constants were first determined. Disappearance of the target
compounds was then monitored in methanotrophic mixed cultures. Henry's constants were
measured over a range of ionic strengths to enable use of these measurements in

environments beyond those of the present study, such as seawater. Knowledge of

47
methanotrophic transformations should assist in selecting environmentally acceptable

HCFCs and HFCs, modeling environmental fate, developing treatment technologies for
fugitive manufacturing emissions, and remediating future wastewater and groundwater

contamination.

Rationale for experimental work

Chlorofluorocarbons (CFCs) are widely used refrigerants and aerosols in industry and
domestic life. Over the past decade, they have been implicated as agents of depletion of
stratospheric ozone and as contributors to global warming (Molina and Rowland 1974;
Rowland and Molina 1975). As a result, worldwide production of CFCs will be banned
under the terms of Montreal Protocol. Nevertheless, CFCs will continue to be released into
the environment due to past production and continued use. In aerobic aquatic
environments, CFCs are recalcitrant, but they are transformed anaerobically (Denovan and
Strand 1992; Lesage et al. 1992; Lesage et al. 1990; Lovely and Woodward 1992;
Semprini et al. 1992).

The ban on CFCs has inspired a major research effort to assess two classes of CFC
substitutes - the hydrochlorofluorocarbons (HCFCs) and the hydrofluorocarbons (HFCs).
HCFCs and HFCs are one- and two-carbon aliphatics, similar in structure and physical
properties to the CFCs, but containing one or more hydrogen atoms. The presence of
hydrogen makes HCFCs and HFCs more susceptible to tropospheric oxidation than the
CFCs, and thus less likely to migrate into the stratosphere. To date, there is relatively little
information on the fate of HCFCs or HFCs in aquatic environments. Lesage et al. (1992)
reported transformation of HCFC-123a to HCFC-133 and HCFC-133b under
methanogenic conditions. DeFlaun et a]. (1992) reported aerobic transformation of three

HCFCs and one HFC by Methylasinus trichosporium OB3b.

48
MATERIALS AND METHODS

Chemicals

HCFC-22, HCFC-142b and HFC-134a were obtained from Asahi Glass Co., LTD.
(Yokohama, Japan). HCFC-l23 was obtained from Allied-Signal, Inc. (Morristown, NJ ,
USA). All chemicals used in media preparation were ACS grade, and all water used was 18

megaohm resistance or greater.

Analytical techniques

The study compounds were analyzed by withdrawing 0.5 mL of headspace from the test
bottles using a Precision gas-tight syringe and injecting the sample onto a Perkin Elmer
8500 Gas Chromatograph (GC) equipped with a squalene packed column and a flame
ionization detector. The GC was operated isothermally at 90°C with helium as carrier.
Concentrations were obtained from an external standard calibration curve bracketing the

concentration range of interest.

Measurement of Henry's constants

The modified EPICS procedure (Gossett 1987) was used to determine Henry's constants
for each of the target compounds. Pure compounds were dissolved in methanol as stock
solutions. To examine possible cosolvent interferences, five serum bottles with same
amounts of HCFC-l34 but with different methanol content (0 to 5%) were prepared. The
result showed no significant cosolvent effect for methanol levels below 2%. Therefore, all
subsequent measurements were conducted under this condition. For each compound,
Henry’s constant was measured in six 158.8 ml serum bottles: three containing 100
milliliters of distilled water, and three containing 25 milliliters. Both sets of bottles were
sealed with Teflon/rubber septa and aluminum crimp caps. HCFCs/HFC solutions were

injected into each bottle using a 0.5 mL gas-tight syringe. The bottles were then incubated

49
in an inverted position for 24 hrs at the desired temperature (6, 12, 22, 30 and 40°C, all

i0.2°C) on a temperature-controlled shaker, and headspace samples were analyzed by gas
chromatography. To assess the effects of ionic strength on the Henry's constant, six
serum bottles were filled with 100 mL solution, each with different concentration of KC]
(0, 0.2, 0.4, 0.6, 0.8, 1.0 M). These bottles were then analyzed by headspace gas

chromatography.

Culture conditions

Mixed culture MM] , a methanotrophic enrichment obtained from aquifer material at Moffett
Field, California, was used for these experiments (Henry and Grbié -Galié 1991a). This
culture is a stable consortium consisting of one methanotroph and three or four
heterotrophs containing predominantly Grain-negative pleomorphic coccobacilli and
prosthecates as well as some Grain-negative bacilli and cocci. The methanotroph in the
mixed culture expresses soluble MMO similar to that of Methylosinus trichosporium OB3b

under similar growth conditions (Henry and Grbié -Ga]ié 1991a).

Mixed culture MM] was grown in Whittenbury Mineral Medium containing (per liter of
deionized water): 1.0 g of MgSO4-7H20, 1.0 g of KNO3, 200 mg of CaC12-2H20, 3.8
mg of FeEDTA, 0.5 mg of NazMoO4-2H20, 0.5 mg of FeSO4-7HzO, 0.4 mg of
ZnSO4-7HzO, 0.02 mg of MnC12-4H20 0.05 mg of CoC12-6H20 0.01 mg of
NiC12-6H20, 0.015 mg of H3BO3, 0.25 mg of EDTA, 260 mg of KH2PO4, and 330 mg
of NazHPO4. One liter of culture was grown at room temperature (~21°C) in a
continuously stirred 4-liter bottle supplied 30% methane in air at 68 mL/min. Growth
curves were monitored and as stationary phase approached, approximately 10 mL of
culture was transferred to a 1 liter of fresh Whittenbury Medium. Cells were harvested in

mid-log growth phase for biotransformation experiments.

50
Batch biotransformation experiments

HCFCs/HFC degradation studies were performed using 158.8 mL serum bottles sealed
with Telfon/rubber septa and aluminum crimp caps. These bottles were incubated with 100
mL of of Whittenbury Mineral Media plus culture. An appropriate amount (measured as dry
weight) of mixed culture MM] was added to each test bottle. Some bottles were autoclaved
after cell addition (autoclaved cell controls) and others were filled with 100 mL pure water
(water controls). HCFCs or HFC solutions (dissolved in water) were added to each bottle
using Precision gas tight syringes, then vigorously shaken upside-down on a rotary shaker

(250 rpm). Headspace samples were periodically analyzed by GC as described previously.

Modeling transformation of HCFCs/HFCs

To quantify the cometabolic transformation of HCFCs and the HFC studied, a second

order rate expression was combined with an expression describing loss of activity due to

endogenous decay (b ) and product toxicity (qc ITC):

 

qc :chL (1)
=dX/dt:_b_gc_ (2)
X 1;
where:

qc = specific rate of transformation (mg substrate/mg cell-d)
k; = second order rate coefficient (1ng cell-d)

CL = liquid phase concentration of the substrate (mg/L)

[1 = specific growth (or decay) rate (d'l)

X = active organism concentration (mg/L)

b = endogenous decay coefficient (d'l)

51

Tc = theoretical or true biomass transformation capacity (mg substrate/mg cell)

The endogenous decay term b includes loss of activity caused by cell death and by
depletion of reducing power required for monooxygenase activity. A more extensive
discussion of these processes and of Eq. (1) and (2) is provided by Criddle (1993). For

batch transformation of a volatile cometabolic substrate, a mass balance at equilibrium

 

gives:
dM
—— C = XV 3
dt qc L ( )
Me = CL(VL + HcVG) (4)
where:

Mc = mass of substrate (mg)
HC = Henry's constant (-)
VL = liquid volume (L)

VG = gas volume (L)

Batch cometabolic transformation can be described by Eq. (1) - (4). These equations can
be solved simultaneously using a Runge-Kutta algorithm. Two simplifying cases should
be noted. The first occurs when product toxicity is absent or insignificant, I) >> qc /Tc and

p = -b. For this case, Eq. (1) - (4) can be combined and integrated to give the mass of

substrate Mc as a function of time:

—(e-'~-z>}

MC = Mcoe{ b (5)

where:

M60 = initial mass of substrate (mg)

52

X0 = initial active organism concentration (mg/L)

A=nkn+m%)

The second simplifying case is also obtained when product toxicity is the dominant factor

causing loss of transformation activity. For this case, qc ITC >> b, and It = qc /TC and Eq.

(1) - (4) can be combined and integrated to give:

 

(—k',AF:)
M=Mw F: _ m)
X __,;_0_e(-k,AF:)
0 T.
where:

Cw: initial concentration of substrate in the aqueous phase (mg/L)

F=Xo_CLo/7;

Disappearance of the target compounds was modeled with both Eq. (5) and (6). Kinetic
parameters were estimated by nonlinear regression using Systat 5.1 (Systat, Inc.). For all
modeling with Eq. (5), an endogeous decay rate b of 0.594/day was assumed. This value
was independently obtained by Clowater (1992) for loss of trichloroethylene (TCE)
transformation activity in aerated batch cultures of mixed culture MM]. Cultures of MM]
were aerated in the absence of methane and periodically assayed to determine the TCE
transformation rate. The endogenous decay coefficient b was then computed as the slope

taken from a plot of the logarithm of specific TCE transformation rate vs. aeration time.

RESULTS

Henry's Law constants

Measured Henry's Law constants are provided in Table 3.], along with coefficients of

variation. With two exceptions, all coefficients of variation were less than 6%. The effects

53
of temperature on Henry's constant followed the van't Hoff relationship (Gossett 1987).

Results from a linear regression of In H vs. Tl( H in m3-atm/mole; T in K) are provided
in Table 3.2. Salting-out coefficients are listed in Table 3.3. Henry's constants were
relatively insensitive to salinity. For the most sensitive compound studied (HCFC-22), the
ionic strength must exceed 0.35 M to cause a greater than 10% increase in the apparent

Henry's constant.

HCFCs/HFC transformation rates

Figures 3.1-3.4 illustrate the methanotrophic transformation of the target compounds. All
four compounds were degraded to different degrees over the concentration range studied
(900-3000 [1 g/L). Mode] fits obtained using equations 5 and 6 are also illustrated in
Figures 3.1-3.4. Estimates for the kinetic parameters used to describe the transformation

of each compound are summarized in Table 3.4 and 3.5.

54

Table 3.] Measured values of Henry's constant vs. temperature.

 

 

compound temperature Hc fiH , W—
‘C H m3.atm/mol %
HCFC-22 6 0.622 0.0142 8.00
12 1.277 0.0298 5.36
22 1.679 0.0406 5.57
30 2.358 0.0586 3.87
40 3.535 0.0907 1.99
HCFC-142b 6 1.390 0.0318 4.33
12 1.749 0.0409 8.24
22 2.432 0.0588 4.75
30 3.213 0.0798 2.24
40 3.926 0.101 2.83
HCFC-123 6 0.571 0.0131 3.26
12 0.825 0.0193 2.96
22 1.057 0.0256 4.26
30 1.463 0.0364 5.02
40 1.979 0.0508 1.19
HFC-134a 6 1.190 0.0272 5.92
12 1.528 0.0357 2.47
22 2.067 0.0500 0.99
30 2.199 0.0546 3.86

 

*Percent coefficient of variation = 100(SD/mean). Triplicate measurements were performed

for each compound and temperature.

55

Table 3.2 Temperature regression for Henry's constant*.

 

 

 

 

H=exp(A?B-/Tl)
Compound A B r2
HCFC-22 1 1.66 4387 0.956
HCFC-142b 7.363 3011 0.995
HCFC-123 7.805 3373 0.990
HFC-134a 5.714 2588 0.979

 

*Computed using values given in Table 3.1. Units of Henry's
constant are m3-atm/mole; T is in degrees Kelvin.

Table 3.3 Salting-out coefficients (22°C)*.

 

 

 

log Y=kI
Compound k, L/mole r2
HCFC-22 0.1 18 0.996
HCFC-142b 0.0838 0.960
HCFC-123 0.0860 0.997
HFC-134a 0.0761 0.972

 

*Based upon measurements from 0 to 1.0 M
KC1 solution. Salting-out coefficients were
determined by plotting log10 (activity coefficient)

vs. ionic strength: 10g10'Y = kIwhere: y =

activity coefficient (-), k = salting-out coefficient
(L/mole), I=ionic strength (M).

56

 

 

 

 

 

 

 

 

 

 

 

0

m

a f f I
.2

Q

to

8

0 0 WC
3 I AC
a A Live
.5 ———-Eq.5
. Eq.6
0

r: J ; *
O ‘5

o _____________
N 400 -
‘2'
0

u. 200 1-
O

I

0 4 l l 1 l
0 25 50 75 100 125 150

Time (hrs)

Figure 3.1 Biotransformation of HCFC-22 by methanotrophic mixed culture MM].
Fitting parameters are summarized in Tables 3.4 and 3.5. Error bars give standard
deviations for three samples. WC = water control (no cells), AC = autoclaved control,
LIVE = 275 mg/L MM] (dry weight).

57

 

 

 

 

 

 

 

 

 

 

 

1000 *
a 900
a
3 800
3-
“A 700

_l
5 T» 600
‘5 3
c 500
o o
o a
a ,2 400 -
N a. 0 we
I

L.
E 200 - __‘__E:°5
o a
I 100 .. Eq.6
O J i L J J
0 25 50 75 100 125 150

Time (hrs)

Figure 3.2 Biotransformation of HCFC-142b by methanotrophic mixed culture MMl:
model fit based on equation 5 and 6, respectively. Fitting parameters are summarized in
Tables 3.4 and 3.5. Error bars give standard deviations for three samples. WC = water
control (no cells), AC = autoclaved control, LIVE = 275 mg/L MM].

58

 

 

 

 

 

 

 

 

 

 

 

3000

o
o
a
.c
a 2500 I
n
3
g 2000 —*
u-

A
“ _l
,5 B, 1500 -

_ a
o v
5
o 1000 - . we
3 I A0
a 500 _ A Live
:3 —---Eq.5
ti Eq.6

o I I J A l
0 25 50 75 100 125 150

Time (hrs)

Figure 3.3 Biotransformation of HFC-134a by methanotrophic mixed culture MM].
Fitting parameters are summarized in Tables 3.4 and 3.5. Error bars give standard
deviations for three samples. WC = water control (no cells), AC = autoclaved control,
LIVE = 275 mg/L MM].

59

 

 

 

 

 

 

 

 

 

 

 

o
0
cu
.:
a
a
a
o
o
:1
u-
“ 3 2000 r
\
5 m
5 31500 L
c
8
1000 - ° WC
3 I AC
'7 A Live
E 500 - ___-Eq,5
o Eq.6
I
o l 1 I
0 25 50 75 100

Time (hrs)

Figure 3.4. Biotransformation of HCFC-123 by methanotrophic mixed culture MM].
Fitting parameters are summarized in Tables 3.4 and 3.5. Error bars give standard
deviations for three samples. WC = water control (no cells), AC = autoclaved control,
LIVE = 275 mg/L MM].

60

Table 3.4 Kinetic coefficients for HCFC/HFC transformation by
methanotrophic mixed culture MM]. Best fit for the parameters
of equation 5: comparison with TCE.

 

 

b k ' Correlation
Compound (l/day) (Umg-day)a coefficient
2
r
HCFC-123 0.594b 0.00054 0.888
10.00014
HFC-134a 0,594b 0.0009] 0.915
10.00002
HCFC-142b 0.594b 0.0030 0.580
10.0002
HCFC-22 0,594b 0.0043 0.61 1
10.0001
TCE 0.594b 1.4 10.23c 0.998b

 

a Determined by triplicate samples at 95% confidence interval.
b Independently determined by Clowater (1992).
c Clowater (1992)

Table 3.5 Kinetic coefficients for HCFC/HFC transformation by methanotrophic
mixed culture MM]. Best fit for the parameters of equation 6: comparison with TCE.

 

 

T k ' Correlation
Compound (11 g Substrate/mg (Umg-day)b coefficient
cell)a r2

HCFC-123 1 .166 0.00090 0.934
10.264 10.00011

HFC-134a 1.636 0.0011 0.937
10.05] 10.0001

HCFC-142b 1.113 0.0096 0.981
10.068 10.0016

HCFC-22 2.468 0.014 0.984
10.050 10.002

TCE 47 10C 1.33 10.24c 0.997c

 

a b Determined by triplicate samples at 95% confidence interval.
C Clowater (1992).

61
DISCUSSION

Different degrees of transformation were obtained for the compounds studied. DeFlaun et
al. (1992) reported that HCFC-123, HCFC-142b, HFC-134a were not degraded by the
pure culture Methylosinus trichosporium OB3b. In the present study, HCFC-123 and
HFC-134a degraded slowly, while HCFC-142b was transformed at a somewhat higher
rate by mixed culture MM]. A possible explanation for the difference between this work
and the DeFlaun study is that the MM] methanotroph may possess enzymes with greater
reactivity toward HCFCs and HFCs. It is also possible that the heterotrophs present in
mixed culture MM] facilitated transformation. Uchiyama (1992) found that TCE was

mineralized to a greater extent by a mixed culture containing heterotrophs.

Pathways of transformation for the compounds evaluated in this study are not known.
Presumably, in each case, oxygen is inserted at the carbon-hydrogen bond yielding an
alcohol intermediate. Halogenated alcohols undergo further hydrolysis and elimination in
aqueous systems giving rise to a variety of products. For HFC-134a, one of the possible
products is trifluoroacetic acid. Trifluoroacetic acid is thought to be stable in aqueous
environments, and it is an expected oxidation product in the troposphere. Analysis of the
culture medium by ion chromatography after completion of HFC-134a transformation
revealed a peak at a run time corresponding to that of trifluoroacetic acid. Additional

analysis is needed to confirm this tentative identification.

For two of the target compounds - HCFC-123 and HFC-134a, use of Eq. (5) and the
independently measured endogenous decay rate of 0.594 day'l provided a reasonable fit to
the data. It should be noted, however, that the model based on product toxicity (Eq. (6))
also fit the data well, indicating that conclusions about the mechanism for loss of

transformation activity cannot be based on model fit alone.

62
As shown in Table 3.4, Eq. (5) provided a poor fit for HCFC-22 and HCFC-142b

indicating that, for these compounds, another mechanism (besides endogenous decay)
apparently contributes to the loss of transformation activity with time. As shown in
Figures 3.1 and 3.2, Eq. (6) provided a good fit to these data. Thus, product toxicity may

explain the loss of transformation activity for these compounds.

To assist in the interpretation of data, the transformation of the targeted fluorocarbons was
compared with trichloroethylene (TCE). TCE is a useful bench mark for comparison
because many researchers have evaluated methanotrophic transformation of TCE, and there
is an extensive dataset on its transformation kinetics. As indicated by Table 3.5, rates of
transformation for all of the fluorinated compounds studied were considerably slower than
rates of transformation for TCE. Second order rate coefficients were 100 to 1000 times
smaller for mixed culture MM]. Transformation capacities for HCFC-142b and HCFC-22
were ten to twenty times smaller than the values reported for TCE. The rapid loss of
activity for HCFC-22 seems reasonable inasmuch as this compound is structurally similar
to chloroform, a compound previously known to exhibit product toxicity in methanotrophic
mixed cultures (Alvarez-Cohen and McCarty 1991c). For chloroform, the toxic byproduct
is believed to be carbonyl chloride (phosgene). An analogous carbonyl may be formed
from HCFC-22. Alvarez-Cohen and McCarty (19910) reported a chloroform
transformation capacity of 6.5 ug/mg cell, a value somewhat higher than that observed for

HCFC-22.

