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Institute of Medicine (US) Forum on Microbial Threats. The Science and Applications of Synthetic and Systems Biology: Workshop Summary. Washington (DC): National Academies Press (US); 2011.

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The Science and Applications of Synthetic and Systems Biology: Workshop Summary.

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A4SYNTHETIC BIOLOGY—A NEW GENERATION OF BIOFILM BIOSENSORS

28 and 27,29.

Biofilms: A Survival Strategy

The notion that bacteria live an autonomous, independent, and planktonic lifestyle has been radically challenged with the realization of the abundance of bacterial communities known as biofilms (Hall-Stoodley et al., 2004). Biofilms are structured communities of adherent microorganisms encased in a self-produced complex extracellular polymeric substance (EPS) matrix as shown in Figure A4-1 (Costerton et al., 1999). The advent of microscopy techniques has revealed the complex nature of these structured communities, containing networks of channels for nutrient supply and organized three-dimensional structures. Moreover, bacteria within these biofilms are profoundly physiologically distinct from their planktonic counterparts, and they function in a coordinated manor as a cooperative consortium, more similar to that of multicellular organisms than a unicellular organism (Hall-Stoodley et al., 2004; Lindsay and von Holy, 2006; Parsek and Singh, 2003). The ability to form biofilms is almost ubiquitously found within the bacteria domain of life and indeed within many other classes of microorganisms, including fungi, yeasts, algae, and protoza (Van Houdt and Michiels, 2010). Evidence suggests that biofilm formation is an ancient ability, with biofilm microcolonies being identified in fossils from 3.3 to 3.4 billion years ago (Hall-Stoodley et al., 2004; Westall et al., 2001). Taken altogether, biofilm formation is an evolutionary conserved and widespread phenomenon.

An electron micrograph of Staphylococcus aureus bacterial biofilms on the luminal surface of an indwelling catheter

FIGURE A4-1

An electron micrograph of Staphylococcus aureus bacteria biofilms on the luminal surface of an indwelling catheter. SOURCE: Image obtained from Public Health Image Library. Provided courtesy of R. Donlan, and J. Carr, Centers for Disease Control and Prevention. (more...)

The advantages of bacteria to abandon their independent lifestyle for that of a community is simply that it provides a more protective mode of growth. Biofilms allow bacteria to colonize and sustain favorable niches independent of larger environmental changes. For example, the EPS, a key component of biofilms, can serve as storage for nutrients and water, helping to stabilize local resources (Costerton et al., 1987; Donlan, 2002; Lindsay and von Holy, 2006). In addition, biofilms provide a defensive role, acting as a physical barrier from environmental dangers such as ultraviolet light, metal toxicity, acid exposure, dehydration, and even immune defenses such as phagocytosis (Donlan, 2002; Lindsay and von Holy, 2006; Parsek and Singh, 2003). These selective advantages of biofilm formation are obvious within the context of hostile and dynamic environments such as that of ancient Earth (Hall-Stoodley et al., 2004). Yet even in today’s relatively temperate environment, the formation of biofilms is still selectively advantageous and constitutes a major component of bacterial biomass in natural environments (Costerton et al., 1978, 1999; Lindsay and von Holy, 2006). Bacteria’s attempt to survive by the formation of biofilms extends beyond just natural environments and provides bacteria protection in many man-made environments (Hall-Stoodley et al., 2004). However, the focus of this review is the presence of pathogenic bacterial biofilms within a clinical context.

Biofilms in a Clinical Context

It is only in recent years that the importance of biofilms in clinical settings as a source of pathogenic bacteria has been realised (Lindsay and von Holy, 2006). The persistence of biofilms in hospitals is undoubtedly a significant contribution to hospital-acquired infections or nosocomial infections (Costerton et al., 1999; Donlan and Costerton, 2002; Hall-Stoodley and Stoodley, 2009; Hall-Stoodley et al., 2004; Lindsay and von Holy, 2006). Indeed it has been estimated that biofilms contribute to 65 percent of nosocomial infections (Potera, 1999; Smith and Hunter, 2008). Table A4-1 shows the most frequent pathogenic strains associated with nosocomial infections, the most frequent and problematic being Pseudomonas aeruginosa, Staphylococcus aureus, and S. epidermidis (Costerton et al., 1999; Hall-Stoodley et al., 2004). Recent studies estimate that at any one time 9 percent of all in-patients in England and Wales (United Kingdom) suffer from hospital-acquired infections, resulting in approximately 5,000 deaths per year. The costs associated with these hospital-related infections for the U.K. National Health Service are estimated at £1 billion per year (Smith and Hunter, 2008).

