Research review paperCurrent advances in molecular, biochemical, and computational modeling analysis of microalgal triacylglycerol biosynthesis
Introduction
Climate change, human population growth, and depletion of natural resources impose a serious burden on the planet. Addressing these challenges while concurrently expanding the global economy will require new technologies that generate renewable resources for food, health products, chemicals, and fuels (Georgianna and Mayfield, 2012; Harvey and Pilgrim, 2011). Oleaginous (oil-producing) microalgae hold promise as a sustainable feedstock for each of these needs, as they can accumulate large amounts of triacylglycerol (TAG) under proper conditions (Benemann, 2013; Hu et al., 2008). TAG is a major storage lipid that plays an important role in energy and carbon storage in many microalgae (Chisti, 2007). It is known that TAG synthesis and storage occur in distinct subcellular compartments within microalgae, and recent evidence indicates that TAG is not only a storage molecule but also plays a crucial role in maintaining intracellular lipid homeostasis, proper membrane structure, and cellular function in response to environmental and developmental cues (Holthuis and Menon, 2014; Kohlwein, 2010). Understanding how these functions and processes are coordinated and regulated will arguably be critical for any sort of rational re-design of TAG biosynthesis in microalgae.
Engineering algae to accumulate high levels of TAG while growing rapidly is an ongoing challenge and only limited success has been reached (Li et al., 2010a; Li et al., 2010b; Tsai et al., 2014). Such efforts benefit from knowledge of the biochemical and molecular mechanisms regulating fatty acid (FA) and TAG biosynthesis, accumulation, and turnover in other organisms, much of it inferred from studies of higher plants. Interestingly, TAG biosynthesis is relatively complicated in species such as Arabidopsis, for which acyl-lipid metabolism and its regulation involve over 600 genes and at least 120 enzymatic reactions in different organs of the plant (Li-Beisson et al., 2013). In comparison, in microalgae, TAG biosynthesis and storage occur within a single cell and involve fewer proteins. For instance, based on the JGI v5.5 of the Chlamydomonas reinhardtii genome, there are just 113 genes predicted to be involved in major steps of acyl-lipid metabolism (Li-Beisson et al., 2015) in that organism, and the number of genes involved in FA metabolism plus TCA cycle is inferred to be just 106 in the diatom Phaeodactylum tricornutum (Mühlroth et al., 2013). Clearly there are other interesting and important metabolic and cell biological differences between plants, green algae, and chromophyte algae, including differences in carbohydrate synthesis and glycolytic pathways, and in the arrangements of their thylakoid membranes (Wilhelm et al., 2006). As our focus is on TAG metabolism, these topics are not discussed in detail in this review.
Indeed, whole genome sequencing, de novo transcriptomics, and proteomics analysis of algae have accelerated the basic understanding of FA and TAG biosynthesis for strain improvement (Guarnieri et al., 2011; Merchant et al., 2007; Nguyen et al., 2011; Radakovits et al., 2012). With analytical advancements in lipidomics, it is now possible to quantitatively identify hundreds of natural TAGs and their regioisomers from algae (Rezanka et al., 2014). Using such omics and biochemical information from the model alga C. reinhardtii, attempts have been made to derive a draft metabolic network using computational modeling and bioinformatics methods (Boyle and Morgan, 2009; May et al., 2009; May et al., 2008). Mathematical and kinetic models have also been developed to study growth parameters and lipid production in green algae (Packer et al., 2010; Tevatia et al., 2012; Yang et al., 2010). Taken together, current developments in molecular genetics and biochemistry, omics (including genomics, transcriptomics, proteomics, metabolomics, and fluxomics), integrative modeling, and genome engineering tools have the potential to enable precise predictive capabilities and rational engineering of algae for high lipid content, see Fig. 1 (Jiang et al., 2014; Recht et al., 2014; Reijnders et al., 2014). The above technological advances offer a unique opportunity to understand algal lipid metabolism in a holistic manner.
This review begins with an overview of the current knowledge of TAG production in microalgae, based on biochemical, physiological, multi-omics, and pathway modification studies, with a focus on the role of major enzymes involved in TAG metabolism. Then we discuss computational methods and their use in data analysis and pathway prediction, with an emphasis on the application of these methodologies to algal TAG production. Finally, we offer a perspective on recently developed genetic engineering tools that may be used to rationally engineer algal strains for favorable biofuel traits (Fig. 1) and summarize reported genetic modifications outcomes, highlighting some strategies that modelers and experimenters could collaboratively investigate.
Section snippets
Fatty acid and triacylglycerol biosynthesis in microalgae
FAs are amphipathic molecules consisting of a carboxylic acid group at one end and a hydrocarbon group at the other, with length and degree of saturation of the hydrocarbon moiety variable among different FA species. Acyl-lipids derived from FAs, primarily glycerolipids, form the basic structural components of cell membranes (Li-Beisson et al., 2013). The physiologically inert storage lipid TAG has three fatty acyl groups esterified with the glycerol backbone. Many important mechanistic
State of the art computational modeling of microalgae
As computational power has improved (primarily within the last ten years), computational models have begun to be applied for data analysis and pathway prediction in microalgae, enabling rational genetic engineering of algae for biofuels and bioproducts. There are many different types of computational models with varying degrees of quantitative versus qualitative characteristics, each having their own strengths and limitations. Many species have been modeled, although the major focus has been on
Technical advances that facilitate metabolic engineering
As more and more studies integrate experiments with mathematical modeling of FA and TAG metabolism, our understanding of these molecules and how to manipulate their production pathways will improve. In this section we review some of the tools that are (or may soon be) useful in engineering microalgal genomes, in ways that facilitate the testing of model predictions and that should enhance TAG production.
Theoretical maximum yield of TAG
Before discussing various metabolic engineering strategies for enhanced TAG production, it is useful to consider the upper limit of any improvements: what is the theoretical maximum yield of TAG that can be obtained from photosynthetic microalgae? Below, we calculate the theoretical maximum yield of TAG per mol of photons to establish the magnitude of improvement for TAG production that is possible in principle. The approach taken is similar to that described by Johnson and Alric in their
Conclusion and perspectives
Directed efforts towards understanding the regulation of lipid metabolism at the physiological and genetic levels have brought us closer to a more efficient production of lipid-based biofuels and high value compounds. These efforts have been mainly guided by biochemical pathway analysis, and more recently by omics data that make it easier to identify genes that play critical roles in lipid metabolism. The results of such work have enabled metabolic engineering of microalgae with an experimental
Acknowledgments
This work was supported by an award from the National Science Foundation to S.M.M. (NSF-EFRI-1332344) and awards to Y.L. from the National Science Foundation (CBET-1511939), Climate Change and Emissions Management Corporation (CCEMC), and Office of Naval Research (N00014-15-1-2219).
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These authors contributed equally to this work.