Yeast-based biosensors: design and applications.

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Received Date : 19-May-2014 Revised Date : 12-Aug-2014 Accepted Date : 19-Aug-2014 Article type

: MiniReview

Editor

: Verena Siewers

Yeast-Based Biosensors: Design and Applications Adebola Adeniran*, Michael Sherer*, Keith E.J. Tyo**

Department of Chemical & Biological Engineering, Northwestern University, Evanston, IL 60208

Manuscript in preparation for: FEMS Yeast Research *These authors contributed equally to this work **Corresponding author: Telephone: +1 847.868.0319 Fax: +1 847.491.3728 E-mail: [email protected]

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/1567-1364.12203 This article is protected by copyright. All rights reserved.

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ABSTRACT Yeast-based biosensing (YBB) is an exciting research area, as many studies have demonstrated the use of yeasts to accurately detect specific molecules. Biosensors incorporating various yeasts have been reported to detect an incredibly large range of molecules including but not limited to odorants, metals, intracellular metabolites, carcinogens, lactate, alcohols, and sugars. We review the detection strategies available for different types of analytes, as well as the wide range of output methods that have been incorporated with yeast biosensors. We group biosensors into two categories: those that are dependent upon transcription of a gene to report the detection of a desired molecule, and those that are independent of this reporting mechanism. Transcription dependent biosensors frequently depend on heterologous expression of sensing elements from non-yeast organisms, a strategy that has greatly expanded the range of molecules available for detection by YBBs. Transcription independent biosensors circumvent the problem of sensing difficult-to-detect analytes by instead relying on yeast metabolism to generate easily detected molecules when the analyte is present. The use of yeast as the sensing element in biosensors has proven to be successful and continues to hold great promise for a variety of applications.

KEY W ORDS biosensing, receptors, environmental sensing, synthetic biology

1. Introduction A key activity in Synthetic Biology is the detection and quantitative measurement of environmental and intracellular conditions. It is not surprising that biosensing can be readily engineered as biosensing in nature is a key biological capability for fitness and allows an organism to avoid harm as well as exploit opportunities for growth and reproduction.

Cell-based biosensing, in general, and yeast-based

biosensing (YBB) in particular have many advantages for certain diagnostic and analytical applications. High specificity biosensing can be carried out in a micron-scale unit (the cell) without power requirements

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or the need for a highly trained operator. Contrast this with a Western blot or a gas-chromatograph massspectrometer (GC-MS), which are orders-of-magnitude larger, and require power and operators. However, compared to a Western blot or GC-MS, engineered cell-based biosensors typically only detect one analyte. In niche applications where only one analytical measurement is required, the advantages of cell-based biosensors are quite profound. The literature has many examples of exploiting yeasts for biosensing. Yeast is ideal for engineering

biosensors for a number of reasons: (a) it can be made in “active dry” form cheaply and stored for long periods of time, (b) it can serve as a chassis for higher-eukaryotic sensing modalities (e.g. G-protein coupled receptors, GPCRs), and (c) it can tolerate rather harsh environments, especially compared to bacteria. These advantages combine with the wealth of background information on Saccharomyces cerevisiae and its role as a model organism with simple genetic manipulations to make yeast an excellent choice for use in cell-based biosensors. In the broader Synthetic Biology literature, there is a strong emphasis on biosensing that results in promoter activation/repression, as this is quite amenable to building genetic circuits. In this review, we cover a range of different transcription-dependent biosensors in yeast. These include both receptors that are specific to organic compounds and heavy metals, as well as non-specific stress responses that can be triggered by a number of analytes.

We extend beyond these transcription-based biosensors by

discussing a fascinating range of non-transcription dependent sensing examples that use yeast metabolism to convert the analyte (a sugar, alcohol or other compound) to another analyte that is easily measured (redox potential, dissolved oxygen, etc.). Figure 1 highlights the range of different analyte classes that can be detected with YBBs with a supporting narrative that focuses on the mechanisms that were engineered to achieve the biosensing goal. As Synthetic Biology finds its way into new applications, an essential technology gap will be to exploit detection strategies that can occur on short time scales (e.g. seconds), which will require transcription-independent strategies.

2. Transcription Dependent Biosensors The majority of yeast-based biosensors use a reporter gene under the control of an inducible

promoter. The promoters are activated in the presence of a ligand of interest, either directly by the


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ligand/receptor dimer or via a signaling cascade. Both the receptor and the promoter may be native to yeast or adapted from other organisms. Furthermore, both native and non-native systems can be evolved to further refine their functionality. The most common reporting systems for yeast biosensors are fluorescence, luminescence,

colorimetry, electrical methods, and growth rate. The operating principles and properties of these systems have been described extensively in the literature, and the advantages and disadvantages of each reporter are listed along with relevant reviews in Table 1. Several variations and yeast specific improvements on these traditional reporters are discussed in Section 2.7.

2.1. GPCR Engineering for Functional Biosensors A large class of yeast-based biosensors is dependent upon the signaling capabilities of G-protein

coupled receptors (GPCRs). Though all members of the GPCR superfamily share the same basic structure, GPCRs are able to selectively detect an incredibly diverse range of molecules including photons, ions, small molecules, and proteins (Pierce, et al., 2002). The mechanism of GPCR signal

transduction is only concisely presented here but has been covered elsewhere (Bardwell, 2005, Ladds, et al., 2005, Dupré, et al., 2009). Briefly, the GPCR is coupled to a G protein composed of Gα, Gβ, and Gγ subunits, from which the Gβ and Gγ subunits form a heterodimer. Ligand binding results in a conformational change of the GPCR, freeing the Gβγ dimer from the Gα subunit to travel laterally through

the membrane and activate downstream effectors (Figure 2). This is the signaling method used in the pheromone mating pathway of S. cerevisiae (Bardwell, 2005). Many yeast based biosensors require expression of heterologous GPCRs that interface with the S.

cerevisiae pheromone mating pathway. The incorporation of GPCRs from higher organisms with functionalities not naturally available to yeast greatly expands the capability of yeast based biosensors. Additionally, GPCRs are a natural starting point in the development of yeast based biosensors due to: 1) key similarities between the yeast mating pathway and the signaling mechanisms of higher organisms including both the structural and functional similarities between Gpa1, the yeast G-protein alpha subunit, and the mammalian Gα subunit (Dohlman & Thorner, 2001), and the dependence of signal transduction


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on the mitogen-activated protein kinase (MAPK) cascade (Ladds, et al., 2005), 2) the fact that yeast have only two endogenous, non-interacting GPCR pathways for which the corresponding GPCRs can be readily knocked out (Versele, et al., 2001), and 3) the ability of GPCRs to mediate intracellular changes in response to extracellular signals. However, there are many challenges associated with incorporating heterologous GPCRs into yeast.

