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8/01 cJCIRC/Datoouepes—p. 15

LONG-TERM EFFECTS OF PHENOL AND PHENOL PLUS
TRICHLOROETHENE APPLICATION ON MICROBIAL COMMUNITIES IN
AEROBIC SEQUENCING BATCH REACTORS

By

Hector Luis Ayala-del-Rio

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Microbiology and Molecular Genetics

2002

ABSTRACT

LONG TERM EFFECTS OF PHENOL AND PHENOL PLUS
TRICHLOROETHENE APPLICATION ON MICROBIAL COMMUNITIES IN
AEROBIC SEQUENCING BATCH REACTORS

By

Héctor Luis Ayala-deI-Rio

One of the most common bioremediation strategies to treat contaminated
effluents is the use of bioreactor systems. Although there is well established
theory on the design and operation of bioreactors, there is scarce knowledge
about the microbial ecology of these systems. The effects of more than two
years of trichloroethene (TCE) application on the microbial communities of
sequencing batch reactors were studied. One reactor was fed phenol and the
other phenol and TCE in sequence. Exposure to TCE resulted in a community
with reduced and relatively stable TCE transformation rates. Community
analysis of the phenol plus TCE-fed reactor by terminal restriction fragment
length polymorphism (T-RFLP) revealed that after initial changes in community
structure, a stable community was selected. In contrast, the absence of TCE in
the second bioreactor community resulted in higher but unstable TCE
transformation rates. The community structure of the phenol-fed reactor was
highly dynamic, showing only transient stability as revealed by T-RFLP analysis.
In both reactors the changes in community structure correlated with changes in

function. Scanning electron microscopy and fractionation of the suspended

solids revealed that TCE application induced major changes in the physiology of
the system by increasing the accumulation of extracellular polymeric substances
(EPS) and the formation of “star-like” flocs. In contrast, low amounts of EPS,
and cells forming small aggregates were observed in the phenol-fed reactor.
Isolation and phylogenetic analysis from both reactors revealed that the
cultivable members of these communities belonged to the Proteobacteria (alpha,
beta and gamma classes) Firmicutes, Actinobacten'a, Bacteriodetes, and
Deinococcus-Thermus phyla. Characterization of the phenol- and TCE-
degrading capabilities of the isolates revealed that only 28 % degraded phenol
under the conditions tested, and that 57 % of the phenol degraders
cometabolized TCE. Enumeration of selected isolates from the communities by
SYBR green real-time PCR revealed that a group of isolates belonging to the
Comamonadaceae of the beta Proteobacten’a were the predominant populations
among the isolates tested in both reactors. These isolates, which contain
phenol hydroxylase genes, grow on phenol and cometabolize TCE suggesting
that long-term application of TCE does not result in the disappearance of TCE

degrading phenotypes.

Copyright by
Héctor Luis Ayala-del-Rio
2002

Dedication

To my family and my wife, for all their love, support and encouragement
during this long journey. T hanks for helping me complete this dream.

ACKNOWLEDGEMENTS

I would like to express my deepest gratitude to many individuals who have
helped me to complete this goal. First, I would like to thank my advisor Dr.
James M. Tiedje, for his guidance, support, patience and encouragement during
the completion of this dissertation. Thanks for giving me the creative freedom to
pursue my own research path; I am sure the experiences I have gained under
your guidance will help in my future career. I have always felt privileged to be
here at MSU and for being your student. Special thanks to Dr. Craig S. Criddle,
for allowing me to work in these microbial communities. Your creativity,
encouragement and enthusiasm made my research more enjoyable and
exciting. Thanks for introducing me to the world of environmental engineering. I
have always considered you as my second advisor. Thanks to the other
guidance committee members: Dr. Thomas Schmidt, Dr. Patrick Oriel and Dr.
Greg Zeikus; for their constructive criticism that helped me to maintain focus on
what was important, and for making my dissertation defense the most enjoyable
discussion that I could ever imagine. Finally, thanks to Dr. Rawle Hollingsworth
and Mindock, from the Department of Biochemistry at Michigan State University
for helping me with the polysaccharide analysis.

I am indebted to Mr. Stephen J. Callister for operating the reactors during
the duration of this experiment. Without your efforts in maintaining and
monitoring these reactors none of this dissertation would have been possible.
THANKS STEVEIII. Thanks to past and present members of the Tiedje’s lab;

Ana Fernandez, Shannon Flynn, Frank Loeffler, Klaus Nilsslein, Gesche Braker,

vi

Bekki Helton, Ben Griffin, John Davis, John Urbance, James Cole, Alison
Murray, Jorge Rodrigues, Vincent Denef, Tamara Tsoi, Joon Park, David
Treves, Monica Ponder, Krystal Baird, Robert Sanlnocencio... and many others
for their friendship and help throughout my studies. Special thanks to past and
present CME staff, Lisa Pline, Pat Englehart, Nikki Mulvaney and Brenda Minnot
for making my life easier.

I would like to thank my friends; Lycely Sépulveda, Andres Chong,
Verbnica Griintzig, Carlos Rodriguez, Claribel Cruz, Gustavo Bonaventura, Olga
Hernandez, Jaime Graulau, Vladimir Ferrer and Carmen M. Medina for being
there in the good and bad times; and for being my family at MSU. I hope our
friendship lasts forever!

None of this would have been possible without the love, support and
encouragement of my family at home, Papi, Mami, Javier and Sarita. They
always had cheering words when I needed them the most, and they did
everything they could to help me complete this dream.

I own the greatest gratitude to my wife, Lizy Fernandez. Atlhough at the
beginning you were far away, you always gave me strength and endless love to
complete this journey. Thanks for being with me every day during the last two
years, for encouraging me the days that things did not work out and for believing
in me even when I had doubts in my abilities. Your patience and
encouragement during this last stage were essential to complete this
dissertation. I am grateful to have your love and I look forward to spend eternity

together.

vii

Last but not least, I want to thank God my lord, for your love, blessings, for
giving me this opportunity and the strength to complete it. You were always with
me, especially when I needed you the most. You have guided me through all
my life, and especially during this last six years. You will remain my steward

until the end of my days.

viii

PREFACE

The work presented in this dissertation was a collaborative effort between
the Center for Microbial Ecology and the Department of Civil and Environmental
Engineering at Michigan State University. Mr. Stephen J. Callister, from the
department of Civil and Environmental Engineering, was in charge of bioreactor
operation. and in gathering the functional measurements presented in Chapter
2. The analysis of the polysaccharide fraction of the extracellular polymeric
substances isolated from the phenol plus TCE-fed reactor presented on Chapter
3, was performed under the guidance of Dr. Rawle l. Hollingsworth and Dr. Carol
C. Mindock, from the Department of Biochemistry at Michigan State University.
Dr. Mindock, performed the GC-MS and NMR analyses. The scanning electron
microscopy images presented on Chapter 4 were taken by Ewa Danielewicz,

from the Center for Electron Microscopy at Michigan State University.

TABLE OF CONTENTS

LIST OF FIGURES .......................................................................................... xiii
LIST OF TABLES ........................................................................................... xviii

CHAPTER 1: TRICHLOROETHENE OXIDATION BY MICROBIAL

COMMUNITIES ................................................................................................... 1
Trichloroethene ................................................................................................ 1
Characteristics and applications .................................................................. 1
Why TCE is a concern? ............................................................................... 2
TCE biodegradation ......................................................................................... 3
Aerobic vs. Anaerobic .................................................................................. 3
Cometabolism .............................................................................................. 5
Aerobic degradation pathways ..................................................................... 6
Metabolic diversity ....................................................................................... 9
Toxicity ....................................................................................................... 12
Bioreactors .................................................................................................... 14
General ...................................................................................................... 14
Sequencing Batch Reactor ........................................................................ 15
Microbial communities ................................................................................... 19
The culture dependent era ......................................................................... 19
Culture independent community analysis ................................................... 20
Thesis Overview ............................................................................................ 24
Objectives .................................................................................................. 24
Experimental design .................................................................................. 26
References .................................................................................................... 29

CHAPTER 2: SUCCESSIONAL AND FUNCTIONAL RESPONSES IN

PHENOL AND PHENOL PLUS TCE FED REACTORS ................................... 36
Introduction .................................................................................................... 36
Materials and Methods .................................................................................. 38

Reactor design and operation .................................................................... 38
TCE and phenol analysis ........................................................................... 4O
Reactor sampling and DNA extraction ....................................................... 41
PCR primers and conditions ...................................................................... 42
PCR purification, restriction digest and gel separation ............................... 43
Analysis of T-RFLPs. ................................................................................. 44
Results .......................................................................................................... 45
TCE transformation rates ........................................................................... 45
Phenol transformation rates ....................................................................... 48
Community structure by T-RF LP ................................................................ 48
Discussion ..................................................................................................... 63
References .................................................................................................... 68

CHAPTER 3: THE ABUNDANCE OF EXTRACELLULAR POLYMERIC
SUBSTANCES AND THE SPATIAL DISTRIBUTION OF BACTERIA IN FLOCS

IN PHENOL AND PHENOL PLUS TCE FED REACTORS. ............................. 72
Introduction .................................................................................................... 72
Materials and Methods .................................................................................. 75

Reactor sampling ....................................................................................... 75
Scanning electron microscopy ................................................................... 75
Epifiuorescent microscopy ......................................................................... 76
Fractionation of the TSS ............................................................................ 76
Purification and characterization of EPS .................................................... 77
Results .......................................................................................................... 78
Visual inspection and microscopy. ............................................................. 78
EPS abundance ......................................................................................... 81
EPS composition ........................................................................................ 84
Discussion ..................................................................................................... 88
References .................................................................................................... 92

CHAPTER 4: CHARACTERIZATION OF THE CULTIVABLE MEMBERS
FROM PHENOL AND PHENOL PLUS TRICHLOROETHENE-FED REACTOR
COMMUNITIES, AND THEIR DENSITIES OVER 2 YEARS OF REACTOR

OPERATION. .................................................................................................... 98
Introduction .................................................................................................... 98
Materials and Methods ................................................................................ 100

Pure culture isolation and characterization .............................................. 100
DNA isolation and purification .................................................................. 102
16S rDNA and phenol hydroxylase sequencing, and phylogenetic analyses
................................................................................................................. 102
In-silico generation of terminal restriction fragments and community
comparisons ............................................................................................. 104
SYBR green quantitative PCR ................................................................. 105
Results ........................................................................................................ 109
Strain isolation and identification .............................................................. 109
Phylogeny of the isolated populations ...................................................... 112
Degradative characterization. .................................................................. 120
Primer design and specificity. .................................................................. 124
Sensitivity and reproducibility ................................................................... 124
Bacterial abundances. ............................................................................. 129
Additional phylogenetic analysis of the dominant populations ................. 137
Discussion ................................................................................................... 140
References .................................................................................................. 152

CHAPTER 5: CONCLUSIONS, SYNTHESIS AND FUTURE DIRECTIONS. 160
Conclusions ................................................................................................. 160
Synthesis ..................................................................................................... 161

xi

Future research ........................................................................................... 166

APPENDIX A: PROTOCOL FOR T-RFLP ANALYSIS OF MICROBIAL

COMMUNITIES ............................................................................................... 171
APPENDIX B: HAEIII T-RFLP DATA ............................................................ 177
APPENDIX C: TRANSMISSION ELECTRON MICROGRAPHS ................... 179

xii

LIST OF FIGURES

Some of the figures in this dissertation are in color.

Figure 1.1. Proposed TCE degradation pathway catalized by aromatic
degrading oxygenases. The toluene dioxygenase pathway was derived
from studies in Pseudomonas putida F1 (Li and Wackett 1992), while the
toluene 2-ortho monooxygenase pathway was determined from studies in
Burkholderia cepacia G4 (Newman and Wackett 1997). Generation of CO;
and cells from the TCE intermediates has been observed in mixed cultures.
..................................................................................................................... 8

Figure 1.2. Normal operational cycles used in SBRs. Dashed lines indicate
inflow or outflow of liquid, suspended solids or waste. Modified from Irvine
et al. (1989). ............................................................................................... 18

Figure 1.3. Overview of the molecular methods of community analysis based
on rRNA genes. PCR, polymerase chain reaction; DGGE. denaturing
gradient gel electrophoresis; TGGE, temperature gradient gel
electrophoresis; ARDRA, amplified ribosomal DNA restriction analysis; T-
RFLP, terminal restriction fragment length polymorphism; RFLP, restriction
fragment length polymorphism; and FISH, fluorescent in-situ hybridization.
................................................................................................................... 23

Figure 1.4. Components of the Sequencing Batch Reactors. Phenol and TCE
were fed via syringes to each reactor individually. For the phenol-fed
reactor, the TCE syringe was replaced with a mineral medium syringe. The
mineral medium, concomitantly fed with TCE. was fed from a carboy using a
peristaltic pump. In order to provide oxygen, an air pump was used. To
control temperature, a peristaltic pump was used to recycle water between
a water bath and a cooling plate inside the reactor. ................................... 28

Figure 1.5. Sequencing Batch Reactor cycling scheme. This cycle occurs twice
a day. The cycle begins with the feed step, where phenol is fed. The
phenol recharge stage is the period when cell grow and phenol degrading
enzymes are activated. In the fill phase, TCE is added to the phenol plus
TCE-fed reactor while the phenol-fed reactor receives the same volume of
mineral medium. The react phase is when the second compound is
consumed. In the settle phase, no aeration or mixing occurs to allow
suspended solids to settle. Finally, the supernatant is removed on the
decent phase before the cycle starts again. ............................................... 28

xiii

Figure 2.1. Long term functional responses of the phenol-fed and phenol plus
TCE-fed reactors. (A), TCE transformation rates. (B), phenol degradation
rates. .......................................................................................................... 47

Figure 2.2. T-RFLP electropherograms of PCR amplified 16S rDNA bacterial
genes from replicate reactor communities before TCE application to reactor

B. Samples were digested with six different restriction enzymes individually
................................................................................................................... 51

Figure 2.3. Changes in bacterial community structure over time. T-RFLP
electropherograms were generated from a Cfol digest of the 168 rDNA PCR
product. Shaded peaks indicate dynamic ribotypes. ................................. 55

Figure 2.4. Correspondence analysis of T-RFLP data from reactor
communities. Results from independent digests with Cfol and Haelll, were
combined for the analysis. Circles with letters indicate periods where the
community structure of the two reactors was similar. (A) phenol-fed reactor
days 50-198, 605-811; phenol plus TCE-fed reactor 50-104. (B) phenol-fed
reactor days 357-541; phenol plus TCE-fed reactor days 294-361, 399, and
414-811. T=0, day 0 of each reactor before TCE application to one of them.
Dim, dimension. ......................................................................................... 56

Figure 2.5. Relative abundance of Bacterial T-RF’s from reactor samples after
digestion with Cfol. Numbers in the legend indicate the sizes of the T-RF’s
in base pairs ............................................................................................... 59

Figure 2.6. Correspondence analysis of T-RFLP data for each reactor
community individually. (A), phenol-fed reactor; (B) phenol plus TCE-fed
reactor. Numbers in the legend next to a symbol indicate day periods that
were grouped together to facilitate interpretation. Arrows and numbers
inside the graph indicate the order of the changes in commuity structure.
Results from independent digests with Cfol and Haelll were combined for
the analysis. Circles delineate the different clusters formed. Dim,
dimension ................................................................................................... 60

Figure 2.7. Community restriction patterns of PCR amplified phenol
hydroxylase genes digested with Rsal and Mspl. Numbers on top of each
lane indicate time in days. Bars on tops represent the samples with similar
fingerprinting pattern within and between reactors as determined from
cluster analysis. ......................................................................................... 62

Figure 3.1. Electron scanning micrographs of reactor suspended solids.
Images were taken after 900 days of reactor operation. Micrographs A-C
and D-F represent different levels of resolution for the phenol-fed and the

xiv

phenol plus TCE-fed reactors, respectively. EPS, extracellular polymeric
substances. ................................................................................................ 79

Figure 3.2. Epifluorescence microscopy of the phenol plus TCE-fed reactor
flocs. Samples were stained with SYTO-9. Panel A, max magnification,
panel B, 700x magnification. ..................................................................... 80

Figure 3.3. Fractionation of the TSS (A) and percentage of the fractions relative
to the TSS (B). Values represent means :I: 95 % confidence intervals (n=6).
................................................................................................................... 82

Figure 3.4. Gas chromatogram of alditol acetates from the phenol plus TCE-fed
reactor purified EPS. The identities of the peaks were determined by
coelution with standards and analysis of mass spectra. The peak before
rhamnose is an unidentified compound. N-acetylglucosamine is not shown
due to its long retention time. ..................................................................... 85

Figure 3.5. 1H NMR spectrum of the extracellular polysaccharide. Signals
characteristic of rhamnose include two singlets between 1.2 and 1.5 ppm
that were assigned to the 6-deoxy groups and signals between 5.0 and 5.5
ppm that were assigned to the alpha linkages of rhamnose. The two
singlets between 2.0 and 2.2 ppm correspond to N-acetyl groups of N-
acetylglucosamine and the signals between 4.7 and 4.9 ppm correspond to
beta linkages of N-acetylglucosamine. Signals between 3.4 and 4.3 ppm
correspond to the remaining sugar protons. PPM, part per million. .......... 86

Figure 3.6. Composition of the EPS from the phenol plus TCE-fed reactor.
Values represent averages :I: one standard deviation of triplicate injections.
................................................................................................................... 87

Figure 4.1. OTU composition of the isolate collection from the two reactors. An
OTU was defined as one or more sequences with more than 97 %
sequence similarity ................................................................................... 113

Figure 4.2. Phylogenetic tree of 168 rDNA sequences of isolates that belonged
to the a-Proteobacten'a class. The tree was constructed from 605
unambiguously aligned positions using the maximum-likelihood method,
and was rooted using Nitrosomonas eutropha. Numbers at nodes represent
the percentage of 100 bootstraps. The nodes without bootstraps values
represent either nodes with bootstrap values below 50 %, or inconsistent
branch order when compared against the consensus tree. The scale bar
represents the number of substitutions per nucleotide position. T, type
strain. ....................................................................................................... 115

XV

Figure 4.3. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to the B-Proteobacten‘a class. The tree was constructed from 677
unambiguously aligned positions using the maximum-likelihood method,
and was rooted using Rhizobium sp. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values
represent either nodes with bootstrap values below 50 %, or inconsistent
branch order when compared against the consensus tree. The scale bar
represents the number of substitutions per nucleotide position. T, type
strain. ....................................................................................................... 116

Figure 4.4. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to the Deinococcus-Thermus phylum, y-Proteobacten'a class, and
Bacteroidetes phylum. The tree was constructed from 659 unambiguously
aligned positions using the maximum-likelihood method. Numbers at nodes
represent the percentage of 100 bootstraps. The nodes without bootstraps
values represent either nodes with bootstrap values below 50 %, or
inconsistent branch order when compared against the consensus tree. The
scale bar represents the number of substitutions per nucleotide position. T,
type strain. ............................................................................................... 118

Figure 4.5. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to Actinobacteria and Firmicutes phylums. The tree was constructed from
449 unambiguously aligned positions using the maximum-likelihood
method, and was rooted using Acidovorax avenae. Numbers at nodes
represent the percentage of 100 bootstraps. The nodes without bootstraps
values represent either nodes with bootstrap values below 50 °/o, or
inconsistent branch order when compared against the consensus tree. The
scale bar represents the number of substitutions per nucleotide position. T,
type strain. ............................................................................................... 119

Figure 4.6. Unrooted phylogenetic tree of the phenol hydroxylase amino acid
sequences of the isolates. The tree was constructed using 169 aligned
amino acids, between positions 83 and 252 of Pseudomonas sp. CF600
dmpN gene, using the protein maximum-likelihood method. Numbers at
nodes represent the percentage of 100 bootstraps. The nodes without
bootstraps values represent either nodes with bootstrap values below 50 %,
or inconsistent branch order when compared against the consensus tree.
The scale bar represents the number of substitutions per amino acid
position. The asterisk indicates strains that degrade TCE. ..................... 123

Figure 4.7. Specificity of the SYBR green PCR reaction at 1.8 (A), and 1.5 (B),
mM MgCl2 using melting peak analysis. —dF/dT, negative derivative of
fluorescence with respect to temperature. Data points shown represent
every tenth measurement. ....................................................................... 125

Figure 4.8. SYBR green I real time PCR calibration curves using cell lysates as
template. Values represent averages of duplicate reactions :t one standard
deviation. CT, cycle at which the fluorescence crosses an arbitrary
threshold determined using a reference dye. ........................................... 126

Figure 4.9. Reproducibility of SYBR green I real time PCR. Two different
primer sets were used for the test. Values represent averages :I: 95 %
confidence intervals (n = 8). ..................................................................... 128

Figure 4.10. Abundance of phenol degraders in the phenol-fed reactor. Values
represent averages :t one standard deviation from duplicate reactions... 131

Figure 4.11. Abundance of phenol degraders in the phenol plus TCE-fed
reactor. Values represent averages i one standard deviation from duplicate
reactions. ................................................................................................. 133

Figure 4.12. Abundance of non-phenol degraders in the phenol-fed reactor.
Values represent averages :I: one standard deviation from duplicate
reactions. ................................................................................................. 135

Figure 4.13. Abundance of non-phenol degraders in the phenol plus TCE-fed
reactor. Values represent averages i one standard deviation from duplicate
reactions. ................................................................................................. 136

Figure 4.14. Phylogenetic tree of 168 rDNA sequences of the unidentified B
Proteobacteria group. The tree was constructed from 1336 unambiguously
aligned positions using the maximum-likelihood method, and was rooted
using Nitrosomonas europaea. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values
represent either nodes with bootstrap values below 50 %, or inconsistent
branch order when compared against the consensus tree. The scale bar
represents the number of substitutions per nucleotide position. .............. 138

Figure 5.1. Summary of community analysis results. Ph-RFLP, phenol
hydroxylase restriction fragment length polymorphism analysis. *, phenol
degrader; **, phenol and TCE degrader. Grey, white and black bars
represent time periods when the community structure was similar within and
between reactors (based on multivariate analysis). ................................. 164

xvii

LIST OF TABLES

Table 1.1. Examples of substrates, microorganisms and enzymes involved in
TCE cometabolism*. on, alpha Proteobacteria; [3, beta Proteobacten'a; and y,
gamma Proteobacten'a. .............................................................................. 11

Table 2.1. Reproducibility of the T-RFLP data. Means and 95% confidence
intervals (CI) for Sorensen and Steinhaus similarity indexes were calculated
from pairwise comparisons. The TAMRA G32500 standard is a molecular
weight marker used to determine the sizes of the fragments in the gel. 52

Table 3.1. Relative abundance of the EPS fraction in the phenol-fed and the
phenol plus TCE-fed reactor. Values represent the percentage of the EPS
relative to the TSS ...................................................................................... 83

Table 4.1. Primers and magnesium concentrations used for SYBR green
quantitative PCR of the isolates. [5 proteo, beta proteobacteria .............. 108

Table 4.2. Assignment of isolate T-RFs to community T-RFLPs. Isolate names
in bold, indicate strains that T-RFs from both enzymes matched a
community T-RF. NR, no restriction site. NM, no match with community T-
RFLPs using a 1: 3 bp window. ................................................................. 110

Table 4.3. Percentages of community T-RFs that matched the isolates T-RFs.
n, number of isolates ................................................................................ 112

Table 4.4 Phenol and TCE degrading properties of the reactor isolates. Values
represent percentage of removal. For the TCE degradation test fresh
phenol medium at the appropriate concentration and TCE were mixed
simultaneously. SBR-P, phenol-fed reactor; SBR—T, phenol plus TCE-fed
reactor. NT, not tested. WV, Wolin’s vitamins. ....................................... 121

Table 4.5. Sequence similarity values of selected sequences from Figure 4.14.
................................................................................................................. 139

xviii

CHAPTER 1
TRICHLOROETHENE OXIDATION BY MICROBIAL
COMMUNITIES

Trichloroethene

Characteristics and applications

Trichloroethene (TCE) is a non-polar chlorinated compound widely used by
industry as a solvent. TCE has been found in marine algae and volcanoes
(Gribble 1994), but its major source is industrial production. It was first
synthesized by Fisher in 1864 by the reduction of hexachloroethane with
hydrogen (Hewer 1975). Ever since, TCE has been popular for industrial uses.
Although the US government has imposed severe restrictions on the use and
release of TCE, the demand for this chemical remains high. The US. demand
for TCE increased from 128 million pounds in 1995 to 171 million pounds in
1998, an increase of 33 % (HSIA 1996, 2001).

The increased use of TCE is because of its favorable physico-chemical
properties which include low flammability, high stability, non-corrosiveness, low
specific heat and solubility with other industrial solvents. TCE is mainly used as
a degreasing agent and for chemical synthesis of other products (HSIA 2001;
ATSDR 2002; U.S.-EPA 2002). For instance, high purity TCE is used as a
precursor to synthesize hydrofluorocarbon 134a, a replacement for the ozone
depleting chlorofluorocarbon 113. As a cleaning and degreasing agent, TCE is
fast and efficient, being used by the metal industry to clean steel before

galvanizing, and previously by the United States Air Force to clean jet engines.

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Miscellaneous uses of TCE include as an extraction solvent, waterless dying in
the textile industry, dry cleaning, a component of paint, spot and paint removers

and typewriter correction fluid.

Why TCE is a concern?

Although TCE is an excellent industrial solvent it is toxic to humans and
animals. Acute short term exposure to TCE vapors can affect the central
nervous system causing headaches, vertigo, drowsiness, irritation of the
respiratory system and unconsciousness (ATSDR 1994). However, the most
severe adverse effects are caused by oral ingestion. Animal studies on rodents
revealed an increase on the incidence of liver, lung and kidney tumors (Bruckner
et al. 1989). Re-analyses and interpretation of previous toxicological studies
suggest that the effects of TCE on rodents can not be extrapolated to humans
because of differences in the metabolism of TCE between the two groups
(Green 2001). Large epidemiological studies, however, have found an
association between TCE exposure and increased risk of different types of
cancer (US-EPA 2001). Nevertheless, the International Agency for Research
on Cancer currently considers TCE to be “probably carcinogenic to humans”
(WHO. 1993), while the US-EPS is currently reassessing the cancer
classification of TCE (US-EPA 2002).

The widespread use of TCE by industry comes with some consequences.
The lack of early government regulation and inappropriate handling and disposal

caused TCE to be one of the most frequently detected soil and groundwater

pollutants in the United States. In 1994, TCE was the most prevalent
contaminant found on the National Priorities List (NPL), being identified in 37 %
of all NFL sites (n=951), and was a groundwater contaminant in 34 % of them
(ATSDR 1994). Furthermore, 87 % of the contaminated plumes (n=322) were
active sources of drinking water representing a major hazard for the human
population. For these reasons, TCE is listed on the top 20 priority pollutant list

of US-EPA.