The results of this work suggest that methanotrophic transformation is not likely to be a
significant sink for the removal of HFCs and HCFCs globally. As indicated in Table 3.5,
the fastest second order rate observed in this study was 0.014 L/mg cell-day for HCFC-22.
Typical microbial densities in the soil and marine environment are on the order of 106 - 107

organisms per cm3 of soil or water. Assuming that active methanotrophs constitute about

63

1% of these communities, first order rates of 10'10 to 10'9 3'1 would be possible for
HCFC-22. In order to have a significant affect (>2-3%) on global lifetime estimates, first

order rates exceeding 103 8'1 are required (Rodriguez et al. 1991).

REFERENCES

1.A1varez-Cohen, L., and P. L. McCarty. 1991a. A cometabolic biotransformation model

for halogenated aliphatic compounds exhibiting product toxicity. Environ. Sci. Technol.
25:1381-1387.

2.Alvarez-Cohen, L., and P. L. McCarty. 1991c. Product toxicity and cometabolic
competitive inhibition modeling of chloroform and trichloroethylene transformation by
methanotrophic resting cells. Appl. Environ. Microbiol. 57:1031 - 1037.

3.Clowater, L. M. 1992. M. S. thesis. Michigan Stata University.
4.Criddle, C. S. 1993. The kinetics of cometabolism. Biotechnol. Bioeng. 41 : 1048-1056.

5.Criddle, C. S., J. T. DeWitt, and P. L. McCarty. 1990. Reductive dehalogenation of
carbon tetrachloride by Escherichia coli k-12. Appl. Environ. Microbiol. 56:3247-3254.

6.DeFlaun, M. F., B. D. Ensley, and R. J. Steffan. 1992. Biological oxidation of

hydrochlorofluorocarbons(HCFCs) by a methanotrophic bacterium. Biotechnology.
10:1576—1578.

7.Denovan, B. A., and S. E. Strand. 1992. Biological degradation of Chlorofluorocarbons
in anaerobic environments. Chemosphere. 24:935-940.

8.Galli, R., and P. L. McCarty. 1989. Biotransformation of 1,1,1-trichloroethane,

trichloromethane, and tetrachloromethane by a Clostridium sp. Appl. Environ. Microbiol.
55:837-844.

9.Gossett, J. M. 1987. Measurement of Henry's law constants for C1 and C2 chlorinated
hydrocarbons. Environ. Sci. Technol. 21:202-208.

10.Henry, S. M., and D. Grbié -Galié. 1991a. Influence of endogenous and exogenous
electron donors and trichloroethylene oxidation toxicity on trichloroethylene oxidation by

methanotrophic cultures from a groudwater aquifer. Appl. Environ. Microbiol. 57:236 -
244.

11.Lesage, S., S. Brown, and K. R. Hosler. 1992. Degradation of Chlorofluorocarbon-
113 under anaerobic conditions. Chemosphere. 24:1225-1243.

12.Lesage, S., R. E. Jackson, M. W. Priddle, and P. G. Riemann. 1990. Occurence and

fate of organic solvent residues in anoxic groundwater at the Gloucester Landfill, Canada.
Environ. Sci. Technol. 24:559-566.

13.Lovely, D. R., and J. C. Woodward. 1992. Consumption of freons CFC-11 and CFC-
12 by anaerobic sediments and soils. Environ. Sci. Technol. 26:925-929.

14.Molina, M. J., and F. S. Rowland. 1974. Stratospheric sink for chlorofluoromethanes:
chlorine atomcatalysed destruction of ozone. Nature. 2492810-812.

15.Rodriguez, J. M., M. K. Ko, N. D. Sze, and C. W. Heisy. 1991. Modeling lifetimes
and sinks for HCFCs/HFCs. Presented at the AFEAS Workshop on Aqueous Loss
Processes for Halocarbons, Washington D. C. .

65

16.Rowland, F. S., and M. J. Molina. 1975. Chlorofluoromethanes in the environment.
Rev. Geophys. Space Phys. 1321-35.

17.Saéz, P. B., and B. E. Rittmann. 1991. Biodegradation kinetics of 4-chlorophenol, an
inhibitory co-metabolite. Research J. WPCF. 63:838-847.

18.Schmidt, S. K., S. Simkins, and M. Alexander. 1985. Model for the kinetics of
biodegradation of organic compounds not supporting growth. Appl. Environ. Microbiol.
50:323-331.

19.Semprini, L., G. D. Hopkins, P. L. McCarty, and P. V. Robert. 1992. In-situ
transformation of carbon tetrachloride and other halogenated compounds resulting from
biotransformation under anoxic conditions. Environ. Sci. Technol. 26:2454-2461.

20.Uchiyama, H., T. Nakajima, O. Yagi, and T. Nakahara. 1992. Role of heterotrophic
bacteria in complete mineralization of trichloroethylene by Methylocystis sp. strain M.
Appl. Environ. Microbiol. 58:3067-3071.

CHAPTER 4
KINETICS OF COMETABOLISM BY GROWING CELLS“

ABSTRACT

An unstructured model for cometabolism is presented and verified experimentally for a
defined methanotrophic mixed culture. The model includes the effects of cell growth,
endogenous cell decay, product toxicity, and competitive inhibition with the assumption
that cometabolic transformation rates are enhanced by reducing power obtained from
oxidation of growth substrates. A theoretical transformation yield is used to quantify the
enhancement resulting from oxidation. A systematic method for evaluating model
parameters is described. The applicability of the model is evaluated by comparing
experimental data for methanotrophic cometabolism of TCE with model predictions from
independently measured model parameters. Propogation of errors is used to quantify
errors in parameter estimates and in the final prediction. The model successfully predicts
TCE and methane transformation successfully for a wide range of concentrations of TCE

(0.5 - 9 mg/L) and methane (0.05 - 6 mg/L).

INTRODUCTION

Many compounds of environmental and toxicological significance are transformed by
cometabolism. In this study, cometabolism is defined as transformation of a nongrowth
substrate by cells that are growing in the presence of growth substrate or by resting cells

in the absence of growth substrate (Criddle 1993; Horvath 1972). With oxidative
* This study was submitted to bigtghnglggy and bioengineering, Jun. 1996.

 

66

67
cometabolism, the growth substrate may compete with nongrowth substrate for positions

at the enzyme active site, hindering transformation of the nongrowth substrate. However,
in the absence of the growth substrate, the ability to sustain cometabolic transformation is
eventually exhausted. Loss of transformation capacity may also result from damage to

cellular material caused by toxic transformation products.

Recent studies indicate that cell inactivation can be quantified and incorporated into
kinetic models by introducing a parameter termed biomass transformation capacity. This
concept has been applied to quantify degradation kinetics for serveral oxgenase-
expressing cultures with a range of chlorinated compounds (Alvarez-Cohen and McCarty
1991a; Chang and Alvarez-Cohen 1995b; Chang et al. 1993; Chang and Criddle 1995;
Hopkins et al. 1993b). Alvarez-Cohen and McCarty (1991a) assumed that biomass
transformation capacity was equal to the mass of nongrowth substrate ultimately
degraded divided by the initial biomass used. Criddle (1993) defined a “theoretical”
biomass transformation capacity by subtracting the effect of endogenous decay. The later
definition represents a theoretical maximum value in the absence of external reducing
power. Studies indicate that transformation capacity is not only a function of the
availability of reducing power, but also of the specific cometabolized compound and the
toxicity of its transformation products (Alvarez-Cohen and McCarty 1991c; Henry and

Grbié -Gali<’: 1991a; Wackett and Householder 1989).

Several models have been proposed to describe the cometabolic transformations of
nongrowth substrate in the absence of growth substrate, many of which have been
reviewed by Criddle (1993). Saéz and Rittmann (1991; 1993), for example, linked
biomass decay with transformation of nongrowth substrate. Models have also been
proposed to describe cometabolic degradation in the presence of growth substrates.

Broholm et al. (1992) and Strand et al. (1990) modeled the interaction between growth

68
and nongrowth substrates by competitive inhibition, neglecting product toxicity and

reducing power effects. Anderson and McCarty (1994) proposed a biofilm model that
incorporated both product toxicity and competitive inhibition, but did not account for
reducing power limitations. Recently, Chang and Alvarez-Cohen (1995a) proposed a
general model that includes reducing energy explicitly as a limiting reactant during
cometabolism. They applied the model to describe degradation of TCE by
methanotrophs. The effects of reducing power were separated from toxicity effects and
quantified by supplying the cells with formate. For methanotrophs, formate provides
energy as NADH, but does not support growth. Thus, the experimental approach of
Chang and Alvarez-Cohen is somewhat specific to organisms for which energy substrates

can be identified that do not support growth.

In this paper, a general model is presented that combines the effects of cell growth,
endogenous cell decay, product toxicity, and competitive inhibition with the assumption
that cometabolic degradation rates are enhanced by reducing power obtained from
oxidation of growth substrates. The proposed model does not require use of energy
substrates, such as formate. A theoretical transformation yield is used to quantify the
enhancement of cometabolism resulting from oxidation of the growth substrate (Chang et
a]. 1993; Criddle 1993). A systematic method for evaluating model parameters is
developed. The applicability of the model is evaluated by comparing experimental data
for methanotrophic cometabolism of TCE with model predictions from independently

measured model parameters.

Rationale for experimental system

The experimental system selected for investigation in this work was methanotrophic
transformation of trichloroethylene. In 1985, Wilson and co-workers reported aerobic

oxidation of TCE by soil microorganisms provided natural gas as a primary source of

 

 

69
energy (Wilson and Wilson 1985). Since then, the ability of methane-utilizing bacteria to

cometabolize TCE and other chlorinated organic solvents has been firmly established by
many researchers (Fliermans et a]. 1988; Fogel et al. 1986; Little et al. 1988). The
enzyme methane monooxygenase (MMO) oxidizes TCE to an epoxide, which
spontaneously degrades to intermediates that can be further metabolized, including
glyoxylic acid, dichloroacetic acid, carbon monoxide, and formate (Little et al. 1988;
Uchiyama et al. 1992) . Some researchers have reported that, in addition to products
resulting from epoxide hydrolysis, intramolecular halide or hydride migration can occur,
yielding 2,2,2-trichloroacetaldehyde (chloral hydrate). Chloral hydrate can be reduced to
trichloroethanol and oxidized to trichloroacetic acid. All these products are potentially
toxic and may cause cellular inactivation (Fox et al. 1990; Newman and Wackett 1991).
For TCE transformation, formate addition resulted in increased initial specific
transformation rates and elevated transformation capacity. Significant declines in
methane conversion rates were observed following exposure to TCE for both resting and
formate-fed cells, suggesting toxic effects by TCE or its transformation products
(Alvarez-Cohen and McCarty 1991b; Alvarez-Cohen and McCarty 1991c). Oldenhuis
and co-workers suggested that TCE epoxide can bind covalently to proteins and nucleic
acids. Other possible reactive metabolites that might bind irreversibly are chloral,
dichloroacetyl chloride, and formyl chloride (Oldenhuis et al. 1991; Oldenhuis et al.
1989).

MATERIALS AND METHODS

Culture and culture conditions

The methanotrophic culture used for these experiments was a mixed culture originally
derived from aquifer material at Moffett Field, California (courtesy S. M. Henry). This
culture is a stable consortium consisting of one methanotroph, one hyphomicrobium, and

' several heterotrophs containing Gram-negative thin and fat rods as well as some gram-

70
positive rods and cocci. The methanotroph in the mixed culture expresses soluble MMO

similar to that of Methylosinus trichosporium OB3b under similar growth conditions
(Henry and Grbic’ -Galié 1991a). Mixed culture MM] was grown in Whittenbury Mineral
Medium containing (per liter of deionized water): 1.0 g of MgSO4-7H20, 1.0 g of
KNO3, 276 mg of CaSO4-2H20, 3.8 mg of FeEDTA, 0.5 mg of NazMoO4~2H20, 0.5
mg of FeSO4-7H20, 0.4 mg of ZnSO4-7H20, 0.02 mg of MnC12-4H20 0.05 mg of
CoC12-6H20 0.01 mg of NiC12-6H20, 0.015 mg of H3BO3, 0.25 mg of EDTA, 260 mg
of KH2PO4, and 330 mg of NazHPO4. One liter of culture was grown at room
temperature (~21°C) in a continuously stirred 2-liter bottle supplied 30% methane in air
at 68 mL/min. Growth curves were monitored and as stationary phase approached,
approximately 10 mL of culture was transferred to 1 liter of fresh Whittenbury Medium.

Cells were harvested in mid-log growth phase for biotransformation experiments.

Analytical methods

A TCE-saturated water solution was used as the spike solution in all experiments. The
spike solution was prepared by adding excess TCE (991% pure ACS reagent, Aldrich
Chemicals Co., Milwankee, WI) to a 250 ml glass bottle capped with TFE-lined Mininert
valve. The bottle was vigorously shaken and allowed to settle at least 24 hrs. The upper
layer of the solution was transferred to another bottle and capped with a Mininert valve.
The spike solution was stored in a refrigerator until needed. One hour before use, it was

shaken again and allowed to settle.

TCE was analyzed by withdrawing 0.1 ml of headspace from the test bottles using a 0.5
ml Pressure-Lok Series A-2 gas syringe and injecting the samples onto a Hewlett Packard
5890 gas Chromatograph (GC) equipped with a column (DB624, 30m x 0.53mm ID.) and
a flame ionization detector. The GC was operated isothermally at 90°C with helium as

' carrier. The temperature at the injection port and detector was 250°C.

71

Methane and oxygen were analyzed by withdrawing 0.1 ml of headspace from the test
bottles using a 0.5 ml Pressure-Lok Series A-2 gas syringe and injecting the samples onto
a Hewlett Packard 5890 series H gas chromatograph equipped with a column (6 ft x 1/8
in SS packed with 80/ 100 washed molecular sieve 13X) and a thermal conductivity
detector. The GC was operated isothermally at 50°C with helium as carrier. The

temperature of the injection port and detector were 50°C and 90°C respectively.

Cell biomass was determined on a dry weight basis using 0.2 pm filters (Gelman
Sciences Inc., Ann Arbor, MI). The filters were prepared by first soaking them in mineral
media for 10 minutes, rinsing on a vacuum filter with deionized water, drying overnight
in a 103°C oven, and cooling in a desiccator until needed. The filters were weighed, and
once a known amount of culture was filtered through them, they were rinsed, dried,

cooled and reweighed.

Batch biotransformation experiments

TCE degradation studies were performed using 25 ml glass vials capped with teflon-lined
Mininert valves. These vials were incubated with 5 mL of of Whittenbury Mineral Media
plus culture. An appropriate amount (measured as dry weight) of mixed culture MMl was
added to each test vial. TCE solutions (dissolved in water) were added to each bottle
using Precision gas tight syringes. Methane were withdrawn from Scotty II cyclinders
(99.0% CH4, Alltech Associate, Inc., Deerfield, IL) at fixed exit pressure and injected
into batch vials. After adding substrates, the vials were vigorously shaken upside-down
on a rotary shaker (250 rpm). Headspace samples were periodically analyzed by GC as

described previously.

72
Model development

A cometabolic model was evaluated that included terms for the loss of microbial biomass
or enzyme activity caused by autooxidation (endogenous decay), proteolysis, depletion of
cofactors (such as NADH), product toxicity, and suicide inactivation. A theoretical
discussion of this model is provided elsewhere (Criddle 1993). For cometabolism in the
presence of growth substrate, the following equations provide a complete mathematical
description of the specific growth rate and the specific rates of utilization of the growth

and the nongrowth substrates throughout the growth and decay periods.

 

 

s
=k
q: 3(KS+S) (1)
=(T +k)( C > (2)
q‘ "q" ‘ KC+C
fl=qu.-- 1'- (3)

C

When there is competitive inhibition between the growth substrate and the nongrowth
substrate, (Ks)obs and (Kc)ob3 replace K, and Kc , respectively, in Eq. (1) and Eq. (2),

where:

C

(K: )obs : Ks(l + 2:) (4)
S
(12),,” — Kc(1 +7) (5)

(5

In the absence of growth substrate, the mode] simplifies to:

C
K+C

 

q. = k.( ) (6)

(7)

73

Two important stoichiometric parameters are the observed transformation

capacity,(Tc)obs, and the observed transformation yield, (7;)053- (72),,“ is obtained by

dividing -qc by u:

I
(72).)!» = b— qus +1. (8)
q. T.

 

For resting cells (qs =0), Eq. (8) simplifies to:

(72),“ =—,,——'—, (9)

_+_
q. T

C

For resting cells, the observed transformation capacity is determined by the theoretical
transformation capacity, 7;, and by the ratio of the endogenous decay rate b to the

specific rate of TCE transformation, qc. The observed transformation yield, (7;)0b3, is

obtained by dividing qc by qs.

In the absence of cometabolism, electrons from the growth substrate are used exclusively

for cell synthesis and respiration so f, + fl = I, where f: = fraction of electrons for cell

synthesis and fi = fraction of electrons for energy generation. In cometabolic reaction,

however, electrons are consumed for growth, respiration and cometabolism. In this case,
f, + fl + fw = I , where fa, = fraction of electrons used for cometabolism (Criddle 1993;
Criddle et al. 1991). For oxygenase-mediated reactions, two moles of electrons are
consumed for every mole of nongrowth substrate transformed, but this ratio will decrease
if the byproducts of transformation are further oxidized by the cometabolizing

community.

74
For batch transformation of a volatile cometabolic substrate in the presence of growth

substrate, a mass balance for growth and nongrowth substrates at equilibrium gives:

 

 

_nd =chVL (10)
(It
MC :C(VL+HCCVG) (11)
dM
_ s -_—, XV 12
dt qs L ( )
Ms =S(VL+HCSVG) (13)

Batch cometabolic transformation in the presence of growth substrate can be described by 5
Eq. (1), (2), (3), (4), (5), (10), (11), (12) and (13). Once the parameters of the model (b,
kc, Kc, 7;, k,. K3, Y, K.

tc’

K“, 7;) are determined, these equations can be solved
simultaneously using a Runge-Kutta numerical method. Simplified cases of the model
(C << Kc in the absence of growth substrate) have been previously verified (Chang and

Criddle 1995).

Model verification

The model evaluated in this work was verified using the procedure illustrated in Figure
4.1. Experimental data were compared with predictions from separate measurements of
the kinetic parameters. Using the measured parameters, degradation rates for methane and
TCE were predicted for specified initial conditions. The predictions were evaluated

experimentally. Four independent series of experiments were conducted to measure the

maximum specific rate of utilization of substrate (kc and IQ). the half-saturation

. coefficient (K. and K,), growth yield (Y), endogenous decay constant (b) and the

75

theoretical transformation capacity (72) in the absence of endogenous decay. To measure

the remaining parameters (KINKL, and 7;), an additional set of experiments was

conducted over a range of concentrations of growth substrate with high initial TCE
concentrations. Details of the experimental evaluation for each of these parameters is
described in the following sections. Propogation of errors was used to quantify errors in
parameter estimates and in the final prediction (Mandel 1984). All non-linear parameter
estimates were obtained by nonlinear regression using Systat 5.2.1 (Systat, Inc.,

Evanston, IL).

