Systems biology and metabolic engineering of Arthrospira cell factories

Arthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented.


Introduction
The term "systems biology" appears widely in the literature and is defined as an emerging approach applied to biomedical and biological scientific research [1]. Systems biology mostly focuses on integrative omics data analysis, mathematical modeling, cellular components interactions, and quantification of dynamic responses in living organisms. Systems biology is typically proposed to achieve a quantitative biological system under study and this is often shown in the form of a mathematical model. A number of case studies use the model for capturing reporter features of the biological system and can hence be later used to predict cellular behaviors at different conditions [2]. In other cases, the modeling of the mathematical model rather serves as a toolbox to extract information of the biological system and to perform data enrichment and classification. Generally, this mathematical model goes hand in hand with laboratory experimental work. Through this, the essence combination exemplifies the core of systems biology and leads to gaining new insight into the fundamental molecular mechanisms occurring in living cells.
In recent years, systems biology has moved towards metabolic engineering [3]. Metabolic engineering is a fascinating science which has several definitions. Most of these are mostly aimed at improving existing cell factories or developing new ones, which are similar to the use of genetic engineering to perform directed genetic manipulations of cell factories with the overall objective to improve their properties for industrial applications. Nonetheless, metabolic engineering clearly differentiates itself by the use of advanced analytical techniques for the identification of suitable targets for genetic manipulation and the use of mathematical models to perform in silico design of optimized cell factories.
Focusing in the field of industrial biotechnology, there is much studied on how systems biology and metabolic engineering can impact the development of efficient cell factories, especially moving forward in improving industrial production processes and developing new products. In this present era, there are certainly rapid jumps towards the use of another type of microalgae, the cyanobacteria Arthrospira (formerly known as Spirulina), as sustainable cell factories. These bacteria are well-known to be used in the production of many industrial products, including high value compounds, such as healthy food supplements, animal feed, cosmetics and pharmaceutical products. In addition, Arthrospira are currently used as important green cell factories for biofuel production (e.g. hydrogen, bioethanol and biodiesel) which serve for renewable energy resources [4,5].
With the advancement of high-throughput omics technologies and bioinformatics accompanied by systems biology and metabolic engineering, these processes have rapidly allowed for the obtainment of a more comprehensive understanding of Arthrospira cell factories.
The basic researches of high-throughput omics technologies are generally focused on how metabolisms are operating at different environmental conditions. Therefore, integrative technologies like genomics, transcriptomics, proteomics, metabolomics and fluxomics as well as bioinformatics, which provide qualitative and/or quantitative information on the operation of the metabolism in a context of network, are playing a key role in systems biology. Besides these technologies, systematic techniques underlying metabolic CSBJ Abstract: Arthrospira are attractive candidates to serve as cell factories for production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. In connection with the development of sustainable bioprocessing, it is a challenge to design and develop efficient Arthrospira cell factories which can certify effective conversion from the raw materials (i.e. CO2 and sun light) into desired products. With the current availability of the genome sequences and metabolic models of Arthrospira, the development of Arthrospira factories can now be accelerated by means of systems biology and the metabolic engineering approach. Here, we review recent research involving the use of Arthrospira cell factories for industrial applications, as well as the exploitation of systems biology and the metabolic engineering approach for studying Arthrospira. The current status of genomics and proteomics through the development of the genome-scale metabolic model of Arthrospira, as well as the use of mathematical modeling to simulate the phenotypes resulting from the different metabolic engineering strategies are discussed. At the end, the perspective and future direction on Arthrospira cell factories for industrial biotechnology are presented． engineering for the analysis of metabolic pathways and guiding rational strategies for the direct improvement of cellular activity [6,7] are additionally valuable. Considering all possible advanced technologies and tools available, there are great opportunities for mapping correlations between genotypes and phenotypes and gaining further insight into our understanding of Arthrospira.
To show an overview of systems biology and metabolic engineering through industrial biotechnology for the development of Arthrospira cell factories, we therefore illustrate the entire processes as shown in Figure 1. Initially, the process of developing Arthrospira cell factories from systems biology, i.e., multi-level omics, bioinformatics and metabolic networks, can be used to perform fundamental analysis of the cells. Afterwards, such Arthrospira cell factories are developed through metabolic engineering, i.e., mathematical modeling, and can be used to guide directed genetic manipulations. Finally, the developed cell factories of Arthrospira are used as workhorses for the industrial production process, i.e., conversion from raw materials to product formations.
In the following description, we firstly review how Arthrospira cell factories are mainly used for industrial applications. We then focus on the systems biology and the metabolic engineering toolboxes available for Arthrospira, i.e., genome sequencing projects on different Arthrospira strains for which the sequence data are publicly available, proteomics, genome-scale metabolic network and in silico modeling as well as their updated status and applications. Towards the end, we describe the perspective and future direction of Arthrospira cell factories.