TCE biodegradation

Aerobic vs. Anaerobic

Biodegradation is the biologically catalyzed conversion of a pollutant into
biomass or less toxic products. Microbes play the major role in the
biodegradation of pollutants since they possess a wide variety of enzymes and
physiological adaptations that allow them to metabolize various toxic
compounds. Studies in the mid 1980’s provided the first evidence that TCE can
be degraded biologically by microorganisms (Vogel and McCarty 1985; Wilson
and Wilson 1985). Anaerobic degradation of TCE occurs by a process known
as reductive dehalogenation (Mohn and Tiedje 1992). In this process, TCE
serves as an electron acceptor, due to its highly oxidized state, and Cl is
removed simultaneously in the process. Although the process can completely
dechlorinate the substrate to ethene, it carries the risk that daughter products
generated from the dechlorination, i.e. cis-DCE, 1,1-DCE and vinyl chloride may
accumulate. These dechlorination products are more toxic than TCE, and hence

maybe even more problematic.

Aerobic biodegradation of TCE was first demonstrated by Wilson and
Wilson in 1985, when they observed a significant reduction in the concentration
of TCE in soil enriched with natural gas (77% methane) compared to the TCE in
non-enriched soils. Subsequent studies by other investigators demonstrated
that methanotrophic bacteria were responsible for the aerobic degradation of
TCE (Fogel et al. 1986; Little et al. 1988; Tsien et al. 1989). Following this
discovery methane was successfully used to stimulate TCE degradation both in
bioreactors and in soils and aquifers (Phelps at al. 1990; Semprini et al. 1990;
Alvarez-Cohen and McCarty 1991b).

Aerobic bacteria that grow on aromatic hydrocarbons are another group of
microbes capable of TCE degradation. Nelson and coworkers isolated the first
aerobic TCE degrader. Burkholden'a cepacia G4, from a waste treatment facility
at the Naval Air Station in Pensacola, FL (Nelson et al. 1986). They noted that
oxygen and an unidentified component in the wastewater, latter identified as
phenol, were essential for TCE oxidation. In subsequent studies they
demonstrated that enzymes from the aromatic degradation pathway of G4 and
other phenol and toluene degraders were involved in the degradation of TCE
(Nelson et al. 1987; Nelson et al. 1988). A key observation made by Nelson’s
group was the requirement of phenol for TCE degradation. Similar results were
obtained by Little and coworkers, who showed that active metabolism of the
growth substrate (methane) by the methanotrophic isolate 46-1 was necessary
for the degradation of TCE (Little et al. 1988). These findings, and the fact that

no pure culture was able to grow on TCE as the sole carbon source lead to the

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conclusion that TCE is degraded aerobically by a process know as

cometabolism.

Cometabolism

Cometabolism is defined as the transformation of an organic compound by
a microbe, with a non-specific enzyme, that does not result in energy or carbon
for cell growth (Alexander 1994). This means that cometabolism will not provide
any direct nutritional benefit to the microorganisms degrading the compound, Le.
a fortuitous transformation. Cometabolism represents a dilemma for bacteria
since the oxidation of a non-grth compound may require energy that could
otherwise be devoted to growth (Chang 1996). Alexander (1994), suggests
three possible explanations for this phenomenon: (a) the metabolites generated
from the fortuitous reaction can not be further transformed by other enzymes to
yield intermediates that could be used for growth; (b) the initial transformation
generates products that inhibit the activity of later enzymes; and (c) the
organism needs a second substrate in order to metabolize the intermediate
generated. Although the first alternative is the most probable one, the potential
toxic effects of the cometabolic transformation products can not be disregarded
since it could result in a tradeoff for the microorganism carrying out cometabolic
reactions. For cometabolism to occur the microorganisms will always require a
growth substrate, which besides providing carbon and energy for growth, may
be needed to activate the enzyme required for the fortuitous reaction. Hence,

the enzymes involved should possess a broad substrate range to recognize

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different compounds. As described earlier, phenol and methane are two
compounds that serve both as growth and inducing substrates for TCE
cometabolism. The broad substrate range enzymes involved in this case are
known as oxygenases. enzymes that activate molecular oxygen and incorporate

it into organic and inorganic compounds as part of the catalyzed transformation.

Aerobic degradation pathways

Since the first reports on the biological degradation of TCE, there have
been extensive efforts to determine how this compound is metabolized.
Knowledge of the metabolic pathway of TCE degradation is necessary to
understand how microbes perform this reaction. and to determine the final fate
of TCE in the environment (Wackett 1995). The first insights into the
mechanism of TCE degradation were provided by Little et al. using a pure
culture of a methanotrophic bacterium (Little et al. 1988). Using 1“C-TCE they
obtained preliminary data that suggests that the products generated from TCE
cooxidation were glyoxylic and dichloroacetic acids. They proposed that TCE
was converted to an unstable epoxide that decomposes in aqueous solution to
yield the products mentioned above. However, in other studies, chloral, carbon
monoxide and glyoxylate were observed as products from TCE cometabolism by
methanotrophs, suggesting the existence of multiple TCE degradative pathways
within this bacterial group (Fox et al. 1990; Newman and Wackett 1991). The

majority of these products can be consumed by other heterotrophic bacteria to

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yield C02 and cells (Little et al. 1988; Uchiyama et al. 1992), and hence might
provide a positive growth response.

As described earlier, the involvement of aromatic degradative pathways in
the degradation of TCE was established early on by Nelson and coworkers
using strain G4 (Nelson et al. 1987). Further support for this observation was
provided by Wackett and Gibson using todC mutants of Pseudomonas putida F1
(Wackett and Gibson 1988). Defects in the todC gene, which produces the iron-
sulfur protein of the dioxygenase component of the toluene dioxygenase,
resulted in no TCE oxidation, while spontaneous revertants recovered the TCE
oxidation ability. Using purified F1 toluene dioxygenase generated from
recombinant Escherichia coli strains and 14C TCE, Li and Wackett (1992) found
that glyoxylic and formic acids were the products of TCE oxidation, and by using
deuterated TCE, formic acid was the main product. This pathway does not
involve the formation of an epoxide (Figure 1.1).

The fact that no epoxide was formed during the degradation of TCE by F1
does not mean that all aromatic oxygenases will exhibit the same phenomenon.
Newman and Wackett (1997) confirmed that indeed there are differences in the
metabolism of TCE between a toluene dioxygenase and a toluene
monooxygenase. Using purified toluene 2-monooxygenase from Burkholden'a
cepacia G4, they determined that the major oxidation products were formate,
glyoxylate and carbon monoxide (Figure 1.1). Although the products are similar
to the ones found in F 1, they proposed that in G4 the TCE was converted to an

epoxide intermediate that spontaneously decomposed to the major oxidation

products observed (Newman and Wackett 1997). Different from the
dioxygenase, that incorporates two oxygen atoms to one TCE molecule, the
monooxygenase reaction incorporates only one oxygen atom which leads to the

formation of the epoxide.

   

   

Toluene
2-ortho monooxygenase

 

 

 

 

 

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C02 + Cells

Figure 1.1. Proposed TCE degradation pathway catalyzed by aromatic
degrading oxygenases. The toluene dioxygenase pathway was derived from
studies in Pseudomonas putida F1 (Li and Wackett 1992), while the toluene 2-
ortho monooxygenase pathway was determined from studies in Burkholden'a
cepacia G4 (Newman and Wackett 1997). Generation of CO; and cells from the
TCE intermediates has been observed in mixed cultures.

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Metabolic diversity

Although methane and toluene are two of the most studied substrates for
TCE cometabolism, a diverse collection of compounds have been found to
stimulate TCE degradation (Table 1.1). Ammonia, butane, 2,4-
dichlorophenoxyacetate, isopropylbenzene, ethylene, phenol, propane and
propylene are all the substrates known to stimulate TCE cometabolism, and in
an equally diverse range of microbes. Members of the Actinobacteria and the
alpha, beta and gamma classes of the Proteobacteria, are able to cometabolize
TCE, indicating that this is a widespread phenomenon.

While oxygenases have the common characteristic of activating oxygen for
catalysis, a survey of broad-substrate oxygenases revealed that many of them
can not cometabolize TCE (Wackett et al. 1989), although later, a variety of
enzymes were found to perform this reaction (Table 1.1). This enzymatic
diversity is further accentuated by the fact that different enzymes that degrade
the same growth substrate can oxidize TCE. For instance, there are five
different pathways for toluene oxidation if judged by the position of the initial
attack to the molecule, and three of them are capable of degrading TCE (Fries
1995). PCR amplification, cloning and sequencing of multicomponent phenol
hydroxylase genes from pure cultures and aquifer samples revealed substantial
sequence diversity for an enzyme with a common reaction mechanism
(Watanabe et al. 1998; Futamata et al. 2001b). These authors found that
different clades of sequence correlated with different affinities for TCE, i.e. Km.

Diversity can also be seen at the strain level, since some strains contain multiple

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and very different metabolic pathways capable of degrading TCE. Ralstonia
eutropha JMP134, possesses a chromosomally encoded phenol hydroxylase
and a plasmid encoded 2,4-dichlorophenol hydroxylase, both capable of TCE

degradation but at different rates (Harker and Kim 1990).

10

 

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Table 1.1. Examples of substrates, microorganisms and enzymes involved in

TCE cometabolism*.

gamma Proteobacteria.

a, alpha Proteobacten'a; (3, beta Proteobacten’a; and y,

 

 

Growth Bacterial Microorganism Enzyme Reference
substrate classt
Ammonia on Nitrosomonas eumpea Ammonia (Arciero et al. 1989)
monooxygenase (Rasche et al. 1991)
Butane Y Pseudomonas butanavora Butane (Hamamura et al. 1997)
monooxygenase
Ethylene/propytene a Xanthobacler Py2 Alkene (Ensign et al. 1992)
monooxygenase (Reij et al. 1995)
lsopropylbenzene Actinobacteria Rhodococcus erythropolis lsopropylbenzene (Dabrock et al. 1992)
dioxygenase (Pflugmacher et al. 1996)
Methane a Methylosinus tn'chospon'um Particulate methane (DiSpiriIo et al. 1992)
083b monooxygenase (Lontoh et al. 1999)
Methane 0L Methylosinus tn’chospon'um Soluble methane (Tsien et al. 1989)
083b monooxygenase (Oldenhuis et al. 1989)
Phenol and [3 Ralstonia eutropha Phenol hydroxylase, (Harker and Kim 1990)
2,4-dlchloro- JMP 134 2.4-dichlorophenol
phenoxyacetate hydroxylase
Phenol B Comamonas testosteroni R5 Phenol hydroxylase (Futamata et al. 2001a)
Phenol B Variovorax sp. HAS-29 Phenol hydroxytase (Futamata et al. 2001b)
Phenol [3 Ralstonia sp. E2 Phenol hydroxylase (Futamata et al. 2001b)
Propane Actinobacteria Mycobaclen‘um vaccae JOBS Propane (Wackett et al. 1989)
monooxygenase
Propylene Actinobacten‘a Rhodococcus corallinus B-276 Alkene (Saaki et al. 1999)
monooxygenase
Toluene y Pseudomonas putida F 1 Toluene (Wedtett and Gibson 1988)
dioxygenase (Zylstra et al. 1989)
Toluene B Burkholden'a cepacia G4 Toluene (Newman and Wackett 1995)
2-monooxygenase (Shields 01 al. 1995)
Toluene B Ralstonia pickettii PKO I Toluene 3- (Olsen 91 al. 1994)
monooxygenase (Johnson and Olsen 1995)
Toluene Y Pseudomonas mendocina KR Toluene 4- (Yen et al. 1991)
monooxygenase

 

 

* Modified from Arp et al. (2001 ). T, according to the taxonomic outline of the Bergey's Manual
for Systematic Baceriology (Garrity et al. 2001 ).

11

Toxicity

The major hurdle for sustained TCE cometabolism is from the toxicity of
the metabolites generated. One of the first observations of TCE metabolite
toxicity was with Pseudomonas putida F1, in which the initial TCE degradation
rate decreased 98 "/0 during the first 20 min of TCE exposure leading to the
conclusion that TCE related toxicity was occurring (Wackett and Gibson 1988).
Subsequent studies using 14C-TCE revealed that the decrease in degradation
rates observed was caused by cytotoxicity of glyoxylic and formic acid, the major
products generated from TCE (Wackett and Householder 1989). Furthermore,
an F1 mutant defective in the oxygenase component of the toluene dioxygenase
did not show any signs of toxicity when exposed to TCE, confirming that the
TCE metabolites were responsible for the toxic effects.

The cytotoxic effects of TCE metabolites have also been observed on
different microorganisms and oxygenases. Multiple studies on strain G4 indicate
that this strain suffers from metabolite toxicity. Newman and Wackett (1997),
using purified enzyme preparations of G4, concluded that similar to F1, the TCE
metabolites damage the active enzyme which results in enzyme inactivation.
This result was different from previous studies using whole cells that indicated
that the TCE degrading ability of this strain was not lost during TCE exposure
(Folsom et al. 1990). However, Mars et al. (1996) found that G4 grown in a
chemostat on toluene had higher maintenance energy costs when exposed to
TCE. Cells, exposed to TCE in the absence of toluene produced a number of

mutants unable to grow on toluene. The G4 mutants had lost the TOM plasmid

12

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involved in toluene monooxygenase formation. Recently, Yeager and coworkers
(Yeager et al. 2001) measured cell cultivability, TCE activity and general
respiratory activity of G4 cells exposed to toluene. A decrease in respiration
rates and cultivability was observed after TCE exposure confirming that the
cometabolic transformation of TCE damages cells and reduces viability.

Microbial communities have also been used to measure the toxic effects of

TCE- Mu and Scow (1994) measured the effects of toluene and TCE on
bacterial densities in soil exposed to these compounds. An increase in TCE
concentration from 1 to 20 ug/ml resulted in a decrease in TCE degradation
rates and densities of TCE degraders in soil (Mu and Scow 1994). Alvarez-
COhen and McCarty (1991a), using a mixed methanotrophic culture, also
Observed a decrease in methane consumption rates after methanotrophic
reactors were exposed to TCE.

Although the majority of the studies suggest that the toxicity of the TCE
intermediates is very high, it will vary among different microbes. Zylstra and
csibson (1989), who cloned the toluene dioxygenase of strain F1 in Escherichia
c0”. noted that the wild type strain had higher initial TCE degradation rates than
the genetic construct. However, the degradation rates of the wild-type strain
decl‘eased rapidly after TCE exposure but remained stable at degradation rates
IO‘Ner than that of the genetic construct, suggesting that different hosts will have
different sensitivities to TCE toxicity. Recently, studies on a constitutive mutant
or Ralstonia eutropha JMP 134 revealed that this strain does not suffer TCE

mediated toxicity (Ayoubi and Harker 1998). No reduction in TCE degradation

13

 

rates was observed at TCE concentrations as high as 800 (M (~ 100 mg/I),
indicating that some microorganisms can avoid TCE toxicity. The explanation

for the important difference is not known.

Bioreactors
General

Since the discovery that TCE can be degraded by microbes, many
approaches have been tried for using this process to remove TCE contamination
from the environment. The design and implementation of bioreactors has been
one of the approaches that has received major attention. A bioreactor is a
Controlled environment where microorganisms are supplied with all the
necessary substrates and environmental conditions that allow them to grow and
perform a specific function in an optimal manner. Bioreactors are frequently
Used for biotechnological applications such as the production of biomass,
antibiotics and other metabolites due to the high degree of environmental
Control, high substrate conversion efficiency and high reaction rates among
other favorable characteristics (Scragg 1991 ).

Bioreactors are also used for biodegradation, since they convert pollutants
into biomass and innocuous products. Ideally, bioreactors should be used to
prevent future contamination by processing waste streams before release into
"‘6 environment. However, prevention of contamination was not always the
case, especially in years before effective government environmental regulations.

Hehoe, bioreactors are frequently used to treat previously contaminated

14

 

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material. For instance, bioreactors have been used to treat contaminated
ground water using a pump and treat strategy. In this approach, contaminated
ground water is pumped from the contaminated plume into a bioreactor where it
is treated to remove the contaminant before being re-injected into a clean
aquifer. To avoid the cost of extraction of the groundwater, a bioreactor can be
established in-situ using recirculation wells to create a mixing zone where
biodegradation can occur (Semprini 1997).

Bioreactors are usually classified based on feeding strategy and type of
growth. The two main feeding strategies are: continuous feeding i.e. chemostat
reactor; and intermittent feeding, i.e. batch feed reactor. Microbial growth can
be classified as dispersed or attached growth (biofilm). A variety of bioreactor
configurations have been created based on these reactor types in combination

with different physical implementations.

Sequencing Batch Reactor

One of the challenges presented by TCE cometabolism is that the primary
substrate and TCE compete for the active site of the oxygenase, thereby making
the TCE degradation inefficient (Alvarez-Cohen and Speitel 2001). Thus, a
reactor system that separates the addition of growth substrate and TCE should
be more efficient. Previous attempts to solve this problem employed the
movement of cells through different reaction chambers, where each compound
Was applied individually to avoid competitive inhibition (Phelps et al. 1990;
Alvarez-Cohen and McCarty 1991b). The other strategy is to separate the

substrates in time, such as accomplished with Sequencing Batch Reactor

15

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(SBR). The SBR system was introduced in 1977 by Irvine and coworkers as an

alternative, cost-effective system for waste water treatment (Irvine et al. 1977).

The system, different from typical activated sludge systems, consists of one tank
with five different sequential periods of operation (Figure 1.2). This means that
the multiple stages of waste treatment, i.e. mixing, aeration, decant and solids
removal, were accomplished in a single tank but at different time periods. This
approach differs from the continuous flow strategy usually employed in
wastewater treatment plants that requires multiple tanks for each stage of
treatment (Irvine and Ketchum jr. 1989).

A typical SBR cycle begins with a FILL period, the stage where new
wastewater is added to the reactor. The reactor could be mixed or remain static
during this stage depending on the waste requirements and the type of
microorganisms to be selected. The REACT phase is when most of the waste

degradation occurs. Similar to the FILL phase, aeration and mixing are

Parameters that can be adjusted depending on the operational needs. Solids
SeDaration is achieved in the SETTLE phase, the period where no mixing or
aeration occurs to allow the biomass fraction to settle. DECANT is the phase
when the clean effluent is removed from the system by pumping a predefined
8"mum of the clarified surface liquid. Finally, the IDLE phase is when the
reactor is ready to receive new wastewater before the cycle begins again.
Wasting (i.e., removal) of suspended solids usually occurs during this period.

The length of the periods will be determined by the operational needs and the

composition of the influent wastewater.

16

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As described by Shih, SBRs could be easily adapted for TCE
cometabolism by the addition of a RECHARGE step during the IDDLE period
(Shih 1995). The RECHARGE step is the period when growth substrate is
added for cell growth and enzyme activation, to be followed by FILL with TCE
and REACT, thus minimizing competitive inhibition. The SBR operation mode
has been successfully used for TCE cometabolism using phenol or methane as
growth substrates in bioreactors with attached growth (Speitel and Leonard
1992; Segar et al. 1995), and dispersed growth (Chang 1996). Although these
studies have provided a wealth of information about the kinetics and efficiency of
TCE cometabolism, knowledge of the microbial communities involved is still
missing. Microorganisms are the key components of these systems, and
determine the success or failure of the reactors. Information about the microbial

communities could be helpful for designing better systems and operational

Conditions that could favor the beneficial microbial populations.

17

Wastewater

Wasting

 

FT

 

V DECANT SETTLE

Clean effluent

FiQUre 1.2. Normal operational cycles used in SBRs. Dashed lines indicate

nflow or outflow of liquid, suspended solids or waste. Modified from Irvine et al.
( 1 989).

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Microbial communities

The culture dependent era

Different from disciplines such as macroecology, botany and zoology, the
subjects of study in microbial ecology can not be observed or enumerated
without using a microscope. If used, this methodology usually provides limited
information about the structure of natural communities since physiologically
different microbes often have similar morphologies. Thus, isolation of microbes
has been the long-standing method of choice to study microbial communities.
One of the pioneers in the isolation of important microorganisms from the
environment was Martinus Beijerinck, who popularized the technique of
enrichment culture. He isolated important microorganisms by tailoring culture
conditions to favor only microbes with a particular metabolism. This
methodology led to the discovery of important microbial processes such as

nitrate reduction, methanogenesis and methanotrophy (Atlas and Bertha 1988).
PhYsiological studies of pure cultures helped us understand the isolates’ role in
d i1zferent environments.

Although isolation by enrichment or direct plating methods has provided a
Wealth of information about metabolic diversity in nature, it only provides a
Ii"‘Iited view of the composition of natural communities. Microorganisms live in
unique niches determined by a variety of physical, chemical and biological
faCtors. Niche, specialization reduces competition and promotes coexistence of
(“Verse microbes, resulting in high microbial diversity. On the other hand, niche

specialization is one of the factors that limit our ability to characterize

19

communities by cultivation. Many microbes live in unique niches that are hard, if
not impossible to replicate in laboratory. Hence, the use of synthetic medium
usually selects for a small group of microorganisms that is not representative of
the entire microbial community. To gain a comprehensive view of a microbial

community, methods that do not rely on cultivation are needed.

Culture independent community analysis

In the last decade, molecular techniques have been used to overcome the
limitations of culture isolation for community structure analysis. Biological
markers such as DNA, RNA and cellular components can be readily extracted
from natural communities and used to distinguish many of the members. The
ribosomal RNA genes (rRNA) have been the most frequently used molecular
marker to study microbial communities since they provide an evolutionary
framework that phenotypic methods do not provide (Olsen and Woese 1993).
All forms of life can be divided in three major evolutionary lineages: Bacteria,
ArChaea and Eucarya, each with distinctive rRNA sequences. The strength of
the rRNA gene for community analysis is because (Olsen et al. 1986; Muyzer
and Ramsing 1995): (1) rRNA genes are necessary for protein synthesis, hence
they are present in all known organisms; (2) they posses variable and conserved
reQions, providing different levels of resolution; (3) sequence information is
s'~11‘I‘icient to be used as a phylogenetic marker to estimate evolutionary

relatedness among microorganisms; (4) rRNA is abundant in the cell, making it

eaSily extractable, and (5) the rate of change of these genes is slow.

2O

Furthermore, the availability of large databases that include both sequence and
taxonomic information facilitate the use of this methodology for community
analysis (Maidak et al. 2001).

Since the advent of using rRNA genes as a biomarker, a variety of
strategies to analyze microbial communities have been developed (Figure 1.3).
The choice of method will depended on the question asked, information
available and level of resolution desired. For instance, if the purpose of the
investigation is to assess changes in community composition across different
samples or treatments, rDNA community fingerprinting and rDNA clone libraries
will provide two different levels of resolution to answer the same question.
Community fingerprinting methods rely on the generation of a representative
mixture of rDNA amplicons from the community by PCR, which is further
resolved based on the melting behavior of the entire product (DGGE and

TGGE), or size differences of the products after restriction digestion (T-RFLP,
ARDRA) (Figure 1.3). Fingerprinting methodologies provides a quick way to
Compare microbial communities under different treatments, although no first
glance identification of the individuals is possible. On the other hand, cloning
and sequencing of the rRNA genes either directly from the sample, or after PCR
amplification provides detailed information about the phylogenetic composition of
the community, but is very labor intensive.

When the focus of the research is to determine the abundance of different
ph)Ilogenetic groups, hybridization and quantitative PCR are the methods of

choice. Hybridization methods are based on the use of a labeled, single

21

stranded nucleic acid probe that binds to a complementary region of the target
gene. Since the probes are labeled, the intensity of the signals can be used to
quantify the abundance of the target. rRNA genes are relatively stable in cells,
hence this method will provide an estimate of the abundance of microorganisms
without the need for cultivation. However, it is difficult to relate rRNA abundance
to cell densities since knowledge about the average rRNA content per cell is
needed. Fluorescent in situ hybridization (FISH) uses the same principle of
membrane hybridization but instead of using extracted nucleic acids, whole cells
are hybridized with the probe. Since the probe is fluorescently labeled, cells will
fluoresce under the proper excitation. The levels of resolution of the
l")lbridization methods range from division to genus level depending on the
availability of discriminatory regions in the rRNA sequence. Finally, quantitative
pCR, similar to the hybridization methods, relies on discriminatory regions to
a""“Iplify a specific target gene from a mixture. However, due to the exponential
t“attire of PCR, the amount of product can not be directly related to initial copy
I‘- L'li‘nber. The first approach to overcome this limitation was the addition of an
internal standard to the reaction, a method known as competitive PCR. The
acldition of known amounts of a competitor, a plasmid with the target gene
QQl'itaining a 50-100 bp deletion, allows the quantification of the initial copy
“I KI mber relative to known amounts of the standard. This method, while reliable,
ig slow and laborious. A more recent approach has been the monitoring of the
reaction progress over time with the aid of fluorescent reporters, a method

I‘riown as real-time PCR. This method allows the identification of the linear

22

 

range of amplification, i.e. the period where the amount of PCR product will be
proportional to the initial amount of template. Using the appropriate calibration
curves, this method could quantify very low numbers of targets in environmental

samples (Grflntzig et al. 2001).

SEE

Extraction ! Fixation
I Representative Sample I

[Nucleic Acids] [Whole Cells]

 

(DNA/RNA)

 

 

 

 

I I I I
I

I

I I I I

 

 

 

 

 

I Community I Quantitative : Clone 'l'7""""""" 'in'SItU' 7""

I fingerprinting I PCR I "bra” : '..M'_ Balm}: wabfajle 2.1. I PCR I 'fLSyJ
I ____________ 4 I- - - .. .0 A
I l I
I I I I . . I
DGGE. TGGE Real-time Sequence RFLP : I : l
ARDRA, T-RFLP Competitive analysis . l . l

I
A A : I : I
L ______ : ____________ . . ______ : ______ . _______ :
I
Isolates/Clones
Abundance P - - -' Diversit ICommunit com siton - - Methods that benfit from
[:I '- - - - -' y y po ( isolate or clone information

isure 1.3. Overview of the molecular methods of community analysis based
h rRNA genes. PCR, polymerase chain reaction; DGGE, denaturing gradient
SI electrophoresis; TGGE, temperature gradient gel electrophoresis; ARDRA,
tTnplified ribosomal DNA restriction analysis; T-RFLP, terminal restriction
ragment length polymorphism; RFLP, restriction fragment length polymorphism;
e. no FISH, fluorescent in-situ hybridization.

23

 

Although methods based on the rRNA have been extremely useful and
powerful for microbial community analysis, isolation of community members is
reQuired for a more complete understanding of microbial communities (Wagner

et al. 1993; Tiedje 1995; Dahllbf 2002). Physiological analyses of isolates from
microbial communities provide clues about their possible role in the community.
Furthermore, all molecular methods of community analysis could benefit from
the study of isolates from the environment (Figure 1.3). The isolates could serve
to test the specificity of primers or probes for quantification; and more
importantly, to aid in interpretation since frequently a sequence does not reveal
much information about the physiology of the organism. I adopted this strategy
and I used a combination of cultivation and cultivation-independent molecular

te(zlnniques to analyze community structure.

