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2 Lessons from Field Observations and Enrichment Cultures

2 Lessons from Field Observations and Enrichment Cultures

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13  Electron Acceptor Interactions Between Organohalide-Respiring Bacteria …



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Fig. 13.1  Overview of common chlorinated solvent dechlorination pathways and o­rganisms

responsible. Chlorinated ethenes, chlorinated ethanes, and chlorinated methanes make up

the largest fraction of the organic halogenated contaminants found at contaminated sites, and

­frequently co-occur. Figure 13.1 illustrates the bottleneck at the level of cDCE and VC for

many dechlorination pathways. For example, chloroform and 1,1,1-TCA inhibit VC dechlorination, consequently affecting dechlorination of all upstream compounds. To date, cDCE and VC

dechlorination have been observed only within members of the Dehalococcoidia, while many of

the other dechlorination steps can be catalyzed by several different genera. Abbreviations: TeCA

Tetrachloroethene; PCE Perchloroethene; TCE Trichloroethene; DCE Dichloroethene; VC Vinyl

Chloride; TCA Trichloroethane; DCA Dichloroethane; CF Chloroform; DCM Dichloromethane



patterns of dechlorination and cross-inhibition. Prime examples include the polychlorinated biphenyls (PCBs), polychlorinated benzenes, polychlorinated phenols,

polychlorinated alkanes, and brominated flame retardants. Research is on-going

for many of these contaminants and others to further the understanding of dehalogenation patterns and to help promote detoxification, although quantifying inhibition is challenging, as described further below.



13.2.1 Simulations of Field and Laboratory Observations

Various models of microbial growth and biodegradation with or without transport have been used extensively to simulate the fate of contaminants and their

daughter products observed in laboratory studies and at field sites, ranging from



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very simple models to extraordinarily complex multidimensional and multiphase

models (Chambon et al. 2010, 2013; Sleep and Sykes 1993; Fennell and Gossett

1998; Smatlak et al. 1996). The United States Geological Survey (USGS) provides resources for some widely used commercial models (e.g. MODPATH and

MT3D transport models). Statistician George Box stated aptly that “all models

are wrong, but some are useful”. Many contaminant fate and transport models

are tremendously useful for evaluating scenarios, establishing rate-limiting steps,

and discovering knowledge-gaps in different contexts. In these models, certain

assumptions about microbially catalyzed reactions have to be made. Often they

are modeled using first order decay constants, sometimes Monod kinetic expressions are included, perhaps incorporating fermentation reactions, and possibly

with additional thermodynamic constraints. As the models increase in complexity,

considerably more knowledge on the responsible microbial populations and their

abundance, reaction pathways and kinetics is needed. Simple first order kinetic

models of the stepwise reductive dechlorination rarely fit experimental data well.

Slower than predicted rates of dechlorination of lesser chlorinated daughter products, an observation practitioners and site owners like to call “cDCE- or VC-stall”,

lead to investigations into causes and mechanisms for inhibition of these key

dechlorination steps.

Substrate inhibition is manifested when specific rates of reaction decrease at

higher substrate concentration. In the context of sequential reductive dechlorination, substrate inhibition is sometimes referred to as “self-inhibition” when a compound and any of its daughter products cause inhibition of any dehalogenation

step. The frequently modeled mechanism assumes a single enzyme (or organism)

and substrates and dechlorinated products competing for the active site. As such,

it is very difficult to distinguish between specific mechanisms of inhibition (substrate inhibition versus competitive inhibition) because at any given time, the parent compound as well as several dehalogenated products may be present. PCE and

TCE and their daughter products cDCE and VC have been the subject of the most

research. The more chlorinated ethenes have been found to competitively inhibit

the reductive dechlorination of the less chlorinated ethenes and, to a lesser extent,

vice versa (Cupples et al. 2004; Yu et al. 2005; Yu and Semprini 2004). This type

of competitive inhibition, as well as substrate inhibition, have been incorporated

into some contaminant fate and transport models used for evaluating remediation

alternatives and effectiveness (Chambon et al. 2010; Chen et al. 2013; Christ and

Abriola 2007; Haest et al. 2010, 2012; Heavner et al. 2013; Huang and Becker

2011; Lai and Becker 2013; Lee et al. 2004; Mendoza-Sanchez and Cunningham

2012; Pon et al. 2003; Popat and Deshusses 2011; Sabalowsky and Semprini 2010;

Yu et al. 2005). These models typically assume either a single aggregate dechlorinating population, or perhaps two distinct populations, one for more chlorinated and another for less chlorinated ethenes. Neither reflects the true situation

because microbial populations are diverse assemblages of subpopulations with

different substrate preferences that often vary spatially and temporally. A thorough review of kinetic models describing reductive dechlorination of chlorinated

ethenes was recently published (Chambon et al. 2013). Inhibition constants (Ki)



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for self-inhibition among chlorinated ethenes were reviewed and summarized, and

generally fall within the range between 3–10 µM (0.5–15 mg/L), although one of

the many challenges noted in this review is that the range of published kinetic and

inhibition constants used to describe these phenomena varies over four orders of

magnitude in some cases, meaning that the fundamental underlying mechanisms

are still poorly understood, not independent, very complex, experimentally difficult to analyze, and dynamic. Experiments to quantify the magnitude of inhibition

are difficult to control as the concentration of substrates and products are always

changing during sequential stepwise dechlorination reactions. The enzymes and

microbes catalyzing these dechlorination reactions vary from site to site, and

enzymes from different microbes have different inherent kinetic and inhibition

properties that affect rates and extent of dechlorination. Despite these challenges,

models have proven very useful to predict the most likely ranges of outcomes, to

evaluate scenarios, and especially to point towards knowledge gaps that need better understanding or more data.