Sensitivity analysis (Robinson and Characklis 1984; Robinson 1985) were performed to
evaluate the uniqueness of parameter estimates and the relative importance of parameters

over the range of substrate concentration. Three equations derived from model, Eq. (1),
(6), and (2) were used to estimate three sets of parameters (k, and K3 , kc and Kc , KL, and

Ty). The derivatives of dependent variable with respect to each set of parameters

(qu I dk, and dqs/sz, dqc ldkc and dqc lch,qu/dKis and dqc/d7; ) were evaluated for
a range of substrate concentration. If sensitivity equations for each pair of parameters are
not multiples of each other over a wide range of substrate concentration, a unique
combination of parameters can be estimated from the data set. To determine the relative
importance of each parameter on the specific rate of transformation of growth and
nongrowth substrates, the derivatives of qc and qs with respect to related parameters were
also evaluated over a range of substrate concentrations.

-x_1_1__ 1.1 .0; '_ -. e 0. f u at” f h nu: wt sustrat and half-sat ratin
' t e w r

K.- and kc were determined by adding a range of concentrations of TCE to batch cultures

of resting cells that were fully induced for the desired cometabolic activity. The initial
concentrations spanned a range that bracketed the concentration above which specific rate

' of transformation are maximum and the concentration corresponding to the half-

76
saturation coefficient. The initial slope of the resulting degradation curves for each initial

concentration was determined. A nonlinear regression on Eq. (6) was used to estimate kc

and Kc.

.11 1 ucf at 0. 1112-10! ofrowth us ..t- 1. -a :01 cof' in of
w s rv i

K, and k3 were determined by adding a range of concentrations of growth substrate to
batch cultures. The initial concentrations spanned a range that bracketed the
concentration above which specific rate of transformation are maximum and the
concentration corresponding to the half-saturation coefficient. The initial slope of the
resulting degradation curves for each initial concentration was determined. A nonlinear
regression on Eq. (1) was used to estimate k, and K. Y was determined by measuring
the increase of dry weight of biomass with the consumption of growth substrate during a
period of time. The value was obtained during the growth phase, before decay of cell

biomass was significant.

En n f't

Subsamples were withdrawn from the decaying culture and spiked with high

concentrations of TCE, so that Co >> K. The initial slope of the resulting degradation

curve was proportional to the concentration of cometabolizing cells. The active fraction
remaining at any time was computed by dividing the initial slopes for each subsample by
the initial slope at the beginning of the decay period. A semilog plot of active fraction vs.

time yields a straight line with slope of -b.

I] .1 E . i

Once the endogenous decay coefficient b and maximum specific rate of utilization kc

77

were quantified, 7; was determined by adding a high concentration of TCE to a batch
culture of resting cells . For C0 >> K; , Eq. (6) simplifies to q; =kc. By combining this

result with Eq. (9) and allowing time to become infinite, the actual or observed

 

 

transformation capacity, (7; )ohsis given by
dM VL+HVdC VL+HV Co— —C,, 1
(T,),,,_,= : =(——£—G>— =( C: 6x )=—-———— ,
VLdX VL dX VL X0 -b—+—
kc T.
7; was calculated from the above relationship.
[ICL‘ 1. ‘f'l.1.!_ 1"] ° ioO‘M'ioanfii frw _1, - rt 0. or: wh
. trat "zain an inhibiti oeffi ien f n wrw 1 . t 1 e 01 ° OWL!

substrate utilization

To evaluate these three parameters, initial specific rates of utilization of growth substrate
and nongrowth substrate were measured over a range of concentrations of growth

substrate with high initial TCE concentration (10 mg/L). A nonlinear regression on Eq.
(1), (2), (4) and (5) with previously determined values for k; , K; , k; and Kc was used to

estimate K1 , K; and 7;.

RESULTS AND DISCUSSION

Cometabolism is a complex phenomenon, especially when both growth and nongrowth
substrates are simultaneously present. For oxygenase-mediated reactions, nongrowth
substrate competitively inhibits utilization of the growth substrate, yet utilization of
growth substrate is needed for sustained transformation of nongrowth substrate. The

model evaluated in this work attempted to capture this paradox.

In this research, a systematic method was developed to predict simultaneous degradation

78
of growth and nongrowth substrates. Parameters for growth and nongrowth substrate

degradation were first measured alone in the absence of competitive interactions.
Thereafter, parameters indicating interaction between growth and nongrowth substrates

(K.

I; , K; and 7;) were measured in the presence of both substrates.

The sensitivity equations for each pair of parameters are not multiples of one another for
the wide range of substrate concentration (Figure 4.2). This implies that unique
combination of parameters can be estimated from the data set. Sensitivity equations with
respect to parameters in Eq. (1), (2), (4), and (5) were also evaluated. The results show
much greater sensitivity to maximum specific rate of utilization of growth and nongrowth
substrates than to the respective half-saturation coefficients. Of all parameters, K1: was
the most sensitive parameter affecting the specific utilization rate of nongrowth substrate.

K‘; was less sensitive. The sensitivity equation with respect to K is reached a maximum at

the lower concentration of growth substrate.

79

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Adequate amount
of cells in vials
(fully induced)
Measure Measure ks , Ks& Y Measure kc & Kc Measure
endogenous for a series of growth for a series of transfo-
decay substrates in the nongrowth substrates rmation
constant, absence of nongrowth in the absence of capacrty,
b substrate growth substrate TC
Measure Kic for a Measure Kis &Ty for
series of growth a series of growth
substrates and high substrates and high ‘9—
initial cone. of initial conc. of
nongrowth substrate nongrowth substrate

 

 

 

l

Solve model with
Runge-Kutta
numerical method

1

Prediction of degradation
of growth & nongrowth
substrates

 

 

 

 

 

 

 

 

Figure 4.1 Approach for prediction of degradation of growth and nongrowth substrates
by the proposed model.

(a)

sensitivity

Parameter

(b)

sensitivity

Parameter

80

 

   

0.15 dqs/dks

   

.0
—L

0.05

-0.05 -

“
§
‘-
Q
‘-
~-
‘-
‘--
‘--
-
‘--
-_-

-0.1

 

 

 

-oo15 l l l l
0 0.5 1 1.5 2 2.5

Methane concentration (mg/L)

0.8

 

0.7 -

 
 

0-5 ” dqc/dkc

  
  

0.5

0.4

0.3

0.2

 

 

0.1
dQC/ch
O ................................................ ..
0.1 l J l l

TCE concentration (mg/L)

 

81
(c

v

 

9°
01

 

10 mg/L TCE

 

 

 

sensitivity

N
I

_L
01
I

 

Parameter

dQC/dTy

 

 

0 __----"' 'l'

0 0.5 1 1.5 2 2.5 3
Methane concentration (mg/L)

Figure 4.2 The sensitivity equations for parameters estimated nonlinearly from model,
concentrations shown here are the ranges for each parameter determination.

The half-saturation coefficients (K, and K;) are often assumed to be equal to the

inhibition coefficients for the respective substrates (KL; and KC) (Alvarez-Cohen and

McCarty 1991c; Anderson and McCarty 1994; Broholm et a]. 1992; Chang and Alvarez-
Cohen 1995a). Our data indicate that they were significantly different under the present
experimental conditions (Table 4.1). This may be caused by the fact that the
measurements were performed with whole cells rather than purified enzymes. Factors
other than competition for the active site of the enzyme may influence the interactions of
growth and nongrowth substrates, e.g., reductant supply or substrate transport to the

enzyme (Landa et a]. 1994). Thus, the assumption that both values are the same and that

82
the substrates are substitutable appears to be inappropriate in this case. A similar

observation was reported for toluene and TCE by Landa et a]. (18).

Independently measured kinetic parameters for TCE degradation are summarized in
Table 4.1. Using these parameters, the mode] was solved numerically to predict methane
and TCE degradation under various conditions. Figure 4.3 illustrates model predictions
and experimental data for batch transformation of growth substrate in the presence of a
high initial TCE concentration. To confirm the consistency of predictions, further
independent batch degradation experiments were conducted with lower initial TCE
concentrations (Figure 4.4). Methane degraded more slowly in all cases when TCE was
present, indicating that methane utilization was strongly inhibited by TCE transformation.
The results show that the model can predict methane and TCE degradation with

reasonable accuracy.

Addition of a limited level of methane enhanced TCE degradation over a specific range
of methane concentrations (Figure 4.5). However, competitive inhibition between growth
and nongrowth substrates also played an important role. Thus, further increases in
methane concentration eventually decreased TCE degradation. This result is in agreement
with the observations of Chang and Alvarez-Cohen (Chang and Alvarez-Cohen 1995b).
The effect of methane is less significant for lower initial TCE concentration. Also,
maximum degradation rates for TCE are achieved at lower methane concentrations for
lower initial TCE concentrations. This phenomenon can be explained by competitive
inhibition between growth and nongrowth substrates. When TCE concentration is low,
methane has more chance to occupy active sites in the methane monooxygenase, and

TCE transformation is inhibited.

83

Table 4.] Kinetic and stoichiometric parameters for methane utilization, growth, and
TCE degradation for methanotrophic mixed culture MM].

 

Parameters

maximum specific rate of utilization of
methane, k;

half-saturation coefficient of methane, K3

maximum specific rate of utilization of
TCE, k;

half-saturation coefficient of TCE, Kc
theoretical transformation capacity in the
absence of endogenous decay, 7;
first-order endogenous decay constant, b
observed yield, Y

inhibition coefficient indicating the effect
of methane on TCE utilization rate, Ki;

inhibition coefficient indicating the effect
of TCE on methane utilization rate, Ki;

growth substrate transformation capacity,
T

.V

Value1
3.77 (+/- 0.83) mg/mg cell-day

6.85 (+/- 1.86) mg/L
0.152 (+/- 0.018) mg/mg cell-day

1.94 (+/- 0.46) mg/L
0.0602 (+/- 0.0005) mg TCE Img cell

0.549 (+/- 0.044) /day
0.426 (+/- 0.023) mg cell/mg methane
0.119 (+/- 0.052) mg/L

10.8 (+/- 1.45) mg/L

4.01 (+/- 1.20) mg TCE/mg methane

1. Values represent the 95% confidence interval for triplicate data.

Table 4.2 Stoichiometry of TCE cometabolism by methanotrophic mixed culture MM]

for different initial TCE concentrations.

 

 

Initial TCE Initial CH4 ATCE/ACH4 AOzlACH4 AX/ACH41’2

concentration concentraton (mole TCE/ (mole 02/ (mole cells/

(mg/L) (mg/L) mole CH4) mole CH4) mole CH4)

0 6.42 0.88 0.059

0.98 6.19 0.003 1.01 0.054
3.98 6.88 0.232 2.09 0.008
10.2 6.73 0.563 2.35 0
17.2 7.03 0.759 2.34 -0.021

 

1. The formula of cells was assumed C 5H7OzN.

 

2. Yields were measured after two-day incubation.

84

Table 4.3 Electron flow in methanotrophic mixed culture MM] for different initial TCE
concentrations.

 

 

Initial TCE Initial CH4 Fraction of Fraction of Fraction of
concentration concentraton electrons for electron for electron for
(mg/L) (mg/L) cometabolism, energy, fe synthesis, fs 2’3
fco 1
0 6.42 0 0.44 0.832
0.98 6.19 0.008 0.50 0.760
3.98 6.88 0.058 1.05 0.1 19
10.2 6.73 0.141 1.18 0
17.2 7.03 0.190 1.17 0

 

 

1. Assumes two moles of electrons required per mole of TCE transformation.
2. The formula of cells was assumed C5H702N.
3. Yields were measured after two-day incubation.

Table 4.4 The effect of methane concentration on observed transformation yield when
both substrates are simultaneously present: comparison of measured and predicted values.

 

 

 

Initial TCE Methane
concentration concentration Observed transformation yield (mg TCE/mg CH4)
(mg/L) (mg/L) Predictedlv2 Measured2
4.] 0.8 0.88+/-0.42 0.94+/-0.22
1.6 0.52+/-0.25 0.60+/-0.13
3.4 0.28+/-0.13 0.22+/-0.02
4.4 0.22+/-0. 10 0. 14+/-0.03
6.4 0. 15+/-0.07 0.12+/-0.03
8.5 0.6 1 .99+/-0.89 1.84+/-0.36
1 .2 1 .34+/-0.62 0.74+/-0.09
2.9 0.73+/-0.35 0.52+/-0.08
3.4 0.65+/-0.30 0.50+/-0.06
5.8 0.42+/-0.19 0.38+/-0.05

 

1. Errors calculated from the law of propagation of error.
2. Values represent the 95% confidence interval for triplicate data.

85

 

 

 

 

 
 
   

 

 

9.0

8.0
A
_l
3, 7.0 -
E I: TCE,2
v 6.0 " q- I
c 50 _ 1 TCE,1 E
o .
I:
e 4.0 -
on
g 3.0 1
0 Methane,1
g 2.0 g g,
0

1.0

Methane.2
0.0 ‘ ___.__._._ ‘
0.0 0.5 1.0 1.5 2.0

Time (days)

Figure 4.3 Biotransformation of TCE and methane by methanotrophic mixed culture:
comparison with model predictions (for different initial methane concentration). Error
bars indicate the 95% confidence interval for triplicate samples.

86

 

 

 

 

 

 

 

 

9.0
8.0
A
d
m 7.0 "
E
"' 6° ' TCE1
: _ I n
O 5.0
"3
h 4.0 P
on
5 3.0 -
2
O 2.0 "
O Methane,1,2
1.0 .————111 4
.. TCE.2
0.0 I J l
0.0 0.5 1.0 1.5 2.0
Time (days)

Figure 4.4 Biotransformation of TCE and methane by methanotrophic mixed culture:
comparison with model prediction (for different initial TCE concentration). Error bars
indicate the 95% confidence interval for triplicate samples.

87

 

 

 

 

 

(a)
1.8
3 1.5
A
g $1.4
GU
1: ' 1.2
'6;
E 8 1
m 08
52
U\ 0.6
U'lu
50
0|- 0.4
a. 0.2 ._
0

 

 

 

Methane concentration (mg/L)

Figure 4.5 The effect of methane concentration on TCE and methane degradation rate
when both substrates are present at same time. (a) Initial concentration of TCE is 8 mg/L
and (b) Initial concentration of TCE is 4 mg/L. Error bars indicate 95% confidence
interval. Dashed lines indicate error range for the prediction.

88

 

 

 

 

(b)
2.5 f f
. 4mg/LTCE
o
A
01>.
gm
=3
“
0:
Em
o
m
Em
0E
\
Um
$3
a
a'

 

 

 

 

Methane concentration (mg/L)

Figure 4.5 The effect of methane concentration on TCE and methane degradation rate
when both substrates are present at same time. (a) Initial concentration of TCE is 8 mg/L
and (b) Initial concentration of TCE is 4 mg/L. Error bars indicate 95% confidence
interval. Dashed lines indicate error range for the prediction.

89
The effect of nongrowth substrate on stoichiometry of cometabolic transformation was

also evaluated. The results (Table 4.2) show that an increase in TCE concentration
increases the observed transformation yield. It appears that methane is more efficiently
used for transformation of TCE at high concentrations of TCE. This result is confirmed
by the results in Table 4.4 and Figure 4.7. For higher ratios of TCE to methane, the
cultures exhibited net decay after two days of incubation. Therefore, some minimum level
of methane was needed to sustain transformation of TCE. The amount of methane needed
to sustain transformation of TCE was related to incubation time in a batch system.
Microorganisms can not sustain transformation of TCE if TCE concentration is too high
with respect to methane. The stoichiometric ratio for oxygen to methane consumed
increases with increasing TCE concentration. Electron flow calculations (Table 4.3)
indicate that almost all electrons supplied by methane go to energy generation and TCE
transformation with none left for cell synthesis at high TCE concentrations. Under these
conditions, utilization of growth substrate is inhibited by transformation of nongrowth
substrate, and cell growth can not occur. In Table III the sum of f; , f; and fa, exceeded
1. This indicates some error in the electron balance assumptions- perhaps due to errors in
the assumed biomass formula or, more likely, the assumption that TCE transformation
byproducts are not further utilized. Calculation of fco assumed two moles of electrons
were required per mole of TCE transformed. In a mixed culture, this assumption is likely

to be incorrect.

Inactivation of methane monooxygenase can be caused by the availability of reducing
power, endogeneous decay, and transformation of TCE. When there are no external
energy sources, a theoretical transformation capacity (7;) can be computed by correcting
for cell inactivation caused by endogenous decay and depletion of reducing power. This

is a finite value that is independent of the presence of growth and nongrowth substrate

and represents a maximum value that cells can theoretically attain. For this culture, 7;

90
appeared to be a characteristic value under the specified experimental conditions.

Observed transfomation capacity values predicted using 7; and b are shown in Figure

4.6. (7;) is much lower than 7; when growth substrate concentration is low. This is

obs
caused by the fact that endogeneous decay becomes more significant at low concentration
of growth substrate, when toxicity and use of reducing power become less significant.

(T;)0,,s approaches the theoretical value when the concentration of growth substrate is

sufficiently high.

Table IV shows that observed transformation yield decreased with increasing methane
concentration. The observed transformation yield is very small compared with the
theoretical value (7;) measured by the mode] when higher levels of methane are present.
Apparently, inhibition of growth substrate plays an important role. Transformation of
TCE was seriously inhibited when high levels of methane were present. The effect of
inhibition of methane offset its enhancement effects. On the other hand, the observed
transformation yield was higher than the theoretical value when trace methane was
present. Under this condition, enhanced transformation of nongrowth substrate by growth
substrate was dominant and a higher observed transformation yield was attained.
Therefore, the theoretical transformation yield represents a theoretical value that the
cultures could have and is independent of the effect of growth and nongrowth substrates.
The observed transformation yield was a function of concentration of growth and
nongrowth substrates, as shown in Figure 4.7: the lower the concentration of TCE, the

less significant is the effect of methane on observed transformation yield.

In summary, the model presented here can be used to predict transformation of growth
and nongrowth substrate accurately. A systematic method was developed to measure
parameters to describe simultaneous degradation of growth and nongrowth substrates. In

previous work, a simplified form of the present model (in the absence of growth

91

substrate) was verified for I-IFC/HCFC degradation by the same methanotrophic mixed

culture (Chang and Criddle

1995). These results suggest that the proposed model can be

applied to other cometabolic transformations for a range of concentrations and substrate

types.

~‘

 

TCE
concentration
(m 9! L)

 

\
\ \

\‘

Observed
transformation
capacity (mg
TCE/mg cells)

Methane
concentration
( m 9 I L)

Figure 4.6 The observed transformation capacity as a function of the concentrations of
growth and nongrowth substrates. Prediction is based on parameters listed in Table I.

92

  
   

MW“?
1"“ 1
i = 4.5
1 5 4
1 1 3'5 Observed
3 transformation
yield, qclqs
0 (mg TCE/mg
methane)
TCE
concentration
(In 9/L)
Methane
concentration
(m glL)

Figure 4.7 The observed transfomation yield as a function of the concentration of
growth and nongrowth substrates. Prediction is based on parameters listed in Table I.

93
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1.A1varez-Cohen, L., and P. L. McCarty. 1991a. A cometabolic biotransformation mode]

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2.Alvarez-Cohen, L., and P. L. McCarty. 1991b. Effects of toxicity, aeration, and
reductant supply on trichloroethylene transformation by a mixed methanotrophic culture.
Appl. Environ. Microbiol. 57:228 - 235.