For example, in mammalian systems each GPCR is coupled to a specific G-protein, and often the Gprotein, or parts thereof, must be replaced for receptor functionality (King, et al., 1990, Crowe, et al.,

2000, Pajot-Augy, et al., 2003). Also, the N-terminus is known to be important for membrane localization (Krautwurst, et al., 1998, Schlinkmann, et al., 2012); thus, it is not surprising that using a heterologous receptor without any modification can result in receptor degradation or mislocalization (Ladds, et al., 2005). Despite the inherent challenges, the use of heterologous GPCRs has been and continues to be a key tool in the development of yeast biosensors. Following the development of one particular class of yeast sensors known as ‘bionoses’, sensors that incorporate olfactory receptors from higher organisms, and highlighting some advancements made using directed evolution to create biosensors reveals many of the innovations that overcome the challenges of integrating heterologous signaling pathways in yeast.

2.1.1 Yeast Bionoses Areas of key modifications on the GPCR used in bionoses are highlighted in Figure 2. One successful

strategy in bionose development has been the replacement or slight modification of the alpha subunit (Ladds, et al., 2005), which plays a key role in both activating and resetting the G-protein after a ligand binding event. The Gpa1 protein is the G-protein alpha subunit of the yeast pheromone receptor (Ladds, et al., 2005), and the Golf protein is the alpha subunit for olfactory receptors (Pajot-Augy, et al., 2003). The

Golf protein was reported to complement null mutations to Gpa1 in S. cerevisiae (Crowe, et al., 2000). This strategy proved successful for heterologous expression of the rat I7 olfactory receptor (Crowe, et al., 2000, Pajot-Augy, et al., 2003, Minic, et al., 2005) and a human olfactory receptor (Minic, et al., 2005) in


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S. cerevisiae. An alternate strategy is to modify the subunit by creating chimeric alpha subunits in which the C terminus is replaced with the corresponding region of the mammalian subunit (Brown, et al., 2000). A second strategy used in the development of bionoses has been to replace portions of the receptor

itself, specifically the terminal domains and the first transmembrane regions. The N terminal region has been found to be important for GPCR membrane localization (Krautwurst, et al., 1998, Schlinkmann, et al., 2012), and the C terminal region has been to shown to have a key role in modulating the interaction of the GPCR with its associated G protein (Hara, et al., 2012). In one example of regional replacement of specific receptor portions (Fukutani, et al., 2012), the N and C terminal regions of a mouse olfactory receptor were replaced by the corresponding regions of the rat I7 receptor. This replacement allowed for the detection capabilities of the mouse receptor to be coupled to the improved functionality obtained by using the rat I7 terminal regions for heterologous expression in yeast. The amino acid residue replacements, along with the use of the Golf subunit, were found to improve receptor expression,

localization, and orientation in the yeast outer membrane.

The ability of bionoses to interface with the cyclic adenosine monophosphate (cAMP) pathway in

yeast began to lay the foundation for a modular, heterologous receptor. Increased cAMP levels in mammalian cells results in the upregulation of genes under promoters with the cAMP response element (Lalli, et al., 1994). A cAMP pathway-dependent modular detection system was constructed such that signaling of detection events were relayed in yeast using some cAMP pathway components of the mammalian olfactory system, ultimately ending in activation of a cAMP responsive fluorescent reporter (Radhika, et al., 2007). The system receptor was composed of the N and C terminal regions of the rat I7 receptor and the ligand binding pocket of any receptor with a desired detecting capability. Maintaining the N and C termini of the rat I7 receptor allowed for signaling to be relayed using the cAMP related signaling components of the mammalian olfactory system. The modularity of this receptor design expands the possibilities for molecular ligands of bionoses, and the ability to interface with the cAMP pathway lends this technology to be feasibly incorporated into cAMP dependent systems.


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2.1.2. Directed Evolution of GPCRs for Yeast Biosensors Directed evolution has been successfully employed to create yeast based biosensors (Ault & Broach,

2006, Armbruster, et al., 2007, Conklin, et al., 2008, Dong, et al., 2010) based around the functionality of the yeast pheromone signaling pathway. In one study, directed evolution and the principles of olfactory sensing were combined to create a combinatorial biosensor starting from the human glucose GPCR (Ault & Broach, 2006). The power of the olfactory system to discriminate amongst thousands of stimuli lies in the combinatorial mechanism by which a single compound is able to activate multiple receptors to a specified degree, effectively establishing a fingerprint among multiple receptors (Malnic, et al., 1999). Evolved receptors were combined in a pairwise manner to discriminate the composition of a solution of several analytes, a task that could not be accomplished with the use of a single receptor (Ault & Broach, 2006). The power of directed evolution has also been harnessed to establish protocols to generate

heterologous GPCRs with novel binding capabilities in yeast (Armbruster, et al., 2007, Dong, et al.,

2010). Though these protocols have largely been used for the screening of pharmacological compounds, there are lessons from these protocols that easily translate into the creation of new yeast biosensors and could greatly expand the potential for yeast biosensors. In the development of these GPCRs, multiple iterations of mutagenesis and screening were needed to obtain mutants that displayed low activity for the native ligand and high sensitivity for the non-native ligand. Similar to bionoses, an appropriate mammalian Gα subunit needed to be co-expressed with the heterologous GPCR to maintain pathway functionality. The choice of expression plasmid was also considered; though a high copy plasmid would be advantageous in the expression of a GPCR that is not highly sensitive to a novel ligand, the use of a high copy plasmid may be disadvantageous if the expression of the non-native GPCR is metabolically taxing to cells. Directed evolution of heterologous GPCRs expressed in yeast (Ault & Broach, 2006, Armbruster, et al., 2007, Dong, et al., 2010) has highlighted the importance of considering targeted regions for mutagenesis, as very specific point mutations often proved to yield the largest changes in receptor specificity while random mutagenesis across the entire receptor often yielded mutations that increased activity but not specificity (Ault & Broach, 2006). Additionally, allosteric mutations were also


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found to yield functional mutagenized receptors, implying that targeted mutagenesis should not be restricted only to ligand-binding regions.