2. Arthrospira as cell factories for industrial applications Arthrospira (Spirulina) are filamentous non-N2-fixation cyanobacteria in a group of cyanobacteria which naturally grow in a high-salt alkaline open pond system in tropical areas [8-10] and utilize sunlight and CO2 to produce essential chemical compounds for life. The genus Arthrospira is comprised of approximately 51 strains within two common species, namely, Arthrospira platensis and Arthrospira maxima, that play an important role for industrial applications [11]. Table 1 summarizes the available information of various important biotechnological aspects of Arthrospira strains.
A. platensis (Spirulina platensis) is widely used as a source for commercially-produced food supplements and animal feeds since the 1970s [12]. In addition, A. platensis has been consumed as a protein source for many years by North Africans and Mexicans [1]. As A. platensis contains high amounts of healthy nutritional molecules, such as beta-carotene, phycocyanin, vitamins, trace minerals, and polyunsaturated fatty acids [13][14][15], it is currently promoted as functional foods with safe consumption and widely sold in various health food stores in the forms of capsules, tablets, and powders.
Furthermore, A. platensis is potentially one microalgae that is capable of renewable energy production, which could decrease the effects of global warming. This is due to its potential to capture and convert CO2 and sunlight into various value-added products through cellular processes, e.g., photosynthesis, glycolysis, TCA cycle, and fatty acid and lipid biosynthesis. Hereby, A. platensis has become an attractive photobiological cell factory as presented in Figure 2.
Among the different species of microalgae [29], A. platensis, as well as A. maxima, were identified as being capable of growing in outdoor environments at a high rate showing high total lipids and biodiesel yields (9.2% and 7.5%, respectively). In particular, A. maxima strain CS-328 exhibited the high growth rate and showed the hydrogen yield by auto-fermentation among other cyanobacteria (up to 18% of hydrogen in the headspace) [30]. A noticeable example reported by Ananyev GM et al. showed that enhanced hydrogen production with 11-fold of hydrogen yield and 3.4-fold of hydrogen production rate from A. maxima was done by manipulating the equilibrium and thermodynamics of the hydrogenase reaction and removing the excreted products [31]. As described, these benefits correspond with other relevant nutritional value achievements from Arthrospira, showing the favorable industrial applications of cell factories.  3. Systems biology and metabolic engineering toolboxes for Arthrospira The cellular machinery complexity is always a biological challenge. The complex regulatory circuits occurring at different levels of cellular control within the cells are often a bottleneck for understanding living cells. To overcome this, the systems biology and metabolic engineering toolboxes are required. Here, we discuss these toolboxes through global information from genomics and proteomics as well as genome-scale metabolic models available for Arthrospira. At the end, we describe the status of existing metabolic models for Arthrospira and their applications. Regarding all possible characteristics of Arthrospira genomes available as listed in Table 2, the typical genome sizes are in between 5.0-6.7 Megabases (Mb) with an average GC content of 44.3-44.8%. In terms of gene prediction and functional annotation using bioinformatics, Arthrospira genome sequences include 5,370-6,630 protein-encoding genes and 30-45 RNA genes. Among these, 800-1,400 protein-encoding genes correspond with the KEGG metabolic pathway (Table 2). For the other functional enrichment analysis, interestingly the Arthrospira species have abundant functions involved in defense mechanisms (e.g. restriction modification, group II intron, CRISPR, and insertion elements (ISs) [44,46,47]. Through the genomic description as mentioned above, it suggests that Arthrospira species are feasibly versatile cell factories at a genetic level. Table 2 shows the status of genomics information available for the Arthrospira genomes.

Arthrospira cell factories
Proteomics is one of highly-studied omics technologies for Arthrospira in order to achieve quantitative description of protein  Figure   3. In brief, the metabolic network reconstruction of A. platensis strain C1 was initially started with automated construction using Pathway Tools software [52] to generate a draft metabolic network. This automated construction facilitates the top-down reconstruction approach which rapidly gives an overview network and visualization.