3.Alvarez-Cohen, L., and P. L. McCarty. 1991c. Product toxicity and cometabolic
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4.Anderson, J. E., and P. L. McCarty. 1994. Model for treatment of trichloroethylene by
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7.Chang, H.-L., and L. Alvarez-Cohen. 1995b. Transformation capacities of chlorinated

organics by mixed cultures enriched on methane, propane, toluene, or phenol. Biotechnol.
Bioeng. 45:440-449.

8.Chang, M.—k., T. C. Voice, and C. S. Criddle. 1993. Kinetics of competitive inhibition
and cometabolism in the biodegradation of benzene, toluene, and p-xylene by two
Pseudomonas isolates. Biotechnol. Bioeng. 41:1057-1065.

9.Chang, W.-k., and C. S. Criddle. 1995. Biotransformation of HCFC-22, HCFC-142b,
HCFC-123, HFC-134a by methanotrophic mixed culture MM]. Biodegradation. 6: 1-9.

10.Criddle, C. S. 1993. The kinetics of cometabolism. Biotechnol. Bioeng. 41 : 1048-1056.

11.Criddle, C. S., L. A. Alvarez, and P. L. McCarty. 1991. Microbial processes in porous
media, p. 639-691. In J. Bear and M. Y. Corapcioglu (ed.), Transport processes in porous
media. Kluwer Academic Publishers, Netherlands.

12.Fliermans, C. B., T. J. Phelps, D. Ringelberg, A. T. Mikel], and D. C. White. 1988.

Mineralization of trichloroethylene by heterotrophic enrichment cultures. Appl. Environ.
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13.Fogel, M. M., A. R. Taddeo, and S. Fogel. 1986. Biodegradation of chlorinated ethene
by a methane-utilizing mixed culture. Appl. Environ. Microbiol. 51:720-724.

14.Fox, B. G., J. G. Bomeman, L. P. Wackett, and J. D. Lipscomb. 1990. Haloalkene
oxidation by the soluble methane monooxygenase from Methylosinus trichosporium
OB3b: mechanistic and environmental implication. Biochemistry. 29:6419-6427.

15.Henry, S. M., and D. Grbf: -Galié. 1991a. Influence of endogenous and exogenous
electron donors and trichloroethylene oxidation toxicity on trichloroethylene oxidation by

94

methanotrophic cultures from a groudwater aquifer. Appl. Environ. Microbiol. 57:236 -
244.

16.Hopkins, G. D., J. Munakata, L. Semprini, and P. L. McCarty. 1993b.
Trichloroethylene concentration effects on pilot field-scale in-situ groundwater

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2547.

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18.Landa, A. S., E. M. Sipkema, J. Weijma, A. A. C. M. Beenackers, J. Dolfing, and D.
B. J anssen. 1994. Cometabolic degradation of trichloroethylene by pseudomonas cepacia

G4 in a chemostat with toluene as the primary substrate. Appl. Environ. Microbiol.
60:3368-3374.

19.Little, C. D., A. V. Palumbo, S. E. Herbes, M. E. Lidstrom, R. L. Tyndall, and P. J.
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22.0]denhuis, R., J. Y. Oedzes, J. J. van der Waarde, and D. B. Janssen. 1991. Kinetics of
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of trichloroethylene. Appl. Environ. Microbiol. 5727-14.

23.0ldenhuis, R., R. L. J. M. Vink, D. B. Janssen, and B. Witholt. 1989. Degradation of
chlorinated aliphatic hydrocarbons by methylosinus trichosporium OB3b Expressing
soluble methane monooxygenate. Appl. Environ. Microbiol. 55:2819-2826.

24.Robinson, J., and W. G. Characklis. 1984. Simultaneous estimation of Vmax, Km, and
the rate of endogenous substrate production (R) from substrate depletion data. Microbial
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25.Robinson, J. A. 1985. Determining microbial kinetic parameters using nonlinear
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26.Saéz, P. B., and B. E. Rittmann. 1991. Biodegradation kinetics of 4-chloropheno], an
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27.Saéz, P. B., and B. E. Rittmann. 1993. Biodegradation kinetics of a mixture containing
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95

29.Uchiyama, H., T. Nakajima, O. Yagi, and T. Nakahara. 1992. Role of heterotrophic
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30.Wackett, L. P., and S. R. Householder. 1989. Toxicity of trichloroethylene to

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Appl. Environ. Microbiol. 49:242-243.

CHAPTER 5
THEORY OF ADAPTATION OF COMETABOLIZING COMMUNITIES

INTRODUCTION

Selection in a continuous culture on a limiting carbon source can lead to the development of
stable microbial communities. The relative dominance of populations within these
communities can be altered by environmental perturbations, such as exposure to nongrowth
substrates. Selection among microbial populations usually occurs because of competition
between different species or competition between an ancestor and its derived genotypes. As
a result of such processes, a new microbial community can develop that is better adapted to

its new environment.

For the microbial communities considered in this work, commensal interactions are likely.
For such interactions, one species produces compounds which serve as an energy or
carbon source for a second species. Such commensal realtionships are often strung together
in a chain so that over time a succession of commensal pairs appears. For example,
Wilkinson et al . (1974) studied the nature of commensal interactions for a microbial
community utilizing methane. The community consisted of a methane-utilizing
Pseudomonas sp., a methanol utilizing Hyphomicrobium sp., an Acinetobacter sp., and a
F lavobacter sp. The authors concluded that the Hyphomicrobium sp. served to remove the
small amounts of methanol which are produced during methane utilization by the
Pseudomonas sp. and which inhibited its growth. Acinetobacter and F lavobacter sp.

removed the complex products of growth or cell lysis.

96

97
The commensal relationship has a twist under cometabolic conditions. The species that are

responsible for utilization of growth substrates also transform nongrowth substrates
simutaneously. Cometabolizing pure cultures do not mineralize nongrowth substrates and
accumulation of transformation products is typical (Henry and Grbié -Gali<’: 1990; Little et
al. 1988; Oldenhuis et al. 1989). Not surprisingly, mixed cultures or communities have
advantages for mineralization of nongrowth substrates (Uchiyama et a1. 1992). Since pure
cultures with oxygenase activity typically suffer from product toxicity, the presence of
heterotrophs capable of degrading such products may facilitate detoxification, enhancing
the growth rate of the community. Thus, selection in cometabolizing environments may

favor community structures with higher capabilities for detoxication.

In this chapter, the fitness concept (Lenski et a]. 1991) is adapted for evaluation of
cometabolism and the changes that occur in a cometabolizing community. The major
concept for the model is that changes in a microbial community can be described by a
"fitness" parameter which is quantified by changes in the kinetic parameters of the
community. The selection gradient for each parameter is defined by the partial derivative of
fitness with respect to that parameter. The gradient therefore reflects the direct selection
acting on each fitness component, with the other components held constant. The
experimental systems selected for investigation in this work is TCE transformation by a

methanotrophic mixed culture and by a phenol-degrading community.

MODEL DEVELOPMENT

Adaptive change of microbial communities

In a competitive situation, we are interested in discovering whether a species enjoys a
natural advantage. Microorganisms (r strategist) with the fastest growth rate should come to
dominate when resources are temporarily abundant, since by virtue of their rapid growth,

they will be able to utilize more of the limiting factor than the slower-growing organism. K

98
strategists, which reproduce more slowly than r strategist, tend to be successful in

resource-limited situations (Andrews and Hall 1986). However, considerations should also
be given to application of this concept for mixed cultures, consortia, and communities

where interactions other than simple competition are important.

For simple communities fed a single substrate, one or a few species are predominant.
Intermediates from oxidation of the growth substrate or products of decay of the dominant
species sustain other species within the community. Since the whole community is
interconnected by amensual or mutualistic relationships, we assume that the whole
community can be viewed as a single population with a single phenotype. Under this
assumption, phenotypic changes during exposure to nongrowth substrate will be expressed
as changes in the phenotype of the whole community. The Malthusian parameter for a

specific species over any time period t is given by (Lenski et al. 1991).

m=ln(N/N0)/t (1)

Where No and N are initial and final cell number during a growth period, respectively.

We assume that the above definition can be applied to a whole community derived from a

single growth substrate:

m = ln(X/X0)/t (2)

Where X0 and X are initial and final cell density, respectively.

The fitness, W of a derived community relative to the ancestor community is expressed as

the ratio of their respective Malthusian parameters :

99

W:_ (3)

For any short period, the specific growth rate, 11 is a constant . Fitness at any instant is then

expressed as:

(4)

For a continuous culture, the specific growth rate, 11 is the actual Malthusian parameter.

Generally, we can measure the concentrations of growth and nongrowth substrate, and

kinetic parameters to obtain 11 at any instant. The specific growth rate prior to TCE
exposure is 1.10. The selection gradient for any trait P is the partial derivative of fitness with
respect to that trait, 8W I 3P. The gradient therefore reflects the direct selection acting on
each fitness component, with the other components held constant. To facilitate comparison
among selection gradients for the several fitness components, gradients are normalized to

create dimensionless quantities that reflect the proportional sensitivity of fitness to each

component (Vasi et a]. 1994):
P [W
GP - (WK?) (5)

Component fitness (fitness contributed by the change of a trait) can be calculated from :

 

aWMP

AW =
n (aPn n

(6)

Therefore, the change of fitness for a derived community can be expressed as:

100

AW = 2 —AP (7)
n=1

The above equation is assumed to express the development of the community. In principle,

changes in community structure can be quantified in this way.

Fitness of cometabolizing cultures

For the case where growth substrate utilization and cometabolic transformation occur in

separate stages, growth phase change is described by:

as YkS
= m 2 ms 8
fl KS+S KS+S U
and
W=-'—"—=-‘—‘— (4)
mo ”0

Where W=1 for the ancestor community and W=oo for the theoretical maximum fitness in

the community (um z oo).

During the death phase, cell decay occurs as a result of endogenous decay and

transformation of nongrowth substrate (Chapter 3):

 

q
=—b——° 9
fl 7; ()
and
kC
= c 10
q, K+C ( )

C

In order to have a consistent trend in fitness over periods of net growth and net decay, a

101
modified definition of fitness and selection gradient is proposed:

Wzfi; 00
01111-111

Where W=-1 for the ancestor community and W=0 for the maximum fitness of the

community (b = 0 and qC I7; = 0)

For cometabolism in the presence of growth substrate, the following equations provide a
complete mathematical description of the specific growth rate and the specific rates of

utilization of growth and nongrowth substrates throughout the growth and the decay

 

 

periods (Chapter 4).
S
q, = k,( C ) (13)
le+——)+S
1C C
q. = (T,.q, + k.)( S ) (14)
K;(1+ —) + C
Kis
u=na— -$- 03

C

and the definition of fitness given by Eq. (1 1) can be used for all cases.

a

W = ——
Incl

(11)

Where W=1 or - 1 for the ancestor community and W=oo for the theoretical maximum

fitness in the community (11,m 2 co, b=0 and q; / 7; = 0).

102

Fitness in a cometabolizing community can begin with 1 or - 1 for the ancestor community
depending upon whether there is sufficient growth substrate to support growth.

Subsequently, fitness of the community can evolve throughout the whole ‘fitness space’.

 

negative positive
growth growth
in in
ancestor ancestor
community community
-oo -1 0 1 oo
- 1 1 1 >
————>

increasing fitness

Figure 5.1 Fitness space of cometabolizing community

Selection gradients

The selection gradient is a normalized parameter defined so as to indicate the relative effects
of different fitness components on fitness and to enable comparison of selection gradients

for several fitness components. The definition of selection gradient has been shown in Eq.

(5). Selection gradients with respect to um and K, can be derived for growth phase using

Eq. (8):

0.1-:11)

._. £1 1%: 16
0,, (Wxaflm) I < >

103

 

 

KS 8W —Ks
G - — = 17
"5 (W)(8Ks_) KS+S ( )
The change of fitness for the derived community can be expressed as:
8W 8W
AW: A —AK 18
an," ”771+ 8K5 S ( )

Selection gradients for um and K8 are zero when there is no growth substrate. Selection

gradients for b, qc and T; can then be derived as follows:

           

G, =(|_v%|)(%):_b +711)” (19)
G=(1115—|’(aq)= bf—IITT— (20)
(7:01am 1231.77; (21)

The change of fitness for the derived community can be expressed as:
AW=a—W-Ab+a—W—AT+flAq (22)

3b 312. (94. "
Selection within a community is more complex when both growth and nongrowth
substrates are present at the same time. However, referring to Eq. (13), (14), and (15), the

change of fitness for the derived community can be expressed as follows:

104

AW=-a—W—AY +2—W—Ab+flA7; +2—W-A7; +9341: +flAk

 

                 

ay 36 are ar, 31 31
+8W AK 8W 8W 8W
3K, 8K 8K "‘ (3K; "‘

(23)

The effect of fitness components on fitness can be deduced from Eq. (16), (17), (19), (20),
and (21). Fitness can increase if maximum specific growth rate, 1.1m and theoretical
biomass transformation capacity, 7; increase. Alternatively, fitness decreases if half-
saturation coefficient of growth substrate, Ks, endogenous decay constant, b and specific

rate of utilization of nongrowth substrate, qc increase. These predictions are summarized in

Table 5.1.

Table 5.1. The effects of fitness components on fitness

 

 

Increase of fitness components Effects on fitness,W
maximum specific growth rate, pm Increase
half-saturation coefficient of growth substrate, K s Decrease
endogenous decay constant, b Decrease
specific rate of utilization of nongrowth substrate, q; Decrease
Theoretical biomass transformation capacity, 7; Increase

 

Implications of theory for different model systems

Several model systems have been considered for the evaluation of cometabolism. In most
cases, both growth and nongrowth substrates are introduced into the model systems. Thus,
the interactions between both substrates are important. The presence of toxic transformation
products can be expected to have some impacts on the microbial community structure

during long-term TCE exposure. These factors will have different effects in simple mixed

105
cultures compared to complex communities and in batch reactors compared to continuous

reactors. In sequencing batch reactors (SBRs), for example, the microbial community is
exposed to a range of growth and nongrowth substrate concentrations. This may select for
a more diverse community with "specialist" organisms that occupy variety of niches created
by substrate concentration gradients. In contrast, a chemostat favors selection of specific
populations at a fixed specific growth rate. This can be expected to result in a less diverse
culture. Since nongrowth substrates are not mineralized by cometabolizing species and
since heterotrophs are known to play important roles in detoxification, more diverse

cultures should have advantages for cometabolism.

In this work, two model communities are evaluated, representing different extremes in the
continuum of community. A methanotrophic mixed culture in a chemostat is used to
represent one extreme in the continuum. The phenol-degrading SBR community represents
a more complex case. The simple methanotroph community is essentially a consortium,
with very limited diversity. Methane utilizers, methanol utilizers, and associated
heterotrophs are bound together in a commensal relationship, with methane as the sole
source of carbon and energy. By contrast, more complex interactions are likely in the

batch-fed phenol-degrading community.

As discussed previously, the selection gradient is a normalized parameter that indicates the
effects of different parameters on fitness. This definition may be useful in predicting

adaptation of specific systems. For example, the selection gradient with respect to K, could

be -1 and O for chemostat and SBR respectively because substrate concentration is low for
a chemostat (S << K,) and periodically high for a SBR (S >> K). It may also be possible
to use selection gradients (especially more sensitive ones) as criteria for the stability of a
community under different environments or different communities in the same

environment. Magnitude of selection gradients for different organisms and different

106
environments can be compared. Lower values of selection gradients indicate a more stable

community for a specified environment.

MODEL ANALYSIS

The experimental systems selected for investigation in this work were TCE transformation
by a methanotrophic mixed culture and a phenol-degrading community. The model for
cometabolism given by Eq.(13), (14), and (15) was verified previously (chapters 3 and 4).
Kinetic parameters for the methanotrophs were listed in Table 4-1. The parameters for
phenol-degrading community are provided in chapter 7. As discussed previously, the
selection gradient is an important indicator of the relative importance of parameters (fitness
components) on fitness. These parameters were evaluated over the ranges of growth
substrate (methane and phenol) and nongrowth substrate (TCE) concentration. Selection
when both substrates are present at the same time is compared to selection when both
substrates are supplied at different times for the methanotrophic mixed culture. The phenol-
degrading community was also evaluated when both substrates were present separately.
For this case, the phenol-degrading community and the methanotroph culture can be
compared. To determine the relative importance of each parameter on the specific rate of

transformation of growth and nongrowth substrates, the derivatives of q; and q; with

respect to related parameters were also evaluated over a range of substrate concentrations.

RESULTS AND DISCUSSION

Selection gradients for the methanotrophic mixed culture when growth and nongrowth
substrates are supplied separately is shown in Figure 5.2. Apparently, there is no selection
with respect to the inhibition constants. The selection gradients of growth and decay phases
(including nongrowth substrate transformation) are evaluated separately. The effects of

fitness components are in agreement with predictions in Table 1. Similar results were also

107
observed for the phenol-degrading community when growth and nongrowth substrates are

not supplied simultaneously (Figure 5.3).

Comparing Figures 5.2 and 5.3, both the methanotrophic mixed culture and the phenol-

degrading community have the same selection gradient with respect to Y," and k; (or um).
However, there are some differences for selection gradients with respect to 7; , b , kc, and
K;. For example, a 100% increase in theoretical transformation capacity (7;) causes 50%
increase of fitness at 2 mg/L TCE for phenol-degrading community. The same TCE
exposure would cause 70% increase in fitness for the methanotroph culture. There are
similar observations for other parameters. Thus, the phenol-degrading community is more
stable than methanotrophic mixed culture based on these criteria. This implies that it may be
possible to use selection gradients (especially more sensitive ones) as criteria for the

stability of a community under a given perturbation.

Figure 5.4 and 5.5 show selection gradients for each fitness component as functions of
T CE and methane concentration when both substrates are present at the same time. Over a

wide range of concentrations, K; and K; are the most sensitive gradients with respect to

TCE and methane concentration. The selection gradients are higher at higher substrate
concentration for these two parameters. This implies that a smaller change in K; and K .1 is
needed to attain the same level of change in fitness. As a result, the half-saturation
coefficient of nongrowth substrate and inhibition coefficient have more significant effects
on fitness at high substrate concentrations. Some other differences can also be observed.

The selection gradient for k; shifts to negative values and the selection gradient for K;

shifts to positive values when both substrates are present at the same time. This indicates

that improvements in the transformation of TCE (such as an increase of K; and 7;)

become more critical for adaptive changes.

108
Figure 5.6 to 5.9 also provide a sensitivity analysis for q; and q; as a function of TCE and

methane concentrations, respectively. The results show that an increase in K; and a
decrease in K; can increase qc. However, the effect on q; is less significant. Compared to

fitness with respect to several parameters, q; and q; are not so sensitive to substrate

concentration and change in parameters.

Various model environments also have different effects on selection. The batch growth
environment selects strongly for a higher maximum specific growth rate, with much
weaker selection for populations that have higher affinity for substrate. The ratio between
selection gradients with respect to um and K; represents a measure of fitness that can be
attributed to these two parameters. Dividing Eq. (16) by (17), the proportional selection

gradient is obtained:

_Esfi

Gm/stz K
S

11

In continuous culture, the dilution rates through the reactor, D, are usually a fraction of the

maximum specific growth rate, um. Assuming p. = umS/(KS +S) = 0.211,“, the
proportional selection gradient for um and KS is -1.25. By comparison, the proportional
selection gradient for um and KS in a batch reactor can differ by a factor of several

hundred(since S>> Ks ).