2.2. Human Steroid Receptors Perhaps the most prolific class of yeast biosensors is based on heterologous expression of human

steroid receptors. These biosensors are typically used to test compounds that may activate or inhibit the receptors, and/or screen environmental samples for the presence of such compounds. These exogenous ligands or inhibitors are classified as endocrine disruptive compounds (EDCs), which may pose significant human health risks (Casals-Casas & Desvergne, 2011, Marques-Pinto & Carvalho, 2013), although the topic remains highly controversial (Nohynek, et al., 2013). The relative ease, portability, and rapidity of the yeast assay make it a cost effective choice for preliminary screening. Some endocrine biosensors have also found non-environmental monitoring uses, such as estrogen detection for the in utero determination of chicken gender (Tran, et al., 2010). The human estrogen receptor (hER) has been known to function in yeast since 1988 (Metzger, et

al., 1988). Since that time, dozens of studies have used engineered estrogen responsive elements upstream of various reporter systems to develop screening systems. The system architecture for the BLYES (BioLuminescent Yeast Estrogen Screen) is shown in Figure 3 as a representative example (Sanseverino, et al., 2005). Readers are directed to the references for a more thorough history of

estrogen sensor development (Eldridge, et al., 2011). It is also worthwhile to note that while most sensors have used the alpha isoform of the hER, the beta form is also functional in yeast and can be used for sensing (Lee, et al., 2007, Chu, et al., 2009). Of course, the detection of EDCs is not limited to estrogen and its analogues. The human thyroid

receptor (Li, et al., 2008, Shiizaki, et al., 2010), androgen receptor (Eldridge, et al., 2007, Michelini, et al.,

2008), glucocorticoid receptor (Wright & Gustafsson, 1992), mineralcorticoid receptor (Miller, et al., 2010) and progesterone receptor (Klotz, et al., 1997, Berg, et al., 2000), have all been recombinantly expressed, often multiple times with different reporting strategies. The general framework for most of


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these systems is the same as the BLYES estrogen system above – the receptor is expressed continuously and a plasmid contains reporter genes induced by the receptor-ligand dimer. One concern with the standard system is that the receptor and reporter are often on different

plasmids, or the receptor is integrated into the genome and the reporter is not (such as the BLYES system shown Figure 3). Apart from making cloning tedious, particularly for high throughput applications, this opens up the possibility of copy number imbalances between the receptor and the reporter, which can in turn affect signal strength. This problem was recently addressed by creating a set of single copy plasmids containing both the receptor and the reporter for all six human type I steroid receptors (Miller, et al., 2010). The resulting low copy number “pRR” plasmid contains the CEN and ARS DNA sequences that confer efficient episomal replication and segregation to help ensure stable copy numbers over many generations.

One technique to enhance signaling from heterologous receptors is the expression of accessory

proteins. In humans, steroid receptor cofactor I (SRC1) binds to a number of nuclear hormone receptors within the nucleus and helps stimulate transcriptional activity. Coexpression of SRC1a or SRC1e with hERα led to an increase in estradiol induced signaling in S. cerevisiae, however studies with fragmented proteins indicated a different binding mechanism was responsible than that in mammalian cells (Sheppard, et al., 2003). Nevertheless, SRC1 coexpression has been successfully incorporated into EDC biosensors. SRC1e considerably improved the sensitivity of an estrogen EDC biosensor used to sample rivers in Japan (Chu, et al., 2009) and SRC1a led to a 50% increase in maximum signal from a biosensor using the aryl hydrocarbon receptor (discussed in section 2.3) (Leskinen, et al., 2008). Use of the native human steroid receptors provides the most physiologically relevant information

when screening an environmental sample. However, disadvantages of this approach include the inability to distinguish between different agonists, or to separate out the effects of multiple contributing agonists/antagonists. These issues can be solved with receptors that are more specific to a ligand of interest, and such receptors have been generated by directed evolution. For example, researchers sought


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to create a sensor capable of identifying bisphenol A (BPA) specifically and distinguishing it from other estrogenic compounds. They performed directed evolution on hERα with a positive growth based screen for BPA induction and a negative fluorescence based screen for estradiol induction (Rajasärkkä, et al., 2011). One of the resulting mutants was tested and shown to be 4 fold more sensitive to BPA and 166,000 fold less sensitive to the native ligand, as shown in Figure 4 (Rajasärkkä & Virta, 2013). Further examples of directed evolution studies on hER in yeast include developing affinity for other hormones (Chen, et al., 2004) and synthetic ligands (Chockalingam, et al., 2005). Thus, as with GPCRs, directed evolution of intracellular receptors provides a powerful tool for improving assay specificity.

2.3. Other Nuclear Receptors In addition to the major human endocrine receptors, organic compounds can influence other human

nuclear receptors such as the aryl hydrocarbon receptor (AhR) and retinoid X receptor (RXR). AhR affects development and cell differentiation (Quintana, et al., 2008), while RXR is a required cofactor for several hormone receptors (Rowe, 1997) – thus many ligands and inhibitors of these receptors are also classified as EDCs. Yeast biosensors have been built around both of these receptors. Some follow the traditional reporter gene model described above, such as an AhR-lacZ assay (Miller, 1999, Kawanishi, et al., 2013). However, two other strategies have also emerged for engineering organic pollutant biosensors: (a) engineering receptor fusions capable of detecting the desired ligand in yeast, and (b) using a functional genomics approach to identify native yeast promoter activation that correlated with exposure to the desired ligand. In certain situations, heterologous expression of the receptor system is not adequate to create an

operational biosensor.

Instead, heterologous subunits must be fused with known functional

transactivators. Such is the case with the RXR, which is often activated by organotin compounds. To sense such compounds, a chimeric receptor was engineered with the ligand binding domain from RXR and the DNA binding domain from hERα, which (as discussed above) is a well characterized yeast

transcriptional activator. With the use of a luciferase reporter, several organotin compounds were


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detected at nanomolar levels (Kabiersch, et al., 2013). This approach thus offers another method to construct yeast biosensors when no native sensing elements are available. By fusing heterologous domains to well-characterized transactivators, such as hERα, the range of sensing domains available in yeast could be greatly expanded.

When specific receptors are not known, an alternative approach is to use functional genomics,

where native yeast promoters are identified that have the desired properties. Organophosphates, best known for their use in pesticides and chemical weapons, are also a suitable target for biosensors. A global transcription profile of S. cerevisiae was used to identify promoters responsive to the application of paraxon, a model organophosphate (Schofield, et al., 2007). Several candidate promoters were identified and investigated; eventually the promoter regulating ORF YLR346C was chosen and successfully used to express yeGFP in the presence of paraxon. However, the authors desired a strain that could not only detect paraxon but hydrolyze it, thus making the yeast both a biosensor and a bioremediator. To achieve

this, an organophosphate hydrolase enzyme (E.C. 3.1.8.1) from Pseudomonas diminuta was recombinantly expressed, and another global transcription profile was used to select a promoter active under conditions of paraxon hydrolosis. Unfortunately both systems were not tested in the same cell at the same time, but the results nevertheless highlight the applicability of functional genomics to biosensor development.