Thesis Overview

0 hi ectives

The biodegradation of toxic compounds by microorganisms has been a
major area of research for economic and health reasons. The use of
r~'N‘icroorganisms for waste processing is one of the most efficient and cost-
effective approaches available. In this regard, microbial communities are
essential since they provide a range of ecotypes and metabolic diversity
rssuiting in a more robust process (Tiedje et al. 1995). These characteristics
I“ave been exploited in sewage treatment plants around the globe, where
I“'T‘icxobial communities are the key component of wastewater treatment.

M i crobial communities are also important in the degradation of toxic compounds;

24

because the range of ecotypes and the metabolic diversity allows synergistic
interactions to occur. Microbial interactions such as metabolite transfer, allows
microbial communities to completely mineralize compounds that pure cultures
can only partially degrade (Slatter and Lovatt 1984). The biodegradation of
pollutants by microbes can result in cleaner environments and reduced health
risks to the human population.
Previous studies suggest that TCE cometabolism is a viable approach for
the degradation of this pollutant. Although various reactors have been
developed for this purpose, there is not much information about the microbial
Communities involved. Information about community structure, dynamics and
abundance of different members may lead to improved design and operation of
biOreactor systems. For example, integration of community structure data with
r eactor operation could be used to link microbial populations with reactor
e1z‘lriciency, including anticipating functional changes from community structure
data. Furthermore, once key members of microbial communities are identified,
e"‘Vironmental parameters, e.g. temperature, pH and substrate, could be
Qletimized to meet the requirements of the relevant community members,
tlH'ereby increasing process control and efficiency. The overall aim of this
dissertation was to determine the long-term effects of TCE application on
"I‘- i(:robial communities in sequencing batch reactors. This study was organized
a "Qund the following questions:
1. Does long-term TCE application decrease, increase or cause no

change in TCE removal efficiency?

25

Elllerirr

To

. Does TCE application results in a shift in community structure?

. How stable is the microbial community structure in the TCE and
non-TCE treated reactors? If there is community succession, are
there distinguishable patterns to the succession?

. Does TCE application affect the physiology of the microbial
community? If so, does it affect the spatial arrangement of
bacteria?

. What portion of the community can be obtained in culture? How
many that grow on the primary substrate (phenol) can also
cometabolize TCE?

. How diverse are the isolates? Which have phenol hydroxylase
genes and how diverse they are? Are any of the isolates dominant
members of the microbial community?

. Does long-term TCE application results in the extinction of TCE

degrading phenotypes?

E)(lzierimental design

To address the questions stated above, two bench scale SBRs reactors

'“ere used (Figure 1.4). The control reactor was fed phenol (100 mg/I final

CO ncentration), while the experimental reactor was fed phenol (100 mg/l) and

TCE (5 mg/l final concentration). The reactors were operated using the cycle

gt:I'ieme developed by Shih (1995) that involved the separate addition of phenol

a I“(I TCE at different times during the reactor cycle (Figure 1.5). In Chapter 2

26

mammal
essays fit
monumi
1,3,1; the

nh‘nfl .
v I: 'V' I

Yp:
it. a;
mmrinatI

WI EUIII

Vi'invl
icxmn

'irilc'AIF.q
h'tlv‘ivIUu
‘ v

. u .

In 86‘.
‘=,-:':xI-ia
EQIICOr

As
\IR "I‘
“I wii'.

the function of the reactors was determined by phenol and TCE degradation
assays from samples taken from the reactor during the REACT phase. The
community structure and stability of the microbial communities were assessed
using the T-RFLP community fingerprinting technique. Additionally, RFLP of
phenol hydroxylase genes was used to validate the T-RFLP results. The effects
of TCE application on EPS accumulation and aggregation were evaluated by a
combination of solids fractionation and microscopy in Chapter 3. Finally, in
Chapter 4 the diversity of the microbial communities was determined by isolation
0f community members. The isolates obtained were characterized
physiologically for phenol and TCE degradation, and phylogenetically by 168
r DNA sequencing and tree construction. Additionally the diversity of phenol
“Yd roxylase genes among the isolates was determined using PCR primers that
ta I‘get conserved regions of the gene. Selected isolates were enumerated in the

8 BR communities by SYBR green quantitative real-time PCR.

27

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Temperature controllec-
water bath

Figure 1.4. Components of the Sequencing Batch Reactors. Phenol and TCE
Were fed via syringes to each reactor individually. For the phenol-fed reactor,
the TCE syringe was replaced with a mineral medium syringe. The mineral
medium, concomitantly fed with TCE, was fed from a carboy using a peristaltic
pu mp. In order to provide oxygen, an air pump was used. To control
temperature, a peristaltic pump was used to recycle water between a water bath
and a cooling plate inside the reactor.

TCE addition to the
phenol plus TCE-fed reactor

 

 

2000
1500
1000
Phenol R
500 Recharge eact
I I f T I I
0123456789101112

Time (hours)

It i sure 1.5. Sequencing Batch Reactor cycling scheme. This cycle occurs twice
day. The cycle begins with the feed step, where phenol is fed to a final
Qanentration of 100 mg/l. The phenol recharge stage is the period when cell
"ow and phenol degrading enzymes are activated. In the fill phase, TCE is
aCided to the phenol plus TCE-fed reactor (final concentration of 5 mg/l) while
e phenol-fed reactor receives the same volume of mineral medium. The react
Dhase is when the second compound is consumed. In the settle phase, no
aeration or mixing occurs to allow suspended solids to settle. Finally, the

% Ll pernatant is removed on the decant phase before the cycle starts again.

28

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35

CHAPTER 2
SUCCESSIONAL AND FUNCTIONAL RESPONSES IN PHENOL
AND PHENOL PLUS TCE FED REACTORS

Introduction

Trichloroethylene (TCE) is a potentially carcinogenic, volatile, chlorinated
solvent that is one of the most common groundwater pollutants in the United
States (Bruckner et al. 1989; ATSDR 1994). TCE can be degraded
anaerobically by reductive dechlorination, but the degradation is often
incomplete yielding cis-dichloroethene and vinyl chloride, a carcinogen (lnfante
and Tsongas 1987; Mohn and Tiedje 1992). However, TCE can be degraded
aerobically by cometabolism, a fortuitous oxidation that does not result in energy
Or carbon for cell growth (Alexander 1994). Cometabolism of TCE produces an
epoxide that is spontaneously converted to innocuous products such as forrnate
and glyoxylate (Vogel et al. 1987; Wackett 1995), but some epoxide reacts with
the catalytic enzyme destroying these cells (Wackett and Householder 1989).
Hence, long-term TCE exposure can select against TCE degraders (Mars et al.

1 996). or possibly for a more active TCE degrading community if active
Organisms grow on forrnate and glyoxylate.

Cometabolism, has been successfully used for the degradation of TCE in
biOreactors (Segar et al. 1995; Chang 1996) and in ground water communities
(Hepkins and McCarty 1995; McCarty et al. 1998) provided the appropriate
growth substrate. However, the majority of the studies have focused on the

kinetics and efficiency of TCE degradation without considering the microbial

36

 

communities involved. Only one study has investigated the dynamics and
diversity of microbial communities undergoing TCE cometabolism in a test
aquifer (Fries et al. 1997a; Fries et al. 1997b). Understanding the structure and
stability of microbial communities is of extreme importance for the success of
bioremediation strategies, since microbial communities are responsible for the
success or failure of the process. Insights into processes such as community
succession are necessary to understand and predict future changes in
community structure in natural and managed ecosystems. Often, bioreactor
systems are operated for extended periods of time assuming that the microbial
community structure will remain unchanged. However, studies on a functionally
stable methanogenic reactor revealed that microbial communities can be very
dynamic, suggesting that stable function is not always correlated with stable
Community structure (Fernandez et al. 1999). On the other hand, perturbation
Studies using 1,1-dichlorethene, a very toxic compound (Dolan and McCarty
1 995; Anderson and McCarthy 1997), suggest that microbial communities can
be quite resilient since the community structure recovered to its original state
(Fries et al. 1997b). Hence, is clear that the dynamics and stability of microbial
Communities will depend on many environmental factors.
In the study reported here, we compared the structure and function of two
r eactor communities; one fed phenol only and the other phenol plus TCE over
two years of continuous operation. Our objectives were to determine the effect
of long-term TCE application on community structure, succession, and reactor

performance, the latter measured as rates of phenol and TCE degradation. We

37

used sequencing batch reactors (SBR) so that the phenol consumption phase
was temporally separated from the TCE degradation phase to avoid competitive
inhibition between the two substrates. Periodic changes in community structure
and function were observed in the SBR that received phenol alone. These
changes were not observed in the phenol plus TCE-fed community, where long-
term exposure to the TCE selected for a community that was more stable in
structure and function. Overall, long-term exposure to TCE did not result in a

community with higher TCE transformation rates.

Materials and Methods

Reactor design and operation
Two aerobic bench scale SBRS (Chang 1996) were inoculated from a
phenol fed reactor that showed high and stable phenol and TCE kinetics (Shih et
al- 1996). Each reactor consisted of a 2.2 l glass vessel and mixer system
(Wheaton minijar ferrnentor M-100, Millville, NJ.). Reactors were mixed at 180
rpm, except during the settling phase, and the temperature was maintained at
22°: 1°C by water re-circulation from a temperature controlled water bath
through a cooling plate inside the vessel. Reactors were operated aerobically by
pumping air at a flow rate of 220-280 ml/min. Both vessels were covered from
"Q ht to avoid photosynthetic growth. Phenol and TCE solutions were fed using
60 ml syringes and syringe pumps (Harvard Apparatus lnc., Holliston, Mass).
SLlpernatant was decanted and mineral medium was added by peristaltic pumps

(Watson-Marlow, Wilmington, Md.). Peristaltic, air and syringe pumps were

38

controlled by a programmable timer (Fisher Scientific, Pittsburg, Penn.) Both
reactors were fed phenol for 30 days before adding TCE to one of them.
Reactor operation was as follows: initially, both reactors received 25 ml of a
mixture of phenol (7,200 mg/l) and mineral medium (2.13 g of NazHPO4, 2.04 g
of KHzPO4, 1 g of (NH4)2SO4, 0.067 g of CaCl2-2H20, 0.248 g of MgCl2-6H20,
0-5 mg of FeSO4-7H20, 0.4 mg of ZnSO4°7HZO, 0.002 mg of MnCl2-4H20, 0.05
mg of CoCl2-6H20, 0.01 mg of NiCl2-6H20, 0.015 mg of H3803, and 0.25 mg of
EDTA per liter) over a 0.5 h interval for a final concentration of 100 mg phenol/I.
This concentration was selected because it was sufficient for cell growth and the
fortuitous oxidation TCE in the next stage. Higher concentrations of phenol
could result in growth inhibition. Six hours after phenol addition the TCE-fed
I‘eactor received 25 ml of TCE mixed with mineral medium (0.2 mg/ml) and
additional mineral medium (1 l) during 1 h for a final concentration of 5 mg
TCE/l, while the phenol-fed reactor received mineral medium only. The TCE
Concentration of 5 mg/l was chosen because pure culture studies indicate that
Values below this concentration were sufficient to observe TCE mediated
toxicity. Three hours later, suspended solids were allowed to settle for 30 min,
and the supernatant (~1.1 I) was removed before another 12 h cycle began.
Suspended solids (200 ml) were removed every other day 1.5 h after phenol

i "Ijection to maintain a mean cell residence time of 11 days.

39

TCE and phenol analysis

Reactor performance was evaluated by calculation of kinetics for phenol

and TCE removal in batch assays. Samples from each reactor (5 ml) were
taken 1.5 h after the end of the phenol addition step of the cycle and were
transferred to three 20-ml glass vials and crimp sealed with Teflon-coated butyl
ru bber stoppers. For TCE removal measurements, different amounts of TCE in
water (~ 1,000 mg/l) were injected (10-50 ul) into the vials which were then
incubated at 120 rpm in a rotary shaker, at room temperature. Periodically, 0.1
ml of headspace was removed using a 0.5 ml Pressure-Lek Series A-2 gas
syringe (Hamilton company, Reno, Nev.) and injected into a Hewlett-Packard
5890A gas chromatograph (GC) equipped with a capillary column (08624, 30 m
X 0.53 mm l.D.), a flame ionization detector (FID) and a electron capture
detector (ECD). The 60 was operated isothennally at 90°C with helium as
c>arrier gas (12 ml/min) and the injection port was set at 250°C. The temperature
of the FID and ECD were 250°C and 350°C respectively. Second order rate
Constants for TCE removal, which normalize for biomass, were calculated from
the TCE concentrations as described previously (Chang 1996). For phenol
re moval rates, different amounts of phenol were injected into each of the vials
which were then incubated as described above. Periodically, samples were
r en'loved with a syringe and filtered through 0.2 pm nylon syringe filters
(Wheaton). Filtrate was collected (2 ml) to measure phenol concentrations by
an HPLC equipped with a C18 column (Waters, Milford, Mass.) and a UV

cietector set to 235 nm. The solvent used was an acetonitrile water mixture

40

(60:40) at a 1 ml/min flow rate. Zero order kinetic transformation coefficients for
phenol removal were calculated from the phenol concentrations as described

previously (Chang 1996).

Reactor sampling and DNA extraction
Samples of suspended solids (~ 12 ml) were taken for community analysis
at the same time the kinetic assays were performed. Samples were centrifuged
for 15 min at 6,000 x g (4°C), the supernatant fraction was decanted, and cell
pellets were stored at -80°C. No major wall growth was observed in the reactor.
For nucleic acid extraction, cells were resuspended in TE buffer (10 mM
Tris [pH 8.0] and 1 mM EDTA [pH 8.0]) and ATL lysis buffer (Qiagen,
Ch atsworth, Calif.) before being mixed by a vortex mixer. The cell suspension
was subjected to two freeze-thaw cycles before being digested with lysozyme
(50 pl of a 100 mg/ml solution, Gibco BRL, Gaithersburg, Md.) and
86h romopeptidase (10 pl of a 25 mg/ml solution, Sigma, St. Louis, Mo.). The
Cell lysate was digested with RNAse (10 mg/ml Boehringer Mannheim,
'nd ianapolis, lnd.) followed by proteinase K (45 pl of a 25 mg/ml, Gibco BRL) for
1 h at 60°C. Nucleic acids were purified by three phenol-chloroforrn-isoamyl
al‘3C3l‘iol extractions and were precipitated overnight at —20°C by addition of
NaOAc (3M pH 5.3) and 0.8 volumes of 100% isopropanol. The DNA pellet was
reslJSpended in 100 pl of sterile deionized water. Purity and quantity of the DNA
we '8 determined by UV spectrophotometry at 260 and 280 nm. DNA was stored

at ~20°c.

41

PCR primers and conditions

Amplification of bacterial 16$ rDNA genes from community DNA was
performed with primers 8F (5’-AGAGTTTGATCMTGGCTCAG-3' where M was A
or C) (Giovannoni 1991), fluorescently labeled at the 5’ end with the
phosphoramidite dye 5-hexachlorofluorescein (Operon lnc., Alameda, Calif.),

and 1392R (5’-ACGGGCGGTGTGTACA-3’) (Amann et al. 1995). PCR
reactions were carried out as described previously (Braker et al. 2001) with the
following modifications: 20 ng of template DNA was added to each PCR
reaction, and 25 cycles of amplification were performed. Amplification of the
phenol hydroxylase genes from the community was performed with primers FDZ
(5’-GGCATCAARATYNNNGACTGG-3’ where N was a mixture of A,C,G,T; R
Was A or G; and Y was C or T) and Phe212 (5'-
cs‘FTI‘GGTCAGCACGTACTCGAAGGAGAA-3’) (Watanabe et al. 1998). These
primers produce a 490 bp PCR product from the large subunit ((1) of the
mu lticomponent phenol hydroxylase gene family. Primer FD2 was designed by
comparative analysis of amino acid sequences of this protein from
p89 udomonas sp. str. CF600, Pseudomonas putida str. H, Pseudomonas
putida, P35X, Burkholderia cepacia G4 and Burkholderia sp. str. JS150. Primer
Specificity was tested using a collection of isolates known to degrade phenol,
and Burkholderia cepacia G4. The PCR products were sequenced, and were
f0kind to be highly similar to phenol hydroxylase genes from known phenol
Clegraders. PCR amplification was performed using the same thermal cycler and

reaction mixture described previously (Braker et al. 2001), with the modification

42

 

that 25 pmol of each primer was used and five replicate reactions were prepared
for each sample. The following touch down thermal profile was used for the
amplification: an initial denaturation step of 5 min at 95°C, followed by 10 cycles
at 94°C 303, 305 of annealing (where the temperature started at 58°C and
decreased 1°C every cycle) and 45 s at 72°C. In addition, 25 more cycles were
carried out using an annealing temperature of 55°C. The quality and quantity of
the PCR products was determined by electrophoresis of an aliquot (10 pl) of
each PCR reaction on a 2.0 % (wt/vol) agarose gel (Gibco BRL) in 1X TAE (Tris

a cetate-E DTA).

P C R purification, restriction digest and gel separation.

PCR purification, restriction digest and gel separation for T-RFLP of 168
rRNA genes were performed as described previously (Braker et al. 2001) with
Haelll and Cfol restriction enzymes (Gibco BRL).

For the phenol hydroxylase genes the PCR products from the five replicate
reactions were combined and concentrated by roto-evaporation. The
concentrated PCR product was separated in a 2.5% (wt/vol) agarose gel (Gibco
BIRL) in 1X TAE, and the band of correct size was excised from the gel and
6'thed in 60 pl of sterile filtered distilled water using a QlAquick gel extraction kit
(Qiagen). The purified PCR products were concentrated by rote-evaporation
and digested with 15 U of Mspl and Rsal restriction enzymes combined (Gibco
BI§L). The resulting fragments were separated in a 4% (wt/vol) Metaphor

aQarose gel (FMC, Indianapolis, Ind.) in 1X TBE (Tris borate-EDTA) buffer at

43

 

 

140 volts for 3.5 h at 4°C, and then stained with ethidium bromide (0.5 pg/ml).

Gel images were analyzed using GelCompar 4.5 (Applied Maths, Kortrijk,

Belgium).

Analysis of T-RFLPs.

Electropherograms from each sample from both reactors were manually
aligned against each other using Genotyper 2.5 (Applied Biosystems
Instruments, Foster City, Calif.). To avoid primer artifacts, fragments smaller
than 35 base pairs were excluded from the analysis. The alignment resulted in a
matrix in which different peaks were considered to be indicative of different
ribotypes present in the community, and the peak heights were used as a
measure of ribotype abundance. After alignment, samples where the sum of the

peak heights was less than 10,000 units were re-digested and separated, since
data below this threshold in unreliable for statistical analysis (Blackwood 2001).
Histograms with the abundance of each peak normalized by the total signal in
ea Ch sample were used to compare the changes in community structure in each
reel¢::tor. Similarity between samples from the same reactor and between the two
rea<2tors was determined by Correspondence Analysis (CA) (ter Braak 1995;
LeQendre and Legendre 1998) with the software package ADE-4
(httpzllbiomservpniv-lyom.fr/ADE-4.html) (Thioulouse et al. 1997). CA was
chcDsen for the analysis of the T-RFLP profiles because this ordination method
rec><Jvers the underlying structure of the data by analysis of the proportional

changes in species abundances, providing a global view of the community

44

structure without being severely affected by noise. For CA, peak height was
used as described above and the data analysis was performed in a step-wise

process. CA was initially performed on all the samples (from both reactors) to

compare the two reactors (between and within treatment variability).

Subsequently, each reactor data set was analyzed individually to evaluate within

treatment variability.
To evaluate the reproducibility of the T-RFLP methodology nine replicate

samples from each reactor (taken after 811 days of operation) and 32 lanes of
internal standard were analyzed with two similarity indices. Similarity indices
were used because they generate direct sample to sample comparisons,
providing more precise estimates of the variability. The Sorensen similarity
i'"ldex takes into account presence or absence of peaks while the Steinhaus

Similarity index takes into account the abundance of each peak (Legendre and

Legendre 1998). Similarity matrixes were generated for each set of nine

replicates and 32 lanes of the internal standard (Gene scan 2500, Applied
Bi<>systems, Alameda, Calif.) with the SIMIL module of the R-package 4.0

(httpzllwwwfasumontreal.ca/BlOL/Iegendrel) (Legendre and Legendre 1998).

Means, 95 % confidence intervals and coefficients of variation were calculated

from the similarity values.
Results

To E transformation rates
IReactor communities showed different TCE transformation rates after long-term

phenol and phenol plus TCE application (Figure 2.1, panel A). From days 60 to

45

100 both reactors showed similar TCE transformation rates, that were as high as
0.22 and 0.24 I mg" d" for the phenol and phenol plus TCE fed reactors,
respectively. By day 109, TCE transformation rates decreased to 0.03 I mg" d"
for the phenol-fed reactor, and to 0.05 | mg" d" for the phenol plus TCE-fed
reactor. At this point, the TCE transformation rates of the reactors diverged.
The phenol-fed reactor had variable TCE transformation rates ranging from 0.01
to 0.24 l mg" d". In contrast, the TCE transformation rates in the phenol plus
TCE-fed reactor were less variable in the range of 0.01 to 0.12 l mg" d". Visual
inspection of the TCE transformation rates in the phenol-fed reactor suggests
that changes TCE kinetics occur periodically in this reactor (Figure 2.1, panel A).
Unimodal TCE transformation rates were observed approximately between days
60-109, 400-500 and 575-725 in the phenol-fed reactor. On the other hand, the
phenol plus TCE-fed reactor did not show continuous periodicity, with only
I'T'Iarked changes in transformation rates during the first 110 days, and minor
Changes afterwards (Figure 2.1, panel A). The average TCE transformation
rates from 150-825 days were 0.082 i 0.12 and 0.044 :I: 0.05 l mg" d" for the
phenol and the phenol plus TCE-fed reactor, respectively. Analyses of
headspace from the phenol plus TCE-fed reactor indicate that the injected TCE

was completely degraded before the settle and decant phases.

46

 

0.25

.O
N
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0.1

Ill

TCE transformation rate
(I x mg'I x day")

0.05

 

0.7

11111111111

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Phenol degradation rate
(mg phenol x mg dry weight'l x h")
.0 .o
on A

 

 

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Days

 

——0— Phenol-fed reactor —e— Phenol plus TCE-fed reactor

 

 

 

I: i Qure 2.1. Long term functional responses of the phenol-fed and phenol plus
CE-fed reactors. (A), TCE transformation rates. (B), phenol degradation rates.

47

Phenol degradation rates

The maximum specific rates of phenol degradation for the phenol-fed
reactor were variable during the first 200 days of operation, ranging from 0.1 to
0.4 mg phenol x mg dry weight" x h" (Figure 2.1, panel B). After 200 days of
operation, the phenol degradation rates were stable in the range of 0.1-0.3 mg
phenol x mg dry weight" x h", except for day 747 when for unexplained reasons
the rates increased to 0.6 mg phenol x mg dry weight" x h". Visual inspections
of the phenol degradation did not reveal any marked periodicity, such as in the
TCE transformation rates (Figure 2.1, panel A).

In the phenol plus TCE-fed reactor, phenol degradation rates started at an
initial value of 0.5 mg phenol x mg dry weight" x h" and then cycled, with
conspicuous decreases (0.19 and 0.22 mg phenol x mg dry weight" x h") and
increases (0.6 mg phenol x mg dry weight" x h") (Figure 2.1, panel B). After
day 145 the phenol degradation rates did not increase as a high as before, and
less pronounced changes occurred. No periodicity on the phenol degradation
rates was observed in the phenol-fed reactor. Analyses of samples from both
reactors after phenol injection indicate that phenol concentrations decreased to
zero after the first 2 h of the phenol recharge stage. No major wall growth was

observed.
Community structure by T-RF LP

To determine if the two reactor communities were similar after 30 days of

operation on phenol, and before TCE was applied to one of them, T-RFLP was

48

performed with six different tetrameric restriction enzymes (Figure 2.2). Each of
the enzymes tested produced almost identical patterns for each of the two
reactors, with only minor differences in the height of some small peaks.

In the T-RFLP procedure, enzyme selection is critical to judge if changes in
community structure have occurred. Therefore, to select the best restriction
enzymes for the analysis, a subset of all the samples was tested with the six
enzymes previously used. Restriction enzymes Haelll and Cfol were selected
for community analysis because they generated the highest number of sharp
and distinguishable fragments in all the samples tested. Restriction enzymes
such as Mspl and Rsal were initially promising (Figure 2.2), but in later samples
several peaks overlapped.

To assess if T-RFLP is a suitable method to monitor population dynamics,
a reproducibility analysis was conducted (Table 2.1). The indices used
measured similarity based on the presence or absence of a fragment
(Sorensen) and abundance of each fragment (Steinhaus). To have some
reference for comparison, 32 lanes from the DNA marker 682500 were
analyzed. For the two indices the G82500 standard resulted in the highest
values as expected (Sorensen 1.0, Steinhaus 0.931 ), and the phenol and phenol
plus TCE-fed reactors T-RFLP patterns showed good reproducibility (Table 2.1).
Variability of the community measurements was lower than from previous
studies (Dunbar et al. 2000; Osborn et al. 2000; Scala and Kerkhof 2000; Braker
et al. 2001), with percentages of variation that ranged from 3.3 % to 6.2 %. This

is an important finding since in previous studies there were some disagreements

49

on the reproducibility of the T-RFLP method (Osborn et al. 2000; Scala and
Kerkhof 2000; Braker et al. 2001), probably caused by the limited number of

samples analyzed.

50

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52

The samples from the phenol plus TCE-fed reactor have a notable amount of
polymer (Chapter 3) that could have affected DNA extraction procedures and
might account for the slightly higher variability in this reactor. Nevertheless,
good reproducibility was observed for the nine replicate samples from each
reactor indicating that the samples were homogenous and representative of the
microbial community in each system.

T-RFLP analysis of the two reactors showed marked changes in
community structure over time (Figure 2.3). Fragments between 340-375 bp
had different peak heights for the two reactors. Other fragments increased in
relative peak height over time (200 bp fragment in the phenol plus TCE-fed
reactor) or remained relatively stable (58 bp fragment in the phenol-fed reactor).