In the absence of growth substrate, there is no selection with respect to um and K5 in

cultures conducting cometabolic transformations. However, batch cultures are exposed to a

higher concentration of nongrowth substrate initially than continuous cultures. Therefore,

the selection gradient with respect to qc is much higher in batch culture since q; is directly
proportional to concentration of nongrowth substrate. However 7; can offset the effect of

q;, so it is difficult to differentiate the selection advantages of batch and continuous

109

culture. The proportional selection gradient for qc and 7; is a constant factor

(Ge. IGT;_ = —-I). This implies that there is counter selection with respect to qc and 7; over

any range of nongrowth substrate concentration.

In summary, this work extended the fitness concept to describe adaptation of microbial
communities. Selection gradient is the key concept underlying a theory for the quantitative
description of stability of communities. It appears possible to use selection gradients
(especially more sensitive ones) as criteria for the stability of a community under a given
perturbation. Communities are likely to exhibit adaptive changes in response to given
perturbations (such as, long term nongrowth substrate exposure). A more stable
community shows a smaller change in kinetic parameters as a result of a given perturbation.
However, a less stable community has a high capacity for change in performance because it
is characterized by larger selection gradients. Thus, fitness may be used to track the
stability and adaptation of communities under perturbation. An unstable community
characterized by high selection gradients and low fitness can be adapted to create a more

stable community characterized by low selection gradients and high fitness.

110

 

 

 

 

 

 

 

 

 

 

 

 

 

 

(a)
1.5
‘5 1 - Ym, ks
.2
u
2
a: 0.5 -
C
.2 0 -
fl
0
.2
° 05 -
m ' ,Ks
-1 P 1 l
0 1 2 3 4
Methane concentration (mg/L)
(b)
Tc
u
C
.2
'u .
u Kc
h
a:
C
o b
a:
0
.2
«1’5
kc
0 1 2 3 4

TCE concentration (mg/L)

Figure 5.2 Dimensionless selection gradient, (P/W)(8W/8P) as a function of methane and
TCE concentrations for methanotrophic mixed culture when both substrates are present
separately.

1]]

 

1.4 -

1.2 '

 

1 - Ym, ks

(L8 -

0u4 -

Selection gradient

(L2 -

 

 

o I I I
0 50 100 150 200

Phenol concentration (mg/L)

 

(b)

(16

 

 

To
054 -

0.2 L

 

 

Kc

 

 

-0.6 ~ kc

Selection gradient

 

 

 

TCE concentration (mg/L)

Figure 5.3 Dimensionless selection gradient, (P/W)(BW/3P) as a function of phenol and
TCE concentrations for phenol degrading culture when both substrates are present
separately.

112
(a)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

2 1
Methane:
1.5 - 0.2 mg/L
fl
5
'5' Tc
8 Ks
a) Kc
Kis

: o
O Ym, KIC
33 b
2 kc
a ks, Ty
(I)

-1.5 I I I I

O 2 4 6 8 10
TCE concentration (mg/L)

(b)

14 ,

Methane:

12 ' 2 mg/L
‘5
.2 10 ~
'0
a 3 L
h
a: 5 _
c
o 4 ' .
'3 KC,K|S
8 2
- Tc, Ks
(g 0 :1 Ym, Kic, b

' kc, ks, Ty
-2
-4 , 1 4 - 1 ,
O 2 4 6 8 10

TCE concentration (mg/L)

Figure. 5.4 Dimensionless selection gradient, (P/W)(3W/8P) as a function of TCE
concentration for methanotrophic mixed culture at a specified methane concentration.

113

(a)

 

 

 

 

 

 

 

 

Kc
Kis
“
I:
.2
'U
a
b
O)
t:
O
'3
0
2
o
(I)
0 W Tc, Ks, Ym
5 . . ] Kic, kc, b , ks,Ty
0 2 4 6

Methane concentration (mg/L)

 

 

 

 

 

 

 

 

(b)
9
Kc
3 Kis
‘5 7
c1
'5 6
2
U) 5
4
t:
.2 3
‘6
m 2
0 To
U) 1 Ks
O Ym,Kic,kc,b
_1 ks, Ty
O 2 4 6

Methane concentration (mg/L)

Figure. 5.5 Dimensionless selection gradient, (P/W)(8W/3P) as a function of methane
concentration for methanotrophic mixed culture at a specified TCE concentration.

114

m

 

 

 

0.15
Methane:
g 0.1 b 0.2 /L
2:.
«I
g 0.05 . ks(dqs/dks)
0
/' Kic(dqs/dKic)
> 0
.2:
>
E
a Ks(dqs/sz)
:
0
(I)

 

 

 

-0.15- ' 1 ' .
0 2 4 6 8 10

TCE concentration (mg/L)

 

 

I Methane:
N:
ks(dqs/dks)

Kic(dqs/dKic)

_L
U"

-L
I

 

Sensitivity equation
0

I

o

0"
I

 

 

Ks(dqs/sz)

 

I I I I

0 2 4 6 a 10
TCE concentration (mg/L)

 

I
A

Figure. 5.6 Sensitivity equation for q; as a function of TCE concentration for
methanotrophic mixed culture at a specified methane concentration.

115

(a)

 

ks(dqs/dks)

 

Kic(dqs/dKic)

Sensitivity equation

 

 

 

 

- 1 ‘ ' ‘ ‘ Ks(dqs/sz)
O 1 2 3 4 5

Methane concentration (mg/L)

A

b)

 

ks(dqs/dks)

 

Kic(dqs/dKic)

Sensitivity equation

 

 

 

Ks(dqs/d Ks)

 

0 1 2 3 4 5
Methane concentration (mg/L)

Figure. 5.7 Sensitivity equation for q; as a function of methane concentration for
methanotrophic mixed culture at a specified TCE concentration.

116

(a)

 

 

    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.8 Kic(dqc/dKic)
0 6 bMethane:
: ’ 0.2 m/L ks(dqc/dks)
.2 0.4 -
fl
" 02
g. ' _, --—---— ------ — -------------------- Ty(dqc/dTy)
o O kC(dqc/dkc)
o 2 Kis(dqc/dKis)
3‘ ' ' Kc(dqc/ch)
'S' -0.4
.3
"5 -0.6
E. -0.8
m _1 _ Ks(dqc/sz)
_1.2 I I I A
O 2 4 6 8 10
TCE concentration (mg/L)
(b)
2
, ks(dqc/dks)
,_._ 1.5 - figs/1° Kic(dqc/dKic)
O
“:- 1 '
a
g. 0.5 TY(dqc/dTY)
m Kis(dqc/dKis)
0 kC(dqc/dkc)
E. _0.5 Kc(dqc/ch)
>
E -1
a)
C -1.5
I)
m -2
Ks(dqc/sz)
-2.5 I I I I
O 2 4 6 8 10

TCE concentration (mg/L)

Figure. 5.8 Sensitivity equation for q; as a function of TCE concentration for
methanotrophic mixed culture at a specified methane concentration.

117

 

"”3
0.25 - 0.5 m/L

ks(dqc/dks)

 

 

Ty(dqc/dTy)
Kis(dqc/dKis)
Kic(dqc/dKic)
kc(dqc/dkc)
Kc(dqc/ch)

 

 

 

 

 

 

 

Ks(dqc/sz)

Sensitivity equation

 

 

 

 

0 1 2 3 4 5

A
U'
V

 

Kic(dqc/dKic)
ks(dqc/dks)

A Ty(dqc/dTy)
Kis(dqc/dKis)

kc(dqc/dkc)
Kc(dqc/ch)

 

 

 

 

 

Ks(dqc/sz)

Sensitivity equation

 

 

 

 

 

-3 I I I I
0 1 2 3 4 5

Methane concentration (mg/L)

Figure. 5.9 Sensitivity equation for q; as a function of methane concentration for
methanotrophic mixed culture at a specified TCE concentration.

1 18
REFERENCES

1.Andrews, J. H., and R. F. Hall. 1986. r- and K selection and microbial ecology.
Advances in Microbial Ecology. 9199-147.

2.Henry, S. M., and D. Grbié -Galic. 1990. Effect of mineral media on trichloroethylene
oxidation by aquifer methanotrophs. Microbial Ecology. 20:151-169.

3.Lenski, R. E., M. R. Rose, S. C. Simpson, and S. C. Tadler. 1991. Long-term
experimental evolution in Escherichia Cali. 1. Adaptation and divergence during 2,000
generations. The American Naturalist. 138:1315-1341.

4.Little, C. D., A. V. Palumbo, S. E. Herbes, M. E. Lidstrom, R. L. Tyndall, and P. J.
Gilmer. 1988. Trichloroethylene biodegradation by a methane-oxidizing bacterium. Appl.
Environ. Microbiol. 54:95] - 956.

5.0ldenhuis, R., R. L. J. M. Vink, D. B. Janssen, and B. Witholt. 1989. Degradation of
chlorinated aliphatic hydrocarbons by methylosinus trichosporium OB3b Expressing
soluble methane monooxygenate. Appl. Environ. Microbiol. 55:2819—2826.

6.Uchiyama, H., T. Nakajima, O. Yagi, and T. Nakahara. 1992. Role of heterotrophic
bacteria in complete mineralization of trichloroethylene by Methylocystis sp. strain M.
Appl. Environ. Microbiol. 58:3067-3071.

7.Vasi, F., M. Travisano, and R. E. Lenski. 1994. Long-term experimental evolution in
Escherichia Cali. 11. Changes in life-history Traits During Adaptation to a Seasonal
Environment. The American Naturalist. 144.

8.Wilkinson, T. G., H. H. Topiwala, and G. Hamer. 1974. Interaction in a mixed
bacterial population growing on methane in continuous culture. Biotechnol. Bioeng. 16:41-
59.

CHAPTER 6

CHANGE IN COMMUNITY STRUCTURE IN RESPONSE TO LONG
TERM TCE EXPOSURE:
METHANOTROPHIC MIXED CULTURE IN CHEMOSTAT‘

INTRODUCTION

Trichloroethylene (TCE) is widely found in soil and groundwater near industrial sites. In
1985, Wilson and co-workers reported on the possibility of aerobic oxidation of TCE by
soil microorganisms with natural gas as the primary energy source (Wilson and Wilson
1985). Since then, the ability of methane-utilizing bacteria to cometabolize TCE has been
reported and confirmed by several researchers (Fliermans et al. 1988; Fogel et a]. 1986;
Little et al. 1988). This work was concentrated on the kinetics (Alvarez-Cohen and
McCarty 1991c; Anderson and McCarty 1994; Chang and Alvarez-Cohen 1995a; Criddle
1993; Folsom et a]. 1990; Strand et a]. 1990) and pathways of degradation (Fliermans et
a]. 1988; Fogel et a]. 1986; Fox et al. 1990; Little et al. 1988; Nakajima et al. 1992;
Newman and Wackett 1991; Oldenhuis et a]. 1989). To date, few researchers have
investigated changes in community structure and performance in response to long-term

TCE exposure.

Wilkinson et al . (1974) evaluated a stable mixture of four bacterial species in continuous
culture with methane as the sole carbon source. The community consisted of a methane-

utilizing Pseudomonas sp., a methanol utilizing Hyphamicrobium sp., and, in addition, an

 

* The genetic analyses described in this study performed with the assistance
of Dr. Denise Searles

119

120
Acinetobacter sp. and a F lavobacter sp. The Pseudomonas was the only species that

could utilize methane as a carbon and energy source, and it constituted the dominant
member of the community (ca. 90% of the biomass). The authors concluded that
Hyphomicrobium sp. removed small amounts of methanol produced during methane
utilization by the Pseudomonas sp.. Methanol was believed to be inhibitory to the
Pseudomonas sp.. The Acinetobacter and F lavobacter sp. apparently removed complex

products generated during cell growth or lysis.

Recent studies indicate that TCE transformation capacity is not only a function of the
availability of reducing power, but also of the specific cometabolized compound and the
toxicity of its transformation products (Alvarez-Cohen and McCarty 1991a; Henry and
Grbic’: -Galié 1991a; Wackett and Householder 1989). Formate addition resulted in
increased initial specific TCE transformation rates and elevated transformation capacity.
Significant declines in methane conversion rates following exposure to TCE were observed
for both resting and formate-fed cells, suggesting toxic effects caused by TCE or its
transformation products (Alvarez-Cohen and McCarty 19913; Alvarez-Cohen and McCarty
1991b). Only a few researchers have examined on the toxicity of transformation products.
Oldenhuis and co-workers suggested that TCE epoxide can be expected to bind covalently
to proteins and nucleic acids. Other possible reactive metabolites that might bind
irreversibly are chloral, dichloroacetyl chloride, and formyl chloride (Oldenhuis et a].
1991). Organisms capable of degrading a large amounts of TCE should possess active

detoxification systems for these compounds.

The accumulation of stable TCE breakdown products in methanotroph pure cultures
indicates that methanotrophic bacteria alone are not be able to mineralize TCE completely
(Henry and Grbit’: -Galié 1990; Little et al. 1988; Oldenhuis et al. 1989).Some research has

shown that methanotrophic mixed cultures are advantageous for mineralization of TCE.

121
Since methanotrophs suffer from product toxicity when transforming TCE, heterotrophs

that degrade the toxic products may play an important role in detoxification. Heterotrophic
bacteria in methanotrophic mixed cultures apparently can degrade most of the water-soluble
T CE breakdown products, decreasing levels of water-soluble radiolabel and increasing
production of l"’COz (Little et al. 1988). Futherrnore, Uchiyama and co-workers reported
that a heterotrophic bacterium isolated from a methanotrophic mixed culture, Xanthobacter
autotrophicus, can oxidize dichloroacetic and glyoxylic acid completely and can reduce
trichloroacetic acid to lower levels. These results indicate that heterotrophic bacteria play an

important role in TCE degradation (Uchiyama et al. 1992).

The presence of toxic transformation products can be expected to have some impacts on the
development of microbial communities during long-term TCE exposure. Changes in the
populations are likely related to the level of TCE exposure, turnover of transformation
products and utilization of growth substrate. Lackey et al . (1994) used total-recycle
expanded-bed bioreactors to evaluate the degradation potential of TCE by a microbial
consortium. Ester-linked phospholipid fatty acid profiles (PLFAME) were used to monitor
the change of TCE-affected community during short-term perturbation. The results showed
that a propane-utilizing bacteria] biomaker increased as TCE was degraded and propane
consumed. However, the relationship between community structure and extent of TCE

exposure was not clear for these short-term exposures.

Several genetic analysis techniques have been developed for identification of
methanotrophic bacteria. Tsuji et al . (1990) demonstrated that it is possible to distinguish
and classify methanotrophic bacteria using 16S rRNA sequence analysis. Another report
described the use of PFGE (pulsed-field gel electrophoresis) -restriction fragment length
polymorphisms and the use of a cloned DNA fragment carrying the component B gene to

detect soluble MMO genes from methanotrophs on Southern blots prepared from gels on

122
which large DNA restriction fragments were separated by PFGE. This technique, when

combined with fluorescence-labeled oligodeoxynucleotide signature probes and Western
blot analysis, enabled characterization of methanotrophs and detection of methanotrophs
that synthesize soluble methane monooxygenase (MMO) (Tsien and Hanson 1992). More
recently, PCR primers specific for four of the five structural genes in the soluble MMO
gene clusters for several methanotrophs were used to amplify specific DNA sequences for

direct detection of methanotrophs in natural environments (McDonald et al. 1995).

Molecular biology techniques offer new opportunities for the analysis of the structure and
species composition of microbial communities. Some approaches obtain information about
microbial communities directly without the need for sequencing. Amplified Ribosomal
DNA Restriction Analysis (ARDRA) or Restriction fragment length polymorphism
provides a fingerprint of the microbial community under study(Martinez-Murcia et al. 1995;
Massol-Deya et a1. 1995).If the rDNA fingerprints for individual bacteria in a community
are sufficiently different, then one can examine the amplified products for a series of
distinct patterns resulting from the different populations that make up the community.
Another technique is based on the separation of PCR-amplified fragments of genes coding
for 16S rRNA, all the same length, by denaturing gradient gel electrophoresis (DGGE).
DGGE analysis of different microbial communities demonstrated the presence of up to 10
distinguishable bands in the separation pattern, which were likely derived from 10 different
species constituting these populations. It is possible to identify constitutes which represent
only 1% of the total community with this technique (Muyzer et al. 1993). These methods
could be used for a quick assessment of genotypic changes over time or between different

location reflecting different environmental conditions.

In this study, a methanotrophic mixed culture was exposed to different levels of TCE for

extended time periods. A chemostat was chosen as the model environment for the

123
methanotrophic mixed culture. Changes in the consortia were monitored and the effects of

TCE exposure on community structure were evaluated. Various phenotypic parameters
were also monitored. Community analyses were also conducted to monitor shifts in
microbial community structure. Changes in the community were analyzed using the fitness

theory presented in chapter 5.

MATERIALS AND METHODS

Culture conditions

A methanotrophic enrichment obtained from aquifer material at Moffett Field, California,
was used for these experiments. This culture is a stable consortium consisting of one
methanotroph, one hyphomicrobium, and several heterotrophs containing Gram-negative
thin and fat rods as well as some Gram-positive rods and cocci. The methanotroph in the
mixed culture expresses soluble MMO similar to that of Methylosinus trichosporium OB3b

under similar growth conditions (Henry and Grbié -Galié 1991a).

Mixed culture MM] was grown in Whittenbury Mineral Medium containing (per liter of
deionized water): 1.0 g of MgSO4-7HZO, 1.0 g of KNO3, 276 mg of CaSO4-2H20, 3.8
mg of FeEDTA, 0.5 mg of NazMoO4-2H20, 0.5 mg of FeSO4-7H20, 0.4 mg of
ZnSO4-7H20, 0.02 mg of MnC12-4HzO, 0.05 mg of CoC12-6HzO, 0.01 mg of
NiC12-6HzO, 0.015 mg of H3BO3, 0.25 mg of EDTA, 260 mg of KH2PO4, and 330 mg
of NazHPO4. One liter of culture was grown at room temperature (~21°C) in a
continuously stirred 2-liter bottle supplied 30% methane in air at 68 mL/min. Growth
curves were monitored and as stationary phase approached, approximately 10 mL of
culture was transferred to a 1 liter of fresh Whittenbury Medium. Cells were harvested in

mid-log growth phase for inoculation of two chemostats.

1 24
Perturbation of TCE

Possible feed modes for TCE peturbation are pulse input and step input. Pulse input should
be able to adapt consortia for enhanced transformation of TCE. After exposure to TCE for a
period of time, the consortia is allowed to recover and grow back. Alternatively, a step
input can be used to evaluate the impact of toxicity of TCE transformation on the
community. In this study, an input mode combining the two basic modes was used. TCE
was injected in an impulse way but with step increases of the TCE level for more extended

periods of time .

Duplicate chemostats were seeded with the methanotrophic mixed culture taken from the
batch reactor. The set-up of chemostats is shown in Figure 6.1. After the reactors had
grown to steady state, one was injected with TCE solution. TCE exposure was continuous
over a finite period, halted to allow recovery of the consortia, and then reintroduced for
another period at a higher concentration with another recovery period. This pattern was
repeated ten times (Figure 6.2). The operating conditions for the chemostats are

summarized in Table 6.1.