It is also possible, at least theoretically, to go beyond simply identifying promoters of interest and

actually engineer said promoters to have more desirable properties. It has been demonstrated that simple sequence changes in S. cerevisiae promoters can lead to a variety of phenotypic effects (Nevoigt, et al., 2007). Such efforts could be used to amplify signal strength or lower background signals by removing undesired cross talk from different transcription factors. We feel such techniques have been underutilized to date and hold great promise for further improving biosensor performance.


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2.4.

Heavy Metals

Microbial biosensors are well suited to monitoring heavy metals due to their availability to distinguish

between bioavailability and concentration - an important distinction, as only certain forms of ions have biological effects (Lehmann, et al., 2000). Bacterial heavy metal biosensors have seen significantly more

development than yeast based systems, including biosensors for Cd, Pb, Hg, Cr, Ni, Co, Zn, Cu and As (Ivask, et al., 2009, van der Meer & Belkin, 2010, Woutersen, et al., 2011, Branco, et al., 2013). This disparity may be due in part to the existence and characterization of numerous metal-specific resistance operons in E. coli which made it easy to identify specific metal responsive promoters (Daunert, et al., 2000). Only two metal-specific toxicity resistance genes (for As and Cd) are well characterized in S. cerevisiae (Wysocki & Tamás, 2010). Nevertheless, several yeast based metal biosensors have been developed using either optical or electrical reporting systems. Two such systems measure copper, which is an essential cofactor for all life forms at low concentrations, but is toxic and an environmental concern at high concentrations. Both systems take advantage of the 2+

Cu

inducible promoter which regulates expression of the CUP1 copper binding protein. The electrical

method uses the promoter to drive expression of LacZ to enable lactose metabolism (Lehmann, et al., 2000, Tag, et al., 2007). The yeast are incubated with lactose, and the increase in metabolism, and therefore oxygen consumption, associated with promoter activation is measured by an electrode, similar to the biochemical oxygen demand systems discussed in Section 3.1. The optical approach uses the CUP1 promoter to drive the firefly luciferase gene (Leskinen, et al., 2003). The luciferase gene was modified from the wild type to remove peroxisome targeting peptides, which improved cellular viability and signal strength. In the end, both systems report a similar detection limit (~1uM). The electrical system requires cell immobilization and more specialized Flow Injection Analysis equipment, however the assay itself is faster as no incubation period is required. The electrical biosensor demonstrated agreement with chemical analyses on wastewater samples (Tag, et al., 2007), while the optical biosensor was used to demonstrate copper bioavailability in a series of metal nanoparticle toxicity studies (Aruoja, et al., 2009, Kasemets, et al., 2009).


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2.5. Intracellular Metabolites As we have seen, the most common form of biosensors uses the organism as a transducer,

detecting and reporting the presence of an analyte in the environment. However, it can also be useful for an organism to report on its own metabolic activity. Real time monitoring of sugars, phosphates, redox carriers, and even protein activity in living cells can provide valuable insight into cell behavior as well as inform metabolic engineering decisions (Umeyama, et al., 2013). Thus, several such sensors have been developed in yeast. Most use posttranslational systems and are discussed in Section 3.6. However, transcription factor based biosensors have also been developed. One recent example provides an excellent demonstration of how such sensors can assist metabolic

engineering efforts. Umeyama and colleagues designed sensors for S-adenosylmethionine (SAM), an industrial product and research reagent produced in S. cerevisiae. There is no known native transcription factor responsive to SAM in S. cerevisiae, so a synthetic transcriptional activator was created using the

yeast activation domain B42 and the SAM responsive MetJ repressor from E. coli. This novel fusion is capable of binding to the metO operator sequence (also native to E. coli) and driving transcription in yeast. (Umeyama, et al., 2013). The system showed dose dependent activation of a fluorescent reporter, and was further engineered into an AND gate circuit with the doxycycline dependent tetR repressor. By using the sensing strains in a genomic screen, the researchers were able to identify GAL11 as an enhancer of SAM production.

2.6.

Nonspecific Toxicity

It is sometimes useful to have a low-specificity biosensor respond not to a particular analyte but

instead indicate an overall level of toxicity in a sample. This is particularly true for environmental monitoring applications involving a wide range of (possibly unknown) contaminants, as well as pharmaceutical screening assays. Yeast are particularly well suited for these applications - they are fast growing and easily manipulated, but their eukaryotic nature also makes them a reasonable model


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organism for toxicity (Wei, et al., 2013). Heat shock promoters, which are active under a wide variety of cellular stresses, are a good example of generic distress signals. Such promoters have been shown to activate reporter genes in the presence of sub-lethal concentrations of disinfectant (Fujita, et al., 1998). S. cerevisiae also has native promoters activated by DNA damage, which are responsive to a wide

range of mutagens and carcinogens. Studies have engineered various reporter genes downstream of several such promoters, including HUG1, RAD54, and RNR3, to investigate their potential as genotoxicity screening tests (Afanassiev, et al., 2000, Benton, et al., 2007). A Rad54 based biosensor has even been ®

commercialized as the GreenScreen assay (Cahill, et al., 2004). Yeast based tests have several advantages over commonly used bacterial screens such as the Ames test, including detection of genotoxins that are bactericidal and/or have a eukaryote specific mechanism (Cahill, et al., 2004, Wei, et al., 2013). Given the variety of background strains and reporter genes used in the various assays, Wei and colleagues sought to combine various improvements from different studies and construct standardized systems using HUG1 or RNR3. A fluorescent assay was chosen for its ability to image live cells. Five gene knockouts that had been previously shown to enhance genotoxin sensitivity (cell wall proteins cwp1 and cwp2, membrane efflux transporters pdr5 and snq2, and a stress response regulator yap1) were combined with two new deletions (mag1 and rad1) involved in DNA repair. The resulting biosensor had anywhere had between 2 and 300 fold enhanced sensitivity to eight different genotoxic compounds (Wei, et al., 2013). Furthermore, the sensor is not responsive to several compounds that ®

cause false positive results in either the Ames test or the GreenScreen assay. The use of knockout strains is unique because it allows for signal enhancement without direct modification of a receptor or transcription factor. Furthermore, these results suggest that, at least in some cases, multiple knockouts can be synergistically combined for enhanced effects. Bakhrat and colleagues report a novel use of another S. cerevisiae DNA damage sensitive promoter,

specifically UFO1 (Bakhrat, et al., 2011). By using the promoter to drive a luciferase reporter, they measured activity in response to ultraviolet light (which consists of UVA, UVB and UVC rays). They found that the promoter was particularly responsive to UVB (315-280nm) light. However, the application of commercial sunscreens resulted in complete protection from the UV light, suggesting these biosensors


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could be used to evaluate such products. Interestingly, the system was also sensitive to sub-nanomolar concentrations of arsenic when implemented in a background strain (pdr5) lacking the transporter used to remove such toxic chemicals.

