Genetically-encoded biosensors for analyzing and controlling cellular process in yeast
Graphical abstract
Introduction
Yeast has been a robust workhorse to manufacture bioproducts and enable the expression of a broad range of heterologous pathways for various biotechnological applications. This is partly because of the existence of the spatially separated organelles to allow for gene expression and specialized metabolic activity taking place at various cellular locations and time-scales. For example, gene transcription and mRNA translation are separated by nucleus membrane. Catabolic and anabolic metabolism are distinctly localized in mitochondria, peroxisome and cytoplasm et al. The separation and compartmentalization of different genetic and metabolic events presents us the opportunity to precisely control and program gene expression for more advanced biological functions. Other industrially-relevant traits include its tolerance to a number of biotic or abiotic stress factors, such as acids, reactive oxygen species, carbon or nitrogen starvations, osmotic pressure, and mechanical shear force. These genetic and phenotypic advantage have made yeast a powerful host organism for production of biofuels [1, 2, 3], commodity chemicals [4], natural products [5] and pharmaceuticals [6, 7, 8].
As for fermentation process, we have been able to monitor cell metabolism by tracking cell growth, pH, oxygen uptake rate, the CO2 emission rate, respiratory quotient and a number of nutrients including glucose, glutamate, lactic acid, ammonia et al. These parameters allow us to precisely interpret cell physiology and predict the microbial process kinetics as well as apply control strategies to improve the economics of industrial fermentation [9,10]. As we move forward in the post-genomic era, one area that is largely underexplored is to integrate genetically encoded biosensors with hardwired optical sensors or electrochemical signals that may translate the metabolic activity into readable output, permitting us to rapidly screen mutant strains and seamlessly optimize fermentation process [11•]. It is hoped that sensors, or ‘electrodes’ that could be installed inside the cell to forecast or troubleshoot the complicated cellular process.
Biosensing in eukaryote, like yeast, is more complicated compared to simple prokaryotic organisms like bacteria. Gene transcription involves the recruitment of multiple transcriptional factors and associated proteins into the nucleus; signal relay in yeast requires the activity of multiple phosphorylase and dephosphorylase to bridge the gap between receptor and the actuator proteins. An essential feature of eukaryote biosensing is the cooperative assembly and de-assembly of multiple regulatory proteins leading to the complex/nonlinear signal processing and cellular decision-making functions [12••]. The dynamic response, sensitivity and operational range of these genetically encoded sensors are determined by the molecular structure and interactions of the related proteins and DNAs [13••]. In this short review, we will cover the design principles of engineering yeast-based allosteric transcriptional factors, and novel biosensors including GPCR-based sensors and optogenetic-based sensors in yeast (Figure 1).
Section snippets
Allosteric transcriptional factor-based biosensors in yeast
Allosteric transcriptional factors (aTF) consist of a DNA binding domain (DBD) and an effector-binding domain (EBD). The DBD of aTF will specifically interact with a cis-regulatory DNA sequence (generally called operator or enhancer) adjacent to the promoter to restrict or enhance the access of RNA polymerase (RNAP), thus repressing or activating gene transcription [9,14]. The EBD of aTF is the sensor domain that can bind with small molecules or environmental stress factors (heavy metals,
GPCR-based biosensors in yeast
G-protein coupled receptors (GPCRs) are large and diverse family of cell surface receptors that respond to an enormous amount of cell signals, including neurotransmitters, hormones, carbon/nitrogen sources and sensations (tastes and odors) [30] et al. Binding of the signaling molecules or the ligand to the GPCRs result in G-protein activation which then triggers the production of other secondary messengers. These molecular systems allow the rapid and dynamic transmission of biochemical
Optogenetics-based biochemical control in yeast
The use of chemical inducer as input signal is powerful to control cellular regulatory networks and reprogram gene expression. But these chemical inducers are less hardwired with our commonly used optoelectrical devise that could give us direct signal to initiate any feedback control. Light-controlled gene expression and protein assembly have been suggested as an extremely useful solution to dissect and analyze cellular network function. It will improve our ability to interrogate and understand
Conclusions and perspectives
For the purpose of pathway engineering, the expression of heterologous pathway to redirect carbon flux toward the targeted metabolism is a challenging task. This may compete with the native metabolism, and the buildup of nonnative metabolites may elicit cellular stress response. It is important to install sensors to control the dosage and temporospatial expression of enzyme [41]. Yeast has been an excellent platform that allows for temporospatial separation of genetic and metabolic events. The
Conflicts of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
This work was supported by the Cellular & Biochem Engineering Program of the National Science Foundation under grant no.1805139 and Bill & Melinda Gates Foundation (grant number OPP1188443). The authors would also like to acknowledge the Department of Chemical, Biochemical and Environmental Engineering at University of Maryland Baltimore County for funding support.
References (41)
Engineering acetyl-CoA metabolic shortcut for eco-friendly production of polyketides triacetic acid lactone in Yarrowia lipolytica
Metab Eng
(2019)Production of chemicals using dynamic control of metabolic fluxes
Curr Opin Biotechnol
(2018)Engineering transcriptional regulator effector specificity using computational design and in vitro rapid prototyping: developing a vanillin sensor
ACS Synth Biol
(2016)Development of a synthetic malonyl-CoA sensor in Saccharomyces cerevisiae for intracellular metabolite monitoring and genetic screening
ACS Synth Biol
(2015)Engineering modular biosensors to confer metabolite-responsive regulation of transcription
ACS Synth Biol
(2017)- et al.