Cross-inhibition occurs when different series of halogenated substrates acted on

by distinct microbial populations interact, such as the observed inhibition of chlorinated ethene dechlorination by chlorinated ethanes. Contaminated sites often contain

mixtures of different types of chlorinated solvents as well as other contaminants and

these mixtures do frequently confound cleanup efforts. An important example is the

co-contamination of sites with TCE and 1,1,1-trichloroethane (1,1,1-TCA) (Scheutz

et al. 2011), which occurs at ~20 % of sites on the U.S. Environmental Protection

Agency’s National Priorities List (Grostern and Edwards 2006a). 1,1,1-TCA was

introduced as a less toxic alternative to TCE (Doherty 2000) but was subsequently

found to be a potent inhibitor of methanogenesis (Adamson and Parkin 2000).

Chloroform (CF) is another frequent co-contaminant with chlorinated ethenes.

1,1,1-TCA and CF have specifically been identified to inhibit VC dechlorination

resulting in the buildup of the toxic intermediates cDCE and VC (Chan et al. 2011;

Chung and Rittmann 2008; Duchesneau et al. 2007; Duhamel et al. 2002; Grostern

and Edwards 2006a; Scheutz et al. 2011; Yu et al. 2005). The reverse is also true:

VC itself inhibits dechlorination of chlorinated ethanes and methanes (Grostern

et al. 2009). A review of sites with comingled halogenated contaminants vividly

illustrates the challenges for cleanup (Scheutz et al. 2011).



13.2.2 Microbial Diversity, Competition, Inhibition,

and Reductive Dehalogenases

Competition and inhibition drive evolution through natural selection. These processes result in highly diverse microbial populations with different patterns of

resource use that occupy different niches. We appreciate now more than ever the

diversity of microbes that can dehalogenate, as well as the diversity of reductive dehalogenases within these microbes that mediate these reactions, as covered in the many chapters of this book. Moreover, these organohalide-respiring



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Fig. 13.2  Substrate specialization in OHRB. Enrichment cultures (referred to as KB-1, ACT-3,

WBC-2, and CW) that dechlorinate PCE, chloroform and 1,1,1-TCA, 1,1,2,2-tetrachloroethene,

and chlorinated benzenes have been developed and in many cases successfully deployed for

remediation. Recent research is revealing that within these dechlorinating enrichment cultures,

different OHRB specialize to individual steps in dechlorination. In some cases, very close relatives carry out distinct steps, such as two nearly identical strains of Dehalobacter (Dhb) in the

case of 1,1,1-TCA and 1,1-DCA dechlorination in the ACT-3 enrichment culture, or very different microbes working together, in the case of Dehalobacter, Dehalogenimonas, and Dehalococcoides to bring about transformation of 1,1,2,2-TeCA to ethene in the WBC-2 enrichment

culture. See Fig. 13.1 for definition of chemical names. Nomenclature Dhb1, Dhb2 refers to presence of distinct strains of Dehalobacter specialized for the dechlorination step indicated



bacteria are critically dependent on fermenting and acetogenic microbes within their

­community to provide essential electron donors, co-factors, and other nutrients for

growth (Chap. 14). It is now becoming clear that OHRB are often extreme “niche-­

specialists”, with rather pronounced strain and enzyme specialization observed

under some conditions (Fig. 13.2). For example, in the studies of chlorobenzene

dechlorination in enrichment cultures from the Chambers Works (CW) site (Nelson

et al. 2011), Dehalobacter was found to be responsible for observed dechlorination, but different, yet closely related strains, were specialized for each of the three

dichlorobenzene isomers (Nelson et al. 2014) and yet a fourth strain for the substrate monochlorobenzene (MCB) (Fig. 13.2). Moreover, some strains could not

dechlorinate the other’s substrate. The same has been observed in the ACT-3 enrichment culture that dechlorinates 1,1,1-TCA and 1,1-dichloroethane (1,1-DCA) (Tang

and Edwards 2013; Tang et al. 2012) with yet again different Dehalobacter strains

(Fig. 13.2). Chloroform and 1,1,1-TCA (methyl chloroform) are dechlorinated by

the same Dehalobacter strain, while 1,1-DCA is dechlorinated by a closely related

but distinct strain (Tang and Edwards 2013). The challenge is in detecting this strain

differentiation, as often these organisms have identical 16S rRNA sequences—the

difference lies primarily in the complement of reductive dehalogenase genes that



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each organism harbors. The evolutionary and ecological implications of this specialization are fascinating to consider and may also provide a mechanism for the

community to overcome competition for and inhibition by closely related electron

acceptors. It is important to therefore develop investigative tools to explore the evolution of microbial communities in the presence of multiple competing substrates.