To determine if there were similarities between both reactor communities,
CA was applied to data from both reactors combined. CA is a multivariate
method that reveals the underlying structure of the data by taking into account
both the peaks present in each sample and proportional changes in peak
intensity. The analysis will result in a graphical representation of the samples
along two or more axis of reference (dimensions) that will contain a fraction of
the total variability. Each dimension represents the possible environmental
factors that affect species distributions. Samples with similar community
structure will be closer together in the multidimensional plot. The amount of data
scatter in the multidimensional plot represents the variability of the community.
CA resulted in data points forming an arch structure, typical of systems

undergoing succession (ter Braak 1995) (Figure 2.4). CA data from the two

53

reactors before TCE addition (T=0) were nearly identical and different from all
the other samples, indicating that the reactor communities were almost identical
at the beginning of the experiment, and then diverged. Sample data from the
phenol-fed reactor were scattered in the plot, which suggests that the phenol-fed
community was highly variable. On the other hand, the data from the phenol
plus TCE-fed reactor were more confined to the left arm of the arch, suggesting
that the community in the phenol plus TCE-fed reactor was more stable than the
community in the phenol-fed reactor. Both reactor communities were similar at
different periods (Figure 2.4, circles A and B). Data points from days 50-104 of
the phenol plus-TCE-fed reactor grouped with data from days 50-198, and 605-
811 of the phenol-fed reactor (Figure 2.4, circle A). Interestingly, the highest
TCE transformation rates were observed in both reactors during those days with
similar community structure suggesting that some community structures were
beneficial for high TCE transformation rates. Similarities were also observed
between days 357-567 of the phenol-fed reactor and days 294-361, 399, 414-
811 of the phenol plus TCE-fed reactor (Figure 2.4, circle B), periods with
intermediate to low TCE degradation rates. All these data together indicate that

the communities of the two reactors were similar at different functional stages.

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Dim 1 (26%)

 

Cl T=0
Phenol-fed reactor
A Phenol plus TCE-fed reactor

 

 

 

Figure 2.4. Correspondence analysis of T-RFLP data from reactor
communities. Results from independent digests with Cfol and Haelll, were
combined for the analysis. Circles with letters indicate periods where the
community structure of the two reactors was similar. (A) phenol-fed reactor days
50-198, 605-811; phenol plus TCE-fed reactor 50-104. (B) phenol-fed reactor
days 357-541; phenol plus TCE-fed reactor days 294-361, 399, and 414-811.
T=0, day 0 of each reactor before TCE application to one of them. Dim,

dimension.

56

Changes in community structure can be seen in more detail in histograms
that show the relative proportions of each fragment over time (Figure 2.5,
Appendix B). The appearance or disappearance of fragments indicates that the
community in the phenol-fed reactor was dynamic. For example, fragment 367
disappeared or was below detection limit after 198 days and simultaneously
fragment 206 appeared and was present from day 127 until day 616. Fragment
367 reappeared after 605 days of operation. CA of each reactor separately was
used to discern temporal patterns in each reactor community individually. CA
confirmed that there were differences in community dynamics between the two
reactors, and the relationship between community structure and function (Figure
2.6). In the phenol-fed reactor, the community structure changed at least three
times converging back to its original state (Figure 2.6, panel A). T-RFLP
patterns of samples from days 0-198 clustered with samples from days 605-811
indicating that the communities from those two periods of high TCE
transformation rates were very similar. The T-RFLP profiles from days 226-310
and 357-567, periods with intermediate to low TCE transformation rates, formed
distinct clusters.

The community in the phenol plus TCE-fed reactor was less dynamic than
in the phenol-fed reactor (Figure 2.5). Most of the changes in community
structure occurred in the first 100 days of operation. Fragment 367 disappeared
during the first 100 days while fragment 206 appeared after 127 days and was
present until the end of the sampling period. In addition to fragment 206,

fragments 60, 66, 95, 376 and 567 were present most of time during days 127-

57

811. CA analysis revealed the majority of the changes occurred during days 0-
104 (Figure 2.6, panel B), while in days 127-811 points tend to cluster together
near the origin indicating that the community structure was similar and relatively

stable during this lengthy period.

58

Phenol-fed Reactor

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Figure 2.5. Relative abundance of Bacterial T-RF's from reactor samples after

digestion with Cfol.

base pairs.

Numbers in the legend indicate the sizes of the T-RF's in

59

 

Dim 2 (17%)

 

 

 

 

-1.5 ' 0 ' ' 1.5
Dim1(28%)

 

0 0-198 A 226-310 0 357-567 0 605-811

 

 

 

 

Dim 2 (19%)

 

 

 

-2.5

 

Dim 1 (30%)

 

I 0-104 0 127-811

 

 

 

Figure 2.6. Correspondence analysis of T-RFLP data for each reactor
community individually. (A), phenol-fed reactor; (8) phenol plus TCE-fed
reactor. Numbers in the legend next to a symbol indicate day periods that were
grouped together to facilitate interpretation. Arrows and numbers inside the
graph indicate the order of the changes in community structure. Results from
independent digests with Cfol and Haelll were combined for the analysis.
Circles delineate the different clusters formed. Dim, dimension.

60

To investigate changes in community structure at the functional level,
phenol hydroxylase genes were amplified by PCR, digested with two restriction
enzymes, and separated by electrophoresis (Figure 2.7). Cluster analysis of the
phenol hydroxylase fingerprints revealed that populations with the gene in the
phenol-fed reactor exhibit periods that correspond approximately to the periods
found by T-RFLP (Figure 2.7, black and gray bars). Fingerprints from days 0-
198 of the phenol-fed reactor where different from days 226-525, where the
intensity of a ~135 bp band increased and a band between 89 and 104 bp
appeared. Furthermore, fingerprints from days 630-811 were similar to days 0-
198 profiles, consistent with the T—RFLP data (Figure 2.5 and 2.6, panel A).
The phenol hydroxylase bearing populations in the phenol plus TCE-fed reactor
showed two periods of change (Figure 2.7). During the first 50 days the
community was stable and then changed with the emergence of a band of ~ 135
bp and the disappearance of two bands of ~210 and ~230 bp. This profile
remained stable until the end of the experiment (day 811). Cluster analysis of
the phenol hydroxylase fingerprints from both reactors confirmed that both
communities were similar during the initial days and the latter period of the
phenol-fed reactor (black bars), when the highest TCE transformation rates were
observed. The communities were also similar during days 226-525 and days
127-811 of the phenol-fed and the phenol plus TCE-fed reactors respectively
(Figure 2.7, grey bars), periods with low of intermediate TCE transformation

rates.

61

Phenol-fed reactor

in
t

lath-1'

l 3
. n v V '
I‘ll.»- -I

--OI-..---I

I
--r

 

Figure 2.7. Community restriction patterns of PCR amplified phenol
hydroxylase genes digested with Rsal and Mspl. Numbers on top of each lane
indicate time in days. Bars on tops represent the samples with similar
fingerprinting pattern within and between reactors as determined from cluster
analysis.

62

Discussion

Shih et al. previously showed that pulse feeding of phenol selected for a
more efficient TCE degrading community than continuous feeding of phenol
(Shih et al. 1996). However, the efficiency of TCE degradation was determined
by assays on reactor samples, where the effects of TCE application on the
community were not measured. In the current study, we employed the phenol
feeding strategy of Shih et al. modified by the addition of a TCE feeding step to
determine the long-term effects of TCE application. Long-term TCE application
resulted in a microbial community with reduced TCE transformation rates (0.093
vs. 0.049 I mg" d" for the phenol and phenol plus TCE fed reactors
respectively), but the rates were relatively stable over two years. Toxicity from
the TCE epoxide is well documented in Pseudomonas putida F1 and
Burkholderia cepacia G4 (Wackett and Householder 1989; Newman and
Wackett 1997; Yeager et al. 2001). Although, the effects of TCE toxicity can be
seen within 1 h of exposure, long-term effects have not been studied. Mars et
al. (1996) suggested that TCE epoxide toxicity resulted in an increase in
maintenance energy cost and the appearance of variants of Burkholderia
cepacia G4 in batch culture which had lost the catabolic plasmid for TCE
cooxidation. After 100 days of operation we observed low but relatively stable
phenol degradation rates in the phenol plus TCE-fed reactor. In contrast, the
phenol-fed community experienced frequent changes in phenol degradation
rates that were linked to changes in TCE degradation rates (Figure 2.1, panel A

and B). Our findings indicate that long-term TCE application in a sequential

63

feeding mode resulted in a slower though more stable TCE degrading
community. It has been proposed that one of the adaptations to TCE toxicity is
reduced rates of TCE cometabolism (Ishida and Nakamura 2000). Apparently,
TCE application selected against the fast TCE degrading organisms but allowed
slower TCE degraders to succeed.

Community analysis by T-RFLP of 16S rDNA genes revealed that there
was correspondence between community structure and function in these
reactors. Changes in TCE transformation rates for the phenol-fed reactor
occurred periodically, and corresponded to changes in community structure. CA
of all the fragments from two enzymes (Haelll Cfol) showed that different day
periods had particular community structures associated with them, and when
TCE transformation rates were high the community structure was similar (Figure
2.6, panel A). Fragment 367 was present in days 0-198 and 605-729 in the
phenol-fed reactor, when the highest TCE transformation rates were registered.
In contrast, fragments 206 and 95 were present during days 226-567, when TCE
transformation rates were intermediate to lower. Interestingly, a similar pattern
was observed in the phenol plus TCE-fed reactor where fragment 367 was
present when the transformation rates were high (days 0-104) and it was
replaced by fragments 206 and 95 when the transformation rates were low (127-
811 days). Changes in the distribution of phenol degrading populations can
result from adaptations to phenol degradation or interactions between
community members, e.g. predation by protozoa which were present in both

reactors. Mutations in the promoter region of the phenol degradation genes

64

(Burchhardt et al. 1997), repression by organic acids (Miller et al. 1996; Ampe
et al. 1998) or activity of unknown gene repressors (Arai et al. 1998) are events
possible in a mixed community and could affect the fitness of phenol degrading
populations causing changes in community structure.

While the T-RFLP data suggests that there was a community structure-
function relationship, it is possible that the changes observed at the 163 rDNA
level were not related to changes in the functional genes. Analysis of phenol
hydroxylase genes (an enzyme often involved in TCE cooxidation) from selected
samples from the two reactors revealed that the changes in community structure
resulted and corresponded to changes in the phenol hydroxylase fingerprints.
Different phenol hydroxylase genotypes were present when the reactors had
higher transformations rates (days 0-198 and 630-811, of the phenol-fed reactor,
and days 0-50 of the phenol plus TCE-fed reactor) suggesting that some phenol
hydroxylases were more efficient in TCE cooxidation (Figure 2.7_).
Furthermore, some phenol hydroxylase genotypes disappeared shortly after
TCE was injected into one of the reactors suggesting that genotypes that were
efficient in TCE cometabolism were also sensitive to TCE toxicity.

Understanding community succession has been one of the most
challenging topics in contemporary ecology. Current ecological theories of
succession predict contrasting outcomes of the process. Succession, as defined
by Clements (1916), is an ordered unidirectional process of species
replacements that culminate in a stable community, Le. a climax community. On

the other hand, more recent theories propose that during succession,

65

disturbances are frequent, and communities never reach a climax stage. Our
findings suggest that microbial community succession was continuous in the
phenol-fed reactor, while a relatively stable community was formed in the phenol
plus TCE-fed reactor. Furthermore, CA of the phenol plus TCE-fed reactor
community revealed that most of the changes in the community occurred during
the first 100 days (Figure 2.6, panel B) and then, the community stabilized for
700 days suggesting that the community reached the climax stage Clements
proposed (Clements 1916). In contrast, the community from the phenol-fed
reactor changed periodically over the two year period and never reached a
stable phase. Interestingly, the community structure of the phenol-fed reactor
during the initial days (0-198) was similar to the community structure at the end
of the experiment (days 605-811). The similarity was mainly caused by T-RFs
present in those periods that were apparently displaced during the intermediate
days (226-567). This is unusual in ecology since lost species usually do not
return to the community. In our case, species were probably never entirely
displaced from the community because they could remain as a refugia and
below the detection limit of our methodologies (MassoI-Deya et al. 1997;
Fernandez et al. 1999). Nevertheless, it can be hypothesized from the changes
in community structure that some microbial communities are dynamic and never
reach a climax stage, but rather are a shifting mosaic where changes are
unpredictable, as observed in some plant communities (McCune and Cottam

1985), and only limited temporal stability occurs.

66

The remaining question now is why the phenol plus TCE-fed community is
more stable than the phenol-fed community. Cook (Cook 1996) reviewed
modern successional theory and concluded that one of the points of
commonality between the new theories is that disturbances are frequent and
they likely affect the outcome of succession. It is possible that the changes in
community structure seen in the phenol-fed reactor are a response to random
disturbances that occur as part of any normal reactor operation. However, the
way the reactors were designed and operated, random disturbance should affect
both communities. Then, why should one of our communities be more stable?
A possible answer of this question lies in the effects of TCE on the community.
Toxicity studies on pure cultures suggest that the epoxide generated from TCE
cometabolism damages cellular components, rendering cells inactive (Wackett
and Householder 1989). Furthermore, recent studies on Burkholderia cepacia
G4 suggest that G4 secretes toxic soluble intermediates from the degradation of
TCE, which affect inactive cells (Yeager et al. 2001). Therefore, it is reasonable
to hypothesize that TCE application selected for a community that is more
resistant to TCE toxicity, and depending on the protection mechanism, the
community could be more resistant to environmental disturbances. For
instance, an increase in EPS accumulation was observed in the phenol plus
TCE-fed reactor (Chapter 3), a phenomenon that resulted in the formation of
“star-like” fiocs, which to some extent resemble biofilms. Biofilm communities
are very stable and resilient to perturbations suggesting that an analogous

mechanism could be conferring increased stability to the TCE-fed community.

67

The results of this study suggest that the stability of the microbial
communities could be dependent upon random disturbances and the selective
pressure applied to the ecosystem. More than two years of phenol and TCE
application resulted in a community with reduced but stable TCE degradation
rates and relatively stable community structure. On the other hand, long-term
application of phenol resulted on a different community with higher TCE
transformation rates but with continuous and unpredictable changes in
community structure and function. These findings suggest that microbial
communities in nature in similar niches could have contrasting dynamics

depending upon particular physical, chemical or biological conditions imposed.

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71

CHAPTER 3
THE ABUNDANCE OF EXTRACELLULAR POLYMERIC
SUBSTANCES AND THE SPATIAL DISTRIBUTION OF
BACTERIA IN FLOCS IN PHENOL AND PHENOL PLUS TCE FED
REACTORS.

Introduction

The majority of the microorganisms in bioreactor systems live in
aggregates often termed as granules, flocs or biofilms. This lifestyle is
convenient for microbes because the close proximity between them allows
interactions such as metabolite transfer, communication via diffusible factors and
horizontal gene transfer (Davey and O'Toole 2000). Aggregate formation can
also serve as a protection mechanism against undesirable environmental
conditions such as predation (Decho and Lopez 1993) and the presence of toxic
compounds (Gilber and Brown 1995). For instance, the addition of ethanol to
liquid and solid cultures of Pseudomonas aeruginosa enhances the production
of alginate, leading to the formation of mucoid cells and biofilms that are
resistant to antibiotics (DeVauIt et al. 1990; Costerton et al. 1999). Starvation is
another condition that favors aggregation. Cells form aggregates under
starvation in order to trap nutrients from the surrounding environment making
them available to the cells in the floc or biofilm (Lewis and Gattie 1990; Davey
and O'Toole 2000; Flemming and Wingender 2001). Aggregate formation is

also an important process for efficient wastewater treatment, where good settling

72

sludge is essential to obtain clean effluents (Urbain et al. 1993; Bossier and
Verstrate 1996; Sponza 2002).

Bacteria form aggregates by attaching to each other forming a
microcolony, a structure that will be colonized by other species of bacteria, and if
stable, will eventually result in the formation of a floc or a biofilm (Watnick and
Kolter 2000). This process will also be strongly affected by physical and
chemical features of the environment. Extracellular polymeric substances (EPS)
serve as the “glue” that maintain cells attached to each other and to solid
surfaces (Li and Ganzarcyk 1990; Urbain et al. 1993; Bossier and Verstrate
1996; Wingender et al. 1999).

Microbial EPS is a mixture of organic compounds secreted by cells either
in aggregates or in isolation. EPS is usually composed of polysaccharides,
proteins, DNA and some lipids (Frolund et al. 1996; Neu 1996; Dignac et al.
1998), with polysaccharides being the most abundant representing 40-90% of
the total organic matter in biofilms (Urbain et al. 1993; Flemming and Wingender
2001; Sutherland 2001). In pure cultures EPS can be found as a capsule
surrounding the cell resulting in a mucoid appearance, or secreted into the
medium (Grobe et al. 1995; Kachlany et al. 2001). Diverse groups of bacteria
are capable of secreting EPS under a variety of conditions. Some of the genera
known to secrete EPS include: Burkholderia, Streptococcus, Bacillus,
Fla vobacten'um, Rhizobium, Mycobacten’um, Lactobacillus, Pseudomonas,
Sphingomonas, Nitrosomonas, Klebsiella, Desulfovibn‘o, and Enterobacter

(Graber et al. 1988; Stehr et al. 1995; Cerantola et al. 2000; Denner et al. 2001).

73

EPS production could be a major factor in bioremediation applications.
Bacteria that produce EPS and attach to surfaces in large quantities affect
ground water flow reducing the success of the bioremediation strategy. In other
cases EPS could help protect cells from the toxic effects of some pollutants. For
instance, studies on a mucoidal strain of Rhodococcus rhodochrorus revealed
that EPS conferred tolerance to 10 % (vol/vol) n-hexadecane and allowed cells
to degrade crude oil (lwabuchi et al. 2000). EPS production is also important for
the success of bioreactors. In biofilm reactors EPS is essential for retention of
the biocatalyst populations on solid surface. In batch reactors EPS is also
needed for good settling and hence retention of the biomass. Despite the
importance of biofilms, generalizations about the conditions that cause EPS
secretion and its role in bioremediation are not yet clear.

Previous studies in activated sludge have examined the abundance of EPS
during normal operation and during anaerobic storage, as well as its physico-
chemical properties (Urbain et al. 1993; Nielsen et al. 1996; Liao et al. 2001;
Sponza 2002). However, there are no studies on the effects of chlorinated
compounds on the EPS content of bioreactor communities. The purpose of this
study was to determine the effects of long-term TCE application on the EPS
content of phenol-fed sequencing batch reactors. Our objectives were: (i) to
determine if the EPS content was affected by TCE addition; (ii) determine the
spatial distribution of bacteria in the different reactors; and (iii) determine the

composition of the EPS. We had noted an increase in EPS abundance and the

74

formation of “star like” flocs in the phenol plus TCE-fed reactor, but not in the

phenol-fed reactor suggesting this phenomenon might be important.

Materials and Methods

Reactor sampling

Samples of total suspended solids (TSS) were obtained from two bench
scale sequencing batch reactors (SBR), one was fed-phenol and the other was
fed phenol and TCE in alternate cycles (Chapter 2). Initially, six samples (~ 10
ml each) from each reactor were taken and processed the same day for EPS
extraction. Samples frozen at -80° C were used to analyze the changes in EPS
content during reactor operation. For scanning electron microscopy (SEM),
samples (~5 ml) were fixed with one volume of 4% glutaraldehyde in 0.1M

sodium phosphate buffer (pH 7.4) for 30 min before processing.

Scanning electron microscopy

One drop of fixed sample was placed on a 12 mm round glass cover slip
pre-coated with 1 % poly-I-Iysine (Sigma Aldrich, St. Louis, Missouri), and air
dried for 5 min. The slide was gently washed in water before dehydration by
successive transfers in ethanol baths (25 %, 50 %, 75 %, 95 %), and three final
changes in 100% ethanol. The sample was critical point dried in a Balzers
critical point dryer (Balzers, Lichtenstein) and then coated with gold in an
emscope sputter coater (Emscope, UK). Images were taken in a JEOL 6400V

scanning electron microscope (JEOL, Ltd., Tokyo, Japan).

75

 

Ep‘

Fral

solli
mod
with
min

091

(7

lie

lint}.

119:9)

Epifiuorescent microscopy

Reactor samples (1 ml) were mixed with the nucleic acid stain SYTO-9
(Molecular Probes, Eugene, Oreg.) to a 1 pM final concentration, and incubated
for 15 min at 25° C. An aliquot (5 pl) was placed on a 6 mm well of a
microscope slide (Cel-line associates, Newfield, New Jersey) and was observed

with a Leitz Orthoplan 2 microscope equipped with a 50 W mercury lamp.

Fractionation of the TSS

The EPS was recovered from the TSS by the potassium hydroxide
solubilization method described by Kim et al. (Kim et al. 2000) with the following
modifications: fresh TSS samples (~ 10 ml) were transferred to a beaker, diluted
with two volumes of 10 % potassium hydroxide (1.8 M) and mixed slowly for 30
min at 25° C. The biomass fraction was separated from the mixture by
centrifugation (2000 x g at 25° C for 15 min), resuspended in distilled water and
filtered through a 0.22 pm Durapore filter (Millipore, Bedford, Mass.) for dry
weight measurement. The supernatant was transferred to a flask and mixed
with three volumes of 95 % ethanol to precipitate the EPS fraction. After
centrifugation (2000 x g at 25° C for 15 min), the EPS was dried at 42° C and
weighted as described previously (De Vuyst et al. 1998). The efficiency of the
fractionation method was determined by comparing the sum of the masses of
the different fractions against the TSS of untreated samples. The TSS of
untreated samples was measured by filtering six replicate samples and dry

weight measurements were obtained as described above.

76

Purification and characterization of EPS

Precipitated EPS was purified by dialysis against distilled water (two water
changes daily) in a 12,000-14,000 Dalton molecular weight cutoff membrane
(Spectrum Laboratories, Rancho Dominguez, Calif.) for 10 days at 22° C. The
purified EPS was frozen to -80° C before being Iyophilized. The composition of
the EPS was determined by analysis of alditol acetate derivatives (Wang and
Hollingsworth 1994). Approximately 0.1 mg of sample was hydrolyzed in 2 M
trifluoroacetic acid (TFA) for 1 h at 120° C. The solution was dried under a
stream of nitrogen while heated at 50° C, dissolved in distilled water, and dried
again. This procedure was repeated two times to remove traces of TFA. The
dried product was reduced to form alditol acetates by incubation with sodium
borohydrate at 25° C for 24 h. To remove the excess of sodium borohydrate
one volume of distilled water and one drop of concentrated hydrochloric acid
were added to the vial and the solution was incubated at 25° C for 10 min before
being dried under a stream of nitrogen as described above. Water,
concentrated acetic acid and methanol were added to the vial, mixed and dried
under nitrogen. Methanol was added and dried three more times to remove
traces of boric acid. The dried product was paracetylated with acetic anhydride
and pyridine, and analyzed by gas chromatography (GC) and gas
chromatography followed by mass spectrometry (GC-MS) as described
previously (Wang and Hollingsworth 1994). The temperature program employed
began with an initial temperature of 170° C for 2 min, followed by an increase to

220° C at a rate of 2° C/min and held for 60 min at that temperature. To further

77

support the GC-MS findings H1 NMR of non-hydrolyzed samples was performed

in 020 using a Varian VXR500 spectrometer at 500 MHz.

Results

Visual inspection and microscopy.

Marked differences in the appearance of the suspended solids were
observed between the phenol and the phenol plus TCE-fed reactors. The
suspended solids of the phenol-fed reactor formed loose granules by visual
inspection of reactor samples. On the contrary, the suspended solids from the
phenol plus TCE-fed reactor formed large and dense cell floccules of slimy
appearance.

Scanning electron microscopy revealed major differences between the
phenol-fed and the phenol plus TCE-fed reactors communities (Figure 3.1). The
microbial community of the phenol-fed reactor formed small aggregates of 50-60
pm in diameter with low amounts of EPS connecting the cells (Figure 3.1, A and
C). The aggregate was composed of rod shaped bacteria of different sizes,
some of which were pleomorphic. In contrast, the microbial community of the
phenol plus TCE-fed reactor formed a “star-like” structure (Figure 3.1, D) with
bacteria embedded within an EPS matrix that together made up the long
filament (Figure 3.1, E and F). The filaments were composed mainly of small
rods (~ 1.2 pm in length) and some longer rods positioned towards the outside

of the filament (Figure 3.1, F).

78

Filaments—>

Agra-gulc-

Ill pm

 

Figure 3.1. Electron scanning micrographs of reactor suspended solids.
Images were taken after 900 days of reactor operation. Micrographs A-C and D-
F represent different levels of resolution for the phenol-fed and the phenol plus
TCE-fed reactors, respectively. EPS, extracellular polymeric substances.

79

This star-like structure could also be observed under epifluorescence
microscopy (Figure 3.2); the filaments were also arranged in conical structures
with relatively similar diameters. The cells are uniform in shape and
arrangement in these filaments (Figure 3.2, B). The aforementioned structures
were observed in different samples throughout the 120 to 811 day period. They
may have formed sooner than 120 days but no samples were examined for the 0

to 120 days period of operation.

 

Figure 3.2. Epifluorescence microscopy of the phenol plus TCE-fed reactor
flocs. Samples were stained with SYTO-9. Panel A, 100X magnification, panel
B, 700X magnification.

80

EPS abundance

Analysis of the TSS by dry weight revealed that there were marked
differences between the two reactors (Figure 3.3, panel A). The TSS of the
phenol-fed reactor was 1,002 1: 39.7 mg/l, 30 % less than the TSS of the phenol
plus TCE-fed reactor which was 1462 t 24.5 mg/l. Fractionation of the TSS in
EPS and biomass revealed that the differences observed between the TSS of
the two reactors were caused by a significant increase in the EPS content of the
phenol plus TCE-fed reactor. The concentration of EPS in the phenol plus TCE-
fed reactor, measured as polymer dry mass (PDM), was 589 1: 88.2 mg PDMII or
40 % of the TSS (Figure 3.3 panel B). In contrast the EPS concentration in the
phenol-fed reactor was 121 t 33.8 mg PDMII or only 12% of the TSS. No major
differences were observed between the biomass fractions of each reactor, with
concentrations of 862 t 68.9 and 735 :I: 87.3 mgII in the phenol-fed and the
phenol plus TCE-fed reactors respectively. However, due to the major amount
of EPS in the phenol plus TCE-fed reactor, the biomass fraction only
represented 50% of the TSS (Figure 3.3, panel B). In contrast the biomass
fraction of the phenol-fed reactor represented 86 % of the T88.

In order to determine the changes in EPS content during the duration of the
experiment, selected frozen samples were used for biomass and EPS
fractionation (Table 3.1 ). The values were expressed as the percentage of EPS
relative to the TSS previously measured (Chapter 2). Analysis of the phenol-fed
reactor samples shows that the proportion of EPS was relatively stable over the

duration of the experiment with an average of 10 % of the TSS. In contrast, the

81

proportion of EPS in the phenol plus TCE-fed reactor increased during the first

90 days of operation and stabilized at an average of 45 % of the TSS.