2.7. Reporter Options The most common reporters for transcription dependent yeast based biosensors are fluorescence

(typically green fluorescent protein) bioluminescence (luciferase) and colorimetry (beta-galactosidase). Each reporter has pros and cons, and there does not seem to be a ‘one-size-fits-all’ reporter system, despite years of research. Some advantages of each reporter are that: •

Colorimetric beta-galactosidase based methods are the most commonly used, require less expensive substrates than luciferase, and can be read with simpler equipment (a spectrophotometer) than luciferase or GFP (Fox, et al., 2008).

•

Bioluminescent luciferase offers similar sensitivity with a much faster and simpler procedure than colorimetric tests (6 hours vs 2-3 days). (Sanseverino, et al., 2005, Sanseverino, et al., 2009). They also do not require a light source for excitation, and luminescence is ideal for the development of fiber optic devices which are well-suited for field monitoring applications (Roda, et al., 2011).

•

Fluorescent proteins do not require any substrate addition or cellular disruption, and can also be completed in 4-6 hours (Bovee, et al., 2004, Beck, et al., 2005).

All three of these techniques are constantly being refined and improved – for example, a GFP system

was designed to make yeast GPCR activation visible via fluorescence microscopy (Nakamura, et al., 2013) and a faster version of the colorimetric assay has been developed (Colosi & Kney, 2011). When choosing a reporter for a transcription based biosensor, it is up to each researcher to determine which factors are most important for a particular application. Further complicating the decision are the emergence of new reporting strategies beyond the traditional “big 3”, which may offer even faster and/or less expensive detection. We will consider two such systems.


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The first uses a chemiluminescent (instead of colorimetric) beta-galactosidase reaction, with a recently developed commercially available substrate (Balsiger, et al., 2010). This assay requires shorter incubations with the environmental sample, resulting in a total assay time of four hours. Perhaps more importantly, the system functions properly without sterilization, extraction, or concentration of the environmental samples. In fact, the authors suggest that sterilization and filtration procedures may actually eliminate some of the estrogenic compounds from the sample and thus corrupt results. The assay is tenfold more sensitive for several estrogenic compounds than the BLYES assay, which may be due to loss of steroidal ligands in processing steps required for BLYES that are unnecessary with the new method. This same procedure has recently been shown to work with thyroid receptor biosensors as well (Li, et al., 2013). Another approach uses an amperometric detector due to its potential as an online, continuous

monitoring system. In this case, phytase is the estrogen induced reporter gene (Pham, et al., 2012).

When the appropriate substrate is added, phytase will catalyze the formation of p-aminophenol, which can be oxidized at an electrode and thus monitored amperometrically via the system shown in Figure 5.

The biocomponent is Arxula adeninivorans, chosen for its osmo-tolerance, and the same cells can be reused up to 15 times without loss of sensitivity. The system was compared to GC-MS for analysis of environmental samples, with mixed results (Pham, et al., 2013). One possible explanation is the fact that GC-MS can only detect the presence of specific reference compounds used as standards, whereas the biosensor is subject to the cumulative agonistic and antagonistic effects of the entire sample.

3. Transcription Independent Biosensors Another large class of yeast biosensors indirectly senses difficult-to-detect molecules by detecting

either reactants or products associated with the molecule’s metabolism for which standard detection methods exist. This approach has enabled yeast for use in the detection of alcohols, sugars, organic material, and lactate. Posttranslational sensing techniques such as FRET, which report ligand binding events directly without transcribing a reporter gene, are also discussed in this section.


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3.1.

Biochemical Oxygen Demand

Biochemical oxygen demand (BOD), the amount of dissolved oxygen needed for an organism to

breakdown organic materials, is often used as a proxy for water health. The standard BOD determination method, BOD5, measures the oxygen required for a consortia of microbes, often termed ‘activated

sludge’, to breakdown organic compounds and requires 5 days of assay time. In contrast, BOD tests using immobilized microorganisms yield results within minutes (Riedel, et al., 1988). To ensure compatibility between the two methods, a common practice is to compare BOD values obtained from the biosensor with those obtained from the standard BOD5 procedure. The basic structure of the BOD

biosensor consists of a measurement probe and immobilized microorganisms (Yang, et al., 1997). Key considerations for BOD sensors include the yeast strain used, the detection method, and the immobilization technique. An ideal strain in a BOD biosensor would have broad substrate specificities in order to react with a

variety of organic substances, resistance to toxic substances in waste water, and high oxidation activity (Jia, et al., 2003, Arlyapov, et al., 2013). Yeast cells in particular are resistant to detrimental environmental factors (Akyilmaz & Dinçkaya, 2005, Arlyapov, et al., 2012) and can utilize simple organic substances as carbon sources (Kulys & Kadziauskiene, 1980). Some yeast strains used in BOD sensors include Trichosporon cutaneum (Hikuma, et al., 1979, Riedel, et al., 1988, Riedel, et al., 1990, Yang, et al., 1997, Reiss, et al., 1998, Catterall, et al., 2001, Jia, et al., 2003) , Hansenula anomala (Kulys & Kadziauskiene, 1980), Torulopsis candida (Sangeetha, et al., 1996), A. adeninivorans (Riedel, et al., 1998, Chan, et al., 1999, Chan, et al., 2000, Tag, et al., 2000), S. cerevisiae (Campanella, et al., 1995),

Issatchenkia orientalis (Heim, et al., 1999)and Debaryomyces hansenii (Arlyapov, et al., 2012, Arlyapov, et al., 2013). Following the development of biosensors with T. cutaneum and A. adeninovorans highlights

some key development in yeast BOD sensors. One of the earliest yeast BOD sensors used T. cutaneum and was reported in 1979 as an alternative

approach to BOD sensors that used biomass composed of mixed populations and could not give reproducible results (Hikuma, et al., 1979). Using the differential time curve method (Yang, et al., 1997)


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as opposed to waiting for a steady state measurement from the electrode of a T. cutaneum biosensor reduced sensor response time from minutes to seconds (Riedel, et al., 1988). Incubating the sensor with wastewater increased biosensor sensitivity, as this pretreatment was thought to induce transport systems and metabolic pathways necessary for catabolism of organic matter (Riedel, et al., 1990). The poor ability of T. cutaneum to report the presence of polymerized, higher molecular weight substances such as starch (Hikuma, et al., 1979) was overcome by integrating the BOD sensor with enzyme columns; the flow through from two columns containing starch-degrading enzymes was then reacted with the sensor biomass (Reiss, et al., 1998), resulting in better agreement between the BOD and BOD5 values. T. cutaneum has been co-immobilized with Bacillus subtilis (Jia, et al., 2003) and Bacillus licheniformis (Suriyawattanakul, et al., 2002) to successfully expand BOD substrate specificity. Heavy metal ion toxicity (Riedel, et al., 1990, Jia, et al., 2003) and high salt (Jia, et al., 2003) proved to be inhibitory to T. cutaneum BOD sensors.