These pressures will select for a different microbial populations compared to experiments with fewer substrates, but will likely develop more interesting models for

understanding adaptation and inhibition in the context of evolution, and certainly

provide a means for generating more robust inocula for bioaugmentation.

At the heart of the processes involved in competition and inhibition are the

diverse reductive dehalogenases that catalyze the different reactions. Different

OHRB harbor and express a different array of enzymes and thus have different

substrate and inhibition profiles. Selection conditions established at field sites and

in enrichment cultures will therefore influence microbial populations and associated kinetic and inhibition properties. Rudimentary understanding of the relationship between enzyme, microbe, and specific activity has already greatly improved

bioremediation outcomes, and further understanding will further advance the

technology. In particular, the recent elucidation of the three-dimensional structures of two reductive dehalogenases (see Chap. 20) is very exciting. Briefly, a

tetrachloroethene dehalogenase, PceA, produced by Sulfurospirillum multivorans

was purified and structurally characterized by Bommer et al. (2014) and a catabolic (nonrespiratory) reductive dehalogenase was characterized by Payne et al.

(2015), with striking similarity to the structure of PceA. These structures revealed

a “letterbox-like” insertion unit or substrate channel that must strongly contribute

to substrate preferences of the enzyme. Enzymes with similar substrates appear

to share “letterbox” substrate channel sequences. These structures, together with

recent advances in the ability to heterologous express reductive dehalogenases in

more tractable hosts (MacNelly et al. 2014; Parthasarathy et al. 2015) now provide the opportunity to examine these enzymes at a more mechanistic level, and

perhaps will eventually enable the design proteins and microbes that are less susceptible to inhibition by competing or co-occurring substrates. Characterization

of single enzymes is essential for fundamental understanding and to inform more

complex systems, and may eventually lead to rationally designed enzymes to deal

with problematic pollutants. At the same time, approaches to characterize activity

and inhibition in complex natural and engineered mixed microbial dechlorinating

communities are also needed, as described below.



13.3 Experimental Approaches to Quantify Inhibition

in Microbial Communities

Inhibition observed in anaerobic dehalogenating microbial communities in the lab

and in the field results from multiple factors, including direct inhibition exerted

by the inhibitor on OHRB, general toxicity to essential bacterial cell functions,



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competition and survival among different bacterial species, and suboptimum site

parameters like pH, redox, or salinity. For example, halogenated compounds are

relatively hydrophobic and impact multiple cellular functions, and may not simply

interact directly or specifically with reductive dehalogenase enzymes. Chloroform

is a well-known inhibitor of methanogens and other C1-metabolizing organisms

that are not OHRB (Yu and Smith 2000), possibly as a result of nonspecific binding to the many porphyrin-like co-factors and methyltransferases involved in

metabolism. OHRB and reductive dechlorination can also be inhibited by other

electron acceptors, such as oxygen, nitrate, and various forms of sulfur and iron,

as well as detergents and solvents used to enhance remediation and factors such

as pH or salinity (Wei and Finneran 2011; McGuire and Hughes 2003; Paul et al.

2013; Paul and Smolders 2014; Townsend and Suflita 1997; Yeh et al. 1999). In

order to specifically classify the mechanisms for inhibition of reductive dechlorination in complex systems, we shall restrict our analysis here to the effect of the

simultaneous presence of multiple distinct halogenated organics on the growth

of OHRB and on dehalogenation kinetics, as seen in the context of groundwater

remediation.



13.3.1 Deconvoluting the Mechanisms of Inhibition

In the context of OHRB living in a mixed anaerobic community of fermenters,

acetogens and methanogens in their environment, we can envisage three general levels of inhibition, ranging from effects directly on the catalytic reductive

dehalogenase enzyme, to effects on the OHRB more generally or effects on other

microbes on which the OHRB depend:

(1) Enzyme level: Inhibition at the level of the membrane-bound reductive dehalogenase enzyme itself. For example the inhibitor can bind to the enzyme at

the active site in the classical notion of competitive inhibition or can specifically bind elsewhere on the enzyme or enzyme–substrate complex, resulting

in decreased activity.

(2) Organism level: Inhibition at the level of the OHRB itself, where the inhibitor interferes with or binds to critical cellular components other than reductive dehalogenases, resulting in impaired growth and dehalogenating activity.

For example inhibitors may interact with vital metabolic enzymes or electron transport components, or impair membrane function via hydrophobic

interactions.

(3) Community level: at this level, the inhibitor interferes with other microbes in

the community responsible for key processes of electron donor fermentation

and provision of hydrogen and other essential nutrients or co-factors to the

OHRB (and possibly vice versa).



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