 

 

 

 

  

 

 

 

 

 

 

 

 

 

   

 

 

1600_ 109

A q A '1 86 B
‘7. 140% 4 I
O 4 1 J-
g 1200-: (0 8°:

.1 (I)
z- 4 l-
0 1000- - ‘
E : g 60- 50
‘5 8003 5’ ‘
s r g «
E 600: a; 40-
c - a a
8 : ,
s 400: .
0 2 20-

200, ~
.1
G1 I G
TSS EPS Biomass Eps Biomass
[:1 Phenol-fed reactor Phenol plus TCE-fed reactor

Figure 3.3. Fractionation of the TSS (A) and percentage of the fractions relative
to the TSS (B). Values represent means :i: 95 % confidence intervals (n=6).

82

Table 3.1. Relative abundance of the EPS fraction in the phenol-fed and the
Values represent the percentage of the EPS

phenol plus TCE-fed reactor.

 

 

 

relative to the TSS.
Day Percentage of EPS
Phenol plus TCE-fed
Phenol-fed reactor reactor

42 8 % 6 %
63 10 % 30 %
90 10 °/o 43 %
119 9 % 46 %
151 10 % 49 %
173 12 % 44 %
350 8 % 41 %
689 11 °/o 45 %
800 11 % 40 %

 

83

EPS composition

To determine the composition of the EPS, large samples from each reactor
were extracted with potassium hydroxide and purified by dialysis. Since we
were not able to recover sufficient EPS from the phenol-fed reactor after dialysis
we focused on the EPS composition of the phenol plus TCE-fed reactor. GC
and GC-MS analysis revealed that this EPS was mainly composed of hexose
sugars, i.e. rhamnose, fucose, arabinose, xylose, mannose, galactose, glucose;
and one amino sugar, i.e, N-acetylglucosamine (Figure 3.4). Further evidence
to support this identification was obtained by H1 NMR analysis (Figure 3.5).
Singlets between 1.2 and 1.5 ppm were assigned to the 6-deoxy groups of
rhamnose, while signals between 5.0 and 5.5 ppm were assigned to the alpha
linkages of rhamnose. Signals between 3.4 and 4.3 ppm were assigned to the
remaining sugar protons. The two singlets between 2.0 and 2.2 ppm were
assigned to the N-acetyl groups of N-acetylglucosamine while the signals
between 4.7 and 4.9 ppm were assigned to the beta linkages. The most
abundant sugar identified was rhamnose representing 56 % of all the

carbohydrates identified, followed by mannose with 22 % (Figure 3.6).

84

Rhamnose
F ucose
Arabinose
Xylose
Mannose
Galactose
Glucose

NODO'I-hOONA

 

 

 

 

 

 

 

 

_j 10:. tn.

Figure 3.4. Gas chromatogram of alditol acetates from the phenol plus TCE-fed
reactor purified EPS. The identities of the peaks were determined by coelution
with standards and analysis of mass spectra. The peak before rhamnose is an
unidentified compound. N-acetylglucosamine is not shown due to its long
retention time.

85

 

 

 

 

 

Figure 3.5. 1H NMR spectrum of the extracellular polysaccharide. Signals
characteristic of rhamnose include two singlets between 1.2 and 1.5 ppm that
were assigned to the 6-deoxy groups and signals between 5.0 and 5.5 ppm that
were assigned to the alpha linkages of rhamnose. The two singlets between 2.0
and 2.2 ppm correspond to N-acetyl groups of N-acetylglucosamine and the
signals between 4.7 and 4.9 ppm correspond to beta linkages of N-
acetylglucosamine. Signals between 3.4 and 4.3 ppm correspond to the
remaining sugar protons. PPM, part per million.

86

 

 

 

Rhamnose H

 

 

Fucose

 

 

Arabinose

Xylose

1| III ll 1

 

Mannose

 

 

Galactose

II II

 

Glucose

 

 

N-acetylglucosamine

 

 

 

IIIIjiIlIIIIIITjIII'IrTIIITjT

0 1 O 20 30 40 50 60

Percentage of abundance

Figure 3.6. Composition of the EPS from the phenol plus TCE-fed reactor.
Values represent averages :I: one standard deviation of triplicate injections.

87

Discussion

The majority of the studies that investigated the effects of TCE on cells
have focused on cell viability, growth rates, toxicity and enzymatic activity and
they suggest that TCE has detrimental effects on the cells that degrade it.
However, studies in Pseudomonas putida suggest that TCE could induce its own
degradation (Shingleton et al. 1998). This suggests that other responses might
be induced by TCE. We observed an increase in the EPS content in the reactor
that was exposed to TCE both by microscopy and solids fractionation (Figures
3.1-3.3, Table 3.1). EPS represented between 40-50 % of the TSS in the phenol
plus TCE-fed reactor, while it represented between 8-12 % in the phenol fed
reactor. Similar ranges in EPS values have been documented in activated
sludge (10-15%) (Urbain et al. 1993) and biofilm reactors (40-80%) (Characklis
and Cooksey 1983). Furthermore, Chang (1996) (Chang 1996) who evaluated
the effects of increasing levels of TCE on the predecessors to these reactors,
noted floc formation and persistence in a phenol plus TCE-fed reactor exposed
to increasing amounts of TCE, but did not further study the observation.
Together, these results suggest that TCE application could induce the formation
of substantial amounts of EPS. Similar results were observed when
Pseudomonas aeruginosa was exposed to ethanol (DeVault et al. 1990) or
kanamycin (Drenkard and Ausubel 2002), since those compounds caused an
increase in alginate production and the formation of biofilms. These authors

propose that biofilm formation is the mechanism used by Pseudomonas

88

aerugl'nosa to reduce the diffusion of the antibiotics inside the cell, thus
increasing its survival (Drenkard and Ausubel 2002).

Although there are several possible explanations for the accumulation of
EPS in the TCE-fed reactor, a plausible one is that EPS is a protection
mechanism against TCE or TCE induced toxicity. As has been proposed by
various investigators, the toxicity of TCE or its metabolites are the major hurdles
in aerobic TCE cometabolism (Alvarez-Cohen and McCarty 1991; Mars et al.
1996; Mars et al. 1998). The TCE epoxide, and some its derivatives are toxic to
the TCE degraders and to other microbes (Alvarez-Cohen and McCarty 1991;
Yeager et al. 2001). Thus, continuous exposure to TCE should favor
microorganisms or communities with increased resistance to the toxic solvent. It
is possible that EPS accumulation could be a mechanism to control toxicity by
limiting the diffusion of the toxicant into the cells. Diffusion limitation by changes
in membrane composition was the resistance mechanism observed in
Pseudomonas putida DOT-T1 when exposed to high concentrations of toluene
(Ramos et al. 1997). On the other hand, recent studies on toluene oxidizers
suggest that EPS production could be a protective mechanism against the toxic
effects of toluene (Putthividhya and Abriola 2002). Studies on FIavobacten'um
cells revealed that they transformed pentachlorOphenol only when immobilized
in polyurethane (O'Reilly and Crawford 1989). Furthermore, studies on a phenol
degrading methanogenic consortium entrapped in agar beads revealed that
immobilization resulted in a reduction in degradation rates, but increased

resistance to phenol inhibition (Dwyer et al. 1986). It is possible that the EPS

89

produced by the microbial community causes the same effect that agar and
polyurethane caused to immobilized cells; a reduction in the diffusion of
inhibitory compound, TCE, inside the cells thereby limiting the toxic effects. If
TCE does not penetrate the cell, the toxic metabolites will not be generated and
TCE mediated toxicity will not occur. This type of protective mechanism could
result in decreased phenol and TCE degradation rates, a phenomenon
previously observed (Chapter 2) and that supports the proposed role of EPS.
Additional research with pure cultures or simple communities is needed to
determine if this hypothesis is correct and to rule out other alternatives such an
opposite effect by retaining the toxic metabolites inside the cells.

The increase in EPS accumulation could have an effect on the structure of
the microbial community. High amounts of EPS will reduce the diffusion of
oxygen to inner layers, resulting in microaerobic or anerobic conditions. Such
conditions will foster the formation of anerobic microbial communities in the
interior of the flocs. For instance, sulfate reducing microorganisms have been
observed in aerobic activated sludge systems suggesting that anerobic areas
within the flocs exist. Alternatively, facultative microorganisms such as
denitrifiers could be enriched under anoxic conditions. Since ammonia oxidizers
were present in these communities, dentirification is a feasible alternative
(Chapter 5). Nevertheless, enrichment cultures for anaerobic microorganisms
and the measurement of anerobic intermediates are needed to determine if

anaerobes or facultative microbes were present in the reactor communities.

90

Carbohydrate analysis of the EPS from the phenol plus TCE-fed reactor
revealed that it was mainly composed of neutral sugars. Although we did not
analyze the EPS for protein content it is likely that it is mainly carbohydrate
because the only method that successfully extracted the EPS was solubilization
with potassium hydroxide. This method is effective for the extraction of tightly
bound EPS composed of neutral sugars, (Graber et al. 1988; Nielsen and Jahn
1999; Sutherland 2001; Liu and Fang 2002), although some proteins can be
solubilized under these conditions.

The EPS was composed of rhamnose, mannose, glucose, galactose,
arabinose, xylose and N-acetylglucosamine. These sugars are commonly found
in the EPS of a variety of bacterial species and in anaerobic microbial
communities (Veiner et al. 1995; Veiga et al. 1997). The most abundant
carbohydrate was rhamnose representing 56 % of the carbohydrates identified.
Rhamnose is a common component of the cell wall and capsule of many
bacteria (Graber et al. 1988; Giraud and Naismith 2000). Studies in
Streptococcus mutans and Vibrio cholerae revealed that mutations in some of
rhamnose biosynthesis genes blocked the synthesis of cell wall polysaccharides
and the cells were unable to colonize tissue and begin infections (Chiang and
Mekalanos 1999; Yamashita et al. 1999). Furthermore, rhamnose is the third
most abundant carbohydrate in methanogenic granules (Veiga et al. 1997).
These observations suggest that rhamnose might be an important carbohydrate
in aggregate and biofilm formation and could explain its high abundance in the

EPS of the phenol plus TCE-fed reactor.

91

Marked differences in the spatial arrangement of bacteria in the two
reactors were observed. While bacteria in the phenol-fed reactor formed cell
aggregates with small amounts of EPS, the bacteria in the phenol plus TCE-fed
reactor formed flocs with high amounts of EPS, and bacteria embedded along
filaments that resembled a star form. Previous structural studies in aerobic
biofilm communities revealed that biofilms are a discontinuous layer of bacteria
with cell-free channel structures that allow the diffusion of oxygen and substrates
(Lawrence et al. 1991; Massol-Deya et al. 1995). If bacteria form a dense
spherical structure, oxygen and substrates may be limiting in the inner layers.
Thus, the formation of “star-like” aggregates could be a strategy that allows
protection while maintaining some diffusion of oxygen and growth substrates to
the inner layers of the filaments.

One of the most remarkable characteristics of bacteria is their ability to
adapt to changes in their surrounding environment. In microbial communities,
the adaptation process is far more complex and robust because of microbial
interactions and metabolic diversity. In this study the long-term effect TCE
application appears to be accumulation of EPS. This major shift in carbon flow
is remarkable, and suggests that it might be beneficial. The reactor operated at
lower but stable phenol and TCE degradation rates for two years, and hence it

should be an ecologically successful adaptation.

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97

CHAPTER 4
CHARACTERIZATION OF THE CULTIVABLE MEMBERS FROM
PHENOL AND PHENOL PLUS TRICHLOROETHENE-FED
REACTOR COMMUNITIES, AND THEIR DENSITIES OVER 2
YEARS OF REACTOR OPERATION.

Introduction

The functional dissection of microbial communities has been a major
challenge for microbial ecologists. Assignment of functions to different microbial
groups, and their enumeration are necessary steps to understand and predict
the effects of perturbations on community function and stability. From an
applied perspective this knowledge will help improve the design and operation of
biological waste treatment processes. Surveys by 168 rDNA cloning and
sequencing have revealed that communities in nature are very diverse, and that
the microbes in culture represent a minor fraction of the microbial world
(Hugenholtz et al. 1998). In many ecosystems the so called “non-cultivable
microorganisms” are numerically abundant and thought to be important and
perhaps necessary for community function. Isolation and physiological
characterization of microorganisms remains the main means to understand their
role in microbial communities. For instance, 16S rDNA analysis of aerobic
sludge revealed that wastewater communities are very diverse, but allowed only
limited inferences about the possible roles of some of the community members

(Bond et al. 1995; Bond et al. 1999; Juretschko et al. 2002). Physiological

98

analyses of pure cultures, in combination with perturbation experiments. can aid
in determining the function of the different microbes in the community.

Trichlorethene (TCE) is an industrial chlorinated solvent that, due to
inappropriate management and disposal, is a frequent contaminant in soil and
groundwater (ATSDR 1994). Aromatic compounds such as phenol and toluene
have been successfully used to stimulate the degradation of TCE, since
aromatic oxygenases fortuitously oxidize TCE in a process known as
cometabolism (Nelson et al. 1987; Wackett and Gibson 1988; Hopkins et al.
1993a). The cometabolism of TCE produces an unstable epoxide that yields
non-toxic products such as formate and glyoxylate (Li and Wackett 1992;
Newman and Wackett 1997b). Although the epoxide lasts ~ 20 sec inside the
cell, some of it reacts with the catalytic enzyme and DNA reducing degradation
rates or killing the TCE degraders. Based on these results, several investigators
suggest that field stimulation of TCE degradation by aromatic compounds would
eventually result in a takeover of the community by TCE inactive populations
that metabolize the primary substrate only (Mars et al. 1996; Fries et al. 1997a;
Mars et al. 1998). On the other hand, toxicity studies on a derivative of
Ralstonia eutropha JMP 134 revealed that this microorganism can resist high
doses of TCE without a decrease in activity, suggesting that adaptations to TCE
toxicity exist in nature (Ayoubi and Harker 1998).

Studies in a field site where phenol and toluene were used to stimulate
TCE cometabolism, and on phenol-fed reactors suggest that the microbial

communities of these two ecosystems are diverse, and the dominant members

99

of the community can be recovered in culture (Fries et al. 1997b; Watanabe et
al. 1998). The purpose of this work was to isolate, characterize and enumerate
the cultivable members of two sequencing batch reactors (SBR), one fed phenol
and the other fed phenol and TCE, for more than 2 years. Although
communities that degrade phenol and cometabolize TCE have been studied
previously, the SBR system used in this study has been exposed to a TCE dose
10 times higher than previous studies (Hopkins et al. 1993a; Hopkins et al.
1993b; Fries et al. 1997b) and has established low but stable degradation rates
(Chapter 2). Therefore, the effects of long-term TCE application on the
abundance of TCE degraders can be determined. The objectives of this work
were: (i) to determine the cultivable diversity present in these reactor
communities; (ii) elucidate if the isolates degrade phenol and TCE, and the
diversity of the phenol hydroxylases among them; and (iii) determine the
abundance of selected TCE active and inactive populations over the 2 years of

exposure.

Materials and Methods

Pure culture isolation and characterization

Reactor samples (10 ml) were aseptically collected from each reactor at
day 340 of operation. Samples were vortexed for 1 min, homogenized by
passing the sample through a syringe with a gauge 30 needle, before being
serially diluted using sterile mineral medium (described in Chapter 2). An aliquot
from each dilution was plated on R2A agar (Difco, Detroit, Mich.) and the plates

were incubated for 3 weeks at 25° C. Colonies representing the different

100

morphologies on the plates were selected and further purified by repetitive
streaking on fresh medium. Additionally, enrichments for ammonia oxidizers
were established from samples taken after 900 days of operation using mineral
medium with 0.01 % phenol red as an indicator (Chapter 2). The motivation to
attempt to isolate this group of bacteria was the observation of structures that
resembled ammonia oxidizing bacteria from transmission electron micrographs
of the community (data not shown). Enrichments were transferred several times
until pure cultures were obtained by direct plating on mineral medium with 0.01
% phenol red, and 15 % w/v agar. Genomic fingerprints from each isolate were
generated using BOX-PCR as described previously (Rademaker et al. 1998),
and were analyzed using GelCompar 4.5 (Applied Maths, Kortrijk, Belgium).
Isolates with identical fingerprints were considered the same strain, and one
representative was kept for further analysis.

The isolates were tested for phenol degradation by inoculating single
colonies into 20 ml vials containing a mix of mineral medium and phenol at 50
and 10 mg/I; and at 10 mg/l with 1X Wolin Vitamins (Wolin et al. 1963). Vials
were sealed with Teflon caps and aluminum crimp seals before being incubated
for 2 weeks at 25° C on a rotary shaker. An aliquot (1 ml) was removed from
each vial to measure the remaining phenol concentration by HPLC with a Hibar
RP-18 column at a flow rate of 1.5 mein, an eluent of water-acetonitrile-
phosphoric acid (66:33:01), and a UV detector set to 218 nm. Isolates that
removed more than 15% of the initial phenol concentration were considered

phenol degraders and tested for TCE degradation. The 15% cutoff was

101

determined based on abiotic losses of phenol from vials without cells or with
killed cells. For TCE degradation tests single colonies were inoculated in phenol
medium at the appropriate concentration, and the vials were amended with TCE
to a final concentration of 1 mg/l. Vials were sealed and incubated as described
above. The amount of TCE removed was quantified by gas chromatography

(GC) as described previously (Chapter 2).

DNA isolation and purification

One colony from each isolate was inoculated in 5 ml of 0.20X nutrient broth
(Difco) and was incubated at 30° C until sufficient turbidity was observed. The
cell pellet used for DNA extraction was obtained by centrifugation of a 2 ml
aliquot at 12,000 x 9, followed by supernatant removal. Nucleic acids were
extracted using freeze-thaw cycles, enzymatic digestions and phenol chloroform
purification as described previously (Chapter 2), and were stored at -20° C until

U88.

16S rDNA and phenol hydroxylase sequencing, and phylogenetic
analyses.

The 168 rDNA gene from each isolate was amplified with primers 8F (5’-
AGAGTTTGATCMTGGCTCAG-B’ where M stands for A or C) (Giovannoni
1991), and 1522R (5’-ACGGGCGGTGTGTACA-3') (Amman et al. 1995) using
the same reaction conditions and cycles described previously (Chapter 2). The
amplicon was checked for purity by separation in a 1 % agarose gel followed by

primer and nucleotide removal using a Qiaquick PCR purification kit (Qiagen,

102

Chatsworth, Calif.). Partial 168 rDNA sequences were obtained using the dye
terminator method at the Michigan State University DNA sequencing facility
using primers 8F and 787R (5’-TACCAGGGTATCTAAT-3’), modified from a
previously published primer (Amman et al. 1995). For selected isolates, nearly
complete 16S rDNA sequences were obtained by additional sequencing using
primers 1522R and 533F (5’-CAGCAGCCGCGGTAA-3’) (Rhodes et al. 1998).

Consensus 16S rDNA sequences generated from the assembly of different
sequence reads, were aligned against the most similar sequences in the
Ribosomal Database Project II (RDP-II) release 8.0 database (Maidak et al.
2001), using the fast align procedure of the ARB software package (Strunk and
Ludwig 1995). Sequences were also compared against the GenBank database
from the National Center for Biotechnology Information (NCBI) using the Basic
Local Alignment Search Tool (BLAST) (Altschul et al. 1990). Sequences with
high similarity scores that were not present in the RDP-II database were
retrieved, aligned as described above, and included in the phylogeny.
Alignments were corrected manually by taking into account primary and
secondary structure considerations, and ambiguously aligned regions were
removed from the analysis. Phylogenetic trees were generated using maximum
likelihood with the fastDNAml program (Olsen et al. 1994). Boostrap values
were generated from 100 replicate trees. Sequence similarities were calculated
using the ARB software package.

The isolates were screened for phenol hydroxylase genes using PCR

primers that target conserved regions of the alpha subunit of the multicomponent

103

phenol hydroxylase gene family (Futamata et al. 2001). All amplifications were
carried out as described previously (Futamata et al. 2001) in a GeneAmp 9600
thermal cycler (Perking Elmer Biosystems, Norvvalk, CT). Isolates that
generated a band of the correct size were sequenced as described above with
the modification that the PCR products were gel purified with a QIAquick gel
extraction kit (Qiagen, Chatsworth, Calif.). The consensus nucleotide sequences
were translated to amino acids using the program BioEdit (Hall 1999), and were
aligned against all the phenol hydroxylase sequences available in the GenBank
database using the alignment program CLUSTALX (Thompson et al. 1997).
Alignments were corrected manually and unambiguously aligned positions were
used for the analysis. Phylogenetic trees were constructed using protein
maximum likelihood with the program PROTML from the Phylogeny inference
package (PHYLIP) version 3.6a (J. Felsenstein, Department of Genetics,
University of Washington, Seattle). Bootstrap values were generated from 100
replicate trees using SEQBOOT, while sequence identities were calculated

using PROTDIST, both from the PHYLIP package.

In-silico generation of terminal restriction fragments and community
comparisons

The 16S rDNA sequences from each isolate were used to generate in-
silico terminal restriction restriction fragments (T-RFs) for two enzymes, Cfol and
Haelll. These computer generated T-RFs were compared against the T-RFLPs
previously generated from the reactor samples (Chapter 2) to determine what

fraction of the community could be accounted for by the isolates. Isolate T-RFs

104

that were within 3 bp from any T-RF in the community, were considered a match

to the community.

SYBR green quantitative PCR

The abundance of selected isolates was quantified using the real-time PCR
method based on the SYBR green I chemistry (Rantakokko-Jalava and Jalava
2001; Collantes-Femandez et al. 2002; De Preter et al. 2002; Dhar et al. 2002).
The method monitors the increase in fluorescence of the DNA binding dye
SYBR green I during the PCR cycles, as a measurement of the PCR product
generated. The cycle number when the fluorescence crosses a threshold (Cr),
calculated using a reference dye, is proportional to the initial amount of template
(Grt'lntzig et al. 2001). This method allows the quantitation of the product in the
linear range of the PCR reaction, thus avoiding the problems of terminal cycle
quantitation.

Primers specific for the 16S rDNA of each isolate were initially designed
using the probe design function of the ARB software package and visual
comparisons against aligned sequences (Table 4.1). The primers were tested
using the Primer Express software package (Perking Elmer Applied Biosystems,
Foster City, CA) to determine if they follow the annealing temperatures,
distances between primers and GC contents suggested by the manufacturer. All
the primers were aligned against all the sequences of the isolates using the
CLUSTALX program to determine their specificity in-silico and the distribution

and number of mismatches against all the sequences. Primers with more than

105

five mismatches against all isolate sequences, and with mismatches close to the
3’ end of the primer were selected for further testing. The primers were also
tested for specificity against the GenBank database using BLAST searches, and
using the check probe function of the RDP-ll (Maidak et al. 2001). To determine
if the primers were specific in the presence of non-target DNA, mixtures
containing 2.0 ng of different non-target DNAs were prepared and added to
individual PCR reactions. Each mixture consisted of DNA from all the
phylogenetically similar isolates. To improve the specificity of the primers
different magnesium chloride concentrations were tested until non-specific
products were not detected. The reaction mixture, prepared using the SYBR
green core reagent kit (Perking Elmer Applied Biosystems), consisted of 1X
SYBR green PCR buffer, 0.2 mM dNTPs (1:1:1:1 mix), 0.005 U/pl AmpErase
UNG, 0.1 U/pl Amplitaq gold Taq polymerase and 150 nM of each primer in a
total volume of 30 pl. All reactions were carried out in a 7700 Sequence
Detector (Perking Elmer Applied Biosystems), with the cycling conditions
recommended by the manufacturer.

Calibration curves based on total cells were constructed to correct for
differences in 16S rDNA operon copy number in different microorganisms. Cells
from early to mid exponential phase cultures were harvested and serially diluted
in both phosphate buffered saline and 0.05 N NaOH. The PBS dilutions were
plated on R2A agar to determine total cell counts, while dilutions in NaOH were
Iysed for 20 min at 99° C in a thermal cycler (model 9600, Perking Elmer Applied

Biosystems), and kept on ice until use. Duplicate PCR reactions were prepared

106

from each cell dilution lysate, and calibration curves were generated using the
viable counts. The abundance of each isolate during the 2 years of reactor
operation was determined from previously extracted community DNA (Chapter
2) by preparing duplicate PCR reactions for two different concentrations (20 and
2 ng) of community DNA. Means and standard deviation for each sample were
determined after conversion of CT values to cell values using the appropriate
calibration curves.

An estimate of the total bacterial abundance of each sample was
determined with the same procedure described above, but using bacterial
primers 1108F (5’-ATGGYTGTCGTCAGCTCGTG-3’) (Amman et al. 1995) and
1123R-b (5’-GGGTTGCGCTCGTTGC-3’) (Wilmotte et al. 1993), and PCR
conditions described previously (Riley-Buckley 2001). Pseudomonas stutzeri
JM300 was used for the standard curve.

The reproducibility of the SYBR green methodology was tested by
quantifying the abundance of a reactor isolate and total bacteria on replicate
samples from each rector. Duplicate PCR reactions were prepared from DNA
from each of eight independent samples from each reactor obtained previously

(Chapter 2).

107

 

 

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108

Results

Strain isolation and identification.

A total of 73 isolates was obtained from both reactors by direct plating on
R2A medium. Fifty isolates with different BOX-PCR fingerprints were retained;
24 came from the phenol-fed reactor (SBR-P) and 26 from the phenol plus TCE-
fed reactor (SBR-T). To determine if these isolates matched any of the T-RFLP
fragments generated previously (Chapter 2), the T-RFs of each isolate
generated in-silico from 16S rDNA sequences with Haelll and Cfol restriction
enzymes were compared against the community T-RFLPs. All the isolates
matched the community T-RFs with at least one restriction enzyme, and the
majority matched with two (Table 4.2). Analysis of the collection with Haelll
restriction enzyme yielded five isolates that did not match any peak in the
community database, while Cfol yielded nine isolates that did not match any
community peak. Interestingly, eight isolates had a Cfol T-RF that matched a 66
bp community fragment while another seven matched a 206 bp fragment. These
T-RFs were among the most abundant T-RFs in the community T-RFLPs at
different times (Chapter 2).

Comparisons between the T-RFs of the isolates and the community at the
sampling day revealed that the isolates represent at least 37 % of all the peaks
in the community (Table 4.3). The isolates from the phenol-fed reactor
represented 47 and 37 % of all the peaks generated with Haelll and Cfol
respectively. In the phenol plus TCE-fed reactor the isolates' T-RFs represented

43 % of all the peaks on the sampling day for the two restriction enzymes.

109

Table 4.2. Assignment of isolate T-RFs to community T-RFLPs. Isolate names
in bold, indicate strains that T-RFs from both enzymes matched a community T-

RF. NR, no restriction site. NM, no match with community T-RFLPs using a 1: 3
bp window.