2330 “02:2 oqubocmfio—Z a .3 souaccommcfih NOE 28 228825 .26 253m

228805 .258 28 59: MOB 23 .

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

 

 

 

 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

 

 

 

 

 

 

 

.So
as,» .53.. 2.5 «Bk? .2 d
348
z_ «Bk? _ f _ .\ .52
023000 _ j L ~=<>Vm0
J a A O 1
O O
«26 O O 38
1 2.32%
o o o o 26
O _ _ O
O O 0
{ii amt;
/. ozsooo
thee 22.5.28 mu...
«Em: _
302
9.0 A
— _ _ _ .52 225:2
. _
.50 250285 _ _ _

m<O

126
Table 6.1. Operation conditions for chemostat

 

 

auditions in chemostats Values
pH 6.8(input)
8.2(output)
Liquid volume 1650 mL
Gas feed,
methane 30 mL/min
air 270 mIJmin
Liquid feed
Nutrient feed 120 mL/day
TCE loading.* Impluse, 6 - 18 mg/day
@ 50 - 150 mg/L in feed
Hydraulic retension time 13.75 days
Solid retension time 13.75 days

 

* w/o TCE input for control chemostat

 

160
c

.2 140-
H

(B

:.-. 120*
C
8A1oo-
=<
8m 80r
fig 60-
o

.2 4o-
3 20-
" o

 

 

 

O 72- 185- 199- 213- 227- 241- 255- 356- 371- 385-
130 192 206 219 234 248 262 365 379 393

Time (Days)

Figure 6.2. TCE feed concentration for long-term TCE exposure experiment
in chemostate

127
To maintain a constant cell density in the chemostat, the higher TCE exposure should

correspond to a longer period of cell recovery. During TCE exposure, the following

relationship was assumed to be valid:

 

 

 

 

AMC t
(Dabs = AMC tc = FLCo’c = FLCo’c

VLsz VLAX VLAX AM,

Where (2)0,” is the observed transformation capacity (mgTCE/mg cells), Me is the mass
of TCE (mg), tc is the length of period of TCE exposure in impulse input (days), Mb is

the mass of cells (mg), X is the cell density in chemostat (mg/L), V1, is the liquid volume

in chemostat (L), FL is the liquid feed rate (mL/day), C0 is TCE concentration in liquid

feed solution (mg TCE/L).

When TCE is absent, the continuing input of methane contributes to the growth of cells.
Under steady state, the specific growth rate is equal to the dilution rate, u=D. The net rate

of cell mass growth is uX, then the amount of cell growth within this period tg is

AMb = laws

At steady state, the cells consumed in the exposure period are equal to those produced

during the growth period:
FLCot

—C = uX Vt

(7;)obs 8

Rearranging the above equation, the following expression is derived:

128
‘g FLCo

tc (7;)obSHXV

The above equation relates TCE exposure period, to to cell growth period,tg. This

derivation supplies a theoretical basis for pulse perturbations in a chemostat.

Monitoring of consortia

From the method described above, TCE was introduced into the reactor, to encourage the
change of consortia that resist toxic products or transform TCE by a different
transformation pathway. Since heterotrophic bacteria play an important role in complete
TCE degradation, long term TCE exposure should have some impacts on community
structure. To monitor the change in the consortia, the following parameters were measured

periodically (methods for measuring these parameter are described in chapter 4) :

(a). Theoretical transformation capacity in the absence of endogenous decay, T

(b). Endogenous decay rate, b

(c). Maximum specific rate of utilization of TCE in the absence of methane, kc
((1). Half saturation coefficient of TCE, Kc

(e). Maximum specific rate of utilization of methane in the absence of TCE, k,

(f). Observed yield of cells, Y

Cell samples were also taken periodically and the change in morphology and community
structure were observed. Genetic analyses were conducted to show the change on
community structure. The history of different parameter values was compared to the results

from morphological or genetic analysis.

129
Adaptation of cometabolizing community in chemostat

The methanotrophic mixed culture chosen in this work is a consortium with closely
commensal interaction and limited diversity. Furthermore, as discussed previously
(Chapter 5), the chemostat selects specific populations by maintaining a minimum specific
growth rate to avoid wash out. Thus, the experimental system may represent a less stable
system when a long-term nongrowth substrate exposure is introduced repeatedly. In this
work, both growth and nongrowth substrates were fed into chemostats simultaneously.
Methane was the only growth and energy substrate, and the intermediates from oxidation
of the growth substrate or products of decay of the dominant species are utilized by other
populations in the community. It is assumed that the community can be treated as a single
population (Chapter 5). With this assumption, the fitness, W of a community relative to an

ancestor community is expressed as the ratio of their respected specific growth rate:

To facilitate comparison among the selection gradients for several fitness components, the
gradients are normalized to create dimensionless quantities that reflect the proportional

sensitivity of fitness to each component (Vasi et al. 1994):
P W
G = — —
” (WK 8P)

where P is the parameters (fitness components) that characterize the microbial system. The

change of fitness for a derived community can be expressed as follows:

 

" 3W
AW=§ 3PM,

ll

130

The above equation was used to quantity adaptive changes within the community.

In a chemostat, non-steady state conditions occur during nongrowth substrate exposure.

During this period, a mass balance can be applied to organisms using the stoichiometric and

kinetic relationships given earlier in this work:

General material balance

Accumulation = Influent - Effluent + Source - Sink

Organism balance

VLAX = 0 — QXAt + uXVLAt — bXVAt

It has been assumed that there are no organisms in the reactor influent. The above equation

may be rearranged as follows:

dX Q

— = —-—X+ —bX

dt V #X
=—DX+fl,mX

The above expression represent a mathematical description of the dynamic nature of a

completely mixed reactor. and is determined when cell density over a period and dilution

rate are measured. This derivation offers a way to measure fitness, W, under conditions of

TCE exposure, Where :

131

W=_”n_er
#0

General procedures for biotransformation measurement

Batch biotransformation exmg’ments

Biotransformation studies were performed using 20 ml glass vials sealed with teflon coated
butyl rubber stoppers and aluminum crimp caps. These vials were incubated with 5 mL of
of Whittenbury Mineral Media plus culture. An appropriate amount (measured as dry
weight) of mixed culture MMl was added to each test vial. TCE solutions (dissolved in
water) were added to each bottle using Precision gas tight syringes. Methane was
withdrawn from Scotty H cyclinders (99.0% CH4, Alltech Associate, Inc., Deerfield, IL) at
a fixed exit pressure and injected into batch vials. After adding substrates, the vials were
vigorously shaken upside-down on a rotary shaker (250 rpm). Headspace samples of TCE
and methane were periodically analyzed by GC .

mm

A TCE-saturated water solution was used as the spike solution in all experiments. The
spike solution was prepared by adding excess TCE (99+% pure ACS reagent, Aldrich
Chemicals Co., Milwankee, WI) to a 250 ml glass bottle capped with TFE-lined Mininert
valve. The bottle was vigorously shaken and allowed to settle at least 24 hrs. The upper
layer of the solution was transferred to another bottle and capped with a Mininert valve.
The spike solution was stored in a refrigerator until needed. One hour before use, it was

shaken again and allowed to settle.

TCE was analyzed by withdrawing 0.1 ml of headspace from the test bottles using a 0.5 ml

Pressure-Lok Series A-2 gas syringe and injecting the samples onto a Hewlett Packard

132
5890 gas chromatograph (GC) equipped with a capillary column (DB624, 30m x 0.53mm

I.D.), a flame ionization detector (FID) and a Electron capture detector (ECD). The GC was
operated isothermally at 90°C with helium as carrier (12 mL/min). The injection port was

set at 250°C. The temperature of PH) and ECD were 250°C and 350°C respectively.

Methane and oxygen were analyzed by withdrawing 0.1 ml of headspace from the test
bottles using a 0.5 ml Pressure-Lok Series A-2 gas syringe and injecting the samples onto a
Hewlett Packard 5890 series 11 gas chromatograph equipped with a column (6 ft x 1/8 in
SS packed with 80/ 100 washed molecular sieve 13X) and a thermal conductivity detector.
The GC was operated isothermally at 50°C with helium as carrier. The temperature of the

injection port and detector were 50°C and 90°C respectively.

Cell biomass was determined on a dry weight basis using 0.2 pm filters (Gelman Sciences
Inc., Ann Arbor, MI). The filters were prepared by first soaking them in mineral media for
10 minutes, rinsing on a vacuum filter with deionized water, drying overnight in a 103°C
oven, and cooling in a desiccator until needed. The filters were weighed, and once a known
amount of culture was filtered through them, they were rinsed, dried, cooled and

reweighed.

Community Analysis

D t' ' i

Samples (15 ml) were collected from reactors at intervals, and cells were harvested by
centrifugation and frozen. Pellets were later resuspended in a buffer consisting of 100 mM
Tris, 100 mM EDTA (pH 8.0), 1.5 M NaCl, 1% CT AB. 2 ml SDS (10%) was added and
the cells were incubated at 65°C for 30 minutes in a rotary water bath (150 rpm). Samples
were cooled to 37°C, at which time 50 pl proteinase K was added (10 [lg/pl) and the

lysates were incubated at 30°C for 2 hours, with shaking. DNA was then purified by

133
extraction with chloroform/isoamyl alcohol (24:1) and phenol/chloroform/isoamyl alcohol

(25:24:1) and precipitated with cold isopropanol.

Changes in the communities were monitored by ARDRA (amplified ribosomal DNA
restriction analysis). Thirty microliters PCR reactions contained approximately 10 ng
template DNA, 0.75U Taq DNA Polymerase (GibcoBRL), 10X buffer supplied by
GibcoBRL, 2 mM Mg”, 0.3 pg BSA, and 200 mM dNTPs, and 0.5 uM of each primer
described by Martinez-Murcia, et al.(Martinez-Murcia et al. 1995). An initial denaturing
step of 92°C for 6 minutes was followed by 30 cycles of 92°C (70 seconds), 55°C (30
seconds) and 72°C (2 minutes 10 seconds), and a final extension of 72°C for 6 minutes.
Fifteen microliters of product were digested with 2.5 U each of Hian and MboI, at 37 °C
for 3 hours and analyzed by electrophoresis on a 10% polyacrylamide gel. Bands were

visualized by silverstaining.

RESULTS

For the long-term TCE exposure experiment with the methanotrophic mixed culture, TCE
was fed continuously over a finite period, halted to allow recovery of the consortia, and
then reintroduced for another period at a higher concentration with another recovery period.
This pattern was repeated ten times (Figure 6.2). Changes of cell density in response to
long-term TCE exposure are shown in Figure 6.3. To monitor the changes in community
structure and function, the following parameters were measured periodically: biomass
transformation capacity, endogenous decay rate, and observed yield. The history of these
parameters along the long-term TCE exposure are shown in Table 6.2 and Figure 6.4 to
6.5. The fitness of the consortia in the TCE exposed and unexposed chemostats are shown

in Figure 6.6.

134

Amplified ribosomal DNA analysis (ARDRA) was used to observe changes in community
structure. ARDRA gives a unique "fingerprint" for each species or community through
amplification and digestion of 168 rDNA. Changes in community structure may be detected
by this technique through the appearance and/or disappearance of bands. Such changes
may indicate the gain or loss of populations within a community, or merely a shift in

dominance in the community structure. Results of this analysis are shown in Figure 6.7.

 

 

 

 

 

 

 

_150 18
"100 12
. .: mgTCE/L mg/d

800 " ‘ O .0.
A O
i 700 - ‘9' . :
m L,
E 600 - h In '5
>. "I
.‘.'.' 500 *- . I
m
C I I
a 400 - I I =
1: . I

300 ' II I I
" I
3 I
0 20° ‘ n .

lExposed
100 ' oControl
O J I l I
0 100 200 300 400 500

Time (Days)

Figure 6.3 Changes of Cell density of methanotrophic mixed cultures in respond to long-
terrn TCE exposure

Observed growth yield (mg
cells/mg methane)

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

-0.1

135

 

 

 

Time (days)

 

Figure 6.4 Observed growth yield for methanotrophic mixed cultures during
long-term TCE exposure

Table 6.2 Endogeneous decay constant for methanotrophic mixed cultures
during long-term TCE exposure

150 1a
100 12
r——'l n n ['1 ll “n so 6
mg TCE/L mg/d
P I I I
’ I
I Exposed
OControl
0 100 200 300 400

 

 

 

-—Time fidogenous decay r2 for regression
(Days) constant, 1/day
Exposed Control Exposed Control
25 0.881 0.804 0.824 0.878
54 0.657 0.921 0.999 0.953
94 0.631 0.988 0.718 0.811
115 0.438 0.786 0.954 0.899
124 0.62 0.758 0.586 0.663
136 0.261 0.616 0.843 0.857
164 0.654 0.459 0.777 0.807
193 0.549 0.225 0.945 0.955

 

136

 

 

 

 

 

 

_150 18
“n” -100 12
g, IExposed mgTCE/L mg/d
V 025 _ OControl
b .
I- A
a 2 f
“-5 0.2 -
IB 0
° I
g 5,015 - I
a E I E
... \
"' ll]
20 0.1 ~ i i
b l-
O
‘3', 0.05 -
g I
h
r- o - - .
0 100 200 300 400

Time (days)

Figure 6.5 Transformation capacity for methanotrophic mixed cultures during long-term
TCE exposure

 

 

 

 

 

1150 18
2 .———-. nnnnnfl nnlidgg° 1:
. mgTCE/L mg/d
1.5 -
I .-
3 1 ' ”’1'
.. 0.5 -
a
3
5 ° '
i: I
-0.5 -
1 __ IExposed
I OControl
_1.5 l l l l L l l
O 50 100 150 200 250 300 350 400

Time (Days)

Figure 6.6 Fitness of methanotrophic mixed cultures in respond to long-term TCE
exposure in chemostats

137

E
_:_1
S

 

 

Figure 6.7 ARDRA fingerprints of control and TCE-exposed methanotrophic
communities.

138
DISCUSSION

Changes in cell density for methanotrophic mixed culture exposed to periods of continuous
loading of TCE are shown in Figure 6.3. Continuous exposure to TCE initially resulted in
loss of capability to utilize methane and degrade TCE . Cell densities dropped drastically
and increased slowly after TCE addition stopped. TCE concentration in aqueous phase
within the exposed reactor changed from 0.01 to 0.1 mg/L approximately when cell
densities dropped. This suggests that product toxicity impacted the microbial community.
Greater TCE concentrations had a similar effect, and more time was needed for recovery of
the biomass. For this culture the strong inhibition of TCE on methane utilization ( Kic:
10.8 mg/L) may be another factor in the decline of cell density. Clearly, simultaneous

feeding of TCE to the community had a great impact on growth.

Continuous exposure to TCE initially resulted in decreased rates of methane utilization and
TCE transformation. Kinetic parameter measurements showed that maximum utilization
rates of TCE decreased initially but returned as cell density increased. Growth yield and
transformation capacity showed similar trends during TCE exposure. However, greater
values of transformation capacity were observed between cell recovery and the next TCE
exposure. This result suggests that the community adapted to TCE exposure by increasing
transformation capacity. The relationship between the improvement in transformation

capacity and diversity of community is still not clear from the data obtained.

Specific growth rates of methanotrophic mixed culture in the chemostat were calculated
from cell density data by mass balance of biomass. Fitness was calculated as the ratio of
specific growth rate of the culture at any time to the respected value before the initiation of
TCE exposure. The results indicate some decline in fitness just after initiation of TCE
exposure (50 mg/L TCE in feeding), with subsequent recovery to the original value. After

'150 mg/L TCE feeding was initiated, a significant drop in fitness was observed(from 1 to

139
-1). The fitness recovered to 1 after TCE feeding was halted. This result indicates that the

microbial community was not sustainable when TCE was fed continuously. A possible
explanation is the limited diversity of this methanotrophic mixed culture, perhaps
contributing to the low tolerance to TCE exposure. Another explanation is the sensitivity of
this particular culture to competitive inhibition of methane utilization by TCE. As discussed
previously, the relatively high selection gradient for this culture indicates a greater

instability for the culture (chapter 5).

ARDRA results indicated changes in both the chemostat cultures from the original batch
culture inoculum. However, there were no further changes in the control reactor and only
minor changes could be seen in the TCE-exposed community, even after TCE feed
concentration was raised to 150 mg/L (Figure 6.7). These results indicate that the
community structure of the methanotrophic mixed culture was stable despite exposure to
TCE. While transitory phenotypic changes occurred, the populations present within the
community remained unchanged. Greater changes in community structure occurred when
TCE feed concentration was subsequently reduced to 100 mg/L. Less diversity was
observed at this exposure. The more diverse community again appeared when influent

levels were restored to 150 mg/L. The results suggest that higher diversity corresponded to

higher TCE exposure.

140
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29.Wilkinson, T. G., H. H. Topiwala, and G. Hamer. 1974. Interaction in a mixed
bacterial population growing on methane in continuous culture. Biotechnol. Bioeng. 16:41-

30.Wilson, J. T., and B. H. Wilson. 1985. Biotransformation of trichloroethylene in soil.
Appl. Environ. Microbiol. 49:242-243.

CHAPTER 7

CHANGE IN COMMUNITY STRUCTURE IN RESPONSE TO LONG
TERM TCE EXPOSURE:
PHENOL-DEGRADING COMMUNITY IN SEQUENCING BATCH
REACTOR‘

INTRODUCTION

Trichloroethylene (T CE) is widely found in soil and groundwater. Considerable research
has established that certain phenol- and toluene-degrading Pseudomonas species can
rapidly cometabolize TCE (Folsom et al. 1990; Nelson et al. 1987; Wackett and Gibson
1988). To date, this research has concentrated on kinetics (Alvarez-Cohen and McCarty
1991c; Chang and Alvarez-Cohen 1995a; Criddle 1993; Folsom et al. 1990; Strand et a1.
1990) and pathways (Harker and Kim 1990; Nelson et al. 1988; Shields et al. 1989;
Wackett and Gibson 1988) with relatively little research on adaptive changes in microbial

community structure.

TCE transformation capacity is a function of available reducing power and toxicity of
transformation products (Alvarez-Cohen and McCarty 1991a; Henry and Grbié -Galié
1991a; Wackett and Householder 1989). Reducing power is required for monooxygenase
activity and can be derived from either the growth substrate or supplemental electron
donors, such as formate. For example, formate addition to a phenol-degrading community

resulted in increased transformation yield and elevated transformation capacity (Hopkins et

 

* The genetic analyses described in this study were performed with the assistance
of Dr. Denise Searles

143

144
al. 1993b). Other reports indicate that the toxicity of certain transformation products can

limit the extent of transformation. Oldenhuis and co-workers suggested that TCE epoxide
can bind covalently to proteins and nucleic acids. Other possible reactive metabolites that
might bind irreversibly are chloral, dichloroacetyl chloride, and formyl chloride (Oldenhuis
et al. 1991). Communities and populations capable of degrading a large amounts of TCE

should possess active detoxification systems for these compounds.

Several reports indicate that TCE cometabolism by pure cultures does not result in the
mineralization of TCE (Henry and Grbic’: -Galié 1990; Little et al. 1988; Oldenhuis et al.
1989). Other research suggests that microbial consortia or community have advantages for
mineralization of TCE. Because the TCE-oxidizers suffer from product toxicity, associated
heterotrophs may play an important role in detoxification (Little et al. 1988). Uchiyama and
co-workers (1992) reported that a heterotrophic bacterium in a methanotrophic mixed
culture, Xanthobacter autotrophicus, can oxidize dichloroacetic and glyoxylic acid
completely and can reduce trichloroacetic acid to lower levels. These results indicate that

heterotrophic bacteria play an important role in TCE detoxification.