A. adeninivorans LS3, a salt tolerant yeast strain with broad substrate range, has been incorporated

into many BOD biosensors (Chan, et al., 1999, Chan, et al., 2000, Tag, et al., 2000) to address BOD detection in saltwater. The strain was successfully used in coastal water with salt conditions that were strong enough to inactivate a commercial BOD sensor (Lehmann, et al., 1999). A. adeninivorans has two stable morphologies, existing either as a budding cell or mycelia. The mycelia morphology was incorporated into a BOD sensor that was similar in response range, stability, and detection limits as budding cell biosensors, but showed improved storage stability, higher sensitivity for select amino acids and sugars, a stronger correlation between BOD and BOD5 values, and a higher tolerance for salt than

the budding cell sensor (Tag, et al., 2000). A second major concern in formation of BOD biosensors is the detection method. Biosensors

coupled to either a potentiometric or amperometric electrode measure the current produced from the reduction of molecular oxygen consumed by yeast as they break down organic material (Hikuma, et al., 1979, Riedel, et al., 1990, Campanella, et al., 1995, Reiss, et al., 1998, Chan, et al., 1999, Lehmann, et

al., 1999, Tag, et al., 2000, Chen, et al., 2002, Jia, et al., 2003, Arlyapov, et al., 2013). Developing a


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platform to be used for online monitoring of wastewater drove the development of flow-based BOD biosensors.

In

particular,

the

print

screen

method,

which

successively

deposits

layers

of

electroconductive inks on a solid support (Cristea, et al., 2009), allowed for the production of a planar electrode measuring just millimeters in size. The small size circumvented the need to incorporate the large, standard oxygen electrode into a BOD device, allowed for the electrode to be easily incorporated in flow-through systems, and was shown to be compatible with sample volumes as low as a few microliters (Heim, et al., 1999, Chan, et al., 2000). In one automated flow-based system, the cells and the electrode were spatially separated (Heim, et al., 1999). This design feature introduces a sense of modularity into yeast BOD biosensors. Fluctuating oxygen levels decrease BOD biosensor reproducibility; thus, one alternative approach is to use ferricyanide as an electron acceptor and measure the current produced from the oxidation of ferricyanide at the electrode (Catterall, et al., 2001, Trosok, et al., 2001).

A third concern for BOD biosensors is the method used to immobilize the cells. The method in which

the yeast are immobilized to the sensor can affect sensor stability and reproducibility of results (Jia, et al., 2003), and an ideal immobilization material has low toxicity, low reactivity, and high diffusivity properties. Among the many immobilization materials used in yeast BOD biosensors are: polyvinylaclohol (PVA) (Riedel, et al., 1990), polyethylene (Riedel, et al., 1990), hydrogels (Chan, et al., 1999), sol-gels (Chen, et al., 2002), cellulose membranes (Hikuma, et al., 1979, Sangeetha, et al., 1996, Heim, et al., 1999),

polycarbonate membranes (Suriyawattanakul, et al., 2002), and poly(carbamoyl)sulfonate (PCS) (Chan, et al., 1999, Chan, et al., 2000). Modification of materials used for immobilization of yeast onto the electrode can greatly improve BOD performance. Thin gels traditionally used for yeast immobilization have characteristically low mechanical strength, which can significantly decrease the lifetime of a BOD sensor (Arlyapov, et al., 2013). PVA is a commonly used immobilization substance as it is biocompatible and chemically stable. Modifying PVA with 4-vinylpyridine in a gel solution eliminated gel swelling and was used in combination with a sol-gel derived glass to improve material strength (Jia, et al., 2003). PVA has also been modified with N-vinylpyrrolidone to improve sensitivity and stability of the BOD sensor (Arlyapov, et al., 2013).


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3.2.

Alcohols

Interest in detecting alcohols for toxicological studies in both the food and biotechnology industries

has driven the development of biosensors for this purpose. In particular, a variety of methylotrophic yeastbased sensors have been developed for methanol and ethanol detection. Two key aspects of alcohol metabolism in yeast, the production of acidic byproduct and the uptake of oxygen required for metabolism, can be readily measured. Methylotrophic yeast strains Hansenula polymorpha 34-19 and Pichia pinus 2468 oxidize alcohols

and consequently acidify their extracellular environment. This extracellular acidification has been coupled with ion sensitive field effect transistors (ISFET) to convert the biological response into an electrical output. Though specific for their respective substrates, the two sensors showed a slight response to formaldehyde (Korpan, et al., 1993). This ability to slightly detect formaldehyde was further developed

into an H. polymorpha formaldehyde specific biosensor (Korpan, et al., 1993). Genetic changes to increase the rate of formation of acidic product and formaldehyde specificity resulted in a 10-fold faster response time and a 2-fold improvement in linear detection range compared to the ISFET alcohol sensors. Though highly specific, the practical use of these sensors are limited by the decrease in sensor output observed in buffered solutions (Korpan, et al., 1993, Korpan, et al., 1993). A separate approach using H. polymorpha with high alcohol oxidase activity coupled formaldehyde detection to both oxygen and screen printed electrodes. The oxygen electrode sensor showed increased sensitivity, but the screen print electrode provided functionality over a larger range of formaldehyde concentrations (Khlupova, et al., 2007). Another approach for yeast based alcohol sensors takes advantage of the coupling of alcohol

metabolism and oxygen uptake. One problem with this class of sensors is noise from interfering compounds. To address this, different combinations of yeast strains and membranes have been tested. For example, in one biosensor, the yeast strain Pichia methanolica proved to be highly specific towards ethanol and methanol (Reshetilov, et al., 2001). The use of a gas permeable membrane in an ethanol

sensor with the strain Trichosporon brassicae eliminated interference from volatile compounds (Hikuma,


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et al., 1979). Saccharomyces ellipsoideus also proved to be a successful in an ethanol sensor when coupled to an oxygen electrode. Though the sensor had a quick response time of 2 minutes and did not respond to other volatile organic compounds such as proponal, methanol or acetic acid, it was inhibited by glucose. The addition of a second membrane composed of Polytetrafluoroethylene (PTFE) eliminated glucose inhibition but also doubled sensor response time (Rotariu & Bala, 2003, Rotariu, et al., 2004).