110

 

 

 

Isolate name T-RF Match with T-RF Match with
Haelll Community Cfol Community
SBR-P1 229 228 144 142
SBR-P2 193 194 341 NM
SBR-P3 225 225 61 60
SBR-P4 230 228 581 580
SBR-P5 193 194 341 NM
SBR-P6 245 NM 84 NM
SBR-P7 243 NM 64 63
SBR-P8 199 199 66 66
SBR-P9 229 228 143 142
SBR-P10 198 199 66 66
SBR-P11 39 38 176 176
SBR-P12 67 66 441 444
SBR-P13 328 330 NR NM
SBR-P14 199 199 206 206
SBR-P15 293 291 81 NM
SBR-P16 295 296 81 NM
SBR-P17 190 189 60 60
SBR-P18 309 307 240 237
SBR-P1 9 165 166 580 580
SBR-P20 39 38 1 76 176
SBR-P21 252 255 206 206
SBR-P22 192 194 340 NM
SBR-P23 201 199 66 66
SBR-P24 78 77 207 206
SBR-T1 192 194 340 NM
SBR-T2 203 205 208 206
SBR-T3 192 194 342 345
SBR-T4 218 218 66 66
SBR-T5 260 258 214 215
SBR-T6 200 199 65 66
SBR-T7 254 255 208 206
SBR-T8 219 218 229 232
SBR-T9 39 38 206 206
SBR-T10 293 291 83 NM
SBR-T1 1 68 66 58 58
SBR-T12 199 199 68 66
SBR-T13 226 225 342 345
SBR-T14 233 235 148 147
SBR-T1 5 199 199 68 66
SBR-T16 310 308 239 237
SBR-T17 39 38 176 176
SBR-T18 194 194 345 345
SBR-T1 9 225 225 62 62
SBR-T20 218 218 204 204
SBR-T21 182 NM 146 147
SBR-T22 312 NM 581 580
SBR-T23 38 38 60 60
SBR-T24 244 NM 84 NM
SBR-T25 201 199 68 66
SBR-T26 77 77 207 206

 

111

Moreover, the isolates’ T-RFs were compared against the total number of peaks
detected during the 800 days of operation, and the phenol-fed reactor isolates
represented only 32 and 16 % of the Haelll and Cfol community T-RFs
respectively, while the phenol plus TCE-fed reactor isolates represented 30 and
24 "/0 of the T-RFs for the same enzymes. Analyses of the T-RFs from the two
collections revealed that all the isolates obtained represented 60 and 47 % of all
the Haelll and Cfol fragments for the sampling day while they represented 41
and 27 % of peaks observed for all the days. These results indicate that the
sampling day was a factor determining the success of isolation approaches in
recovering the diversity in the community, but that isolates like many of these

strains were present throughout the sampling period.

 

 

 

 

Collection n % of Haelll TRFs that % of Cfol TRFs that
match the community match the community
Samplinlday All days Sampliquay All days
Phenol-fed reactor 24 47 32 37 16
Phenol plus TCE-fed
reactor 26 43 30 43 24
All isolates 50 60 41 47 27

 

Table 4.3. Percentages of community T-RFs that matched the isolates T-RFs.
n, number of isolates.
Phylogeny of the isolated populations

The phylogenetic placement of all the isolates was performed by partial
sequencing of the 16S rDNA and phylogenetic analysis. Sequence similarities
among all the isolates revealed that nine different isolates from the phenol-fed

reactor had isolates with identical sequences in the phenol plus TCE—fed reactor.

112

For phylogenetic analysis, the isolates were grouped in the following bacterial
phyla and classes according to the taxonomic outline of the Bergey's Manual of
Systematic Bacteriology (Garrity et al. 2001): Proteobacteria (alpha, beta and
gamma classes), Deinococcus-Thermus, Firmicutes (Low G+C gram positives),
Actinobacteria (High G+C gram positives) and Bacteroidetes. Assignment of the
isolates to operational taxonomic units (OTUs) using a 97 % similarity cutoff
(Stackebrandt and Goebel 1994; Juretschko et al. 2002) resulted in 32 different
OTUs. The predominant phylum was the Proteobacteria representing 60 % of
all the OTUs from the two reactors, followed by Actinobacten'a (15 %),
Firmicutes (13 %), Bacteroidetes (9%) and Deinococcus-Thermus (4 %) (Figure
4.1). Within the Proteobacteria, the beta class (ti-Proteobacteria) was the most
dominant with 25 % of all the isolates, followed by the alpha (or-Proteobacteria)

and gamma (y-Proteobacteria) classes with 22 and 13 % respectively.

 

   

   
 

Actinobacteria
WGCgranposilives
15 %

 

\

. 3/

K. H/
A
/

y-Proteobacteria \ ‘

 
 

‘ _B~Pi9teofie\qtéria: .

   

eyeweqoetmd % 09

 

 

Figure 4.1. OTU composition of the isolate collection from the two reactors. An
OTU was defined as one or more sequences with more than 97 % sequence

similarity.

113

Thirteen isolates belonged to the a-Proteobacten’a class, and were
distributed among six different genera (Figure 4.2). Two of the isolates (SBR-
P5, T3) clustered with Aflpia genosp 7, a human pathogen previously isolated
from hospital water supplies (La Scola et al. 2000). Isolates SBR-P2, T18
clustered with Bosea thiooxidans, a group of heterotrophic organisms that could
oxidize thiosulfate to sulfate (Stubner et al. 1998). One isolate clustered with
Phenylobacten'um immobile (SBR-T23), a member of a, group with an extremely
limited nutritional spectrum, that degrades the poly-aromatic herbicide
chloridazon (Eberspacher 2002). The bacterial family with the highest
representation within the a-Proteobacten'a class was the Rhizobiaceae with five
isolates. One isolate clustered with Rhizobium giardinii (SBR-T13), two
clustered with Mesorhizobium (SBR-P3, T19) and two more isolates had no
specific affiliation based on the bootstrap values (SBR-T1, P22). Finally, three
of the isolates clustered within the Sphingomonas genus (SBR-T10, P16 and
P15), in which some of the representatives degrade aromatic pollutants and
generate extracellular polymeric substances (Fredrickson et al. 1998; Denner et
al. 2001).

A total of 12 isolates clustered within the B-Proteobacten’a class (Figure
4.3). Two of the isolates clustered with Nitrosomonas eutropha str. C-9 (SBR-
P24, T26), and one clustered within the Burkholderia family (SBR-T2), a group
of microbes with some members known to degrade aromatic compounds and
TCE (Folsom et al. 1990). The remaining isolates appear to belong to the

metabolically diverse Comamonadaceae family, with the exception of one isolate

114

that clustered with the Variovorax paradoxus group (SBR-T4); their specific

identities could not be resolved.

 

 

J; Unidentified Sulfur oxidizer str 52211

Caulobacter sp. A1
FLO-0121C SBR-1'23
Phenylobacterium immobile str. E DSM 1986 (T)

~—35 Rhizobium USDA 1920
Rhizobium gallicum str. R6028p
Rhizobium Sp SDWO62
l L SBR-Tl. SBR-I’ll
Rhizobi um giardinii str. H152
Rhizobium huautlense str. S02
82 Mesorhizobium sp. ORS 1259
Mesorhizobium ACCC 19665 (T)
Mesorhizobium sp. WG

Mesorhizobium chacoense str PR5
Sphingomonas malt str., Y-347 IFO 15500 (T)
Sphingomonas sp. oral clone

SBR—1’16

Sphingomonas sp. JRL-S

Sphingomonas J SS-54

SB R-I’ l 5

 

 

 

   
  
 
 
  
  
  

 

 

58

 
 
   

 

0.10

 

Figure 4.2. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to the a-Proteobacten‘a class. The tree was constructed from 605
unambiguously aligned positions using the maximum-likelihood method, and
was rooted using Nitrosomonas eutropha. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values represent
either nodes with bootstrap values below 50 %, or inconsistent branch order
when compared against the consensus tree. The scale bar represents the
number of substitutions per nucleotide position. T, type strain.

115

 

Rhizobium sp. 'USDA 1920'
991—— Nitrosomonas eumpea
Hum-I’M. SBR-1'20
Nitrosomonas eutropha str. C-91
OorBurldwlderia Vietnamensis str. TW75 LMG 10929 (T)
l

—
.i

 

     

 

 

Burkholderia vianamensis str. TW70
Burkholderia cepacia G4
92 Aquaspirillum gracile D4 ATCC 19624 (T)
65 Unidentified sludgebactcrium H2
“"r
97
68 99

Aquaspirillum delicatum str. 146 LMG 4328 (T)
Variovorax paradoxus VAl-C

 

 

 

 

 

 

 

 

 
   
  
   
 
  
  

 

Variovorax paradoxus

Rhizosphere soil bacterium RSI-30

‘ 'l ' Ill

Acidovorax avenae subsp. avenae ATCC 19860 (T)

Nitrogen fixing Bactu'ium BA 128
71 Variovorax sp. ANRB-Zg acetate utilizcr

Unidentified phenol reactor isolate rA 10
\liR-I’B. SBR-10

 
 

0.10

 

Figure 4.3. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to the B-Proteobacteria class. The tree was constructed from 677
unambiguously aligned positions using the maximum-likelihood method, and
was rooted using Rhizobium sp. Numbers at nodes represent the percentage of
100 bootstraps. The nodes without bootstraps values represent either nodes
with bootstrap values below 50 %, or inconsistent branch order when compared
against the consensus tree. The scale bar represents the number of
substitutions per nucleotide position. T, type strain.

116

Isolates SBR-T12, T15 and P10 clustered with an uncultured sludge bacterium
and Aquaspin'llum gracile, while isolates SBR-T20 and P14 were nested within
other sequences without forming a cluster. Finally, isolates SBR-P23, T6 and
P8 formed a single cluster where the closest known relatives were an
unidentified phenol reactor isolate (Watanabe et al. 1998) and an acetate user
that was described as a Variovorax sp.

Two isolates belonged to the Deinococcus-Thermus phylum (SBR-P6 and
T24) with 99 % sequence similarity to Deinococcus radiopugnans (Figure 4.4), a
radiation resistant microorganism. Six isolates grouped with the y-
Proteobacten'a class (Figure 4.4). Two isolates were nested between
Thermomonas haemolytica and an unidentified denitrifying iron oxidizer (SBR-
T16 and P11), while another one clustered near Xanthomonas (SBR-P20) with
89 % sequence similarity. An additional isolate clustered within the
Pseudomonadaceae family (SR-T9) and two isolates clustered with members of
the Acinetobacter genus (SBR-T7 and P21). Finally, three isolates clustered
with members of the Bacteriodetes phylum. One isolate clustered with
Flavobacterium ferrugineum and its sequence was 93 % similar (SBR-T15),
while SBR-P13 was nested between the Flexibacter and Flavobacter genera
and its sequence was 92 % similar to Flexibacter filiformis. The closest known
organism to isolate SBR-P7 was Dyadobacter fermentens although they were
only 86 % similar in sequence.

The Firmicutes phylum (High G+C gram positives) represented five

isolates that belonged to Bacillaceae family (Figure 4.5). One of the isolates

117

(SBR-T8) formed a defined cluster with Brevibacillus while the other four were
affiliated with different Bacillus species. Six isolates belonged to the
Actinobacteria phylum, clustering with Rhodoccous (SBR-P12), Pseudonocardia

(SBR-T11) and Microbacterium (SBR-T14, P1 and P9).

—— Deinococcus radiodurans DSM 20539 (T)

 

 

 

33 SBR-1’0

—Lw{Deinococcus radiopugnans ATCC 19172 (T)
‘ Deinococcus geothermalis str. AG-3a
Unidentified phenol reactor isolate str r .115

T hermomonas haemolytica str. A50-7-3
SBR-110. SBR-I’ll

snuuazu

 

-sn99090ugaq

   
  
  

   

Unidentified denitrifying Fe-oxidizing bacteria str BrG3
Xanthomonas campestris LMG 682

SBR-1’20

Pseudomonas sp., IpA-l (isopimaric acid degrader SBR)
ij
99

98

 

Unidentified phenol reactor isolate str r117
Pseudomonas nitroreducens, [AM 1439 (T)

100 Acinetobacter calcoaceticus, ATCC 23055 (T)

Jill Acinetobacrer sp. str. Ben 59
737 Acinetobacterjunii DSM 1532

1
7 Wei-new acter sp. UH1742TAPH293694

1
”I C F lavobacterium ferrugineum ATCC 13524 (T)
S-—BR-l’l3

F Iexibacter filiformis str. FX, ATCC 29495 (T)
'00 JCFIexibacter sancti, ATCC 23092 (T)

100 Runella slithyformis, ATCC 29530 (T)
100 Runella sp. NS12

99 Dyadobacterfermentens str. NS114
“L- _

 

98

 

vyazavqoazoxd-ll

 

 

 

 

 

 

 

sazapgoxazcvg

 

 

0.10

Figure 4.4. Phylogenetic tree of 168 rDNA sequences of isolates that belonged
to the Deinococcus-Thermus phylum, y-Proteobacten'a class, and Bacteroidetes
phylum. The tree was constructed from 659 unambiguously aligned positions
using the maximum-likelihood method. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values represent
either nodes with bootstrap values below 50 %, or inconsistent branch order
when compared against the consensus tree. The scale bar represents the
number of substitutions per nucleotide position. T, type strain.

118

 

Acidovorax avenae ATCC 19860 (T)

8 Rhodococcus erythropolis DSM 43188 (T)
TEL;

__73 7 Rhodococcus erythropolis str. DCL14
1 Pseudonocardia str. K1
_—::Lfseudonocardia sulfidoxydans str. 592 DSM 44248 (T)

l Microbacteriumfoliorum str. P 333/02 DSM 12966
SBR-1‘21

 

100

 

   

vuazavqouyoy

 

SBR-1‘14. SBR—1’1

_l

Brevibacillus brevis JCM 2503 (T)

65 Brevigacillus agri M1-5

Brevibacillus agri NRRL NRS-12 (T)
100 ——Bacillus licheniformis DSM 13 (T)

l Bacillus pumilus str. M1-9-1
%ifacillus pumilus KL-052
6

 

     

 

 

 

 

Bacillus sp. IFO 12605
1 Bacillus cereus str. ATCC53522

Bacillus thuringiensis 1AM 12077 (T) __J

87

 

samaguugj

 

 

 

0.10

Figure 4.5. Phylogenetic tree of 16S rDNA sequences of isolates that belonged
to Actinobacteria and Firmicutes phylums. The tree was constructed from 449
unambiguously aligned positions using the maximum-likelihood method, and
was rooted using Acidovorax avenae. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values represent
either nodes with bootstrap values below 50 %, or inconsistent branch order
when compared against the consensus tree. The scale bar represents the
number of substitutions per nucleotide position. T, type strain.

119

 

Degradative characterization.

The isolates were tested for growth on phenol as the sole carbon source at
different concentrations in order to minimize the inhibitory effects noted by Fries
et al. (1997a). Twenty eight percent of the isolates tested degraded phenol
under the conditions tested (Table 4.4). Five isolates degraded 100% of the
phenol when tested at 50 mg/l, one isolate from the phenol-fed reactor (SBR-
P12) and four from the phenol plus TCE-fed reactor (SBR-T2, T4, T11 and T20).
Six additional isolates degraded phenol at 10 mg/l; three from the phenol-fed
(SBR-P3, P5 and P18) and three from the phenol plus TCE-fed (SBR-T16, T19,
T23) reactor. Supplementation of the 10 mg/l phenol medium with vitamins
allowed three additional isolates to degrade 93-100 % of the phenol in the vial
(SBR-P10, T12 and T15). These isolates were also able to grow on 50 mg/I
phenol if vitamins were provided.

The 14 isolates that degraded phenol and the two ammonia oxidizers were
tested for TCE cometabolism by re-inoculating them in fresh medium with 1 mg/l
TCE. Five isolates removed 100 % of the TCE, one from the phenol-fed reactor
(SBR-P11), and four from the phenol plus TCE-fed reactor (SBR-T2, T4, T15
and T20). One isolate from the phenol plus TCE-fed reactor removed 80 % of
the TCE (SBR-T12) and two other isolates removed 19 and 17 % respectively
(SBR-P3 and SBR-T18). The remaining phenol degrading isolates and the
ammonia oxidizers did not remove significant amounts of TCE (0-5 %) under the

conditions tested.

120

Table 4.4 Phenol and TCE degrading properties of the reactor isolates. Values
represent percentage of removal. For the TCE degradation test fresh phenol
medium at the appropriate concentration and TCE were mixed simultaneously.
SBR-P, phenol-fed reactor; SBR-T, phenol plus TCE-fed reactor. NT, not
tested. WV, Wolin’s vitamins.

 

 

 

Name Phenol Phenol Phenol TCE
50 mg/l 10 rm/I 1m" WV 1 mg/l
SBR-P1 9 % 5 % 7 % NT
SBR-P2 5 % 0 % 2 % NT
SBR-P3 0 % 17 % 0 % 19 °/o
SBR-P4 3 % 0 % 3 % NT
SBR-P5 3 % 20 % 0 % 5 %
SBR-P6 11 % 5 % 4 % NT
SBR-P7 8 % 13 % 0 % NT
SBR-P8 4 % 0 % 3 % NT
SBR-P9 7 % 0 % 2 % NT
SBR-P10 8 % 12 % 100 % 100 %
SBR-P11 9 % 13 % 8 % NT
SBR-P12 100 % NT NT 0 %
SBR-P13 9 % 9 % 4 % NT
SBR-P14 1 % 9 % 0 % NT
SBR-P15 1 % 6 % 4 % NT
SBR-P16 0 % 4 % 5 % NT
SBR-P17 O % 14 % 12 % NT
SBR-P18 6 % 16 % NT 0 %
SBR-P19 8 % 5 °/o 8 % NT
SBR-P20 0 % 7 % 9 % NT
SBR-P21 7 % 9 % 0 % NT
SBR-P22 8 % 4 % 0 % NT
SBR-P23 6 % 5 % 8 % NT
SBR-P24 NT NT NT 0 %
SBR-T1 2 % 7% 10 % NT
SBR-T2 100 % NT NT 100 %
SBR-T3 2 % 6 % 5 % NT
SBR-T4 100 % NT NT 100 %
SBR-T5 5 % 0 % 0 % NT
SBR-T6 8 % 9 % 5 % NT
SBR-T7 3 % 1 % 4 % NT
SBR-T8 7 % 9% 9 % NT
SBR-T9 5 % 4 % 5 % NT
SBR-T10 8 % 11 % 0 % NT
SBR-T11 100 % NT NT 0 %
SBR-T12 7 % 11 % 93 % 80 %
SBR-T13 1 % 8 % 5 % NT
SBR-T14 8 % 7 % 9 % NT
SBR-T15 7 % 10 % 100 % 100 %
SBR-T16 7 % 19 % 4 % 2 %
SBR-T17 6 % 4 % 7 % NT
SBR-T18 0 °/o 10 % 6 % NT
SBR-T19 5% 16% 12% 17%
SBR-T20 100 % NT NT 100 %
SBR-T21 3 % 7 % 5 % NT
SBR-T22 8 % 0 % 3 % NT
SBR-T23 1 % 16 % 0 % 2 %
SBR-T24 0 % 8 % 5 % NT
SBR-T25 5 % 3 % 0 °/o NT
SNR-T26 NT NT NT 0 %
121

 

DNA from all the isolates was tested with primers that target conserved
regions of the phenol hydroxylase gene to determine which strains carry similar
genes. From the 50 isolates tested six produced a band of the correct size
(SBR-T2, P10, T15, T20, T12 and T4), although some non-specific amplification
was observed. Sequencing of the bands and GenBank searches using BLAST
revealed that there was high sequence variability among the isolates. The
amino acid identities of the phenol hydroxylases among the isolates were from
72 to 100 %, and isolates SBR-P10 and SBR-T15 had identical sequences. All
the phenol hydroxylase sequences from the isolates contained regions
resembling the conserved dinuclear iron binding domains typically found in
monoxygenase enzymes (Fox et al. 1993). Furthermore, the spacing between
these motifs was 94 amino acids, a value that is within the average for toluene
oxygenases further supporting that these sequences belong to the oxygenase
family (Johnson and Olsen, 1995). Phylogenetic analysis of the amino acid
sequences shows that the isolates were distributed among different known
phenol and toluene oxygenases (Figure 4.6). The phenol hydroxylase of SBR-
T2 (Burkholderia sp.) was 100 % identical to the toluene ortho-monooxygenase
(TOM) of Burkholderia cepacia G4. The sequences from the isolates SBR-T15
and P10 formed a distinct cluster that was 90 % similar to TOM of Burkholderia
cepacia G4 and 92 % similar to the phenol hydroxylase of Comamonas
testosteroni strains R5 and R2. The phenol hydroxylase of isolate SBR-T20
clustered with the toluene/benzene-2 monooxygenase of Bukholderia sp. str. JS

150, with 94 % amino acid identity. Interestingly, the phenol hydroxylase from

122

isolate SBR-T12 forms a deep clade and was 82 % identical to the phenol
hydroxylase of Comamonas E6, although the 168 rDNA sequence of SBR-T12
was 98 % similar to SBRT15 and P20. Finally, the phenol hydroxylase of isolate
SBR-T4 (Variovorax sp.) clustered with the phenol hydroxylase of a Variovorax

sp isolated from a TCE contaminated site in Japan.

199' Burkholderia cepacia G4, TOM ’

99 94 \llR-l’lll
\llR-l IS

100 Comamonas testosteroni str. R5 "
Comamonas testosteroni str. R2 "

Burkholderia sp. str. 18150 Toluene/Benzene-Z monooxygenase ’
”I... Burkholderia Waist: KPZ3 ‘
Burkholderia cepacia str. El ’

Pseudomonas putida str. P-6

Pseudomonas putida str. P-8

clone TCE contaminated site, PCRTDl 5

Pseudomonas sp. LABo36 ‘

73 Pseudomonas sp. LAB-21 ‘

Pseudomonas sp. LAB-08

Pseudomonas putida str. H

Pseudomonas putida P35X

Pseudomonas putida BH

Pseudomonas sp. str. CF600

clone TCE contaminated site, PCRTDOZ

——l M.,——

Comamonas sp. str. E6 ‘

 

 

 

 

 

 

 
   
   
 
 

 

 

 

 

 

 

63 Ralstonia sp. P10 ‘

———19+ 93 Ralstonia sp. £2 ‘

Ralstonia sp. HAB-Ol
Ralstonia sp. RAB-02 ‘
85 Ralstonia sp. KN] ‘

clone TCE contaminated site, PCRTDO4

clone TCE contaminated site, PCRLDl 8
53 \ “R- 1 4

Variovorax sp. RAB-22 "
6‘ Variovorax sp. RAB-29 ‘

Variovorax sp. RAB-27 "

 

 

 

 

 

 

 

0.1
Figure 4.6. Unrooted phylogenetic tree of the phenol hydroxylase amino acid
sequences of the isolates. The tree was constructed using 169 aligned amino
acids, between positions 83 and 252 of Pseudomonas sp. CF600 dmpN gene,
using the protein maximum-likelihood method. Numbers at nodes represent the
percentage of 100 bootstraps. The nodes without bootstraps values represent
either nodes with bootstrap values below 50 %, or inconsistent branch order
when compared against the consensus tree. The scale bar represents the
number of substitutions per amino acid position. The asterisk indicates strains
that degrade TCE.

123

Primer design and specificity.

A total of 13 isolates were selected to design primers for quantitative PCR
(Table 4.1). The isolates consist of two major groups: phenol degraders and
non-phenol degraders. The phenol degraders were selected based on the
presence of phenol hydroxylase genes and the T-RF matches against the
community T-RFLPs. The non-phenol degraders were selected because either
they represent genera known to degrade aromatics, or by their flocculation
characteristics on rich medium. The specificity of the primers was tested using
the reactor isolates because they represent a diverse collection (Figures 4.2-
4.5). To obtain maximum specificity, MgClz concentrations were adjusted since
changes in the annealing temperature did not increase the specificity. Melting
curve analysis was used to evaluate the specificity of the primers, since non-
specific products will have different melting curves due to differences in size,
AT/GC ratio and sequence of the amplicon (Ririe et al. 1997). Magnesium
chloride concentrations between 1.5-2.0 mM resulted in PCR products of the
correct melting temperature and the disappearance of non-specific products
(Figure 4.7).

Sensitivity and reproducibility

The sensitivity of the SYBR green PCR assay was evaluated by using
Iysed 10-fold serial dilutions of bacterial cultures as template for the reactions
(Figure 4.8). A decrease in the number of cycles needed for the fluorescence to
cross the threshold (07) was inversely proportional the initial template

concentration. A linear response between CT and the log of total cells was

124

observed over five orders of magnitude ranging from 1.1 cell to 1.6 x 105 cells (r2
= 0.999). Similar results were observed when the universal primers 1108F and
1123b were used with Pseudomonas stutzen' JM300, although the reaction was

less sensitive (two fold) due to fluorescence in the no template negative controls.

0.25

 

A

 

 

 

 

 

 

 

is

E.

'9’

95
Temperature (°C)

+ a—Proteobacten’a mix + Firmicutes and Actinobacteria
—B— B-Proteobacten‘a mix —t— Bacillus sp. SBR-T16
—0— y-Proteobacten’a, Bacteroidetes and Deinococcus —c— Negative conrtol
—6— Fimwicutes, Actinobacteria and Bacillus sp. SBR-T16

 

 

 

Figure 4.7. Specificity of the SYBR green PCR reaction at 1.8 (A), and 1.5 (B),
mM MgCl2 using melting peak analysis. -dF/dT, negative derivative of
fluorescence with respect to temperature. Data points shown represent every
tenth measurement.

125

 

 

 

 

 

40
SBR-T14 (Mlcrobacterium sp.)
35 -*
30 ~
251
20 e
15 —
A —— y = 35.798 - 3.4311log(x) R= 0.99957
1..
B
E 10 1 14111111 1 1 111111L 4 11111111 1 1 1111111
g SBR-T4 (Variovorax sp.)
é
30 a
25 —
20 ~
15 —
—- y = 35.622 - 3.5496log(x) R= 0.99969
10 1 11141111 1 11111111 1 11111111 1 11111111 1 11111111 141111
10° 10‘ 1o2 103 10‘ 105 106

Number of cells

Figure 4.8. SYBR green | real time PCR calibration curves using cell lysates as
template. Values represent averages of duplicate reactions :I: one standard
deviation. CT, cycle at which the fluorescence crosses an arbitrary threshold
determined using a reference dye.

126

This problem has been observed before (Corless et al. 2000; Riley-Buckley
2001; Nadkarni et al. 2002) and two possible explanations have been
suggested: primer dimer formation or contamination in the PCR master mix.
Melting curve analysis of the negative controls resulted in PCR products with
high melting temperatures (80-82° C) which suggest that signals detected were
caused by contamination. Since the CT of the negative reactions was between
33-35 cycles, any test sample with a CT in this range was disregarded. Only test
samples with a CT below 28 were used for further analysis in order to avoid false
positives.