TABLE A4-1. The Most Common Causes of Nosocomial Infections.

TABLE A4-1

The Most Common Causes of Nosocomial Infections.

Although many nonpathogenic bacteria are able to form innocuous biofilms, it is the formation of pathogenic biofilms that is particularly important for contributing to the high level of hospital-acquired infections. It has been suggested that biofilm formation is analogous to a virulence factor, where biofilm formation increases the likelihood of a pathogen causing an infection.

Three general mechanisms have been proposed to explain the increase of virulence seen for pathogenic bacteria in a biofilm microenvironment (Hall-Stoodley and Stoodley, 2005). These are the survival and transmission of planktonic pathogens from biofilms, the phenotypic heterogeneity of biofilm populations and the potential evolution of infectious phenotypes, and the role of biofilm formation in the regulation of virulence mechanisms. Each of these mechanisms is discussed further in Box A4-1.

Box Icon

BOX A4-1

The Mechanisms of Biofilm-Associated Virulence. Clinical environments such as hospitals are constantly exposed to a consortium of pathogenic bacteria. Many pathogenic bacteria can be easily eradicated when in planktonic mode of growth by biocidal agents. (more...)

The seriousness of this problem has provided strong motivations for developing new antibiofilm therapies and detection methods. The development of new antibiofilm therapies is beyond the scope of this paper, although there are a number of extensive reviews (Lynch and Abbanat, 2010). Here we focus on current methods for biofilm detection, as well as their limitations, and the prospect of synthetic biology to develop completely novel methods for the detection of pathogenic biofilms.

Current Methods of Biofilm Detection

The ability of biofilms to form on a variety of abiotic and biotic surfaces has resulted in colonization of many clinical environments. Detection of these biofilms has obvious advantages: to direct suitable sanitation protocols in biofilm niches, to indicate replacement of any contaminated medical devices such as indwelling catheters, and to direct suitable medication if patient infection ensues. It has now become common practice in hospitals to sample both environmental surfaces and patients, typically urine, for the presence of pathogenic biofilms. There are numerous methods used for detection, including conventional plate counting, phenotypic screens, microscopy methods, and genotypic methods. Two of the most established methods for biofilm detection used in laboratories and clinical settings are the genotypic and phenotypic methods that are described in more detail below.

Phenotypic methods rely on culturing bacterial samples and screening these cultures for the phenotype of biofilm producers. These methods rely on the hypothesis that biofilm formation is a marker of virulence. Three variants of phenotypic detection have been commonly used: the tissue culture plate, the tube method, and the Congo red agar (CRA) method. These methods rely on indirectly assessing biofilm producers by accumulation of biofilm components such as EPS, a polysaccharide involved in cell-cell adhesion and essential for biofilm formation.

Genotypic methods are based upon PCR amplification of specific genetic markers associated with pathogenic biofilms. The most notable example is the use of the ica cluster to identify biofilm-producing strains of Staphylococcus aureus and S. epidermidis. The ica cluster contains genes that are essential for these strains to produce EPS. In addition, PCR is commonly used to assess antibiotic resistance; for instance, the presence of the mecA gene encoding methicillin resistance can be used to identify strains of methicillin-resistant S. aureus.