Use of Candida tropicalis yielded a highly specific sensor able to detect ethanol in the presence of other alcohols due to the high specificity of its native alcohol oxidase (Akyilmaz & Dinçkaya, 2005). In a biosensor with H. polymorpha cells, permeabilizing cells to facilitate diffusion and elevating alcohol oxidase activity improved sensor sensitivity for ethanol and methanol (Gonchar, et al., 1998).

3.3.

Sugars

Glucose biosensors take advantage of the presence of the glycolytic pathway. S. cerevisiae was

used to compare the performance of coupling the sensing element to an oxygen sensor, to be used in aerobic conditions, or to a carbon dioxide sensor, to be used in anaerobic conditions. The oxygen sensor was more accurate for glucose concentrations from .01-1 mM, while the carbon dioxide sensor yielded better performance at 1-10mM (Mascini & Memoli, 1986). To reduce interference due to changes in oxygen and CO2 pressures, a potentiometric sensor with H. anomala correlated pH change to glucose concentration (Racek, 1991). An alternative approach for glucose measurement used the surface display of glucose-1-oxidase on the surface of S. cerevisiae to yield a sensor with high glucose specificity that was stable from pH 3.5-11.5 and at temperatures up to 56⁰C (Wang, et al., 2013). Noise from interfering compounds proved problematic with these sensors. In one approach to

directly address interference in a sucrose sensor, the electrode was covered with an outer membrane containing glucose oxidase (GOD) to convert glucose to gluconic acid and hydrogen peroxide, and catalase to convert hydrogen peroxide to water and oxygen. This enzyme layer reduced glucose interference from 15% to 3.5% (Rotariu, et al., 2002).


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To address the challenges of biosensors with low selectivity and inability to distinguish substrates, P. methanolica was combined with Gluconobacter oxydans to make a biosensor able to determine ethanol and glucose concentrations in various ethanol-glucose mixtures. Using an artificial neural network to analyze data provided accurate estimation of ethanol and glucose concentrations from 1-10mM (Lobanov, et al., 2001).

3.4.

Lactate

Lactate sensors are often used in the food, fermentation, sports medicine, environmental and clinical

chemistry industries (Racek & Musil, 1987, Kulys, et al., 1992, Smutok, et al., 2007, Nikolaus & Strehlitz, 2008). Many lactate sensors are based on the oxidation of L-lactate to form pyruvate by the enzyme flavocytochrome b2. The oxidation reaction calls for the use of an electrode to convert biological response

to electrical output. Some key strains used for lactate detection are: H. anomala (Racek & Musil, 1987, Racek & Musil, 1987) , H. polymorpha (Smutok, et al., 2007, Shkil, et al., 2009) and S. cerevisiae (Garjonyte & Malinauskas, 2003, Garjonyte, et al., 2006, Garjonyte, et al., 2008, Garjonyte, et al., 2009). One of the earliest yeast lactate sensors was based on H. anomala suspended in a semipermeable

membrane attached to an amperometric electrode. The sensor was able to determine lactate concentrations in whole blood and blood plasma (Racek & Musil, 1987), but biosensor performance specificity decreased after 2 weeks of use upon showing responsiveness to select sugars and amino acids (Racek & Musil, 1987). H. anomala has also been used in conjunction with a carbon paste electrode for lactate determination in serum (Kulys, et al., 1992). Overexpression of flavocytochrome b2 in permeabilized H. polymorpha expanded the linear lactate

detection range of H. polymorpha. Flavocytochrome b2 is located in the mitochondrial intermembrane space, and permeabilizing the cells allowed for lactate and the electron mediator to more easily diffuse to the site of reaction (Smutok, et al., 2007). An alternative method based on H. polymorpha overexpressing flavocytochrome b2 avoids the need for an electron mediator by monitoring the reduction of molecular


Accepted Article

oxygen as cells metabolize lactate (Shkil, et al., 2009), essentially functioning as a BOD sensor that is lactate specific. S. cerevisiae has been used with a carbon paste electrode to yield functional lactic acid sensors.

Prussian blue was used as a mediator to mitigate leaching from carbon paste (Garjonyte & Malinauskas, 2003). The use of phenazine methosulphate as an electron mediator yielded a sensor that could detect lactic acid in milk and dairy products, though the sensor had a high level of noise and showed some sensitivity to ascorbic acid (Garjonyte, et al., 2006). Permeabilization of S. cerevisiae cells with 30% ethanol yielded a biosensor with improved response output and stability (Garjonyte, et al., 2008). Drying temperature was found to have an effect on lactic acid sensor performance. The K2 killer type strain of S. cerevisiae was dried at temperatures as high as 90⁰C for use in a sensor that showed higher sensitivity, lower noise and a quicker response time than earlier S. cerevisiae lactic acid sensors (Garjonyte, et al., 2009).

3.5.

Metals

A recent copper biosensor offers a unique, transcription independent method for heavy metal

detection. Rhodotorula mucilaginosa, a yeast strain known to have high copper affinity, was lyophilized and incorporated into a carbon paste electrode (Yüce, et al., 2010). When a copper solution is added, 2+

Cu

ions are bound to the yeast cell wall, and thus incorporated into the electrode. Using Differential

Pulse Stripping Voltammetry, the researchers demonstrated linear and specific Cu

2+

dependent current

over the range 100nM-100uM. Of course, the generalizability of this method is limited because it is dependent on the metal affinity of the yeast strain.

3.6.

Intracellular Metabolites

As mentioned above, self-monitoring yeast systems have many applications, particularly for

metabolic engineering. Due to the importance of carbon sources such as glucose to cellular metabolism, several strains have been engineered to sense intracellular sugar levels. These sensors operate primarily