The reproducibility of the SYBR green PCR method was tested by
quantifying total bacteria abundance and SBR-T12, P10 and T15 abundance in
eight replicate samples from each reactor. Quantifications were reproducible
with 95 % confidence intervals within the same order of magnitude (Figure 4.9).
Values for the bacterial primers were 3.2 x 107 :1: 7.15 x 106 and 6.48 x 107 1:
1.84 x 107 cells x pg’1 of community DNA for the phenol and phenol plus TCE-
fed reactors respectively. For the SBR-P10 and T15 primers values were 3.29 x
106 i 7.02 x 105 and 5.26 x 106 :1: 1.48 x 106 for the phenol and the phenol plus

TCE-fed reactors respectively.

127

 

 

108

 

 

 

 

 

 

 

 

 

 

 

 

i T

,7 ~ 1
< ‘ T

z i i

Q -1

b

'5’ 107?

g :

,H -

o _

0.0

1 6

.2 ‘03

o.) a

U :

105 I
Phenol-fed Phenol plus TCE-fed

Reactor

 

[3 Bacterial abundance
I SBR-T12, P10 and T15

 

 

 

Figure 4.9. Reproducibility of SYBR green | real time PCR. Two different
primer sets were used for the test. Values represent averages :l: 95 %
confidence intervals (11 = 8).

128

Bacterial abundances.

Changes in total bacterial abundance were observed in the phenol-fed
reactor during the 810 days of operation (Figure 4.10). The initial bacterial
density in the phenol-fed reactor increased from 3.39 x 106 to 1.55 x 108
bacterial cells x pg of community DNA‘1 after 127 days of operation. Then, the
bacterial cell density decreased 100 fold at day 198 and returned to similar
levels 28 days later maintaining an average of 3.91 x 107 bacterial cells x pg of
wmmunity DNA‘1 during 341 days of operation. Finally, there was a neany one
fold increase in abundance when the bacterial densities reached 1.0 x 108
bacterial cells x pg of community DNA‘1 during days 630-729.

In the phenol plus TCE-fed reactor the bacterial dynamics was relatively
stable compared with the phenol-fed reactor (Figure 4.11). The initial density
was 4.79 x 106 cells x pg of community DNA’1 and reached a maximum of 7.06 x
107 cells x pg of community DNA’1 after 127 days of operation. Bacterial
densities then stabilized with minor sporadic increases averaging 2.69 x 107
cells x pg of community DNA‘1 during the remaining 584 days of operation.

Analysis of a selected group of phenol, and phenol plus TCE degraders
revealed that an unidentified group of B-Proteobacteria isolates (SBR-T12, P10
and T15) were the most abundant population among the isolates evaluated in
the phenol-fed reactor (Figures 4.10-4.13). The abundance of this phylogenetic
group ranged from 4.0 x 104 to 1.64 x 107 cells x pg of community DNA", with
changes in abundance that correlated with changes in total bacterial densities

(R2 = 0.90). In contrast, the abundance of the phenol degrader

129

Phenylobacterium sp. SBR-T23, and the phenol/TCE degraders Variovorax sp.
SBR-T4 and SBR T-20 was between 10 to 100 fold lower than the abundance of
the unidentified B-Proteobacteria isolates (SBR-T12, P10 and T15). The ranking
order of the remaining isolates, from most to least abundant, during the first 500
days of operation was SBR-T20 > Variovorax sp., SBR-T4 > Phenylobacter sp.
SBR-T23, and their dynamics correlated with the bacterial dynamics during the
first 500 days of operation. In contrast, the dynamics during the last 300 days of
operation were variable, with Phenylobacter sp. SBR-T23 and unidentified SBR-
T20 isolates having an opposite trend to Variovorax sp. SBR-T4 and the
unidentified (3 Proteobacteria group (SBR-T12, P10 and T15).

In the phenol plus TCE-fed reactor the unidentified B-Proteobacten’a group
(SBR-T12, P10 and T15) was the predominant cultivable population (Figure
4.11). During the first 50 days, the cell density of the unidentified B-
Proteobacten'a group decreased from 7.8 x 105 to 4.2 x 104 cells x pg of
community DNA“, and returned back to cell densities that were between 106-1o7
cells x pg of community DNA“ during the remaining 684 days. The cell densities
of the other phenol and phenol plus TCE degrading populations were at least
100 fold lower than the dominant group. The dynamics of Variovorax sp. SBR-
T4 and Phenylobacter sp. SBR-T23 were very similar to the dynamics of the
most abundant group with abundances between 103 to 104 cells x pg of
community DNA“. These values represent approximately a 10 fold increase
compared to the values observed in phenol-fed reactor. In contrast, SBR-T20

was less abundant in phenol-fed reactor with values between 101-102 cells x pg

130

of community DNA“ with the exception of two conspicuous increases at days
240 and 541 reaching maximum levels of 5.3 x 102 and 3.2 x 103 cells x pg of
community DNA“, respectively. The ranking order, from most to least abundant,
in the phenol plus TCE—fed reactor was unidentified (3 Proteobacteria group
(SBR-T12, P10 and T15)> Variovorax SBR-T4 and Phenylobacter SBR-T23>
SBR-T20. Although Burkholderia SBR-T2 was isolated from this reactor, it was

detected only on the day of isolation.

 

Cells x pg of community DNA'1

 

 

 

 

100111111111111111111111111111111111111111111111

0 100 200 300 400 500 600 700 800 900
Days

 

—O— Bacterial abundance + SBR-T20
—E}-— SBR-T12, P10 and T15 9 Phenylobacter sp., SBR-T23
—-9— Variovorax sp., SBR-T4

 

 

 

Figure 4.10. Abundance of phenol degraders in the phenol-fed reactor. Values
represent averages 1: one standard deviation from duplicate reactions.

131

In the phenol plus TCE-fed reactor the unidentified B-Proteobacten‘a group
(SBR-T12, P10 and T15) was the predominant cultivable population (Figure
4.11). During the first 50 days the cell density of the unidentified 8-
Proteobacteria group decreased from 7.8 x 105 to 4.2 x 10‘ cells x pg of
community DNA“, and returned back to cell densities that were between 106-107
cells x pg of community DNA“ during the remaining 684 days. The cell densities
of the other phenol and phenol plus TCE degrading populations were at least
100 fold lower than the dominant group. The dynamics of Variovorax sp. SBR-
T4 and Phenylobacter sp. SBR-T23 were very similar to the dynamics of the
most abundant group in the reactors with abundances between 103 to 10‘ cells x
pg of community DNA“. These values represent approximately a 10 fold
increase compared to the values observed in the phenol-fed reactor. In
contrast, SBR-T20 was less abundant in the phenol-fed reactor with values
between 101-402 cells x pg of community DNA“ with the exception of two
conspicuous increases at days 240 and 541 reaching maximum levels of 5.3 x
102 and 3.2 x 103 cells x pg of community DNA“ respectively. The ranking
order, from most to least abundant, in the phenol plus TCE-fed reactor was
unidentified B-Proteobacteria group (SBR-T12, P10 and T15); Variovorax sp.
SBR-T4 and Phenylobacter sp. SBR-T23; and SBR-T20. Although Burkholderia
sp. SBR-T2 was isolated from this reactor, it was detected only on the day of

isolation.

132

 

 

 

 

 

 

v- 84
< 10
Z 7
.é‘
5 wt
2 E\
5
8 10 -i g
3,104— - . /
1 i; 3 -\ g 3 9 fl ' ‘
Z: 103_ f’ -e “o-O ,3, 4.
E) 2 e . “a 0
10 e 9
101—5 I e t: :3 9 e e
O
100111111111111_1__1_1111111111111_1_1_1111411L1111111_1_
0 100 200 300 400 500 600 700 800 900
Days
-—O—— Bacterial abundance —€-— SBR-T20

—Ei— SBR-T12, P10 and T15

9 -~ Phenylobacter sp., SBR-T23

 

 

—9— Variovorax sp., SBR-T4

 

Figure 4.11.
reactor.
reactions.

Abundance of phenol degraders in the phenol plus TCE-fed
Values represent averages :1: one standard deviation from duplicate

133

The abundances of several non-phenol degrading populations were also
examined in the two reactors. The non-phenol degrading populations were
generally below 103 cells x pg of community DNA“ in the phenol-fed reactor with
a few exceptions (Figure 4.12). At day 127 the abundance of all the isolates
increased, with Flexibacter sp. SBR-P13 the most abundant with 1.1 x 105 cells
x pg of community DNA“. Furthermore, Flexibacter sp. SBR-P13 was also the
most abundant non-phenol degrader at days 226 and 811 with 3.3 x 103 and 4.5
x 10‘ cells x pg of community DNA“. On day 567 the abundance of all the non-
phenol degrading isolates increased with Nitrosomonas sp. SBR-P24 the
predominant with 3.3 x 103 cells x pg of community DNA“, an increase between
10 to 100 fold.

In the phenol plus TCE-fed reactor the abundance of the non-phenol
degrading populations was dynamic (Figure 4.13). For instance, the abundance
of Microbacten'um sp. SBR-T14 increased from below 10 cells to 1.6 x 103 cells
x pg of community DNA“ after 385 days of operation, and oscillated afterwards
in the range of 102 to 103 cells x pg of community DNA“. The isolates
Nitrosomonas sp. SBR-P24, Acinetobacter sp. SBR-T7 and Pseudomonas sp.
SBR-T9 had abundances higher than 103 cells x pg of community DNA“ at
particular days of operation, however there was no clear trend over the 800 days
of operation. Finally, the isolates Rhizobium sp. SBR-T1 and Bacillus sp. SBR-

T16 were not detected.

134

 

 

 

Cells x pg of community DNA-1

 

 

 

 

 

1001111111111114—L11ll11111111111111111111111111
0 100 200 300 400 500 600 700 800 900
Days
—-O— Bacterial abundance 9 Pseudomonas sp. SBR-T9
- - El - - Acinetobacter sp. SBR-T7 —+— Nitrosomonas sp. SBR-P24
-—<>— Microbacterium sp. SBR-T14 - - \7 - - Flexibacter sp. SBR-P13
—9— Sphingomonas sp. SBR-P16

 

 

 

Figure 4.12. Abundance of non-phenol degraders in the phenol-fed reactor.
Values represent averages :1: one standard deviation from duplicate reactions.

135

 

 

 
 

 

 

 

 

109
.. 108a
'<
E 107—
g 6
g 10 d
E 5
8 10 e
“5
4
g: 10 ~
><
-"-’ 103—
.0.) ..
U V‘. ..
10:Z - . ./
10' cal", ..
V’a/ a1 : —' V
100 111111L41L111111114L411111111111111111111L41
0 100 200 300 400 500 600 700 800 900
Days
—o— Bacterial abundance 0“ Pseudomonas sp. SBR-T9
- -EJ - -Acinetobacter sp. SBR-T7 + Nitrosomonas sp. SBR-P24

—e— Microbacterium sp. SBR-T14 - - V - - Flexibacter sp. SBR-P13
-—9-— Sphingomonas sp. SBR-P16

 

 

 

Figure 4.13. Abundance of non-phenol degraders in the phenol plus TCE-fed
reactor. Values represent averages :t one standard deviation from duplicate
reactions.

 

136

Additional phylogenetic analysis of the dominant populations

In an attempt to clarify the phylogenetic identity of the most abundant
group of microorganisms, nearly complete 16S rDNA sequences for SBR-T12,
P10 and T15 were used for alignment and phylogeny. Analysis of 1336
unambiguously aligned positions revealed that this group of isolates belongs to
the Comamonadaceae family of the B-Proteobacten’a (Figure 4.14). The isolates
formed a cluster where the closest relative was a clone from activated sludge
and the sequence similarities among them were between 98.4 and 99.3 %
(Table 4.5). The closest cultivable relatives to the isolated strains were an
unidentified phenol reactor isolate (strain rM9) and Aquaspin’llum gracile; with
sequence similarities of 96.3 and 95.6 %, respectively. Different from
Aquaspin'llum gracile that has a helical morphology; SBR-T12, P10 and T15 are
rods of small size and diameter. These isolates form aggregates when grown in

mineral medium supplemented with phenol.

137

1001' Nitrosomonas europaea
6%: Nitrosomonas sp., N224941
Nitrosomonas europaea. AF037106
‘ JQI Herbaspl'rl'llum rubn'subalbl’can, N238356
Herbaspirl'llum rubrisubalbican, AF137508

1 Ralstonia eutro he, DSM 2839. 087999
1940 FRalstonia sp., 0880
Ralstonia sp., 088001
1 Ralstonia eutropha, Y10825

00r- Burkholderia cepacia, M22518
'-D- 1 Burkholderia vietnamiensis, U96928
1 Burkholderia vietnamiensis. U96929 _.

Comamonas testosteroni, M11224
_§£fi Comamonas acidovorans, A8021417
Comamonas sp., AJ002803

Comamonas MC1, AF 149849
Aquaspirillum gracile, AF078752
— Unidentified phenol reactor isolate str. rM9, A8021344

Uncultured sludge bacterium H2. AF234687
%—

beta Proteobacterium, AF236005
Aquaspirillum delicatum. AF078756

Variovorax paradoxus, A8008000
Variovorax paradoxus, lAM 12373 088006

93 Aquaspirillum psychmphl’lum, AF078755
j‘lEEAquaspm‘llum metamorphum. LMG. AF078757
Aquaspirillum metamorphum, DSM, Y18618

 

 

 

 

 

   
  
   

 

 

 

 

 

 

 

—— Aquaspin'llum sinuosum, LMG 439, AF078754
“-1 Acidovorax avenae subsp. citrul, AF 1 37506
Acidovorax avenae subsp. avenae. AF137504
Acidovorax avenae subsp. citrul. AF078761
Acidovorax avenae subsp. cattle. AF078762
Acidovorax avenae subsp. avenae. AF137505
Acidovorax avenae subsp. avenae. AF078759
Acidovorax avenae subsp. avenae. A8021421

 

Allwed eeeoepeuowewoo

 

0.1

Figure 4.14. Phylogenetic tree of 16S rDNA sequences of the unidentified B
Proteobacteria group. The tree was constructed from 1336 unambiguously
aligned positions using the maximum-likelihood method, and was rooted using
Nitrosomonas europaea. Numbers at nodes represent the percentage of 100
bootstraps. The nodes without bootstraps values represent either nodes with
bootstrap values below 50 %, or inconsistent branch order when compared
against the consensus tree. The scale bar represents the number of
substitutions per nucleotide position.

138

 

 

 

 

 

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139

Discussion

Direct isolation on R2A medium resulted in 50 isolates that represented 47
% of all the T-RFs on the sampling day and 27 % when the results were
compared against all days. These results suggest that although spatial
variability plays a role in the success of isolation strategies, plating on R2A
medium was an effective way of recovering a significant fraction of the
community. This medium was previously used to isolate phenol and toluene-
degrading bacteria from groundwater (Fries et al. 1997b), and from activated
sludge (Kampfer et al. 1996) with similar success suggesting that it meets the
nutritional requirements of a variety of microorganisms.

Sequence analysis of the cultivable members of the microbial communities
revealed that reactors were comprised by members of the Proteobacteria (a, 8
and 7 classes), Deinococcus-Thermus, Bacteriodetes, Firmicutes (low G+C,
gram positive bacteria) and Actinobacteria (high G+C gram positives) (Figure
4.1 ). The predominant phylum was the Proteobacteria, representing 60 % of all
the isolates obtained, while the B-Proteobacteria class was predominant among
the Proteobacteria phylum with 25% of the total. Although these results were
affected by cultivation biases, several reports using fluorescent in-situ
hybridization suggest that the Proteobacteria phylum is the most abundant group
in activated sludge representing 50-80% of the bacteria detected (Wagner et al.
1993; Kampfer et al. 1996; Bond et al. 1999; Juretschko et al. 2002).
Furthermore, studies on TCE contaminated aquifers (Fries et al. 1997a; Fries et

al. 1997b; Futamata et al. 2001), and on phenol-digesting sludge (Watanabe et

140

 

al. 1999) revealed that a major proportion of the isolates from these systems
belonged to the B-Proteobacten'a class, and were among the predominant
populations as determined by MPN and quantitative PCR analyses. In the
reactors studied herein, the best phenol and TCE degraders were in the B-
Proteobacteria class. These findings suggest that the B-Proteobacteria class is
an important group of microbes for the degradation of both phenol and the
cometabolism of TCE.

Despite the fact that the reactors were exposed to phenol for at least one
year before the isolations, only 28 % of the isolates degraded phenol under the
different conditions tested. Acinetobacter, Bacillus, Sphingomonas and
Pseudomonas were among the taxa isolated from the reactors that did not
degrade phenol, but members of these genera have been shown to degrade
xenobiotics in other studies (Ehrt et al. 1995; Kim and Oriel 1995; Fredrickson et
al. 1998; Heinaru et al. 2000). Hence, either these strains lacked the ability to
grow on phenol or the assay conditions used were not suited for these microbes.
Without the appropriate cofactors or vitamins available in the community, some
microbes may not be able to metabolize phenol. This was the case for isolates
SBR-T12, P10 and T15 that degraded phenol only when vitamins were added.
Secretion or leakage of cofactors from phenol degrading microorganisms could
allow non-phenol degrading phenotypes to exist. Cross feeding of metabolites
was the phenomenon that allowed the coexistence of the benzyl alcohol
degraders Acinetobacter sp. C6 and Pseudomonas putida R1 to grow on a

single substrate (Christensen et al. 2002). The degradation of benzyl alcohol by

141

 

’5.

 

Acinetobacter sp. C6 resulted in the accumulation of benzoate that was
preferentially used by Pseudomonas putida R1. Similar results were also
observed when TCE was applied to methanotrophic enrichments (Uchiyama et
al. 1992), where the aerobe Xanthobacter autotrophicus metabolized glyoxylic
and dichloroacetic acid intermediates generated from TCE by Methylocystis sp.
Since the reactor communities were diverse, and many non-phenol degraders
were sustained at low densities, it is very likely that cross feeding is a common
event in these single substrate fed communities.

Amplification with primers that target conserved regions within the alpha
subunit of the multicomponent phenol hydroxylase family revealed that this gene
was present, but that there were differences at the amino acid level among the
isolates, since sequences identities ranged between 72 to 100 % (Figure 4.6).
Because the region amplified by the primers covers the active site, a binuclear
iron cluster that activates molecular oxygen (Fox et al. 1993; Johnson and Olsen
1995; Cadieux et al. 2002), differences in kinetic constants or substrate
specificity among the different oxygenases of the isolates could be expected.
This hypothesis is partially supported by studies made by Futamata and
coworkers (Futamata et al. 1998, 2001), who found that different phenol
hydroxylase sequence clusters exhibit different affinities for TCE. Furthermore,
directed evolution of the toluene ortho-monooxygenase of Burkholderia cepacia
G4 resulted in one amino acid substitution within the alpha subunit that caused a
three fold increase in TCE activity and a six fold increase in naphthalene

oxidation (Canada et al. 2002). Detailed studies using purified enzyme

142

 

preparations from the isolates and the complete sequences of their respective
oxygenases would be necessary to determine if the amino acid differences
affect the kinetic coefficients or substrate range of these enzymes.

An unexpected finding was the disparity between the 168 rDNA and the
phenol hydroxylase sequences of isolates SBR-T12, P10 and T15. Analysis of
almost complete 16S rDNA sequences from these isolates revealed that they
form a coherent cluster within the Comamonadaceae family of the B-
Proteobacteria (Figure 4.14), with sequence identities of 99 % (Table 4.5).
However, analysis of their corresponding phenol hydroxylases revealed that this
enzyme in SBR-P10 and T15 was only 72 % identical in amino acid sequence to
the one in SBR-T12, forming distinct clusters in the phylogenetic tree (Figure
4.6). As discussed above, the marked divergence between the phenol
hydroxylases of the two isolates suggests that the enzymes could have different
kinetic constants or substrate specificities. Thus, it is possible that the three
isolates represent two different ecotypes within the community. Typical SBR
operational conditions generate sharp substrate gradients and periods of
aeration and anoxia, situations that generate multiple ecological niches allowing
different ecotypes to coexist (Chiesa et al. 1984; Irvine and Ketchum 1989). In
the SBRs used in this study, the alternation between phenol and TCE exposure
and their respective concentration gradients, in addition to an 11 hour
endogenous decay phase in the phenol-fed reactor provide several alternatives

for the coexistence of different ecotypes in the same system.

143

 

 

 

The SYBR green real-time PCR method was used to quantify a group of
selected isolates in the reactor. This method has been successfully used to
quantify the abundance of bacteria (Rantakokko-Jalava and Jalava 2001; Riley-
Buckley 2001), viruses (Wang et al. 2002), and cysts (Limor et al. 2002) from
different sources. Since the target gene for quantification was the 16S rDNA,
calibration curves were constructed using known numbers of lysed cells as the
template to account for differences in genome size and rRNA operon copy
number in the different isolates. Calibration curves constructed using this
approach resulted in linear correlations over a range of five orders of magnitude,
with a limit of detection of one cell per reaction (Figure 4.7). Similar results were
observed for Ralstonia solanacearum calibration curves, where the limit of
detection was 0.2 cells per reaction (Weller et al. 2000), a value observed with
some of the calibration curves of the isolates. One of the possible reasons for
the low detection limit of the assay is the high growth rates of microbes used for
the calibration curves. Fast growing microorganisms could have more than one
genome copy per cell, a feature that is not accounted for by direct or viable
counts, and hence will overestimate the sensitivity of the assay (Ludwig and
Schleifer 2000). However, Hristova and coworkers constructed calibration
curves for strain PM1 cells harvested at logarithmic and stationary phases of
growth, and found only a small difference in the slope of the curves (0.08); with a
sensitivity of 0.3 cells per reaction (Hristova et al. 2001). These results suggest
that the low sensitivity of SYBR green real-time PCR using 168 rRNA as a target

could be caused by the operon copy number, although detailed studies

144

 

 

comparing the quantification using single and multi copy genes are needed to
address this question. Nevertheless, SYBR green real-time PCR using cell
calibration curves is a sensitive approach to quantify bacterial populations in
environmental samples and should not require knowledge of rRNA operon copy
number and genome size of the organisms of interest.

The reproducibility of the SYBR green real-time PCR was determined by
analyzing eight replicate samples from each reactor. Cell quantifications using
two different primer sets yielded 95 % confidence intervals within the same order
of magnitude. These findings indicate that the SYBR green real-time PCR
method is reproducible and that reactor samples were homogeneous and
representative of the microbial communities studied.

The total bacterial density in the reactors was determined using bacterial
specific 16S rDNA primers as described previously (Riley-Buckley 2001), but
using Pseudomonas stutzeri JM300 as the reference microorganism for the cell
calibration curves. This microorganism was used as a reference because it has
four copies of the rRNA operon and a genome size of approximately four
megabases, values that could be considered average for prokaryotes. Although
systematic error could occur depending on the microorganism used as a
reference, this approach revealed that there were differences in the abundance
and dynamics of bacteria in the two reactors (Figures 4.10-4.11). The bacterial
abundance in the phenol-fed reactor fluctuated between 106 to 109 cells x pg of
community DNA. In contrast, the phenol plus TCE-fed reactor had more stable

values that ranged between 107 to 108 cells x pg of community DNA“. The

145

differences in bacterial density and stability between the two reactors could have
been caused by the presence of predatory organisms, i.e. protozoa and rotifers.
Schmidt and coworkers studied the interactions between bacteria and
microflagellates in SBRs exposed to 2,4-dinitrophenol (DNP) and found that
DNP exposure alone resulted in decreased densities of predatory organisms
(Schmidt et al. 1992). The SBR that was fed both DNP and glucose had higher
densities of bacteria, but their densities oscillated, suggesting that predator-prey
interactions must have occurred. Since rotifers and protozoa were observed
more frequently in the phenol-fed reactor, it is possible that predator-prey
interactions could have played a role determining the total bacterial densities.
These results support previous findings that suggest that the community in the
phenol-fed reactor was more dynamic than the community in the phenol plus
TCE-fed reactor (Chapter 2).

Quantification of selected isolates in the two reactors revealed that the
unidentified group of isolates belonging to the Comamonadaceae family of the B-
Proteobacten'a class (SBR-T12, P10 and T15) were the predominant group
among the isolates enumerated in both reactors (Figures 4.10413). In the
phenol-fed reactor the dynamics of this group mimic the total bacterial dynamics,
with cell densities that were in the range of 106-107 cells x pg of community
DNA. Interestingly, the abundance of SBR-T12, P10 and T15 increased almost
one fold during days 630-729, a period where high TCE degradation rates were
registered in the phenol-fed reactor (Chapter 2). This finding suggests that,

perhaps, this phylogenetic group could be involved in the increased performance

146

of the system during that time period. In contrast, the abundance of the SBR-
T12, P10 and T15 was relatively stable in the phenol plus TCE-fed reactor with
densities that oscillated between 106-107 cells x pg of community DNA after day
127 of operation. As determined before, this group of microorganisms grows on
phenol and cometabolizes TCE when vitamins were supplied in the medium
(Table 4.3). Furthermore, when grown in phenol these isolates form aggregates
similar to the ones observed in the phenol-fed reactor. This evidence supports
the SYBR green real-time PCR data that the group of isolates SBR-T12, P10
and T15 could be one of the predominant TCE degrading groups in both
reactors.

When the abundances of the remaining phenol and TCE degraders and
the non-phenol degraders were compared, some differences between the two
reactors could be observed. The abundance of Phenylobacter sp. SBR-T23, a
poor phenol degrader, was higher in the phenol plus TCE-fed reactor by one or
two orders of magnitude. Members of this genus are not known to degrade
simple aromatic rings like phenol, but they do degrade polycyclic aromatic
herbicides (Schmidt et al. 1984). Variovorax sp. SBR-T4, a phenol and TCE
degrader was also more abundant in the phenol plus TCE-fed reactor with
densities up to 104 cells x pg of community DNA. In contrast the abundance of
the phenol and TCE degrader SBR-T20 was lower in the phenol plus TCE-fed
reactor.