The presence of toxic transformation products can be expected to impact the development
of microbial communities during long-term TCE exposure. Changes in the populations are
likely related to the level of TCE exposure, turnover of transformation products and
utilization of growth substrate. Lackey et al . (1994) used total-recycle expanded-bed
bioreactors to evaluate the degradation potential of TCE by a microbial consortium. Ester-
linked phospholipid fatty acid profiles (PLFAME) were used to monitor changes in a
propane-fed community during short-term perturbation with TCE. A propane-utilizing
bacterial biomaker increased as TCE was degraded and as propane was consumed.
However, the relationship between community structure and extent of TCE exposure was

not clear for these short-term exposures.

145

The effect of growth substrate feeding pattern on the structure and potential cometabolic
activity of a phenol-degrading community was evaluated by Shih et al. (1996). The results
indicated that the manner of growth substrate addition can have a pronounced effect on
community structure and cometabolic activity. Communities enriched with continuous or
protracted feeding intervals exhibited limited long-term capacity for TCE transformation.
Communities enriched with short feeding intervals maintained higher TCE transformation
rates. Pulse feeding also resulted in more stable and diverse communities. However, this

research did not investigate community changes during long-term exposure to TCE.

Adaptation of communities to changing environments may proceed in several ways.
Genetic recombination is one mechanism that can lead to new genotypes in the absence of
mutation. Genetic elements brought together may enable microorganisms to carry out new
functions, and can result in adaptative changes. Selection among species is another
mechanism of adaptation. The impact of the selection in evidenced by the characteristic of
the community. In chapter 5, a theory of community "fitness" was presented. In this
chapter, the cometabolism model verified previously (Chapter 5) is used to analyze adaptive
changes in terms of the " fitness " concept. In this manner, fitness was quantified in terms

of measurable kinetic parameters.

Sequencing batch reactors (SBRs) have several advantages for cometabolic
transformations. An SBR can alternate between periods of growth on growth substrates
and periods of cometabolism of nongrowth substrate, eliminating the possibility of
competitive inhibition for enzymes between growth and nongrowth substrates. SBRs also
have a great ability to periodically change environmental condition, creating temporal
concentration gradients, selecting or enriching specific microbial populations. Thus, SBRs

offer a good model environment to study the dynamic changes of microbial communities.

146

In this study, sequencing batch reactors were chosen as model environments. A phenol-
degrading community was exposed to increasing levels of TCE over time. Changes in the
community were monitored and the effects of TCE exposure on community structure were
evaluated. Various phenotypic parameters were measured. Community analyses were also
conducted to monitor shifts in microbial community structure. Changes in the community

were analyzed using the fitness theory presented in chapter 5.

MATERIALS AND METHODS

Culture conditions

A stable phenol-degrading microbial community was obtained by seeding a chemostat with
activated sludge from a municipal wastewater treatment plant (East Lansing, Michigan) and
providing a phenol-containing medium for two months at a dilution rate of 0.1 day‘l. The
enrichment was matained at 21.5 i 10°C. This culture was inoculated into a 2-liter stirred
reactor supplied continuously with air. Two hundred milliliters of medium (2000 mg/L
phenol) was provided as a single daily pulse immediately after removing the same volume
of biomass from the reactor (Shih et al. 1996). This fed-batch system was operated for
over 400 days, yielding a stable community with good TCE transformation properties.
Microscopic examination revealed a community with distinctive floc structures of spherical
and rod-shaped bacteria. Some fungi were also observed. The community was then used

to inoculate duplicate SBRs.

Phenol feed medium contained the following (per liter of deionized water): 2 g of phenol,
2.13 g of NazHPO4, 2.04 g of KHzPO4, 1 g of (NH4)2SO4, 0.067 g CaC12-2H20, 0.248
g of MgC12-6H20, 0.5 mg of FeSO4-7H20, 0.4 mg of ZnSO4-7H20, 0.02 mg of
MnC12-4H20, 0.05 mg of CoC12-6H20, 0.01 mg of NiC12-6H20, 0.015 mg of H3BO3,
0.25 mg of EDTA. The pH of the medium was 6.8.

147

Perturbation of TCE

The set-up of SBRs is shown in Figure 7.1. After both reactors had stabilized, one was
injected with TCE over a one-hour interval during the filling period of cycle operation.
Phenol was provided in a separate period for both reactors. Influent TCE levels were
increased gradually from 0.5 to 25 mg/L over 4 months (Table 7.1). The operating mode
of the SBR during a cycle is shown in Figure 7.2. The operating conditions for the SBRs

are summarized in Table 7.2.

Monitoring of consortia

To monitor changes in the community, the following parameters were measured

periodically (the methods for measuring these parameter are described in chapter 4) :

(a). Theoretical transformation capacity in the absence of endogenous decay, Tc
(b). Endogenous decay rate, b
(c). Second order rate coeficient of transfomation of TCE in the absence of methane, k;

((1). Maximum specific rate of utilization of phenol in the absence of TCE, k,

(e). Observed yield of cells, Y

Specific growth rate of the community was obtained by multiplying observed yield of cells
by maximum specific rate of utilization of phenol. Also, cell samples were taken
periodically and the change in community structure were observed. Microscopic
examination was also performed periodically. Genetic analysis was used to evaluate
changes in community structure. The change in kinetic parameter values was compared to

the results from morphological observation or genetic analysis.

148
Adaptation of cometabolizing community in SBR

In this work, phenol was the sole growth and energy substrate, so that tha actual substrate
available to members of the community is phenol, intermediates generated by oxidation of
phenol or products of cell decay. Under these conditions, it is assumed that the community
can be treated as a single population (chapter 5). The fitness, W of such a community
relative to ancestor community is expressed as the ratio of the respective specific growth

rates:

W=— (1)

The phenol-degrading community in SBR utilized phenol according to zero order kinetic,
thus qc = k3. and the specific growth rate of the cells is u = Ymks. Selection gradients with

respect to Ym and k, can be derived as follows:

_ Y... W _
_ k, aw _
G“ _(W)(8k )"1 (3)

The change of fitness for the derived community can be expressed as:

8W 8W

AW: AY +
31;, "'

 

 

A
al., k: (4)

149

Off

 

 

 

 

 

 

 

 

 

Gas

 

 

 

 

r121

 

 

 

—l
—1

F

 

—1

 

‘—l

 

 

80
O

 

 

 

 

 

 

 

 

 

 

 

 

 

Fl

 

 

O
O

 

 

 

 

 

f) i
I

 

OC
0

0

1:1

I

 

O
O

 

 

 

 

 

 

“ 001:1

 

 

Oo-

 

 

 

 

 

 

 

 

 

Air Pump Water Bath

 

Medium Feel TCE Feed
Syringe Syringe
Decant Medium
Pump Pump
0 g D g
l ._l
Phenol Feed Phenol Feed!
Syringe Syringe
_ Recycle
Timer pump
:1 El q
L
Effluent Medium
Tank Tank

Figure 7.1. Experimental setup for bench—scale sequencing batch reactors

 

150

Table 7.1. TCE feed concentration for long-term TCE exposure experiment

 

 

 

 

 

 

Start days (days) TCE feed concentration TCE loading per cycle
(mg/L) (mg)
0 0 0
76 0.5 0.5
100 2.5 2.5
140 5 5
150 10 10
183 15 15
191 25 25
Full Read Settle Decant Recharge
0 1 4 5 6 65 12
l l I l l L l
I l l l I T ”'1
2500 -
TCE feed Pheno| Cell
2 f d wastin
g 2000 - 9° 9
2 1500 -
O
> 7
b 1000 ,
O
4.1
o
3 500 P
a:
o 1 1 1 1 1 1 1 1 1 1 1

 

 

01 2 3 4 5 6 7
Time(hrs)

Figure 7.2. The operating mode of SBR in a cycle

10 11 12

151

Table 7.2 Summary of bench-scale SBR operating condition

 

 

Parameter Value

Regtgr volume

Total volume 2500 mL
Liquid volume 1100 - 2200 mL
Headspace volume 300 - 1400 mL
Initial volume 1100 mL

flow rate

Influent flow rate 16.7 mL/min
TCE concentration 0.5-25 mg/L
Recharge flow rate 1.2 mL/min
Phenol concentration 5000 mg/L

Air flow 220-280 mL/min
Minimum oxygen 2 mg/L
concentration in reactor

Magus.

Fill time 1 hr

Reaction time 3 hrs

Settle time 1 hr

Decant time 1 hr

Recharge time 6 hr

- phenol feed mode 0.5 hr

- phenol react mode 5.5 hr
Operating cycle time 12 hrs/cycle
Sludge age, SRT 10 day

 

General procedures for biotransformation measurement

Bc' f 'n ' ts

Biotransformation studies were performed using 20-ml glass vials sealed with teflon-coated
butyl rubber stoppers and aluminum crimp caps. These vials were incubated with 5 mL of

of Whittenbury Mineral Media plus culture. An appropriate amount (measured as dry

152
weight) of mixed culture MMl was added to each test vial. TCE solutions (dissolved in

water) were added to each bottle using Precision gas tight syringes. Methane were
withdrawn from Scotty H cyclinders (99.0% CH4, Alltech Associate, Inc., Deerfield, IL) at
a fixed exit pressure and injected into batch vials. Phenol was added to vials from a 40 g/L
stock solution. After adding substrates, the vials were vigorously shaken upside-down on a
rotary shaker (250 rpm). Headspace samples of TCE and methane were periodically
analyzed by GC. Cell solution was filtered with 0.2 um syringe filter. Phenol in the filtrate
was analyzed by HPLC.

Analytical methods

A TCE-saturated water solution was used as the spike solution in all experiments. The
spike solution was prepared by adding excess TCE (99+% pure ACS reagent, Aldrich
Chemicals Co., Milwankee, WI) to a 250 ml glass bottle capped with TFE-lined Mininert
valve. The bottle was vigorously shaken and allowed to settle at least 24 hrs. The upper
layer of the solution was transferred to another bottle and capped with a Mininert valve.
The spike solution was stored in a refrigerator until needed. One hour before use, it was

shaken again and allowed to settle.

TCE was analyzed by withdrawing 0.1 ml of headspace from the test bottles using a 0.5 ml
Pressure-Lok Series A-2 gas syringe and injecting the samples onto a Hewlett Packard
5890 gas chromatograph (GC) equipped with a capillary column (DB624, 30m x 0.53mm
I.D.), a flame ionization detector (FID) and a Electron capture detector (ECD). The GC was
operated isothermally at 90°C with helium as carrier (12 mL/min). The injection port was

set at 250°C. The temperature of FID and ECD were 250°C and 350°C, respectively.

Phenol was analyzed by HPLC. Cell samples were collected by syringes and injected

through 0.2 ttm NYLON syringe filters. Filtrate (2 ml) was collected for analysis. Water

153
HPLC (WISP 710B+ Model 510 pump) equipped with a column (Econosil C18, 10

micron, 250 mm, Alltech Cat No. 288138) and a UV detector (Lambda-Max Model 481
LC spectrophotometer) was operated isocratically (60% acetonitrile + 40% water) at a total
flow rate 1 mL/min. The wavelength of UV detector was 235 nm and the injection amount

of samples were 30 (IL. The limit of detection for phenol was approximately 1 mg/L.

Cell biomass was determined on a dry weight basis using 0.2 pm filters (Gelman Sciences
Inc., Ann Arbor, MI). The filters were prepared by first soaking them in mineral media for
10 minutes, rinsing on a vacuum filter with deionized water, drying overnight in a 103°C
oven, and cooling in a desiccator until needed. The filters were weighed, and once a known
amount of culture was filtered through them, they were rinsed, dried, cooled and

reweighed.

Community Analysis

DN ' 'rcin

Fifteen milliliter samples were collected from reactors at intervals, and cells were harvested
by centrifugation and frozen. Pellets were later resuspended in a buffer consisting of 100
mM Tris, 100 mM EDTA (pH 8.0), 1.5 M NaCl, 1% CTAB. Two milliliters SDS (10%)
was added and the cells were incubated at 65°C for 30 minutes in a rotary water bath (150
rpm). Samples were cooled to 37°C, then supplemented with 50 pl proteinase K (10
pig/[.11). The lysates were incubated at 30°C for 2 hours with shaking. DNA was purified by
extraction with chloroform/isoamyl alcohol (24:1) and phenol/chloroform/isoamyl alcohol

(25:24: 1) and precipitated with cold isopropanol.

154
El r sis

Changes in the communities were monitored using DGGE (denaturing gradient gel
electrophoresis). Thirty microliter PCR reactions contained approximately 10 ng template
DNA, 1U Taq DNA polymerase, 10X buffer, 10% DMSO (v/v), 2mM Mg”, 200 mM
dNTPs, and 1 11M of primers GM5F and 907R described by Muyzer, et al. (Muyzer et al.
1995). A CG clamp was added to the 5’ end of GMSF. Products were analyzed on an 8%
polyacrylamide gel (37.5 :1 acrylamide/bisacrylamide) containing a 40 -60% gradient of

urea and forrnamide. The separationwas achieved at 200 V and a temperature of 60°C.

RESULTS

For the TCE exposure experiment, TCE was injected over a one-hour interval during the
fill period. Phenol was provided in a separate period, following the decant step. Influent
TCE levels were increased gradually from 0.5 to 25 mg/L over 6 months (Table 7.1).
Changes of cell density in response to long-term TCE exposure are shown in Figure. 7.3.
To monitor changes in community structure and function, the following parameters were
measured periodically: second order rate coefficient for TCE transformation, maximum
specific rate of utilization of phenol, observed yield, biomass transformation capacity, and
endogenous decay rate. The history of these parameters during long-term TCE exposure is
shown in Figures 7.4 to 7.6 and Table 7.3 to 7.4. Figure 7.7 illustrates fitness of the two
communities during the period when the TCE-exposed reactor was exposed to 25 mg/L of

TCE feed solution.

Typical changes in the apparent second order rate coefficient for TCE transformation during
a SBR operating cycle are illustrated in Figure 7.8. A decline in TCE transformation was
observed during the fill, react, settle and decant periods. However, recovery of activity was

observed during the recharge period. From a mass balance on TCE, more than 95% of the

155
added TCE was removed by microbial degradation and only 3% was removed by air

stripping. TCE concentration in gas and liquid phase within the reactor during the fill
period are shown in Figure 7.9. Gas and liquid phase approach equilibrium except during
the initial fill.

One molecular technique - denaturing gradient gel electrophoresis (DGGE) - was used to
detect changes in community structure. A small fragment (~400 bp) of the 16S gene is
amplified and the products are resolved on an acrylamide gel containing a gradient of urea
and forrnamide. The amplified PCR products from different species are of the same size
and do not resolve on agarose or acrylamide. However, the different GC content of each
fragment allows resolution on the gradient gel as fragments with higher GC content are
transported further into the gel before denaturing. Most species will yield one band so a
simplified community fingerprint is obtained. Changes in community structure can be
detected through the appearance and/or disappearance of bands. DGGE fingerprints from

control and TCE-exposed communities are shown in Figure 7.10.

156

TCE Feed concentration (mg/L)

 

 

 

 

 

0 10.5, 2.5 1 5 1 25
1800 f f 1 I
A 1600 - . 0......”
.1
B: 1400 -I Q I l ’I‘
II
5 1200 :EWfigéézo 9' III
I I
§1000 II °fi .3- .fi'.
2 800 -
o
'0 600 -
= 400 -
8 oControl
20° ’ IExposed
0 l l l l
0 50 100 150 200 250
Time(Days)

Figure 7.3 Changes in cell density of the phenol-degrading reactor communities.

157

TCE feed concentration (mg/L)

 

 

 

 

 

O 10.5 | 2.5 l 5'15 1 25
0.8 l I l T
I
I one. o .
"3 ’° 0’ ,I 5 i 00 o
a: 0'5 ' C I I I I
E b I ” I .- .‘I.
_l 0.4 . I
0.3 - I
3: II'
0.2 - II.
0Control
0'1 ' IExposed
O l l l l
o 50 100 150 200 250
Time(Days)

Figure 7.4 Second order rate coefficients of TCE transformation by phenol-degrading
reactor communities.

158

TCE feed concentration (mg/L)

 

 

 

 

 

 

 

0 .0.512.5 .5-15I 25
06 1 I I J
”53
\
. O
3505 II I :
u I a O
.3 04 0 °. 0'.
.2 r: ' 0’ . I . III
:0 I .00 O i-
0: I 0' fit 0 I
mg0.3 " .I ‘
a O I I I
a
c ’00. *II‘ 0 I
5002 P . I -
a: J I I I l
g“ I.
.. I! 0.1 - OControl
:1: IExposed
E a I l l l
o
0 50 100 150 200 250

Time (Days)

Figure 7.5 Maximum specific rate of phenol utilization by phenol-degrading reactor
communities.

Table 7.3. Theoretical transformation capacity for phenol-degrading reactor communities.

 

 

 

 

Time Theoretical transforrnationcapacity, mg fimg cells T,exposure/
(Days) T,control
Exposed Control
68 0.366 i0.016 0.345 :i:0.045 1.06
96 0.333 21:0.023 0.324 21:0.043 1.03
174 0.342 $0.055 0.355 i0.044 0.96
189 0.222 $0.031 0.344 21:0.005 0.65
210 0.332 i0.017 0.34 i0.022 0.98
222 0.345 21:0.041 0.351 i0.055 0.98

 

159

Table 7.4. Endogeneous decay constant for phenol-degrading communities during long-
term TCE exposure in SBRs.

 

 

 

 

 

 

 

 

 

Time Endogenous decay constant, b,exposure/ r2 for regression
(Days) 1/day b,control
Exposed Control Exposed Control
68 0.398 0.349 1.14 0.875 0.771
96 0.177 0.202 0.88 0.735 0.668
174 0.369 0.318 1.16 0.948 0.685
189 0.267 0.248 1.08 0.744 0.791
210 0.152 0.367 0.41 0.704 0.762
222 0.314 0.718 0.44 0.998 0.923
g 1.4
3 1.2 - I 15 mg/L TCE initiated
3
o 1 _ J
I
g c I ' ' . '
v o 0.8 - . . II I
C I I
E 2 o . I o
0 0.6 r o O o
g Q 0” . O. : :0 : o
'o 0.4 - I 0°
m I
2
i
.o OControl
O 0 . . .
160 180 200 220 240

Time (Days)

Figure 7.6 Long-term changes in observed yield for the phenol-degrading reactor
communities.

160

 

   

 

 

 

 

 

 

 

 

1.4
-—Ii
1.2 - I p
1 P
3 0.8 -
«T
to 0.6 -
0
E.
E 0.4 -
0.2 - ° 89°39“
l Control
0 ' 15mg/LTCE ____CE""°st°f'
initiated °" '°
-o.2 . -
160 180 200 220 240

Time (Days)

Figure 7.7 Fitness of phenol-degrading communities during period when the exposed
reactor received an influent concentration 25 mg/L TCE. Fitness calculations are based on )1

measurements (0 , 0) and fitness components (-—, ---), respectively.