Each of these methods has its own advantages and limitations. The major limitations of these are described below:

  • Expertise and cost. Although techniques such as PCR and microscopy techniques have become commonplace in molecular biology laboratories, in hospitals, particularly in developing countries, access to the equipment and expertise required for analysis can be limited (Bose, 2009).
  • In situ and point-of-care detection. Sampling of biofilms on hospital surfaces can be abrasive to attached cells and may result in damage to cells, rendering them nonculturable (Lindsay and von Holy, 2006). In addition, sampling of indwelling medical devices such as catheters requires the removal of catheters, whether a biofilm is present or not. This can result in unnecessary stress for patients and wastefulness of such devices (Donlan and Costerton, 2002). Finally, the recovery efficiency of sampling methods commonly used is largely unknown, leaving the possibility of not detecting a representative population.
  • Reliability and reproducibility. The genotypic methods are generally considered more reliable and sensitive. However, they can be subject to false positives. This is due to the ica cluster not being absolutely required for biofilm formation. It has been shown that polysaccharide intercellular adhesin (PIA) synthesis from the ica operon alone is not sufficient to produce biofilms and that biofilms can form without producing PIA in staphylococci (Chokr et al., 2006).
    For the phenotypic detection methods there is limited sensitivity, reliability, and reproducibility compared to genetic detection. There have been numerous independent studies comparing the different phenotypic methods, which have shown varying success rates using the different phenotypic methods. For example, studies assessing the sensitivities of the CRA method have shown a detection success ranging from 5.26 to 89 percent (Arciola et al., 2006; Bose, 2009; Knobloch et al., 2002; Mathur et al., 2006; Oliveira and Cunha, 2010). In addition, the phenotypic methods are commonly prone to false positives and nondetection of “weak” biofilm-forming strains.
  • Time of response. The phenotypic detection methods rely upon the culturing of microorganisms collected from samples. These incubation periods are typically 24 to 48 hours and cultures are often analyzed offsite, increasing time for biofilms to develop (Bose and Ghosh, 2011).
  • Informative. Neither the genotypic nor the phenotypic methods elicit an in situ biofilm structure. Although detection of the pathogenic biofilms indicates the potential for the presence of biofilms, it is not an accurate reflection of the actual microenvironment. This can lead to an inaccurate detection in the in situ environment and false diagnosis.

Whole-Cell Biosensors: Bridging the Detection Gap

As discussed above there is a strong motivation for the development of in situ detection methods with increased response time, specificity, and more informative outputs. A potential solution is the development of whole-cell biosensors, using genetically engineered biological cells to directly detect pathogenic biofilms. An essential ability for cells to function is the ability to sense and respond to a diverse range of environmental signals from environmental conditions such as temperature change to the detection of nutrients in the environment. This ability can be broken down to two subfunctions, that of sensing and that of response (signal transduction). Whole-cell biosensors are essentially composed of the same two elements integrated into microorganisms: sensing elements detecting the analyte of interest and transducing elements that are usually coupled to the production of a detectable output, for example, expression of reporter proteins such as natural fluorescent proteins.

Some of the advantages of whole-cell biosensors are that they detect a variety of biological, organic, and nonorganic compounds. In addition, they can produce a number of detectable outputs that can be easily quantifiable, for example, fluorescent proteins measured by a fluorometer, dyes and pigments measured by spectroscopy and by eye, and electrochemical signals such as pH measured by litmus paper or a pH meter. These easily measured outputs require little expertise to use or to interpret. In addition, the timescale for biosensors is generally rapid, with signals being transduced in seconds to generate detectable signals within minutes to hours. In addition, compared to other in vitro biosensors like widely used enzymes or antibodies, whole-cell biosensors are easily produced by cell culture, in contrast to the costly and time-consuming purification of enzymes and antibodies.

Another advantage of whole-cell biosensors can be highlighted using the example of detecting pathogenic biofilms. Genotypic methods detect at a microscopic level the DNA genetic elements that are required for the formation of biofilms rather than the products of these genes, which can therefore lead to false positives. Phenotypic detection detects the products of biofilms but at a macroscopic level. Whole-cell biosensors offer a way to bridge the gap between these two methods, allowing sensing and evaluation at the level of microbial habitats. Whole-cell biosensors could allow the detection of new markers of biofilms to provide more effective detection methods. In recent years, there has been increased interest in the development of whole-cell biosensors, particularly within the field of synthetic biology. In the remainder of this review, the potential of synthetic biology for creating more effective biosensors for the detection of pathogenic biofilms is discussed.

Synthetic Biology: A New Generation of Biofilm Biosensors?