The analysis of non-phenol degrading populations revealed that some

isolates were as abundant as the phenol degraders described above. For

147

 

 

instance, Microbacterium sp. SBR-T14 was one of the most abundant non-
degrading isolates tested with cell densities that reached 103-10‘ cells x pg of
community DNA in the phenol plus TCE-fed reactor (Figure 4.13). Other, slow
growing microorganisms such as ammonia oxidizers were also present in both
reactors. These results suggest that TCE application could increase the
abundance of several microbial populations in the reactor. Some
microorganisms could have reduced affinity for TCE, thus giving them a
competitive advantage over TCE degraders. Others, might grow on the TCE
degradation products or cell constitutes following cell death. Since TCE
metabolites seem to be toxic to some cells and predation occurs in these
communities, substrates from dead cells could have a major effect on
maintaining non-phenol degrading populations in the system. Analysis of
reactor samples using an alternative electron acceptor, 5—cyano-2,3 ditolyl
tetrazolium chloride (CTC), indicated that cells were actively respiring 6 h after
phenol was completely depleted, further supporting this hypothesis. Finally
other microbes might be responding in a secondary manner to the primary
organisms selected by phenol or TCE. For instance, shortly after phenol
addition, the suspended solids from the phenol plus TCE-fed reactor briefly
turned yellow. This color change was likely caused by the accumulation of 2-
hydroxymuconic semialdehyde (2-HMS), a metabolite of the phenol degradation
pathway that often briefly accumulates. This intermediate could diffuse out from
the cell and can be used by other non-phenol degrading strains for growth.

From these results, it is clear that the phenol and TCE degrading characteristics

148

 

" '\

 

of the isolates were not sufficient to predict population abundances in the
community.

An understanding of the selective pressure exerted by TCE is necessary
for the successful use of cometabolism for TCE bioremediation. Several studies
on pure cultures revealed that TCE cometabolism yields intermediates that are
toxic to the cells, such as a short lived TCE epoxide that alkylates proteins, DNA
and RNA (Wackett and Householder 1989; Rasche and Hyman 1991).
Furthermore, studies using purified oxygenases from Pseudomonas putida F1
and Burkholderia cepacia G4 revealed that TCE cometabolism results in
enzyme inactivation and covalent modification of the oxygenases (Li and
Wackett 1992; Newman and Wackett 1997a). Hence, is reasonable to
hypothesize that TCE exposure will result in a negative selective pressure that
will lead to the disappearance of TCE cometabolizing populations and the take
over by non-active populations (Fries et al. 1997a; Mars et al. 1998). In fact,
Munakata-Marr et al. observed a decrease in TCE removal in aquifer
microcosms that were fed lactate and TCE over a period of 280 days
(Munakata-Marr et al. 1997). However, after ceasing the bioaugmentation in the
microcosms, they still saw steady removal of TCE for at least 150 days in one of
them. Studies in mixed cultures of known toluene and TCE degraders suggests
that TCE will have a negative effect on the competitive behavior of toluene
degraders that cometabolize TCE (Mars et al. 1998). In our study several
phenol and TCE degraders were isolated from the phenol plus TCE-fed reactor

after one year of TCE exposure. Moreover, the most abundant group of isolates

149

 

in both reactors degrade phenol and cometabolize TCE. These results suggest
that long-term TCE application will not always result in the disappearance of
TCE degraders by the hypothesized negative selection.

The remaining question is how TCE degrading populations successfully
persisted in a reactor that was exposed to TCE twice per day. As observed
previously (Chapter 3) the addition of TCE resulted in an increase in the amount
of extracellular polymeric substances (EPS) in the total suspended solids.
Yeager and coworkers observed that some the toxic TCE intermediates could
diffuse out from Burkholderia cepacia G4 cells exposed to TCE and affect the
viability of G4 mutants with inactive toluene monooxygenases (Yeager et al.
2001). Furthermore, studies on chemostats continuously fed phenol, and pulsed
with “C-TCE for 15 days revealed that 42 % of the radiolabeled TCE became
soluble transformation products (Shurtliff et al. 1996). Thus, it is possible that
EPS could reduce the diffusion of TCE or some of the diffusible toxic
intermediates, resulting in the survival of TCE degraders. The most dominant
group of phenol and TCE degraders forms granules when grown on phenol or
phenol and TCE providing further support to this idea. Another possible
explanation for the success of TCE degraders is that continuous TCE application
selected for microbes with increased resistance to TCE toxicity. If the
microorganisms can resist TCE intermediate toxicity, negative selection will not
occur, and the abundances of TCE degraders will be determined by factors such
as competition for primary substrate. Experiments on Ralstonia eutropha JM134

derivatives revealed that microbes extremely resistant to TCE exist, since this

150

 

 

 

strain was resistant up to 800 pM TCE, approximately 100 mg/l (Ayoubi and
Harker 1998). TCE degradation rates could also be a factor that determines the
success of TCE degraders under prolonged TCE exposure. Comparative
studies between Ralstonia sp. KN1 constructs carrying a monooxygenase or a
dioxygenase suggest that the degradation rate of the enzyme will determine the
duration of the activity (Ishida and Nakamura 2000). The dioxygenase construct
exhibited faster initial TCE degradation rates that rapidly decreased in
comparison with the construct that was carrying the monooxygenase that had
higher and stable degradation rates. This suggests that fast TCE degraders
become inactive because they can not handle product toxicity as well as slow
degraders. Finally, the success of TCE degrading populations in a TCE-fed
reactor could also be caused by changes in gene expression. The operational
cycles of the SBRs include substrate gradients and periods of endogenous
decay. Since TCE is fed 6 hours after phenol addition it is possible that some
enzymes are inactive by the time TCE is fed, and avoid the toxicity generated
from the intermediates. This will allow TCE degraders to be dominant in a
system without being affected by toxicity. The answer to the question of how
phenol and TCE degraders persist in mixed communities under long-term
exposure to TCE is probably a combination of factors.

The results from this study indicate that long-term TCE application does
not result in the disappearance of TCE degrading populations. Several TCE
degraders with different phenol hydroxylases were isolated from the TCE-fed

reactor. Quantification of the TCE degraders and other isolates indicates that

151

one group of phenol and TCE degraders is a predominant member of both
reactor communities. These findings suggest that TCE degraders can persist in
natural communities exposed to TCE for a prolonged period of time making

cometabolism a feasible treatment for this ubiquitous pollutant.

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159

 

“a

 

CHAPTER 5
CONCLUSIONS, SYNTHESIS AND FUTURE DIRECTIONS

Conclusions

Microbial communities are probably the least studied but most important
component of bioreactors. Knowledge about bioreactor community structure
can likely lead to improved design and operation of these systems. The aim of
this dissertation was to determine the long-term effects of TCE application on
the microbial community of a particular type of reactor system, sequencing batch
reactor. The major findings of this research can be summarized as follows:

1. Long-term TCE application selected a microbial community with low
but stable TCE degradation rates.

2. Community succession was dependent on the substrates applied
and random factors. When phenol was the only substrate applied,
the community never reached stability. On the other hand, when
both phenol and TCE were applied a relatively stable microbial
community was selected.

3. TCE application resulted in an increase in EPS accumulation and
the formation of “star-like” flocs. The EPS was mainly composed of
neutral six carbon sugars, with rhamnose being the most abundant.

4. Sequencing batch reactors fed a single growth substrate harbor
diverse microbial communities. A major proportion of the isolates

belonged to the Proteobacteria phylum.

160

 

‘1

 

. Phenol did not enrich for phenol degraders exclusively since the
majority of the isolated strains did not grow on phenol as the sole
carbon source. Cross-feeding interactions must have occurred
between and among different populations in the reactors.

. Phenol hydroxylases of different phylogenetic groups were present
in the SBR communities. Unknown oxygenase diversity apparently
exists in the phenol degraders whose degradative genes could not
be amplified with the “conserved” phenol hydroxylase primers.

. A novel group of phenol and TCE degraders from the
Comamonadaceae family of the B-Proteobacten‘a were among the
predominant populations in both reactors.

. Long-term TCE application did not result in the extinction of TCE

degrading populations.

Synthesis

In this dissertation complementary community analysis approaches were

used to address the research questions centered on discerning relationships

between community structure and function. Nucleic acids analyses, kinetic

measures, isolation and characterization of strains, and community physiological

measurements were the methods used to determine the effects of long-term

TCE application on the SBR microbial community. This polyphonic community

analysis approach allows the establishment of interrelationships between

community structure, function and the member organisms’ physiology, providing

161

 

 

a more comprehensive overview of the communities studied. The results of
several of these measures are combined in Figure 5.1 so that they can be better

compared and interpreted together.

162

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163

As presented earlier, long-term phenol application (phenol-fed reactor)
resulted in a microbial community with dynamic structure and function (Figure
5.1). TCE transformation rates were also variable during 800 days of operation.
The highest TCE transformation rates were observed during days 0 to 190 and
567-750 in the phenol fed reactor. The community structure during these two
periods was very similar, suggesting that some of the community members were
necessary for high TCE degradation rates. Cell densities of several strains,
including the predominant group of phenol and TCE degraders, increased during
these two periods of high TCE transformation rates. These results suggest that
the particular community structure observed during these periods favor phenol
and TCE degrading populations.

Major changes in community structure and function occurred after 198
days in the phenol-fed reactor. TCE transformation rates on that day decreased
to the lowest values observed followed by rapid increases and decreases.
Marked changes in community structure were also observed after 198 days of
operation while a significant decrease in total bacterial abundance and strain
specific abundance was observed in day 198. Analysis of a second replicate
sample of day 198 yielded similar cell densities, further supporting the observed
decrease in bacterial abundance. These results suggest that the microbial
community was severely perturbed at this point in time. The microbial
community structure changed at least one more time during days 198 to 567.
TCE degradation rates were variable but not to the degree previously observed.

Total bacterial and isolate specific abundances were relatively stable during this

164

period suggesting that the community structure and function were not closely
linked during this period of reactor operation.

TCE application resulted in rapid changes in community structure and
function in the phenol plus TCE-fed reactor (Figure 5.1). TCE degradation rates
decreased during the first 100 days of operation suggesting that the microbial
community was sensitive to TCE exposure. The abundance of isolates SBR-
T12, P10 and T15, an important group of TCE degraders, decreased one order
of magnitude during the first 50 days of operation. Changes in community
structure were also observed during the first 100 days of operation,
corroborating that TCE exposure affected the community structure and hence
likely the function in the bioreactor.

Although TCE caused some initial changes in community structure, a
relatively stable microbial community was selected. The community structure
from day 127 to 811 was different from the community structure observed during
the first 100 days, and was relatively stable. During this lengthy period, TCE
degradation rates were low but relatively stable, suggesting that TCE selected
for a community with low TCE transformation rates. The abundance of several
phenol and TCE degraders was also relatively stable during this period
suggesting that although TCE transformation rates were low, TCE degraders
were present in the community in stable densities.

A remaining question is why the microbial community exposed to TCE
exhibited high stability compared to the phenol-fed community? An unexpected

finding that could explain the survival of TCE degraders and the general stability

165

of the system was the increase in relative amounts of EPS. During the first 100
days of TCE exposure, the relative abundance of EPS in the total suspended
solids increased from 10 to 50 %. EPS concentrations remained relatively
stable afterwards, ranging between 40 to 50% during 650 days of operation.
The increased EPS resulted in the formation of “star-like” flocs which resemble
biofilms. It is possible that EPS protect cells from the toxic effects of TCE and
simultaneously causes changes in the spatial distribution of bacteria in the floc,
generating a structure that resembles a biofilm. Biofilm communities are more
resistant to toxics and the structure is more stable than planktonic communities

such as the granular flocs in the phenol-fed reactor.

Future research

Although the aforementioned research provided new insights about the
effects of long-term TCE application on the dynamics and physiology of
bioreactor communities, new questions emerged that should be intriguing for
future investigations.

Community analysis by TRFLP revealed that changes in community
structure occurred in both reactor communities. However, the successional
patterns in each reactor were different. The data suggests that the community in
the phenol plus TCE-fed reactor reached stability after some initial changes,
while the community in the phenol-fed reactor never stabilized. Since both of
these successional patterns support pre-established ecological theories, further

experimentation is needed to determine their generality. Furthermore, the

166

 

effects of stressors, such as TCE, on successional patterns and on the
resilience of microbial communities to perturbations should be assessed to
understand stress responses at the community level. This research should
contribute to the development of generalized theory about succession in
microbial systems, and to test some of the theories originated in macroecology.
An increase in EPS content was observed in the phenol plus TCE fed
reactor. Although the data suggests that TCE induced this response, further
work is needed to assess whether the EPS and the resulting “star-like” flocs
serve a protective function (Chapter 3). Experiments that assess the effects of
TCE or some of its stable intermediates on the accumulation of EPS and the
viability of the cultures should test this hypothesis. Isolated strains that produce
EPS and degrade TCE will help to better define the role of TCE inducing EPS,
and to more accurately determine if the EPS is protecting the cell. Strains from
a community producing EPS could also help to determine which populations
besides EPS producers are involved in the response. For instance, EPS
production does not have to be from phenol and TCE degraders, but could be
from non-TCE degrading p0pulations sensitive to TCE intermediates. Such a
response could be controlled by quorum sensing. My preliminary tests revealed
that the most abundant isolates by real-time quantitative PCR were positive for
homoserine lactone production when assayed using Chromobacten’um
violaceum as the reporter strain. To assess which populations are involved in
this process, pure and mixed cultures of TCE active and inactive populations

should be evaluated in the presence of TCE or similar toxic compounds. These

167

experiments will increase our knowledge on how individuals or microbial
communities respond to toxic stress, a common phenomenon in contaminated
sites or when a toxicant is added to a community.

The microbial communities in the SBRs were phylogenetically diverse.
The majority of the strains isolated did not degrade phenol under the conditions
tested. Abundance data suggests that although they were in low numbers,
these populations were constantly present during the 2 years of operation. An
important, but difficult to evaluate question is the role of these apparently non-
phenol degrading populations. Are these populations necessary for efficient
reactor operation, are they detrimental, or are they neutral? For instance, some
of these populations could grow on the intermediates of TCE degradation
pathway. This will result in the complete mineralization of TCE and
detoxification of the system if the TCE metabolites are toxic to other cells. On
the other hand, the non-phenol degrading populations could be growing on
intermediates from the phenol pathway, resulting in decreased energy for phenol
degraders. These phenol inactive populations could also be growing on dead or
excreted cell material, or could be involved in the production of vitamins
consumed by other populations. To address some of these alternatives, reactor
samples can be exposed to 13C-Iabeled substrates, e.g. phenol, TCE, glyoxylic
acid, to generate 13C-Iabeled cells. The analysis of labeled ribosomal RNA
could reveal if the non-phenol degraders consume any of the primary
compounds or their derivatives. If the 13C analysis reveals that some of the non-

phenol degrading populations consume the primary compounds or the

168

intermediates then, co-culture experiments could be performed to more
accurately determine the metabolic interactions. Ultimately, the bioreactors
could be bioaugmented with key non-phenol degrading populations to assess
their effect on reactor function and community dynamics.

Phenol and TCE degraders from the Comamonadaceae family of the 8-
Proteobacten’a were the most abundant population in both reactor communities
(Chapter 4). Pure culture studies suggest that TCE degraders are severely
affected by TCE metabolites resulting in oxygenase inactivation and reduced
viability. Hence, these successful strains should be further characterized to
understand why they predominate under TCE exposure. It is possible that these
strains possess toxicity resistance mechanisms that allow them to tolerate TCE
mediated toxicity. On the other hand, it has been proposed that strains with
reduced TCE degradation rates are less prone to TCE metabolite toxicity.
These strains should be tested for TCE toxicity, using TCE and the stable TCE
metabolites at different concentrations and under different growth conditions to
assess if they are more resistant than known strains. Determination of the
kinetic constants for TCE and phenol degradation in combination with the toxicity
experiments should help to elucidate what phenotypes are selected from long-
term TCE application and why.

In a typical SBR system, cells alternate between growth and endogenous
decay phases within each cycle. Hence, the SBR cycling could have an effect
on the dynamics of cellular components such as RNA and protein. The addition

of growth substrates like phenol will trigger the expression of genes involved in

169

the metabolism of this compound, leading to the synthesis of the necessary
proteins. However, the modified SBR cycle used in this study included the
addition of TCE, a non-grth substrate that induces its own degradation in pure
cultures. To evaluate the effects of the SBR cycles and TCE on the RNA and
protein content of the microbial communities, these parameters should be
measured during the reactor cycle. This will provide information about how the
feeding cycle affects the physiology of the community and as a consequence the
function of the system. For instance, the expression of the phenol hydroxylase
genes from the dominant phenol and TCE degraders could be studied to
evaluate the effects of TCE, and the cycles on the expression of the active
enzyme. This information can be related to kinetic measurements to understand
the dominance or extinction of different microbes over long-term SBR selection.
These experiments will provide more understanding of how microbial

communities respond and change under these growth alternating conditions.

170

APPENDIX A
PROTOCOL FOR T-RFLP ANALYSIS OF MICROBIAL
COMMUNITIES

This protocol was used for the community structure analysis described on
Chapter 2 and in the following published article: Braker, G., H. L. Ayala-deI-Rlo,
A. H. Devol and J. M. Tiedje. 2001. Community structure of denitrifiers, bacteria,
and archaea, along redox gradients in Pacific Northwest marine sediments by
terminal restriction fragment length polymorphism analysis of amplified nitrite
reductase (nirS) and 16S rRNA genes. Applied and Environmental
Microbiology. 67:1893-1901. A version modified for use with capillary
electrophoreses-based sequencers has been posted on the web as: Improved
protocol for T-RFLP by capillary electrophoresis. V. GriJntzig, B. Sires, H. L.
Ayala-deI-Rlo, and J. M. Tiedje. http://rdp.cme.msu.edu/htmlIt-rflp_jul02.html

171

i.
‘11 :—

 

 

T-RF LP Protocol

1 Principle

Ierminal Restriction Eragment Length Eolymorphysim (T-RFLP) is a
reproducible and high throughput method for the analysis of microbial
communities. This method resolves complex microbial communities based on
size differences of the terminal fragments of different populations. The figure
below illustrates the procedure and the rationale of the method.

 

   

 

1 2
: cailIEIIDIEElIEElIg
CD 5
CD "‘ "’ ”W;
Extract DNA PCR with fluorescently
from the community labeled 16S rRNAfordward'y primer 3
4 7
5 Restriction digest of PCR product
=

 

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* . . <- :_-; 21.23.1228...
1 0" tumiélm

Fragment separation and
recoginition in sequencing gel

 

 

Fluorescence

 

 

Fragment size (base pairs)

 

First, DNA is harvested from the sample of interest i.e., enrichment cultures, soil,
sediment or reactor material (1). The gene of interest is amplified from the
community DNA using the polymerase chain reaction (PCR) with a fluorescently
labeled primer (2). The mixture of amplicons is purified and digested with a
restriction enzyme (3). The different fragments (A-F) are separated in a
sequencing gel generating a series of bands (4). A laser reader will recognize
only the labeled fragments, and the computer software will calculate the size of
the identified fragments and their relative intensity (5).

172

II. Procedure
A. DNA Extraction

The DNA from the sample should be a uniform representation of the
microbial community studied. Therefore, it is important to select an
extraction method that allows efficient lysis of the sample and recovery of
the DNA. The quality of the DNA is also very important, since DNA
contaminated with humic acids will be difficult to amplify. The procedure
that I used to extract the DNA from the Sequencing Batch Reactors was a
combination of enzymatic digestion with several freeze-thaw cycles.

8. PCR amplification

Primer Selection: The T-RFLP method can be applied to any gene,
therefore primer selection will depend on the user's interest and the
availability of sufficient sequences to identify conserved regions for
primers. For microbial community analysis the 16S rDNA gene is the
most commonly used gene since it provides an overview of the microbial
community. Several different primers targeting conserved regions of the
168 rDNA molecule have been developed (Amann et al. 1995). Since
much of the variability in the 168 rDNA molecule is present in the first
500bp it is recommended to fluorescently label the forward primer.
Although there are several different fluorochromes that could be used to
label primers, phosphoramidite dye 5-hexachlorofluorescein (HEX) is one
of the most commonly used dyes for T-RFLP. The primers l have used to
study bacterial and archaeal communities are listed below. The only
labeled primers that I have used were forward primers (F), labeled with

 

 

 

 

 

HEX.

E. coli Specificity Sequence (5’—>3') Reference

positioning

8—>27F Domain (Bacteria) AGA GTT TGA TCM (Giovannoni

TGG CTC AG 19911

1392- Universal ACG GGC GGT GTG (Amann et

1407R TAC A al. 1995)

21-—>4OF Domain TTC YGG TTG ATC (Moyer et al.
(Archaea) CYG CCR GA 1998)

958—)976R Domain YCC GGC GTT GAM (DeLong
(Archaea) TCC AAT T 1992)

 

 

 

 

 

 

173

Composition of the PCR mixture. The following is a modified version of
the standard master mix provided by Gibco (Gibco BRL, Gaithersburg,
Md.). This mixture could be modified to accommodate different
polymerases or to amplify difficult samples by changing the amounts of
the different components.

 

10X PCR Buffer (Gibco)

50 mM MgCl2 (Gibco)

2.5 mM dNTP mix (1:1:1:1L
Fowvard primer (1O pMol/yl)
Reverse Primer (1 OpMol/pl)

0.5 BSA (20 mg/ml)

0.5 Taq polymerase (5Ulul) (Gibco)
34 Sterile filtered Mill-Q water

49 Total

 

 

 

 

44.50201

 

 

 

 

 

 

 

 

Usually 1 pl of template (z20-100ng) is added to each PCR
reaction. Since PCR is very sensitive to initial template concentrations,
bias can occur between replicate reactions from the same sample. In
order to reduce the variability between PCR reactions, at least 3 replicate
reactions from each DNA sample should be performed and combined
together before further purification.

PCR cycles
The following cycles were used for amplification
95°C 3min Hot Start/denaturation
94°C 45 sec Denaturation
57°C 1 min Annealing 25-30 cycles
72°C 2 min Elongation
72°C 7min Final Extension

4°C forever

The number of cycles to be used will depend on the difficulty of the
samples and the specificity of the PCR product. Lower cycles (25) will
result in less PCR product but more specific.

C. Purification of the PCR product.

PCR products should be purified before digestion to remove any
excess of primer or non-specific PCR products. I recommend the
following two methods for product clean up: Microcon concentration or gel
extraction. The micron concentration columns will remove any excess of
primer but they will not remove any non-specific PCR products. Gel
purification will remove both, excess primers and non-specific products.
Gel purification is performed by loading all PCR products on an agarose

174

 

gel to separate them. The band of the proper size is excised from the gel,
and the PCR product is recovered with a gel purification kit (QIAquick gel
extraction kit, Quiagen, Chatsworth, Calif.) following the manufacturers
recommendations. ls important to check the amount of recovered PCR
product on a gel, since a portion of the product will be lost during the
purification procedure.

D. Restriction digest of the PCR products

The purified PCR products are digested with a restriction enzyme,
usually a four base pair cutter. Restriction enzymes that recognize more
than four bases do not cut frequently, resulting in poor resolution of
different populations in the community. Haelll, and Hhal (Cfol) are some
of the most effective restriction enzymes for community analysis, although
the user is recommended to test several enzymes in order to determine
which is the best enzyme for the samples to be studied. The master mix
for the restriction reaction is as follows:

1 10X Buffer

1 BSA (1 mg/ml)

0.5 Restriction enzyme (10U/pl)
2.5 H20

5 Total

Add 5 ul of purified PCR product and digest for 2-3 hours at 37°C.
lnactivate the enzymes by heating the digest to 65°C for 20 minutes.
Store digests at —20°C.

Note: The PCR product should be quantified before being added to the
digestion. It is important to use similar amounts of PCR product in order
to compare the profiles obtained. However, if a uniform amount of DNA is
added to each PCR reactions the amount of product generated should be
similar. Quantification can be performed with UV spectrophotometry or
gel quantification.

E. Gel separation

An aliquot of the digested PCR products (2 pl) is combined with the
TAMRA GS 2500 marker (Perkin Elmer). The mixture is separated in a
6% polyacrylamide gel in a slab gel DNA sequencer (373 ABl Stretch) for
14 hours at 1680 volts. The data are viewed and normalized using
GeneScan software version 2.1.

175

 

lll. References

Amann, R. l., W. Ludwig and K. H. Schleifer. (1995). Phylogenetic
identification and in situ detection of individual microbial cells without
cultivation. Microbiol Rev 59: 143-69.

Braker, G., H. L. Ayala-del-Rio, A. H. Devol, A. Fesefeldt and J. M. Tiedje.
(2001). Community structure of denitrifiers, Bacteria, and Archaea along
redox gradients in pacific northwest marine sediments by terminal restriction
fragment length polymorphism analysis of amplified nitrite reductase (nirS)
and 16S rRNA genes. Appl. Environ. Microbiol. 67: 1893-1901.

Bruce, K. D. (1997). Analysis of mer gene subsclass within bacterial
communities in soils and sediments resolved by fluorescent-PCR-restriction
fragment length polymorphism profiling. Appl. Environ. Microbiol. 63: 4914-
4919.

Brunk, C. F., E. Avaniss-Aghajannl and C. A. Brunk. (1996). A computer
analysis of primer and probe hybridization potential with small-subunit rRNA
sequences. Appl. Environ. Microbiol. 62: 872-879.

Clement, B. G., L. E. Kehl, K. L. DeBord and C. L. Kitts. (1998). Terminal
restriction fragment patterns (T RFPs), a rapid, PCR-based method for the
comparison of complex bacterial communities. Journal of Microbiological
Methods 63: 4516-4522.

DeLong, E. F. (1992). Archaea in costal marine environments. Proceedings of
the National Academy of Science 89: 5685-5689.

Giovannoni, S. J. (1991). The polymerase chain reaction. In Nucleic acids
techniques in bacterial systematics. E. Stackebrandt. New York, John Wiley
& Sons 177-201.

Liu, W. T., T. L. Marsh, H. Cheng and L. J. Forney. (1997). Characterization of
microbial diversity by determining terminal restriction fragment length
polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol. 63:
4516-4522.

Marsh, T. L. (1999). Terminal restriction fragment length polymorphism (T -
RFLP): an emerging method for characterizing diversity among homologous
populations of amplification products. Curr. Opin. Microbiol. 2: 323-327.

Moyer, C. L., J. M. Tiedje, F. C. Dobbs and D. M. Karl. (1998). Diversity of
deep-sea hydrothermal vent Archaea from Loihi Seamount, Hawaii. Deep-
Sea Research ll 45: 303-317.

176

APPENDIX B
HAEIII T-RF LP DATA

Relative abundance of Bacterial T-RF’s from reactor samples after digestion with
Haelll. Numbers in the legend indicate the sizes of the T-RF's in base pairs.

177

 

1 00%
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80%
70%
60%
50%
40%
30%
20%
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APPENDIX C
TRANSMISSION ELECTRON MICROGRAPHS

Sampling was performed as described in Chapter 3. Samples were processed
by the Center for Electron Microscopy at Michigan State University. A, phenol-
fed reactor diversity of morphotypes. B, microorganisms with internal membrane
systems that resemble ammonia oxidizers in the phenol-fed reactor. C-D,
phenol plus TCE-fed reactor images that suggest that some accumulation of
storage polymers could be occurring in this community (clear areas inside the
cells).

179

 

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