161

Fill React Settle Decant Recharge

l l l l l
0.9 I l I rJ

 

 

0Control
l Exposed

.0
m
I

 

(leg-d)
O
O-Il

o
03
l-OI'I
l—O—ll

Apparent second order rate

 

 

 

“
C
Q
‘5 05 -
C .
O
O

OAT

0.3 l l l I L

8 10 12 14 16 18 20

Sampling time (hr)

Figure 7.8 Change in the second order rate coefficient for TCE transformation in a typical
SBR operating cycle.

162

 

 

 

 

 

0.7
OCw(mg/L)

3 0.6 _ O . IICg(mg/L)|
\
m 0
E 0
v 0.5 - 0
c
o O
2:- 0.4 -
I
I-
E o
0 0.3 -
o O
c
8

L

0.2 I I I

I.l.l I '
U I
I- o.1 ~ I

it ° ' t

I
0 l l l l l l
0 10 20 30 40 50 60 70

Time (min)

Figure 7.9 TCE concentration in gas (Cg) and liquid (Cw) phase during the fill and react
periods for a feed solution of 25 mg/L TCE.

163

 

Figure 7.10 DGGE fingerprints from control and TCE-exposed communities in
sequencing batch reactors.

164
DISCUSSION

The SBR microbial community cometabolized TCE for extended periods without loss of
transformation activity. No significant changes in phenotypic parameters occurred when the
TCE concentration was less than 10 mg/L. With increased influent TCE concentrations,
declines in biomass, observed yield, specific rates of phenol utilization and specific rates
of TCE transformation were observed. Theoretical transformation capacity (defined as the
mass of nongrowth substrate transformed per unit mass of cells in the absence of
endogenous decay) decreased to 60% of the control value (from 0.344+l-0.005 to 0.222+l-
0.031). After one more month of perturbation, theoretical transformation capacity
recovered to the original value. Of interest was the appearance of a persistent yellow color
after the influent TCE concentration was raised to 15 mg/L. The yellow color may have
been due to the accumulation of a-hydroxymuconic semialdehyde, an intermediate of
phenol degradation resulting from the incomplete oxidation of phenol (Shih et al. 1996).
The endogenous decay coefficient decreased slightly after prolonged TCE exposure. This
suggests that the enzymes present in the TCE exposure culture became more resistant to
endogenous decay. This may be because of adaptation to TCE or its transformation

products.

Fitness of phenol-degrading community in SBR was calculated from the ratio of specific
rate of phenol utilization at any time to the value just before initiating the 25 mg/L TCE
feed. The results show that a decline in fitness occurred just after the initiation of 25 mg/L
TCE feed, with subsequently recovery to the original value. The changes in fitness during
TCE exposure closely match those of the observed yield, specific rates of phenol
utilization, specific rates of TCE transformation and theoretical transformation capacity.
These observations suggest that microbial community can adapt to TCE exposure, and that

this adaptation can be quantitatively described by fitness. To further demonstrate the fitness

' theory, selection gradients with respect to fitness components (Ym, ks ) and history of these

165
fitness components were used to calculated fitness (Eq.(l) to Eq. (4)). The results indicate

fitness can be calculated accurately without knowledge of fitness itself (Figure 7.7).

Microscopic examination revealed that the community has a distinctive floc structure of
spherical and rod-shaped bacteria. This characteristic is similar to that of the inoculum from
the fed-batch pulse reactor (Shih et al. 1996). During the settle period, the TCE-exposed
reactor community had a finer floc structure than the control reactor. This difference
appeared soon after TCE exposure and persisted after higher TCE exposures were
implemented. A possible explanation for lower cell density in the TCE-exposed reactor may

be loss of biomass in the decant period because of poor settling properties.

The apparent second order rate coefficients declined during the fill, react, settle and decant
periods and recovered during the recharge period. For specific growth rate, similar trends
were observed. The apparent first order rate coefficient is equal to the true second order
coefficient (k;) multiplied by the concentration of organisms (X) that can degrade TCE (X).
Therefore, changes in the apparent second order rate coefficient during a operating cycle
may correspond to the changes in the concentration of TCE degrading organisms (lower X
) or to loss of reducing power during TCE transformation (lower k). The organisms that
were deactivated or killed during TCE transformation were reactivated by regrowth on the
phenol. The fact that there was no significant difference in endogenous decay rate between
the exposed and control reactor communities suggests that toxicity did not play an

important role for this level of TCE exposure (25 mg/L in feed).

Changes in the structure of the microbial communities were monitored using denaturing
gradient gel electrophoresis (DGGE). Total community DNA was isolated from samples
taken from the reactor and regions of the 168 rDNA genes were amplified by PCR using

universal primers, and resolved by electrophoresis in an acrylamide gel with a urea and

166
formamide gradient. The control community showed no change over time. The TCE-

exposed community was also largely unchanged, with the exception of the appearance of a
distinct new band after TCE concentration was increased to 2.5 mg/L (Figure 7.10). This
change suggests the gain of a population within a community, or perhaps a shift in

dominance in the community structure.

Previous research established that communities fed phenol over short intervals were more
stable and diverse. This research establishes that such a community is capable of long-term
TCE transformation. SBR reactor environments are well suited to cometabolism because
they offer a wide variety niches for metabolism of phenol and cometabolism. In this work,
the methanotrophic mixed culture chemostat experiment (Chapter 6) and the phenol-
degrading SBR community represent two extreme cases for adaptation of cultures. Higher
diversity within the SBR system offers a more stable community structure and higher
potential for TCE transformation than the chemostat under the ranges tested (1-50 mg
TCE/day and 6-18 mg TCE/day, respectively). As discussed previously, it may be possible

to use selection gradients (especially more sensitive ones) as criteria for the stability of a

community under a given perturbation. The selection gradients with respect to 7; , b , kc,

and Kc for methanotrophic mixed culture are higher than those of phenol-degrading

community (Chapter 5). Thus, the phenol-degrading community was expected to be more

stable than methanotrophic mixed culture. This was confirmed by experimental results.

Mass balance data show that over 95% of TCE was removed by microbial degradation and
only 3% of TCE was stripped by aeration. TCE concentration in gas and liquid phase
within the SBR approached equilibrium. From a process engineering point of view, these

data verify that the current SBR design can treat 25 mg/L TCE with satisfactory results.

167
REFERENCES

1.A1varez-Cohen, L., and P. L. McCarty. 1991a. A cometabolic biotransformation model
for halogenated aliphatic compounds exhibiting product toxicity. Environ. Sci. Techno].
25:1381-1387.

2.Alvarez-Cohen, L., and P. L. McCarty. 1991c. Product toxicity and cometabolic
competitive inhibition modeling of chloroform and trichloroethylene transformation by
methanotrophic resting cells. Appl. Environ. Microbiol. 57:1031 - 1037.

3.Chang, H.-l., and L. Alvarez-Cohen. 1995a. Model for the cometabolic biodegradation
of chlorinated organics. Environ. Sci. Technol. 29:2357-2367.

4.Criddle, C. S. 1993. The kinetics of cometabolism. Biotechnol. Bioeng. 41:1048-1056.

5.Folsom, B. R., P. J. Chapman, and P. H. Pritchard. 1990. Phenol and trichloroethylene
degradation by Pseudomonas cepacia G4: kinetics and interactions between substrates.
Appl. Environ. Microbiol. 56:1279-1285.

6.Harker, A. R., and Y. Kim. 1990. Trichloroethylene degradation by two independent

aromatic-degrading pathways in Alcaligenes eutrophus JMP134. Appl. Environ.
Microbiol. 56:] 179-1 181.

7.Henry, S. M., and D. Grbic’: -Galié. 1990. Effect of mineral media on trichloroethylene
oxidation by aquifer methanotrophs. Microbial Ecology. 20: 151-169.

8.Henry, S. M., and D. Grbié -Galic’:. 1991a. Influence of endogenous and exogenous
electron donors and trichloroethylene oxidation toxicity on trichloroethylene oxidation by

methanotrophic cultures from a groudwater aquifer. Appl. Environ. Microbiol. 57:236 -
244.

9.Hopkins, G. D., J. Munakata, L. Semprini, and P. L. McCarty. 1993b.
Trichloroethylene concentration effects on pilot field-scale in-situ groundwater

bioremediation by phenol-oxidizing microorganisms. Environ. Sci. Technol. 27:2542-
2547.

10.Lackey, L. W., T. J. Phelps, V. Korde, S. Nold, D. Ringelberg, P. R. Bienkowski,
and D. C. White. 1994. Feasibility testing for the on-site bioremediation of organic wastes
by native microbial consortia. International Biodeterioration & Biodegradation. 33:41-59.

11.Little, C. D., A. V. Palumbo, S. E. Herbes, M. E. Lidstrom, R. L. Tyndall, and P. J.
Gilmer. 1988. Trichloroethylene biodegradation by a methane-oxidizing bacterium. Appl.
Environ. Microbiol. 54:951 - 956.

12.Muyzer, G., A. Teske, and C. O. Wirsen. 1995. Phylogenetic relationships of
Thiamicrosopira species and their identification in deep-sea hydrothermal vent samples by
denaturing gradient gel electrophoresis of 168 rRNA fragments. Arch. Microbiol.

13.Ne]son, M. J. K., S. 0. Montgomery, W. R. Mahaffey, and P. H. Pritchard. 1987.
Biodegradation of trichloroethylene and involvement of an aromatic biodegradative
pathway. Appl. Environ. Microbiol. 53:949-954.

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14.Nelson, M. J. K., S. 0. Montgomery, and P. H. Pritchard. 1988. Trichloroethylene
metabolism by microorganisms that degrade aromatic compounds. Appl. Environ.
Microbiol. 54:604-606.

15.0]denhuis, R., J. Y. Oedzes, J. J. van der Waarde, and D. B. Janssen. 1991. Kinetics
of chlorinated hydrocarbon degradation by Methylosinus trichosporium OB3b and toxicity
of trichloroethylene. Appl. Environ. Microbiol. 57:7-14.

16.0]denhuis, R., R. L. J. M. Vink, D. B. Janssen, and B. Witholt. 1989. Degradation of
chlorinated aliphatic hydrocarbons by methylosinus trichosporium OB3b Expressing
soluble methane monooxygenate. Appl. Environ. Microbiol. 55 :28 19-2826.

l7.Shields, M. S., S. 0. Montgomery, P. J. Chapman, S. M. Cuskey, and P. H.
Pritchard. 1989. Novel pathway of toluene catabolism in the trichloroethylene-degrading
bacterium G4. Appl. Environ. Microbiol. 55: 1624 - 1629.

18.Shih, C.-C., M. Davey, J. Zhou, J. M. Tiedje, and C. S. Criddle. 1996. Effects of
phenol feeding pattern on microbial community structure and cometabolism of
trichloroethylene. Appl. Environ. Microbiol. 62:2953-2960.

19.Strand, S. E., M. D. Bjelland, and H. D. Stensel. 1990. Kinetics of chlorinated

hydrocarbon degradation by suspended cultures of methane—oxidizing bacteria. Research J.
WPCF. 62:124-129.

20.Wackett, L. P., and D. T. Gibson. 1988. Degradation of trichloroethylene by toluene
dioxygenase in whole-cell studies with Pseudomonas putida F1. Appl. Environ. Microbiol.
54:1703-1708.

21.Wackett, L. P., and S. R. Householder. 1989. Toxicity of trichloroethylene to

Pseudomonas putida F1 is mediated by toluene dioxygenase. Appl. Environ. Microbiol.
55:2723-2725.

CHAPTER 8
ENGINEERING SIGNIFICANCE

Cometabolism is a complicated phenomenon. Several factors interact simultaneously.
This presents a challenge for engineering application since rational design requires use of
appropriate kinetic expressions. A generally accepted mode] is needed with model
parameters that can be evaluated without difficulty. In this research, a model for the most
general case was verified. The model also covers a simplied case in which growth and
nongrowth substrate transformation occurs separately. Different microbial systems can
therefore be evaluated under this same framework. Furthermore, the model can serve as

the basis for design of treatment processes and for prediction of treatment efficiency.

Cometabolic transformations often generate toxic products. The extent to which these
products affect the microorganisms depends upon the type of organisms and the
compounds transformed. Toxicity can affect microbial communities at both the
population level and the community level. At the population level, specific populations
may experience mutation and selection or changes in gene expression. At the community
level, the community can be expected to undergo dynamic changes in composition. All
of these factors contribute to changes in the phenotype of the community. By analyzing
selection gradients, the sensitivity of different parameters on growth can be evaluated.
This may suggest means whereby microbial populations or communities can be
"designed" or improved. For example, improvements in transformation capacity in
cultures that are sensitive to that parameter would enhance the sustainability of

cometabolic transformation.

169

170

The patterns of addition of growth and nongrowth substrate can have a great impact on
the performance of a system. In this work, two extreme cases were examined. A
continuous system with both substrates fed simultaneously, and a SBR with both
substrates fed at separate stages. When both substrates are provided simultaneously,
growth and nongrowth substrates compete for the same enzymes, growth substrate is used
inefficiently, and growth of cells declines. Futherrnore, product toxicity accelerates cell
decay. All these factors contribute to the instability of continuous systems fed both
substrates simultaneously. On the other hand, sequencing batch reactors have certain
advantages. An SBR can alternate between periods of growth on growth substrates and
periods of cometabolism of nongrowth substrate, eliminating the possibility of
competitive inhibition for enzymes between growth and nongrowth substrates. Since
microorganisms are reactivated after the transformation of nongrowth substrate, a steady
and sustainable community can be maintained. Thus, SBRs offer a good treatment
technology for cometabolic transformation of substance that do not support growth of

microorganisms.

Stability of microbial communities is an important factor for engineering application.
Communities usually undergo dynamic changes in structures and performance in
response to perturbation. In this research, nongrowth substrate exposure is the sole
perturbation that the communities experience. A stable community should be able to
maintain its structure and performance under nongrowth substrate exposure. Based on the
results of this study, a diverse community seems to have better stability. It is feasible to
use sensitive selection gradients as the criteria of stability of community. The parameter
can be used as a standard for comparison between different communities in the same

environment or the same communities in different environments.

CHAPTER 9
SUMMARY AND CONCLUSIONS

CONCLUSIONS

1. An unstructured model for cometabolism is presented and verified experimentally for a
defined methanotrophic mixed culture. The model includes the effects of cell growth,
endogenous cell decay, product toxicity, and competitive inhibition with the assumption
that cometabolic transformation rates are enhanced by reducing power obtained from
oxidation of growth substrates. A theoretical transformation yield is used to quantify the
enhancement resulting from oxidation. A systematic method for evaluating model
parameters is described. The applicability of the mode] is evaluated by comparing
experimental data for methanotrophic cometabolism of TCE with model predictions from
independently measured model parameters. Propagation of errors is used to quantify
errors in parameter estimates and in the final prediction. The model successfully predicts
TCE and methane transformation for a wide range of concentrations of TCE (0.5 - 9

mg/L) and methane (0.05 - 6 mg/L).

2. This research investigated the potential for methanotrophic biotransformation of three
HCFCs -- chlorodifluoromethane (HCFC-22); 1-chloro—1,1-difluoroethane (HCFC-142b);
and l,1-dichloro-2,2,2-trifluoroethane (HCFC-123); and one HFC -- 1,2,2,2-
tetrafluoroethane (HFC-134a). All of these compounds were biotransformed to differing
degrees by methanotrophic mixed culture MM]. Intrinsic rates of transformation were
obtained by combining a second order rate expression with an expression describing loss

of transformation activity due to either endogenous decay or product toxicity. For
171

172
HCFC-123 and HFC-134a, the independently measured endogenous decay rate for mixed

culture MM] (0.594/day) was sufficient to account for the observed loss of
transformation activity with time for the one case examined. However, the endogenous
decay rate did not account for the loss of transformation activity for HCFC-22 and
HCFC-142b. A model based on product toxicity provided a reasonable representation of
the loss of transformation activity for all these compounds. The order of reactivity was
HCFC-22 > HCFC-142b > HFC-134a > HCFC-123, with second order rate coefficients
of 0.014, 0.0096, 0.00091, and 0.00054 L/mg-day, respectively. Transformation
capacities for HCFC-22 and HCFC-142b were 2.47 and 1.1] ug substrate/mg biomass,

respectively.

3. Theoretical transformation capacity is a function of organisms and target compounds.
For two groups of compounds and two types of organisms studied here, the order of
theoretical transformation capacity is phenol degrader/TCE > methanotrophic mixed
culture/ TCE > methanotrophic mixed culture/ HCFC/HFC, with typical values of 0.35,
0.06, and 0.002 mg TCE or HCFC/mg cells. This indicates that product toxicity plays a
much more important effect on methanotrophic mixed culture with HCFC/HFC

transformation, but less important for phenol degrader with TCE transformation.

4. A fitness concept was developed for communities and combined with the
cometabolism model to describe adaptation of cometabolizing communities. A major
concept of the model is that gross phenotype changes within a microbial community can
be quantitified by a " fitness" parameter which can be calculated using kinetic parameters
that describe the community. The selection gradient for each parameter is defined by the
partial derivative of fitness with respect to that parameter. The gradient, therefore, reflects

the direct selection acting on each fitness component, with the other components held

173
constant. It appears possible to use selection gradients as criteria for the stability of a

community under a given perturbation.

5. A methanotrophic mixed culture and a phenol degrading culture were exposed to
different levels of TCE over extended periods of time. The changes of community were
monitored and the effects of TCE exposure on community structure were evaluated.
Various phenotypic parameters were measured. Genetic community analysis (ARDRA
and DGGE) was also used to monitor shifts in microbial community structure. The results
indicate that phenotypic and genetic changes occurred during TCE exposure. Both
microbial communities adapted to TCE exposure with improvement in the observed

transformation capacities and endogenous decay constants.

6. This research establishes that a phenol-degrading community is capable of long-term
TCE transformation. SBR reactor environments are well suited to cometabolism because
they offer a wide variety niches for metabolism of phenol and cometabolism. In this
work, the methanotrophic mixed culture chemostat experiment and the phenol-degrading
SBR community represent two extreme cases for adaptation of cultures. Based on an
analysis of the selection gradients for these two communities, the phenol fed SBR
community was expected to be more stable than methanotrophic chemostat mixed

culture. This was confirmed by experimental results for the ranges tested.

FUTURE RESEARCH

l. Hyphomicrobia in methanotrophic mixed culture were thought to be possible
indicators of MMO activity. However, in this research, no significant change in this
population was detectable by image analysis. This may be because an insufficient number
of images were taken. Further research is needed to determine the number of images

required to accurately quantify a given morphotype.

174

2. SBRs provide flexible operation for cometabolism. Several operating parameters
(length of periods, exposure level, growth substrate concentration and extent of aeration)
can affect performance. Optimization of SBR systems will assist the engineering

application of SBRs.

3. Genetic analyses (ARDRA and DGGE) were used in this research for characterization
of community structure. Other methods of community analysis, such as analysis of fatty
acid methyl esters (FAME), might also be employed for analysis of cometabolizing

communities.

4. Key populations responsible for phenol and TCE transformations in the phenol-

degrading community should be isolated and characterized.

5. Use of selection gradients to quantify community stability should be evaluated further.
More extensive studies are needed for different communities within the same reactor type

and for the same community within the different reactor types.

6. For the range of TCE exposures studied in this work, the phenol-degrading community
was always able to adapt to TCE exposure. Further research should be conducted to
determine the upper limit of exposure and to assess whether TCE could conceivably be

used as a growth substrate.

           

       

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