Synthetic biology is an application-driven field attempting to apply a rational engineering approach to the redesign of biological systems, to produce valuable and novel biological functions. Synthetic biology can be thought of as a natural evolution of biotechnology as opposed to being a separate field, although it does offer a novel and exciting approach. In the past 40 years biotechnology has undoubtedly produced many valuable biological products, yet current approaches tend to be lengthy due to the ad hoc nature of the approach. In addition, there is little lateral transfer of knowledge between projects, meaning knowledge gained in research and development in one area may not assist the post hoc development of projects in other, unrelated areas.

Synthetic biology aims to move away from this ad hoc design by providing a conceptual framework based on systematic design and rational engineering, so that redesigning biological systems would become more equivalent to that of redesigning a plane or a car. Indeed, it is the adoption of an engineering framework that has revolutionized the design and output of applications in the fields of mechanical, electrical, civil, and aeronautic engineering, and it is proposed that such an approach will do the same for biotechnology. The focus of this paper is to discuss the potential of whole-cell biosensors and the synthetic biology approach both in general and for the specific application of detection of pathogenic biofilms, the need of which was highlighted earlier.

Three major advantages of the synthetic biology approach for biosensor designs are highlighted: the advantages of the engineering approach, the development of novel sensing elements, and the complexity synthetic biology could enable.

Putting the Engineering into Genetic Engineering

Modern engineering relies on designing systems from catalogues of well-defined standard parts and higher-level devices that can be assembled into larger systems in a standardized manner. For electrical engineers, catalogues such as RS Components and Farnell provide well-defined parts and devices, each associated with data sheets of the functional characteristics. System designs can then be assessed by applying mathematical models in combination with known behavior characteristics, to computationally simulate their overall functions. This allows rapid assessment in silico of designs that meet the required specifications of design. Through iterations of this engineering approach or engineering cycle, efficient production of well-characterized and robust end products, from electronics to buildings, can be produced.

Synthetic biology is trying to adopt this engineering cycle and develop the tools required for high-throughput engineering of biological systems (Figure A4-2) (Gulati et al., 2009; Kitney et al., 2007). However, living systems are not computer chips and thus it is important to note that synthetic biology aims to provide a conceptual engineering approach that will allow biological engineering to be easier and more predictable. Living systems are intricate, formed from a complex network of interacting chemical components driven by the ability to self-replicate, adapt, and survive. Therefore, in living systems, context dependency and stochastic behavior are common features and therefore the engineering of predictable new cellular systems with defined functions is challenging. Nevertheless, the field of synthetic biology aims to provide a series of foundational technologies and a defined framework that will enable robust biological design and overcome these clear challenges. At present much emphasis in synthetic biology is focused on engineering single-cell organisms like Escherichia coli or yeast, given that our understanding of these organisms is more mature than for multicellular systems. With this in mind, a synthetic biology engineering cycle is discussed below.

A diagram of the engineering cycle as an approach for synthetic biology

FIGURE A4-2

The engineering cycle as an approach for synthetic biology. This adaption includes the four stages of defining specifications, design of biological devices, in silico modeling of the potential designs, and characterisation of performances.

