| Literature DB >> 32413968 |
Helene Martin-Yken1,2,3,4.
Abstract
Biosensors are regarded as a powerful tool to detect and monitor environmental contaminants, toxins, and, more generally, organic or chemical markers of potential threats to human health. They are basically composed of a sensor part made up of either live cells or biological active molecules coupled to a transducer/reporter technological element. Whole-cells biosensors may be based on animal tissues, bacteria, or eukaryotic microorganisms such as yeasts and microalgae. Although very resistant to adverse environmental conditions, yeasts can sense and respond to a wide variety of stimuli. As eukaryotes, they also constitute excellent cellular models to detect chemicals and organic contaminants that are harmful to animals. For these reasons, combined with their ease of culture and genetic modification, yeasts have been commonly used as biological elements of biosensors since the 1970s. This review aims first at giving a survey on the different types of yeast-based biosensors developed for the environmental and medical domains. We then present the technological developments currently undertaken by academic and corporate scientists to further drive yeasts biosensors into a new era where the biological element is optimized in a tailor-made fashion by in silico design and where the output signals can be recorded or followed on a smartphone.Entities:
Keywords: biosensors; cell signaling; detection; environmental contaminants; yeasts
Mesh:
Year: 2020 PMID: 32413968 PMCID: PMC7277604 DOI: 10.3390/bios10050051
Source DB: PubMed Journal: Biosensors (Basel) ISSN: 2079-6374
Figure 1General scheme of a yeast biosensor’s purpose and functioning. Different possible inputs appear on the left, in a non-exhaustive list. Live yeast cells are represented by a budding yeast shape inside of a supporting structure that is coupled to the signal detection system. Three main outputs are generally sought after by designers and users: either a “yes/no” answer in case a threshold level of the target molecule(s) exists, or a quantification value when needed and possible.
Different types of biosensors developed based on yeast cells. The upper part of the table summarizes yeast biosensors targeting pollutants and other environmental contaminants, while the lower part of the table contains bioassays developed for the medical domain to detect pathogens and carcinogens compounds or to be used as screening methods to help medical research (for example, the search for new drugs). The third column indicates the type of detection “Yes/No” or “Quantification,” as well as the limit of detection (LoD) or EC50 when such information was available. However, biosensors sensitivities vary significantly for different compounds and conditions; precise numeric values for specific molecules should be sought for in the original publications cited in the last column.
| Detected Coumponds | Yeast Species | Type of Response (LoD or EC50 if Available) | Detection (Reporter Gene) | References |
|---|---|---|---|---|
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| Coumpounds "toxic to eukaryotic cells" (all types) |
| Yes/No | Luminescence (Luc), viability decrease. | (Hollis et al., 2000) [ |
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| Yes/No (2 ng/L) | Colorimetry (LacZ) | (Routledge and Sumpter, 1996) [ | |
|
| Quantification (20 ng/L) | Fluorescence (LacZ) | (García-Reyero et al., 2001) [ | |
|
| Quantification (0.4 nM) | Fluorescence (yEGFP) | (Bovee et al., 2004) [ | |
|
| Quantification (2 ng/L) | Amperometry or biochemistry (phyK) | (Pham et al., 2013) [ | |
| Androgenic and Anti-androgenic compounds |
| Quantification (15 nM for Testosterone) | Two-hybrids System, (LacZ). | (Lee et al, 2003) [ |
|
| Quantification (5 nM) | Two-hybrids System, (GFP). | (Ogawa et al., 2010) [ | |
| Glucocorticoids (cortisol, corticosterone) |
| Quantification (0.3 μM) | Amperometry or biochemistry (phyK) | (Pham et al., 2016) [ |
|
| Quantification (95 μg/L) | Amperometry or biochemistry (phyK) | (Pham et al., 2015) [ | |
|