Single-cell analysis of G-protein signal transduction
J Biol Chem
(2015) - et al.
GPCR-based chemical biosensors for medium-chain fatty acids
ACS Synth Biol
(2015) The promise of optogenetics in cell biology: interrogating molecular circuits in space and time
Nat Methods
(2011)Spatiotemporal control of intracellular phase transitions using light-activated optoDroplets
Cell
(2017)Metabolic burden: cornerstones in synthetic biology and metabolic engineering applications
Trends Biotechnol
(2016)
Lipid production in Yarrowia lipolytica is maximized by engineering cytosolic redox metabolism
Nat Biotechnol
Engineering oxidative stress defense pathways to build a robust lipid production platform in Yarrowia lipolytica
Biotechnol Bioeng
Engineering Yarrowia lipolytica as a platform for synthesis of drop-in transportation fuels and oleochemicals
Proc Natl Acad Sci U S A
Combining 26s rDNA and the Cre-loxP system for iterative gene integration and efficient marker curation in Yarrowia lipolytica
ACS Synth Biol
Complete biosynthesis of cannabinoids and their unnatural analogues in yeast
Nature
Complete biosynthesis of noscapine and halogenated alkaloids in yeast
Proc Natl Acad Sci U S A
Strategies for microbial synthesis of high-value phytochemicals
Nat Chem
Application of metabolic controls for the maximization of lipid production in semicontinuous fermentation
Proc Natl Acad Sci U S A
Engineering metabolite-responsive transcriptional factors to sense small molecules in eukaryotes: current state and perspectives
Microb Cell Fact
Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies
Science
Cited by (23)
Engineering status of protein for improving microbial cell factories
2024, Biotechnology AdvancesHeme sensing and trafficking in fungi
2023, Fungal Biology ReviewsCitation Excerpt :The analysis of heme use by fungi has benefited recently by the application of genetically encoded sensors, which are powerful tools to monitor and quantify molecules of biological interest including metabolites or environmental signals in real time and in living cells. A number of excellent reviews provide detailed descriptions of genetically encoded sensors that generally consist of a protein that senses a specific signal (e.g., a small molecule, a protein-protein interaction, or an enzyme activity) coupled to a protein that provides a fluorescent or bioluminescent readout (Greenwald et al., 2018; Lin et al., 2019; Qiu et al., 2019; Terai et al., 2019; Kostyuk et al., 2020; Marsafari et al., 2020; Zhou et al., 2020; Kim et al., 2021; Nasu et al., 2021). There are numerous examples of applications of genetically encoded biosensors in fungal research.
Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications
2023, Biotechnology AdvancesCitation Excerpt :This approach is based on the generation of libraries that contain rational or random mutations, and individuals with particular characteristics are screened from libraries, which means that each producer should be experimentally evaluated (Cheng et al., 2018; Schaaf et al., 2018). However, the lack of efficient screening methods for selecting individuals with desirable characteristics is also an inevitable issue (Hossain et al., 2020; Marsafari et al., 2020). For instance, the library of mutations can be constructed up to a threshold of 109 per day by random mutagenesis or genome-wide modifications, whereas traditional analytical methods using chromatographic instruments coupled to various detectors (including mass spectrum) can make only up to 102–104 measurements per instrument a week (Kempa et al., 2019; Rienzo et al., 2021).
Complexity of subcellular metabolism: strategies for compartment-specific profiling
2022, Current Opinion in BiotechnologyCitation Excerpt :However, employing different metabolic tracers (substrates) along with a careful compartmentalized MFA setup could assist in addressing these issues. Finally, biosensors can be employed for profiling subcellular metabolism in intact living cells without destructive fractionation [52–56]. Localized protein modifications can also be used to dissect compartmentalized metabolite pools, for example, tracking histone acetylation for acyl-CoAs, S-adenosyl-methionine, NAD/NADH, and alpha-ketoglutarate and DNA methylation for one-carbon metabolism [57,58].
Mining and design of biosensors for engineering microbial cell factory
2022, Current Opinion in BiotechnologyCitation Excerpt :Improving the intelligence of the microbial cell factory is an important way to solve the above problems, and biosensor is the basis of strains intelligence. Through autonomous regulation of transcription, translation or protein level of microorganisms under the control of sensing specific chemicals or signals, metabolic flow can be radically altered and production can be greatly improved [5,6]. Herein, we reviewed the recent advances in the engineering of biosensors for improving the performance and the application in dynamic regulation and directed evolution of microbial cell factory (Figure 1).
Yeast synthetic biology advances biofuel production
2022, Current Opinion in MicrobiologyCitation Excerpt :Biosensors are often developed and utilized to convert screening targets to the easily detected fluorescence phenotypes (Table 1). Current used biosensors can be categorized as riboswitch-based biosensors, reporter protein-based biosensors and transcription factor-based biosensors [38,39]. For example, Dabirian et al. developed a FadR-based biosensor and demonstrated that the overexpression of RTC3, GGA2 and LPP1 could enhance fatty acyl-CoA production by 80% [40].
- 4
These authors contributed equally to this article.