Engineering cycle

  1. Specification The first stage in the engineering cycle is to define the specifications required. For example, a previous whole-cell biosensor designed to detect arsenic in polluted water defined the detection range as defined by the World Health Organization recommendation of safe drinking water at 10 ppb arsenic. Other specifications can be tunable, for example, threshold, time of response, particular microbial organisms, or host chassis to be used.
  2. Design Once the specifications have been defined, genetic devices have to be designed to give the functions of interest to host cells, often microbes. For the design of a genetic device, the principle of abstraction is applied to help separate out unnecessary layers of complexity (Endy, 2005). At the most complex layer, the design of a biological device involves the writing of a DNA nucleotide sequence to give desired function when implanted in a cell. However, an abstract view can be taken and these sequences modularized into functional parts such as promoters, gene coding sequences, ribosome binding sites, and terminators. These modules of DNA allow the functional separation of biological parts (BioParts) and allow a higher level of design, where the underlying complexity of the DNA sequence can be hidden (Endy, 2005).
    Collaborative and international efforts have been made to make open-source registries to document and physically store a huge variety of BioParts and devices, most notably being the BioBricks registry (http://partsregistry.org) and the International Open Facility Advancing Biotechnology (BIOFAB). Two important criteria of BioParts in these registries are the adherence of standards and the documentation of characterisation data. The BioBricks foundation has proposed a “request for comment” process, encouraging the synthetic biology community to list standards for physically joining DNA and protocols such as for characterisation of BioParts.
    These catalogues are beginning to provide a huge toolbox of biological parts for the engineering of biological systems, from basic BioParts such as promoters, ribosome binding sites, genes, and terminators to more complex composite devices such as toggle switches, oscillators, logic gates, and cell-to-cell communication systems. Moreover, the standards required for submission to these registries ensure compatibility and interchangeability. For example, the BioBricks registry has defined standard restriction sites flanking all parts and devices, which has allowed standardized methods of assembly where any two parts can be combined by following use of a standardized protocol. Finally, the documentation of characterisation data allows transfer of knowledge and experience gained from separate projects to be used in the evaluation of potential designs (Arkin, 2008).
  3. Modeling After the design stage, in silico computational simulation of mathematical models representative of the genetic devices is performed. In the 1960s the mathematical logic in gene regulation of the lac operon was demonstrated (Andrianantoandro et al., 2006; Monod and Jacob, 1961). Since then, systems biology has transformed our understanding of biology using representative mathematical models based on quantitative data, most notably stochastic and ordinary differential equations. These modeling techniques, coupled with the use of experimentally defined characteristics defining model parameters, can predict the functional characteristics of a design without the need to undergo cloning or de novo synthesis, or experimental characterisation both of which increase overall time and cost of development. Ellis et al. (2009) demonstrated the use of computational modeling to predict the behavior of feedforward loops in yeast. To do so, a library of inducible promoters with known minimum and maximum outputs (expression levels) were used in the device design. The characterisation data were incorporated into the modeling, the predictions of which were shown to accurately reflect the experimentally derived behaviors of these devices (Ellis et al., 2009).
  4. Quality control The idea that a device’s behavior can be fully predicted by modeling the known characteristics of individual parts is obviously an oversimplification. It is inevitable that unexpected emergent properties will result when individual parts are put together that have been characterised in isolation and different contexts such as a switch in host chassis of E. coli to Bacillus subtilis (Arkin, 2008; Serrano, 2007). Unexpected interactions can occur from the context of the other BioParts in the device or even the host chassis. For example, in mRNA the untranslated regions of promoters can form secondary structures with ribosome binding sites of mRNA sequences modifying the function of this BioPart (Arkin, 2008).
    These factors necessitate a quality control step, first to assess how well the device meets the specification but also to gain a greater understanding of the device being designed. Although approaches such as BioPart characterisation and modeling might not give an absolute prediction of device function as seen in other fields such as electronics, it nonetheless reduces the number of potential designs that need to be explored and therefore increases the overall efficiency.

Novel Biosensor Targets

As mentioned earlier, biosensors are composed of two subfunctions, that of sensing and that of signal transduction. The example of quorum sensing has already been discussed, where bacteria sense the local population densities based on the concentration of autoinducers and, once a threshold is reached, gene expression is induced. In this example, transcription factors act as the sensing elements binding to autoinducers and the quorum-sensing responsive promoters act as signal-transducer elements, defining the threshold of response and converting the autoinducer concentration to transcriptional output.

The bacterial toolbox of sensing elements is mostly mRNA and protein based, both of which can form complex tertiary structures able to bind a myriad of targets. Coupled to these sensing elements, the signal-transducing elements are generally transcriptional, translational, and posttranslational. Sensing modules and transducer modules can be separate biological components such as in quorum sensing, or within the same biological molecule such as riboswitches that contain an mRNA aptamer element able to sense and convert this sensing to translational output by sequestering or releasing a proximal ribosome binding site.

When considering the design of biosensors, it is essential to identify both the sensing and the transducing elements. In general, synthetic biologists have employed three strategies to use these elements: first, to import exogenous sensing elements into new biological host chassis; second, to use and rewire existing endogenous sensing elements to detect new signals; and finally, in the production of novel elements not found in nature.