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| T-2 toxin and other trichothecenes such as verrucarin A |
| Yes/No | Growth inhibition (disk halo) | (Schappert and Khachatourians, 1984) [ |
| Trichothecene mycotoxins |
| Quantification (1 pg/L) | Colorimetry (LacZ) | (Engler et al., 1999) [ |
| Mycotoxin Zearalenone, and other compounds with estrogenic activity |
| Quantification (1 μg/L) | Metabolic construct | (Mitterbauer et al., 2003) [ |
|
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| Quantification (0.5 mM Cu2+) | Amperometry (LacZ). | (Lehmann et al., 2000) [ | |
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| Quantification (5 × 10-7M Cu2+) | Fluorescence (GFP) | (Shetty et al., 2004) [ | |
|
| Quantification (5 × 10-7M Cu2+) | Luminescnce (Luc) | (Roda et al., 2011) [ | |
|
| Quantification (1 μM Cu2+) | Colorimetry (ADE2) | (Vopálenská et al., 2015) [ | |
| Cadnium, Arsenic. |
| Quantification (1 mM Cd) | Fluorescence (GFP) | (Park et al., 2007) [ |
|
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| Okadaic acid, pectenotoxin-11, portimine |
| Quantification (19 nM OA) | Colorimetry (LacZ) | (Richter and Fidler, 2015) [ |
| Ciguatoxins |
| Quantification (0.1 ng/L PCTX3C) | Colorimetry or fluorescence (LacZ) | (Martin-Yken et al., 2018) [ |
|
|
| Quantification (3 mg/L) | Amperometry | (Hikuma et al., 1979) [ |
| Quantification (2.4 mg/L) | Cellular growth | (Yudina et al., 2015) [ | ||
|
| Quantification (2 mg/L) | Spectrophotometry | (Nakamura, 2007) [ | |
|
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|
| Quantification (nM range) | SPR (antigen cell surface display) | (Venkatesh et al., 2015) [ | |
|
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| Yes/No (nM range) | Colorimetry, Engineered GPCR | (Ostrov et al., 2017) [ |
|
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| Quantification (mg/mL range) | Reversion frequency (DEL assay) | (Brennan and Schiestl, 1998, 2004) [ |
|
| Quantification (variable) | Fluorescence (GFP) | (Benton et al., 2007) [ | |
| Pro-carcinogens |
| Quantification (μg/mL range) | CPR-CYP and RAD54-GFP expression | (Bui et al., 2016) [ |
| PI3K inhibitors (oncogenesis related screen) |
| Quantification (μM range) | Reconstituted PI3K pathway | (Fernández-Acero et al., 2012) [ |
|
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| for Matrix Metalloproteinases (MMPs) inhibitors (anticancer) |
| Quantification (nM range) | Cell surface expression | (Diehl et al., 2011) [ |
| for Anti-Malarial Compounds with artemisinin-like activities |
| Yes/No (μM range) | Growth inhibition | (Mohamad et al., 2012) [ |
| for Inhibitors of Human Cytomegalovirus Protease |
| Quantification (μM range) | Target-specific HTS system | (Cottier et al., 2006) [ |
Figure 2Key features of yeasts cells as biosensor sensitive elements. While some environmental contaminants can penetrate into the yeast cell and directly affect cell growth or viability, others (and particularly the biggest molecules) are retained outside the cells. These molecules can, however, be detected through membrane sensor proteins or channels that transmit signals to intracellular elements comprising signaling cascades and transcription factors. The outputs can be either a direct effect on cellular growth or viability or more indirect signals mediated through enzymatic activity and often, but not necessarily, gene expression.
Figure 3New developments and current research strategies in yeast-based biosensors. Remarkable new developments notably include multi-strain biosensors based on microbial consortia stabilized in a supporting matrix and metabolic reporters allowing users to monitor the metabolic state of yeast cells in a fermenter and hence make possible the progression of a biosynthetic process in real time. Moreover, nanotechnologies have revolutionized the detection systems by allowing both miniaturization and wireless transmission of data. Finally, in silico design of new binding partners for the target molecules and even unnatural amino acids use open unlimited options of new yeasts biosensors in the years/decades to come.