Certainly, the quality of the large-scale reconstructed network depends on the quality of the initial annotated data. Hence, manual annotation was subsequently performed to increase the accuracy of the draft autogenerated network. Nonetheless, there are still a number of incomplete pathways (i.e. missing genes and reactions) indicating disconnections in the network. Missing reactions (referred to gaps) that resulted in dead-end metabolites and prevented the model simulation of cell growth were then identified. Therefore, an additional step for metabolic network refinement of A. platensis strain C1 was performed based on present information about cyanobacteria from biochemical literature, biochemistry textbooks, and online biochemical databases. In order to complete the pathways where no gene could be found in the metabolic reconstruction, the Blast algorithms and the advanced tools for the protein domain prediction against Pfam database were further used to determine the enzymatic gene functions of A. platensis strain C1. Towards the end, the refined metabolic network of A. platensis strain C1 contained 692 metabolic genes, 658 metabolites, and 688 biochemical reactions [51]. Then, a stoichiometric model for flux balance analysis (FBA) modeling was formulated to investigate and predict the cellular physiology of Arthrospira. The stoichiometric model was employed for studying the metabolic states of A. platensis strain C1 under autotrophic, heterotrophic, and mixotrophic growth conditions. Interestingly, iAK692 model-based FBA was shown to correspond with the fermentation experiments during autotrophy, heterotrophy, and mixotrophy. In addition to the growth studies, the topological network analysis was then performed in order to reveal metabolic network properties of the iAK692 model. Through this topological analysis, the model could allow the large-scale in silico gene deletion and reaction activity analysis for a specific growth condition. In addition, iAK692 was employed to investigate the relationship between photosynthesis and respiration during Arthrospira growth by using phenotype phase plane analysis (PhPP). Clearly, the iAK692 model is not only used for global understanding of physiological growth behaviors and product formulations, but also as a platform for systems biology investigation leading to metabolic engineering and strain improvement [53] towards a diverse range of biotechnological abilities of Arthrospira.
Genome-scale metabolic models provide an additional framework for direct integration and analysis with high throughputomics data and bridge the gap between knowledge of observed experiment and global metabolic network structure [54,55]. These advantages of genome-scale metabolic models therefore allow us to investigate all possible behaviors of the cellular systems responding to environmental changes. Certainly, a new strategy for strain improvement and biological process development for Arthrospira can be further identified.  [57] which were involved in the central carbon metabolism, the anaplerotic pathway, the photosynthesis, the GLA synthesis pathway and the biomass synthesis pathway. After Meechai's stoichiometric model was formulated, the metabolic flux analysis was performed for overall metabolic distribution studies in the GLA synthesis pathway between wild-type and mutant strains. Once the model was validated with the physiological data, wild-type BP and a high-GLA producing mutant strain Z19/2 were experimentally studied on the GLA synthesis pathway. Meechai's model suggests that a metabolic reaction converting acetyl-CoA into malonyl-CoA is a bottleneck for GLA synthesis of Arthrospira. Moreover, the model suggests the addition of simulation factors, such as NADPH and MgCl2, which resulted in higher GLA production rates in the wildtype strain.

Perspective and future direction on Arthrospira cell factories
Needless to say, Arthrospira are attractive cyanobacteria to be used as efficient cell factories for the production of many valuable compounds useful for food, feed, fuel and pharmaceutical industries. This presents a grand challenge for the researchers to realize the enormous biotechnological potential of Arthrospira. Unfortunately, a stable and reliable system for efficiently transferring exogenous genes into Arthrospira is not yet available, thus the use of Arthrospira as cell factories is still limited. Nevertheless, much progress has been made in developing systems for gene manipulation in the Arthrospira species.
For instance, a system for gene manipulation by certain restriction endonuclease enzymes were identified and characterized in A. platensis [58,59]. In addition, a transformation using a natural Tn5 transposase-transpon-cation liposome complex with electroporation that efficiently transferred chloramphenicol acetyltransferase gene to A. platensis strain C1 (PCC9438) has been reported [60]. The  limited successful establishment of the genetic transformation system is mainly attributed to the defense mechanism systems found in Arthrospira such as restriction and modification enzymes (RM) as well as CRISPR [47]. Hence, the developing area of the strategies and genetic tools for manipulation of these cyanobacteria are crucial for further success of genetic engineering efforts.
The increasing availability of whole genome sequences and the present knowledge relating to Arthrospira's metabolic and regulatory systems allow the researchers to gain insights into this organism at the systematic levels. This knowledge can now be used to address biological meanings and answers through possible biological questions, such as how the transformation systems in Arthrospira work in terms of function and which alternative ways can be used to establish novel genetic tools for genetic manipulation of Arthrospira. For example, thorough analysis of the Arthrospira genome sequence would provide an in-depth understanding of its unique RM systems, thereby enabling the development of efficient transformation strategies. For instance, with proper design and modification of an exogenous gene, the RM defense mechanism can be avoided, and thus a stable transformation can be achieved. Additionally, the recently published genome-scale metabolic model of A. platensis has also been used as a systems biology platform for analyzing A. platensis highthroughput data at both transcriptomics and proteomics levels within the metabolic context. In the future, once the bioinformatics algorithms, the mathematical models and the Arthrospira phenotypic characterization of different mutants suited for metabolic engineering have advanced, new findings by the genome-scale model shall certainly pave a way for comprehensive studies of cellular systems, as well as the development of desirable Arthrospira cell factories for various industrial applications. Beyond this, we look forward to expand and develop the biotechnological potential of Arthrospira as biological factories for applied use in a greener era.