Biosynthesis of terpene compounds using the non-model yeast Yarrowia lipolytica: grand challenges and a few perspectives
Graphical abstract
Introduction
Extensive research has been aimed at producing terpenes (a vast group of high-value natural compounds) using microbial hosts. Model organisms like Escherichia coli and Saccharomyces cerevisiae have been widely used for the production of terpenes as nutraceuticals [1], fuels (e.g., bisabolene [2]), and pharmaceuticals such as cannabinoids [3], with encouraging results. The microbial hosts and production titers for terpenes of different molecular weights (monoterpenes (C10), sesquiterpenes (C15), diterpenes (C20), triterpenes (C30), and tetraterpenes (C40)) are summarized in Figure 1a . However, for commercial applications, E. coli has drawbacks such as bacterial toxin contamination, acetate inhibition, low activity of expressed plant-based enzymes, and an inability to post-translationally modify and compartmentalize complex proteins. S. cerevisiae also has drawbacks such as relatively slow growth rates, complex growth medium requirements, and the Crabtree effect. Recently, new microbial platforms, including non-model yeasts, microalgae, and consortia, have been developed via their unique traits for terpene biosynthesis [1,4,5] (Figure 1b). For example, the red yeast Rhodosporidium toruloides is capable of using cheap feedstocks such as aromatics, glycerol, and methane, while cyanobacteria can utilize CO2. Among these non-model hosts, the oleaginous yeast Yarrowia lipolytica exhibits high chemical tolerance and robust secretion abilities, and has received strong research interests, facilitating the rapid development of relevant genetic and systems biology tools [6]. These tools include genome editing via CRISPR/Cas9, novel secretion pumps, promoter tuning, pathway assembly, and modular cloning strategies, as well as genome-scale modeling to guide rational metabolic engineering [7]. Thus, Y. lipolytica has emerged as an attractive non-conventional host, and has been demonstrated a capability for high terpenoid production (e.g., 6 g/L β-carotene [8]).
Section snippets
Knowledge gaps in Y. lipolytica metabolism
There are still grand challenges in using non-model yeasts for biomanufacturing of terpenes. Detailed metabolic understanding is necessary for performing rational strain engineering, but knowledge gaps still exist in the metabolism of Y. lipolytica [9••,10]. First, cytosolic acetyl-CoA is the key terpene pathway precursor, but its generation is not completely elucidated, particularly when Y. lipolytica metabolizes non-sugar based feedstock. ATP-citrate lyase (ACL) is known to cleave cytosolic
Technological difficulties in constructing multi-step pathways for terpene synthesis
Engineering terpene synthesis requires multiple heterologous enzymes that may pose rate limitations, flux imbalances, accumulation of toxic intermediates, and metabolic burdens. Some enzymes, such as the cytochrome P450 oxygenase [26,27], are difficult to functionally express in heterologous hosts [28]. Further, many secondary metabolite pathways involve metabolite channeling via membrane or covalent binding of multiple pathway enzymes [29]. The multi-enzyme complexes help to channel the flux
Metabolic shifts, morphological changes, and genetic instability under bioreactor conditions
Y. lipolytica can ferment diverse substrates to various organic acids and its overflow metabolism is highly sensitive to growth conditions and fermentation stages. For example, Y. lipolytica ionone fermentation has been observed tosecrete mevalonate and other organic acids in the early fermentation stage and reuse these acids during the late production phase [38]. Moreover, Y. lipolytica is dimorphic, that is, capable of transition between ovoid and filamentous morphologies, which is influenced
Future perspectives
There has been considerable progress in developing non-model yeast platforms. However, challenges are still present, as shown by studies on Y. lipolytica (Figure 3). Traditional strain development relies on the design-build-test-learn (DBTL) cycle with a significant amount of time spent on strain development and testing of desired phenotypes. Computational strain design can reduce the duration of DBTL cycles and facilitate strain development. For example, genome-scale modeling can predict flux
Funding
This work was supported by the National Institutes of Health (NIH1R41GM13027701), the National Science Foundation (MCB 1616619), and by the National Institute of Food and Agriculture (hatch project HAW05040-H and multistate project HAW05041-R).
Conflict 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
CRediT authorship contribution statement
Alyssa M Worland: Writing - original draft, Writing - review & editing, Visualization. Jeffrey J Czajka: Writing - review & editing. Yanran Li: Writing - review & editing. Yechun Wang: Writing - review & editing. Yinjie J Tang: Writing - review & editing, Conceptualization, Supervision, Funding acquisition. Wei Wen Su: Writing - review & editing, Conceptualization, Supervision, Funding acquisition.
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