Accepted Article

via Förster resonance energy transfer (FRET) techniques, with a bacterial periplasmic binding protein as the sugar recognition element (Bermejo, et al., 2011). These strategies are advantageous in part for their speed – because substrate binding triggers fluorescence directly, without lag time for transcription and translation of a separate reporter, the temporal resolution can be as low as milliseconds. The sugar binding proteins are usually specific to a given sugar such as glucose (Bermejo, et al., 2010) or maltose (Ha, et al., 2007). Much like nuclear hormone receptors and GPCRs, protein engineering techniques have also been successful – however, in this case as a mechanism to broaden substrate specificity rather than narrow it (Ha, et al., 2007). Cellular energy and redox carriers can also be directly monitored. Like sugars, FRET based sensors

have been employed to monitor cytosolic ATP, using a subunit from bacterial ATP synthase as the recognition element (Bermejo, et al., 2010). The protein can also be targeted specifically to the mitochondria by adding a mitochondrial targeting sequence from cytochrome C to the N-terminal, allowing in vivo monitoring of mitochondrial function (Vevea, et al., 2013). Bulk NADH and NADPH redox states can be monitored electrically with the use of mediator compounds that shuttle electrons from the cell to an electrode (Baronian, et al., 2002, Heiskanen, et al., 2004). This technique also allows for nearly real time monitoring – for example, monitoring NAD(P)H levels upon substrate addition to yeast with an overexpressed reductase enzyme (Kostesha, et al., 2009). The system has even been implemented onto a microfluidic platform (Heiskanen, et al., 2013). NAD(P)H can be also be assayed by autofluorescence, although this method has been criticized for low sensitivity and specificity(Hung, et al., 2011). Recently, genetically encoded fluorescent NADH sensors with subcellular resolution have been developed in mammalian cells (Hung, et al., 2011, Zhao & Yang, 2012, Bilan, et al., 2014), however to our knowledge

they have yet to be implemented in yeast. Finally, yeast cells have been engineered to report their own pH. This is commonly achieved through

pH sensitive GFP variants (Miesenböck, et al., 1998, Ullah, et al., 2012), although a new bioluminescence resonance energy transfer (BRET) based system can report pH without excitation from an external light source (Zhang, et al., 2012).


Accepted Article

4. Conclusion As we have shown, there is already a plethora of exciting developments in YBBs. In the future, we

anticipate an increase in the range of possible analytes and a decrease in the response times required for measurement as existing biosensing strategies are tweaked and new biosensing strategies are developed. With directed evolution capabilities, the range of analytes that can be detected by GPCRs and nuclear hormone-type receptors have the potential to greatly expand the number of non-native ligands that can be detected by YBBs. GPCRs are particularly promising as they exist on the cell membrane, and therefore the analyte does not have to diffuse into the cytoplasm/nucleus for receptor activation. As well, there are recent reports of rational protein engineering efforts to create synthetic cell membrane receptors (Daringer, et al., 2014). These receptors use affinity binding domains (e.g., scFvs) to bind

analytes and activate intracellular signaling cascades. Such rational design of de novo receptors would reduce the development time for YBBs, increasing the adoption of YBBs. In all these applications, yeasts, and in particular S. cerevisiae, will be a chassis of choice for environmental sensing, due to the tolerance to a wide range of conditions, the ability to store yeast as “active dry”, the low cost of manufacturing, and the abilities to carry out facile genetic engineering.

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Accepted Article

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Accepted Article

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Accepted Article

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Accepted Article

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Accepted Article

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Tables Table 1. Common reporting methedologies for transcription dependent yeast biosensors. Type

Fluoresce nce

Luminesc ence

Examples

GFP, RFP, YFP

Lux (bacterial) Luc (firefly)

Advantages Many spectral, environmenta l variants No substrate addition No substrate addition High Quantum Yield Very sensitive

Disadvantage s

Reviewe d by

Selected examples from this review

Requires excitatory light

(Daunert, et al., 2000, Frommer, et al., 2009)

(Miesenböck, et al., 1998, Bovee, et al., 2004, Beck, et al., 2005, Schofield, et al., 2007, Wei, et al., 2013)

(Daunert, et al., 2000, Close, et al., 2009)

(Leskinen, et al., 2003, Sanseverino, et al., 2005, Sanseverino, et al., 2009, Bakhrat, et al., 2011, Kabiersch, et al., 2013)

(Daunert, et al., 2000)

(Miller, 1999, Fox, et al., 2008, Miller, et al., 2010)

Background from strains/media Narrow linear range Requires substrate Requires substrate

Colorimetr y

Betagalactosidas e

Electrical

Amperometr y Potentiometr y Conductome try Voltammetry

Very fast Very sensitive

Requires immobilization

Growth

His3, Trp1, Leu2

Isolates the strain of interest

Slow

Visible to naked eye

Requires cellular disruption


(Shimom uraShimizu & Karube, 2010, Su, et al., 2011) (Romano s, et al., 1992)

(Baronian, et al., 2002, Heiskanen, et al., 2004, Tag, et al., 2007, Garjonyte, et al., 2009, Yüce, et al., 2010, Pham, et al., 2012)

(Rajasärkkä, et al., 2011)

Accepted Article

Figure Legends Figure 1: Schematic overview of yeast biosensing strategies. (a) Transcription dependent biosensors recognize the ligand of interest, typically via a native or heterlogous receptor protein. When activated, the protein interacts with the cell’s transcripton machinery, either directly or via a signaling cascade, to induce (or cease) production of a reporter gene. (b) Transcription indepdent biosensors convert signals that are difficult to measure into ones that can easily be detected, using either byproducts of metabolism or reporting systems trigered directly by anylate recognition. *Yeast methanol metabolism figure adapted from (Hartner & Glieder, 2006). EDC – endocrine disrupting compound.

Figure 2: A typical G-protein coupled receptor (GPCR) and the associated G-protein. Heterologous GPCRs must often be modified for functional expression in yeast-based biosensors. Common targets for modification and/or replacement include: the N and C termini, the G-protein, and the alpha subunit of the G-protein. Though less common, the downstream signaling cascade can be altered as well.

Figure 3: Overview of the BLYES system. Binding of estrogenic compounds to the consitutively expressed human estrogen receptor (hER) leads to transcriptional activation at the estrogen responsive elements (ERE) on the reporter plasmid, driving expression of the luciferase reporter. The other plasmid contains the remaining luciferase genes necessary for the system to operate without the addition of a substrate. GPD, ADH1 – native yeast promoters. IRES – Internal ribosome entry site . Adapted from (Eldridge, et al., 2011)

Figure 4: Directed Evolution of hERα leads to increased sensitivity and specificity. The original hERα biosensor (right) responded to both the native ligand Estradiol (E2) and the estrogenic compound Bisphenol A (BPA). After directed evolution (left) the biosensor had almost no response to E2 and a considerably higher sensitivity to BPA. Adapted from (Rajasärkkä & Virta, 2013)

Figure 5: The EstraMonitor amperometric system. (a) EstraMonitor apparatus. 1 – amperometric transducter, 2 – pump module for waste and nutrient exchange, 3 – reservoir, 4 – measuring chamber with immobilized yeast cells, 5 – decontamination system. (b) Scheme of the EstraMonitor. Adapted from (Pham, et al., 2012)


Accepted Article

Figure 1:

Figure 2:


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Figure 3:

Figure 4:


Accepted Article

Figure 5:

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