Three examples are described in Box A4-2 to explain each of these strategies. In addition, each example provides an illustration of biosensors functioning at the level of transcription, translation, and posttranslation.

Box Icon

BOX A4-2

Strategies and Examples of Biosensor Design. Aleksic et al. (2007) engineered E. coli to detect toxic arsenic levels in drinking water, which today is a major pollutant of water in developing countries such as Bangladesh and West Bengal. To do so, exogenous (more...)

Increasing Complexity

Within the field of synthetic biology, a variety of synthetic genetic devices have been developed. Many of these devices have been inspired by those found in electronics and include toggle switches (Atkinson et al., 2003; Bayer and Smolke, 2005; Deans et al., 2007; Dueber et al., 2003; Friedland et al., 2009; Gardner et al., 2000; Ham et al., 2006, 2008; Kramer and Fussenegger, 2005; Kramer et al., 2004), memory elements (Ajo-Franklin et al., 2007; Basu et al., 2004; Friedland et al., 2009; Ham et al., 2006, 2008), pulse generators (Basu et al., 2004), time-delayed circuits (Ellis et al., 2009; Weber et al., 2007), oscillators (Atkinson et al., 2003; Danino et al., 2010; Elowitz and Leibler, 2000; Fung et al., 2005; Stricker et al., 2008; Tigges et al., 2009), and logic gates (Anderson et al., 2007; Guet et al., 2002; Rackham and Chin, 2005; Rinaudo et al., 2007; Win and Smolke, 2008). These synthetic genetic devices are aiding the design of more complex biological systems the likes of which have not been previously seen. For the application of whole-cell biosensors, the implementation of these genetic devices could allow more complex and informative biosensors to be developed. For example, consider the use of logic gates for biosensor design. Logic gates are devices that perform a logical operation based on one or more inputs to produce a signal output. A frequently featured logic gate implemented in synthetic biology is the AND gate. An AND gate integrates two or more signals and will only give an output when all inputs are present. The principle and a biological example of an AND gate is shown in Figure A4-3.

The principle and biological example of an A N Dgate

FIGURE A4-3

The principle and biological example of an AND gate. (A) The symbolic representation of an AND gate and (B) a truth table describing the logic of an AND gate with regard to two inputs. Only when both inputs are present is an output produced. (C) An example (more...)

The example of logic gates highlights the potential complexity of design that synthetic biology could offer for biosensor design. In general, multiple signal integration would allow fewer false positives by relying on more than one signal for detection and thus increasing the reliability. For example, the use of NOT gates could be used to remove known signals contributing to false positives. In addition, by being able to integrate multiple signals, logic gates could offer more complex and informative detection.

A more relevant illustration with regard to developing biosensors for detecting biofilms can be highlighted when we take into account mixed-species biofilms. Consider the case of biofilms on indwelling urinary catheters. A study of 106 biofilms found on catheters showed 14 species of bacteria to be commonly present (Macleod and Stickler, 2007; Stickler, 2008). Furthermore, although single-species biofilms were found, most were mixed-species biofilms—most frequently containing Enterococcus faecalis, Escherichia coli, Pseudomonas aeruginosa, and Proteus mirabilis. With the use of logic gates and multiple signal integration, synthetic biology could develop biosensors to respond not only to multiple targets but also with the ability to distinguish between species. This would allow general detection of biofilms as well as species-dependent detection that could aid correct treatment should the infection worsen.

Summary

Synthetic biology is a newly developing field that aims to provide an engineering framework for the predictable construction of new biological systems. The most immediate exemplars relate to biosensors where synthetic biology has already produced several living cell biosensors that act robustly and predictably. The application of synthetic biology design tools will enable the development of a new generation of biosensors to detect pathogenic biofilms. The implementation of these designs will have a profound impact in many areas including the control of hospital-acquired infections.

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28

Centre for Synthetic Biology and Innovation, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.

27

Center for Systems and Synthetic Biology, University of Texas at Austin.

29

Correspondence: e-mail: ku.ca.lairepmi@tnomeerf.p, telephone: + 44 (0)2075945327.

Copyright © 2011, National Academy of Sciences.
Bookshelf ID: NBK84447

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